Shopping behavior in malls - Pure - Aanmelden · Shopping Behavior in Malls . PROEFSCHRIFT . ter verkrijging van de graad van doctor aan de Technische Universiteit Eindhoven, op gezag

Shopping behavior in malls

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Shopping Behavior in Malls

PROEFSCHRIFT

ter verkrijging van de graad van doctor aan de Technische Universiteit Eindhoven, op gezag van de rector magnificus, prof.dr.ir. F.P.T. Baaijens voor een

commissie aangewezen door het College voor Promoties, in het openbaar te verdedigen op 22 januari 2018 om 16.00 uur.

door

Widiyani geboren te Bandung, Indonesië

Dit proefschrift is goedgekeurd door de promotoren en de samenstelling van de promotiecommissie is als volgt: Voorzitter prof.ir. E.S.M. Nelissen Promotor prof.dr. H.J.P Timmermans Co-promotor ir. A.W.J. Borgers Leden prof.dr.ir. B. de Vries

dr.ir. A.D.A.M. Kemperman prof.dr. H. Marjanen (University of Turku) prof. C.Teller MSc PhD (University of Surrey)

Het onderzoek dat in dit proefschrift wordt beschreven is uitgevoerd in overeenstemming met de TU/e Gedradscode Wetenschapsbeofening.

Shopping Behavior in Malls

A catalogue record is available from the Eindhoven University of Technology Library ISBN: 978-90-386-4426-4 NUR: 955 Cover design by Widiyani - collage figures are taken from shutterstock Published as issue 238 in de Bouwstenen series of the Department of Built Environment of the Eindhoven University of Technology Copyright © Widiyani, 2018 All rights reserved. No part of this document may be photocopied, reproduced, stored, in a retrieval system, or transmitted, in any from or by any means whether, electronic, mechanical, or otherwise without the prior written permission of the author.

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CONTENTS CONTENTS....................................................................................................................... v

List of Tables ............................................................................................................... ix List of Figures ..............................................................................................................xii

ACKNOWLEDGEMENTS .................................................................................................xv

SUMMARY .................................................................................................................... xix

1 INTRODUCTION ........................................................................................................... 1 1.1 Background and Motivation .................................................................................. 1 1.2 Research Goals and Contribution .......................................................................... 2 1.3 Research Approach ................................................................................................ 4 1.4 Outline ................................................................................................................... 4

PART I INDONESIAN SHOPPING ENVIRONMENT AND THEORETICAL BACKGROUND .. 7

2 EVOLUTIONARY PATTERNS IN JAKARTA SHOPPING MALLS ...................................... 8 2.1 The Evolution of Shopping Malls ........................................................................... 8

2.1.1 The Historical Evolution of Shopping Malls ..................................................... 9 2.1.2 Jakarta Shopping Malls .................................................................................. 10

2.2 The Need for Classifying Jakarta Shopping Malls................................................. 14 2.3 Jakarta Shopping Mall Classification .................................................................... 19

2.3.1 Identifying the Key Attributes ........................................................................ 19 2.3.2 Data Preparation ........................................................................................... 21 2.3.3 Classifying Jakarta Shopping Malls: Hierarchical Cluster Analysis ................. 21

2.4 Identifying and Labeling the Clusters ................................................................... 21 2.4.1 Profile of Cluster 1: Classic Shopping Malls ................................................... 24 2.4.2 Profile of Cluster 2: Local Shopping Malls ..................................................... 26 2.4.3 Profile of Cluster 3: Modern Shopping Malls ................................................. 27

2.6 Conclusions and Discussion ................................................................................. 28

3 SHOPPING BEHAVIOR: A LITERATURE REVIEW ........................................................ 29 3.1 Conceptual Framework ........................................................................................ 30 3.2 Motives Underlying Shopping Mall Choice .......................................................... 32 3.3 Choice of Shopping Mall ...................................................................................... 33

3.3.1 Location and Convenience ............................................................................. 34 3.3.2 Store Variety, Merchandise Selection and Quality ........................................ 36 3.3.3 Price, advertising and promotion .................................................................. 41 3.3.4 Atmospherics ................................................................................................. 43 3.3.5 Social Environment ........................................................................................ 47 3.3.6 Personal Service ............................................................................................. 49

3.4 Behavior inside Shopping Malls ........................................................................... 51 3.4.1 Stop Behavior ................................................................................................ 51

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3.4.2 Movement Behavior ...................................................................................... 53 3.5 Conclusions and Discussion ................................................................................. 57

PART II PRE-SHOPPING DECISIONS .............................................................................. 59

4 DATA COLLECTION AND DESCRIPTIVE ANALYSES .................................................... 60 4.1 Overview of the Survey Approach ....................................................................... 60 4.2 Questionnaire Design .......................................................................................... 61

4.2.1 Sociodemographics ....................................................................................... 61 4.2.2 Shopping Motives and Mall Preference ........................................................ 62

4.3 Procedures for Collecting Data ............................................................................ 64 4.4 The Shopping Malls Case Study ........................................................................... 65 4.5 Data Collection .................................................................................................... 65

4.5.1 Sociodemographic Characteristics ................................................................ 66 4.5.2 Shopping Motives and Factor Influencing Mall Choice ................................. 66

4.6 Shoppers’ Characteristics .................................................................................... 69 4.6.1 Sociodemographic Characteristics ................................................................ 69 4.6.2 Shopping Motives and Factors Influencing Shopping Mall Choice ................ 72

4.7 Conclusions and Discussion ................................................................................. 73

5 SHOPPERS’ EVALUATIONS ........................................................................................ 75 5.1 Measurement of Shopping Mall Dimensions ...................................................... 75 5.2 Results of Evaluations: Descriptive Analysis ........................................................ 76

5.2.1 Evaluations of Dimensions ............................................................................ 77 5.2.2 Location and Convenience ............................................................................ 77 5.2.3 Price ............................................................................................................... 79 5.2.4 Store Variety, Merchandise Selection and Quality ........................................ 82 5.2.5 Advertising and Promotion............................................................................ 83 5.2.6 Mall Comfort and Visual Appearance ............................................................ 83 5.2.7 Space Arrangement ....................................................................................... 84 5.2.8 Quality of Facilities ........................................................................................ 84 5.2.9 Social Environment ........................................................................................ 84 5.2.10 Personal Service .......................................................................................... 85

5.3 Comparing Evaluations ........................................................................................ 85 5.3.1 Method: Independent t-Test ......................................................................... 85 5.3.2 Results ........................................................................................................... 85

5.4 Understanding Overall Evaluations of Dimensions ............................................. 92 5.4.1 Method: A Stepwise Regression .................................................................... 92 5.4.2 Results ........................................................................................................... 93

5.5 Summary and Conclusions ................................................................................. 101

PART III MALL-USE SHOPPING DECISIONS................................................................. 103

6 SHOPPING BEHAVIOR INSIDE THE MALLS .............................................................. 104 6.1 Questionnaire Design for Shopping Data .......................................................... 105 6.2 Descriptive Analyses .......................................................................................... 105 6.3 Comparison of Behavioral Characteristics Between Malls ................................ 108

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6.4 Shopping Styles and Store Visits Behavior ......................................................... 112 6.5 Deriving Shopping Style ..................................................................................... 113 6.6 Shopping Style Results ....................................................................................... 113

6.6.1 Shopping Style in the Local Shopping Mall (LM) .......................................... 114 6.6.2 Shopping Style in the Modern Shopping Mall (MM) ................................... 115 6.6.3 Shopping Style in the Classic Shopping Mall (CM) ....................................... 116 6.6.4 Labeling of Shopping Style ........................................................................... 117

6.7 Shopping Styles’ Characteristics ........................................................................ 117 6.7.1 Grocery Shoppers ........................................................................................ 118 6.7.2 Fashion Shoppers ......................................................................................... 121 6.7.3 Social Shoppers ............................................................................................ 124 6.7.4 Recreational Shoppers ................................................................................. 127

6.8 Conclusions and Discussion ............................................................................... 130

7 STOP BEHAVIOR AND MOVEMENT PATTERNS ....................................................... 132 7.1 Overview of the Obsevation .............................................................................. 133 7.2 Procedures for Collecting Data .......................................................................... 137 7.3 The Study ........................................................................................................... 138 7.4 Data Collection ................................................................................................... 139

7.4.1 Respondents’ Profiles .................................................................................. 139 7.4.2 Length of Stay in the Mall ............................................................................ 143

7.5 Results of Stop Behavior and Movement Patterns ............................................ 143 7.5.1 Stop Behavior .............................................................................................. 143 7.5.2 Movement Patterns ..................................................................................... 146

7.6 Composition of Stores and Facilities in the Mall and Shoppers’ Behavior ........ 148 7.7 Conclusions and Discussion ............................................................................... 153

8 UNDERSTANDING SEQUENTIAL SHOPPING PATTERNS .......................................... 155 8.1 Sequential Shopping Patterns ............................................................................ 155

8.1.1 Sequence Alignment Methods .................................................................... 156 8.1.2 Data Preparation ......................................................................................... 159 8.1.3 Common Sub-patterns in Sequential Shopping Patterns ............................ 160

8.2 Results of Sequential Shopping Patterns ........................................................... 162 8.2.1 K-Means Clustering ...................................................................................... 163 8.2.2 Characteristics Clusters................................................................................ 163

8.3 Cluster Membership .......................................................................................... 168 8.3.1 Logistic Regression Analysis ......................................................................... 169 8.3.2 Results ......................................................................................................... 169

8.4 Conclusions and Discussion ............................................................................... 170

9 CONCLUSIONS AND DISCUSSION ............................................................................ 173 9.1 Conclusions ........................................................................................................ 173 9.2 Limitations ......................................................................................................... 178 9.3 Contributions and Managerial Implications ...................................................... 178

REFERENCES ................................................................................................................ 182

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APPENDICES ............................................................................................................... 200 APPENDIX 1 The Questionnaire ............................................................................... 201 APPENDIX 2 Results of Descriptive Analyses of Evaluations of Mall Dimensions across the Selected Shopping Malls ........................................................................ 208 APPENDIX 3 Results of Independent t-Tests of Evaluations of Malls’ Cognitive Dimension ................................................................................................................ 221 APPENDIX 4 The Observation Sheets ...................................................................... 233 APPENDIX 5 Classic Mall: Type of Tenants, Type of Stores, and Number of Visits .. 235 APPENDIX 6 Sub-patterns in Sequential Shopping Patterns ................................... 243

AUTHOR INDEX .......................................................................................................... 251

SUBJECT INDEX ........................................................................................................... 257

CURRICULUM VITAE ................................................................................................... 262

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List of Table

Table 2.1 Population Density in Jakarta's Districts in 2010 ........................................... 11 Table 2.2 Profile Clusters of Jakarta Shopping Malls .................................................... 24 Table 2.3 Specific Profiles Clusters of Jakarta Shopping Malls...................................... 24

Table 4.1 Sociodemographic Characteristics of the Sample (N=670) ........................... 67 Table 4.2 Motive to Visit the Mall (N=670) ................................................................... 68 Table 4.3 Factors to Choose the Mall (N=670) .............................................................. 68 Table 4.4 Sociodemographic Characteristics ................................................................ 70 Table 4.5 Motives to Visit the Mall ............................................................................... 72 Table 4.6 Factors Influencing Mall Choice .................................................................... 73

Table 5.1 Evaluations of Mall Image Dimensions ......................................................... 76 Table 5.2 Mean Evaluations of Mall Image Dimensions ............................................... 80 Table 5.3 Summary of Independent t-Tests .................................................................. 86 Table 5.4 Results of Evaluations.................................................................................... 98

Table 6.1 Behavioral Characteristics (N=670) ............................................................. 106 Table 6.2 Store Classification ...................................................................................... 107 Table 6.3 Type of Stores and Facilities Visited (N=670) .............................................. 108 Table 6.4 Description of Behavior ............................................................................... 110 Table 6.5 Store Visits Behavior ................................................................................... 112 Table 6.6 Shopping Style Clusters in the Local Shopping Mall .................................... 114 Table 6.7 Shopping Style Clusters in the Modern Shopping Mall ............................... 115 Table 6.8 Shopping Style Clusters in the Classic Shopping Mall ................................. 116 Table 6.9 Behavior Characteristics of Grocery Shoppers ............................................ 119 Table 6.10 Grocery Shoppers’ Profiles ........................................................................ 120 Table 6.11 Behavior Characteristics of Fashion Shoppers .......................................... 122 Table 6.12 Fashion Shoppers’ Profiles ........................................................................ 123 Table 6.13 Behavior Characteristics of Social Shoppers ............................................. 125 Table 6.14 Social Shoppers’ Profiles ........................................................................... 126 Table 6.15 Behavior Characteristics of Recreational Shoppers .................................. 128 Table 6.16 Recreational Shoppers’ Profiles ................................................................ 129

Table 7.1 Profiles of Respondents (N=166) ................................................................. 143 Table 7.2 Length of Stay in the Mall (N=166) ............................................................. 144 Table 7.3 Stop Behavior: Visits Activity (N=166) ......................................................... 144 Table 7.4 Stop Behavior: Use of Public Space (N=166) ............................................... 145 Table 7.5 Length of Time of Visits Activity and Use of Public Space (N=166) ............. 146 Table 7.6 Movement Patterns (N=166) ...................................................................... 147

Table 8.1 The Frequency of Stop Type by Floor (N=166) ............................................ 159 Table 8.2 Switch Floor Patterns (N=166) .................................................................... 159 Table 8.3 Sub-patterns of Anchor Store and Food-and-Beverage Store Stop ............ 161 Table 8.4 Profiles of Clusters ....................................................................................... 164

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Table 8.5 Clusters by Number of Different Floors Visited and Types of Stops ........... 166 Table 8.6 Comparison of Shopping Patterns between Clusters ................................ 167 Table 8.7 Comparison of Shoppers’ Profiles between Clusters .................................. 168 Table 8.8 Variables Used in Logistic Regression Analysis ........................................... 169 Table 8.9 Parameter Estimates ................................................................................... 169

Table A3.1 – A3.3 Independent t-Test Results for Evaluation of Mall’s Dimensions Table A3.1 between Shoppers in LM (N=218) and MM (N=225)………………………… 221 Table A3.2 between Shoppers in LM (N=218) and CM (N=227) ………………………… 225 Table A3.3 between Shoppers in MM (N=225) and CM (N=227) …………………..…… 229

Table A5.1 – A5.6 Type of Stores, Store Classification, and Number of Visits: Table A5.1 Lower Ground Floor……………..……………………………………………………....... 235 Table A5.2 Ground floor……………………………………………………………………………………. 236 Table A5.3 Upper Ground floor ……….……………………………………………………………….. 237 Table A5.4 1st floor ……………………………………………………………………………………………. 239 Table A5.5 2nd floor …………………………………………………………………………………………… 240 Table A5.6 3rd floor …………………………………………………………………………………………… 241

Table A6.1 – A6.14 Stop sub-patterns: Table A6.1 Sequence Length 3………………………………………………………………………..…. 243 Table A6.2 Sequence Length 4 ………………………………………………………………………….. 243 Table A6.3 Sequence Length 5…………………………………………………………………………… 243 Table A6.4 Sequence Length 6 ………………………………………………………………………….. 243 Table A6.5 Sequence Length 7 ………………………………………………………………………….. 243 Table A6.6 Sequence Length 8 ………………………………………………………………………….. 244 Table A6.7 Sequence Length 9 ………………………………………………………………………….. 244 Table A6.8 Sequence Length 10 ………………………………………………………………………… 244 Table A6.9 Sequence Length 11 ………………………………………………………………………… 245 Table A6.10 Sequence Length 12 ……………………………………………………………………….. 245 Table A6.11 Sequence Length 13 ……………………………………………………………………….. 245 Table A6.12 Sequence Length 14 ………………………………………………………………………… 245 Table A6.13 Sequence Length 15 ……………………………………………………………………….. 245 Table A6.14 Sequence Length 17 ……………………………………………………………………….. 245 Table A6.15 – A6.28 Floor sub-patterns: Table A6.15 Sequence length 3 …………………………………………………………………………… 246 Table A6.16 Sequence length 4 …………………………………………………………………………… 246 Table A6.17 Sequence length 5 …………………………………………………………………………… 246 Table A6.18 Sequence length 6 …………………………………………………………………………… 247 Table A6.19 Sequence length 7 …………………………………………………………………………… 248 Table A6.20 Sequence length 8 …………………………………………………………………………… 248 Table A6.21 Sequence length 9 …………………………………………………………………………… 249 Table A6.22 Sequence length 10 …………………………………………………………………………. 249 Table A6.23 Sequence length 11 …………………………………………………………………………. 249 Table A6.24 Sequence length 12 …………………………………………………………………………. 250

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Table A6.25 Sequence length 13 …………………………………………………………………………. 250 Table A6.26 Sequence length 14 …………………………………………………………………………. 250 Table A6.27 Sequence length 15 …………………………………………………………………………. 250 Table A6.28 Sequence length 17 …………………………………………………………………………. 250

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List of Figures

Figure 2.1 The City Map of Jakarta ............................................................................... 11 Figure 2.2 The Growth of Jakarta Shopping Malls in 1961-1970 and 1971-1980 ......... 15 Figure 2.3 The Growth of Jakarta Shopping Malls in 1981-1990 .................................. 16 Figure 2.4 The Growth of Jakarta Shopping Malls in 1991-2000 .................................. 17 Figure 2.5 The Growth of Jakarta Shopping Malls in 2001-2010 .................................. 18 Figure 2.6 Dendrogram for Complete Linkage Hierarchical Clustering of Jakarta

Shopping Malls ............................................................................................ 23 Figure 2.7 A Sample of Classic Shopping Malls: Mall Puri Indah (1997) – West Jakarta

.............................................................................................................................. 25 Figure 2.8 A Sample of Local Shopping Malls: Blok M Square (2008) – South Jakarta . 26 Figure 2.9 A Sample of Modern Shopping Malls: Thamrin City (2006) – Central Jakarta

.............................................................................................................................. 28

Figure 3.1 Conceptual Framework ................................................................................ 31

Figure 6.1 Store Visits Behavior of Grocery Shoppers ................................................ 118 Figure 6.2 Store Visits Behavior of Fashion Shoppers ................................................ 121 Figure 6.3 Store Visits Behavior of Social Shoppers .................................................... 124 Figure 6.4 Store Visits Behavior of Recreational Shoppers ......................................... 127

Figure 7.1 Layout of the Shopping Mall ...................................................................... 140 Figure 7.2 The Composition of Stores and Facilities in the Shopping Mall ................. 150 Figure 7.3 Frequency Distributions of Store Visits Behavior in the Mall .................... 152

Figure 8.1 The Scree Diagram ..................................................................................... 163 Figure 8.2 Sequential Movement Patterns Cluster 1 (N=35)…….……………………………. 165 Figure 8.3 Sequential Movement Patterns Cluster 2 (N=131)………………………………….165

Figure A2.1a Evaluation of Location and Convenience Dimension…………………....... 208 Figure A2.1b Frequency Distribution of Transport Mode……………………………………... 209 Figure A2.1c Frequency Distribution of Duration of Travel Time ……………………….... 209 Figure A2.1d Frequency Distribution of the Starting Point of the Trip………………….. 209 Figure A2.2 Evaluation of Price Dimension ……………….…………………………………….... 210 Figure A2.3a Evaluation of Store Variety, Merchandise Selection and Quality

Dimension……………………………………………………………………………………….. 211 Figure A2.3b Evaluation of Store Variety, Merchandise Selection and Quality

Dimension……………………………………………………………………………………….. 212 Figure A2.4 Evaluation of Advertising and Promotion Dimension ……………............ 213 Figure A2.5a Evaluation of the Mall Comfort and Visual Appearance Dimension … 214 Figure A2.5b Evaluation of the Mall Comfort and Visual Appearance Dimension…. 215

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Figure A2.5c Evaluation of the Mall Comfort and Visual Appearance Dimension…. 216 Figure A2.5d Evaluation of Odor and Music ……………….……………………………………..... 216 Figure A2.5e Evaluation of Temperature ……………….……………………………………………. 217 Figure A2.5f Evaluation of Building Style ……………….…………………………………………… 217 Figure A2.6 Evaluation of Space Arrangement Dimension ………………………………… 218 Figure A2.7 Evaluation of Quality of Facilities Dimension ………………………………….. 219 Figure A2.8 Evaluation of Social Environment Dimension …………………………………. 220 Figure A2.9 Evaluation of Personal Service Dimension ………………………………………. 220

Figure A4.1 The Observation Sheet: The Instruction …………………………………………. 233 Figure A4.2 The Observation Sheet: A Sample of Layout Plan and Tracking Data.. 234

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SUMMARY

SHOPPING BEHAVIOR IN MALLS

In the 1980s, new emerging shopping malls which were larger in scale and integrated with entertainment functions in a single complex mushroomed in many South-East Asian cities (Dick and Rimmer, 1998). For example, retail space in Malaysia, despite the economic crisis in 1997-1998, increased more than 20% annually (Ahmed et al., 2007). In 2008, a total of 71 shopping malls was registered in Singapore’s Association of Shopping Centers (Henderson et al., 2011). While in Indonesia, the number of Jakarta’s shopping malls since 1991 has been increased 100% almost in every ten years (Herlambang, 2009). In November 2012, Jakarta, which has an area of 704 km2 and 9.6 million people, had about 140 shopping malls. Surprisingly, most shopping malls were built close to one another in the same part of the city. With this rapidly increasing number of malls, and the consequent fierce competition, retailers and mall managers face the serious challenge to successfully run their business. It is therefore of utmost importance for shopping malls and retailers alike to formulate and execute effective development and marketing strategies.

The ultimate success of shopping malls depends on their ability to attract customers. Beyond market size, which largely depends on the (relative) location of the mall, it depends on the portfolio of stores in the mall, its atmospherics and other influential attributes. An improved understanding of how consumer make their decisions before shopping or what we call pre-shopping decisions (e.g. shopping purposes, shopping mall choice) and inside the mall or what we call mall-use shopping decisions (e.g. store choice, movement or path choice) will therefore contribute to better-informed formulation of marketing plans and development strategies. If the location of a mall has been determined, there is little marketing can do to increase the market area of a mall. Marketing should aim at increasing the total amount of sales generated from inside the market area by stimulating repeated patronage (loyalty) and maintaining or improving the positive reputation of the mall, reflected in high satisfaction ratings. Prior research studies confirm that customer satisfaction with the image dimensions of the retail space, such as location, store variety, merchandise quality, price, advertising and promotion, atmospherics, personal service, and social aspects influence their pre-

SUMMARY

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shopping decisions. However, most previous studies have been limited to the store environment.

One of fundamental strategies for the success of a shopping mall is the tenant mix and its placement. In practice, most decisions about the tenant mix are based on evidence obtained in case studies and only a limited number of studies on retail mix have looked at the shoppers’ shopping behavior. The studies on shopping behavior in the shopping malls demonstrate that the pattern of shopping activities and movements, including duration, route, and type and sequence of store visits provide meaningful insights into different shopper profiles. Most discussed in the literature concern shopping malls in the United States and Europe. Studies which discuss customer satisfaction and shopping behavior in an Asian context, particularly in Indonesia, are still limited.

The purpose of this study is to examine individual shopping behavior in shopping malls regarding pre-shopping and mall-use shopping decisions. In particular, it will investigate the complete shopping process, starting when shoppers plan to go to a shopping mall, when they are in the shopping mall and ending after they leave the shopping mall. The focus of this study aims on sequential shopping behavior because this is an important part in the shopping process and also has not been explored much in retailing studies. More specifically, this study investigates the shopping behavior in the shopping mall regarding two types of behavior: stops made to visit stores and facilities, and movement related to strolling and circulating between stores in the public space of the mall. In this case, the definition of public space is the space outside the stores which is operated by the mall manager. In order to get better movement behavior data, the study concerns a multi-story shopping mall, meaning that the building will consist of two or more floors above ground. This study starts with discussing a wide range of literature studies regarding all aspects in the process of pre-shopping and mall-use shopping decisions which assisted the development of the framework in this thesis.

With respect to fill the research gap in examining shoppers’ shopping behavior in shopping malls in an Asian country in general and Indonesia in particular, the thesis reports the historical evolution of shopping malls in general and the growth of Jakarta shopping malls from 1961 up to 2010. Jakarta has witnessed a different shopping malls’ evolution compared to the Western world, and it appears that Jakarta shopping malls’ characteristics do not follow the Western malls’ characteristics. Therefore, five attributes that reflect Indonesian shopping malls’ are investigated, including size of the malls, number of floors, features of tenants, themes, and type of mall ownership. A

SUMMARY

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complete linkage method is used to classify 106 shopping malls into three types of shopping malls, namely modern, local, and classic shopping mall. The classification serves as the context to collect data in examining pre-shopping decisions in this study.

This study aims to investigate the pre-shopping decisions. More specifically, to examine what factors do shoppers take into considerations before going to a shopping mall. A survey is designed to investigate respondents about their needs before shopping, their evaluation of mall attributes, and their sociodemographics. The respondents were shoppers who just finished their shopping at three types of shopping malls in South Jakarta. First investigation focuses on the needs of shoppers and the specific needs shoppers wish to fulfill when visiting the malls. The analysis addresses that each type of malls has different sociodemographic backgrounds of shoppers. Moreover, shoppers in each mall differ in their motives to visit the mall and their considerations behind the mall choice. The results support articles in the literature study which suggest that different mall classifications lead to different targets of shoppers (for e.g. LeHew and Fairhurst, 2000). The next investigation concentrates in shoppers’ evaluation of shopping mall’s image dimensions, including location and convenience, store variety, merchandise selection and quality, price, advertising and promotion, mall comfort and visual appearance, space arrangements, quality of facilities, personal services, and social environment. The shoppers’ evaluations of mall’s dimensions were examined separately for each type of malls to understand the features of shopping mall which significantly influence shopping malls choice and to inspect whether shoppers’ evaluations show systematic differences between the three types of malls. The findings show that shoppers’ evaluation of mall’s dimensions were not significantly different in each mall regarding overall evaluation of social environment. In the same way trip to the mall, quality of food and beverage stores, helpfulness and friendliness of greeters/receptionists, helpfulness and friendliness of security services, and helpfulness of customer services were not significantly different in each mall. However, evaluation of mall’s dimensions was statistically different for each type of malls, in terms of accessibility by motorcycle, variety of leisure facilities, attractiveness of architecture design, signs and decorations in the public spaces, easiness to find a praying room, cleanliness and quality of the praying room, quality, cleanliness, and odor in the toilets, and number of public seats. Differences may be due to the fact that the malls in this study provide sharp contrasts on mall’s dimensions that are discussed.

In the mall-use shopping decisions the study focuses on all the decisions that shoppers make in strolling around the mall, circulating within the floors, choosing stores to buy

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particular merchandises, and stopping at facilities. To capture behavior patterns in the shopping malls respondents in each mall were asked to mention which stores they entered during their visit of the shopping mall, as well as the total expenditure (in food and beverages and non-food-beverages) and duration of time spent in the malls. In this regards, a wide set of store visits is investigated and is tailored to the shopping style which describes a shopper’s action of choosing stores in the mall. The findings confirm four types of shopping styles regarding the most likely stores visited. the grocery shoppers who mainly visited the food type of stores, the fashion shoppers who were highly interested in visiting the apparel-and-accessories type of stores, the social shoppers who mainly visited the eating-places type of stores, and the recreational shoppers who most likely visited the media-and-special-interest type of stores and some different combination of other types of stores. All types of shopping malls have every types of shoppers’ shopping styles. However, each type of malls has its dominant shoppers’ shopping style and the shopping style characteristics in each type of malls are different regarding shoppers’ sociodemographics and shopping behavior, such as duration of mall visits, number of store visits, expenses, and the time and day of visits. Furthermore, differences in each mall are also defined related to their time duration inside the shopping malls and their expenses. The findings suggest that on average shoppers who stay the longest in the mall, do not spend a higher amount of money. Moreover, a higher number of store visits does not always increase time spent in the mall. This means, while the mall seems successful to make shoppers getting around and visiting the stores, it does not always follow that these shoppers also spend their money. The results enhance the understanding that shoppers had preferences of stores when visiting a type of shopping malls. Correspondingly, mall managers in each type of shopping malls could improve the store variety related to their shoppers.

An essential shopping process is to understand shoppers’ movement and sequential behavior, particularly in the multi-story shopping mall. To provide better insight into the effects of relative location of different types of stores in the mall this study examines the sequence of stops. Data was collected through unobtrusive tracking on random shoppers in a shopping mall in South Jakarta, starting when shoppers enter the mall and ending when they exit the mall. The data includes the sequence of shoppers’ stores visit, the route that shoppers made in visiting the stores inside the mall, gender, and the number of accompanying persons who came with the shopper. The analysis addresses shoppers with accompany spend longer than those without accompany. An interesting finding is that the number of stores on each floor has no relation with the number of visits. This finding indicates that due to stores have different products and

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arrangements, shoppers would perceive the attractiveness of the stores differently. The results also show that the higher the floor, the smaller number of visitors are. These imply that in managing multi-story malls business practitioners and mall managers should arrange compositions of stores which attract shoppers to the higher floor.

To obtain insights in sequential shopping patterns we applied a multidimensional sequence alignment method (MDSAM). The method is used to measure the degree of (dis)similarity between pairs of shopping sequences and is followed by a k-means cluster analysis to classify clusters. The results show two distinct clusters according to sequential shopping patterns: types of stores visited and the floor on which the store is located. As expected the two clusters suggest different shopping behavior and sociodemographic characteristics of shoppers: (1) Cluster with the largest membership presents a type of practical shoppers who visits the mall for a shopping purpose (buying products). More specifically, shoppers in this cluster have the smallest range of sequential length patterns, the shortest time of visiting the mall and of spending time in the stores, and somewhat a limited number of floors exploring. Moreover, shoppers in this cluster have the highest ratio of store-stops with purchasing to total of store stops (2) cluster with the smallest membership presents a type of shoppers who visits the mall not only for shopping but also eating and exploring the mall. The shoppers in this cluster have a large range of sequential length following with a large number of stores and floors visited. Shoppers in this cluster tend to spent long time in the mall and in the stores. In addition, the findings suggest that shoppers who visit the mall only to shop at anchor stores seem to have a larger number than shoppers who combine to shop at anchor stores and to eat. This suggests that the anchor stores have a primary role in this mall.

To further explore shoppers’ behavior regarding their sequential shopping patterns a logistic regression analysis is conducted. The analysis explores whether gender, number of accompany, day of the week and time of day influence cluster’s membership. The results show significant different shopping patterns related to gender, number of accompanying persons, day of the week, and time of day. More specifically, there is a significant tendency for male shoppers, shoppers without accompany, and shoppers who come at the weekdays during morning to afternoon to be in the largest cluster mainly consisting of practical shoppers. From a managerial point of view, it might be of interest that the majority of shoppers in the case study are shoppers who tend to have short time, not to visit many stores, but to have many purchases. Thus, mall managers could focus to identify and target this kind of shoppers.

SUMMARY

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This study represents a comprehensive understanding on the shopping behavior process in the shopping mall. Furthermore, it depicts a small step towards better understanding of sequential shopping behavior issues and multi-story shopper behavior.

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1 INTRODUCTION The research presented in this dissertation deals with shopping behavior in malls. This dissertation will focus specifically on shopping decisions with case studies in Jakarta shopping malls. The aim of the research is to understand the shopping processes. More insight is needed in the decisions that are made when shoppers plan to go to a shopping mall, when they are in the shopping mall, and when they finish shopping and leave the mall.

To provide a better understanding of the research, section 1.1 discusses the research background and motivation to conduct this study. Section 1.2 presents the goals to which this dissertation aims to contribute. Next, section 1.3 gives an overview on the approaches used in this dissertation. The last section presents the outline of the dissertation.

1.1 Background and Motivation

In the 1980s, new emerging shopping malls which were larger in scale and integrated with entertainment functions in a single complex mushroomed in many South-East Asian cities (Dick and Rimmer, 1998). For example, retail space in Malaysia, despite the economic crisis in 1997-1998, increased more than 20% annually (Ahmed et al., 2007). In 2008, a total of 71 shopping malls was registered in Singapore’s Association of Shopping Centers (Henderson et al., 2011). In Indonesia, since 1991 there was a dramatic increase in the number of Jakarta’s shopping malls by 100% almost every ten years (Herlambang, 2009). According to our observation up to November 2012, Jakarta had about 140 shopping malls, some still under construction. Surprisingly, most shopping malls were built close to one another in the same part of the city. With this rapidly increasing number of malls, and the consequent fierce competition, retailers and mall managers face the serious challenge to successfully run their business. It is therefore of utmost importance for shopping malls and retailers alike to formulate and execute effective development and marketing strategies.

The ultimate success of shopping malls depends on their ability to attract customers. Beyond market size, which largely depends on the (relative) location of the mall, it depends on the portfolio of stores in the mall, its atmospherics and other influential attributes. An improved understanding of how consumer make their decisions before

1 INTRODUCTION

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shopping or what we call pre-shopping decisions (e.g. shopping purposes, shopping mall choice) and inside the mall or what we call mall-use shopping decisions (e.g. store choice, movement or path choice) will therefore contribute to better-informed formulation of marketing plans and development strategies. Once a location has been decided, there is little marketing can do to increase the market area of a mall. Marketing should aim at increasing the total amount of sales generated from inside the market area by stimulating repeated patronage (loyalty) and maintaining or improving the positive reputation of the mall, reflected in high satisfaction ratings. In part, as has been evidenced by prior research (Bitner, 1992; Fiore and Ogle, 2000; Chebat and Michon, 2003, Ballantine, et al., 2010) customer satisfaction is influenced by the physical appearance of the mall, and its atmospherics and social dimensions, including servicescape and social aspects. Hence, more information is needed about consumer satisfaction and how this is influenced by these factors. In this regard, it should be emphasized that most previous studies have been limited to the store environment (Lam, 2001; Sinha and Uniyal, 2004, 2005; Ghosh et al., 2010). Studies on atmospherics in shopping malls have been relatively limited in number (Turley and Milliman, 2000; Sit et al., 2003; Chebat et al., 2009; Chebat et al., 2010). Information about shoppers’ satisfaction in shopping malls could deliver some directions on how to improve the environment and broaden the retailing knowledge about marketing strategies related to tangible and intangible aspects.

One of fundamental strategies to the success of a shopping mall is the tenant mix and its placement (McGoldrick and Thompson, 1992a; 1992b; Wakefield and Barnes, 1998; Teller and Reutterer, 2008; Yim Yiu and Xu, 2012). In practice, most decisions about the tenant mix are based on evidence obtained in case studies (e.g., McGoldrick and Thompson, 1992a; 1992b). Only a limited number of studies on retail mix have looked at shoppers’ behavior (Brown, 1991a; 1991b; 1992; Nicholls et al., 2002; Zacharias, 2000; 2006). However, recognizing the pattern of activities and movements including duration, route, and type and sequence of store visits would generate information about shopping styles. Hence, analyses of movement and purchasing patterns in a shopping mall could provide powerful insight into different shopper profiles, which is beneficial for practitioners in making decisions about tenant mix and the location of stores inside the mall.

1.2 Research Goals and Contribution

This thesis aims at examining individual shopping behavior in shopping malls in Jakarta with regard to pre-shopping decisions and mall-use shopping decisions. In particular, it

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will investigate the complete shopping process, starting when shoppers plan to go to a shopping mall, when they are in the shopping mall and ending when they leave the shopping mall. Shopping behavior in the shopping mall relates to two types of behavior: stops made to visit stores and the use of facilities, and movement related to strolling and circulating between stores in the public space of the mall. In this case, our definition of public space is the space outside the stores which is operated by the mall manager.

To achieve this aim, the following research questions will be addressed. With respect to pre-shopping decisions, we will focus our attention to answering the following questions:

1. Which needs do shoppers wish to realize when visiting shopping malls?

2. Which attributes/ features of shopping malls influence shopping mall choice?

With respect to mall-use shopping decisions, the following questions will be addressed in this thesis:

3. Which conditions (tangible and intangible) attract specific patterns of behavior?

4. What kind of behavior patterns do shoppers perform inside the mall?

5. How do shoppers use the mall as reflected in their sequential shopping behavior?

6. What is the nature and strength of the relationship between shoppers’ sociodemographics and shopping behavior patterns?

To embed these research questions in a larger context, first a classification of shopping malls in Jakarta is developed. The classification results indicate the variety of malls in Indonesia. They also show the difference between Indonesian shopping malls and Western classifications.

The study contributes to the international state of the art in retailing research in different respects. First, the vast majority of shopping mall studies have been conducted in the US and European context. Studies in Asia, may be except for Hong Kong and Singapore, have been significantly less in number, while studies about Indonesian retailing are scarce. Thus, due to the fact that historically studies on shopping behavior predominantly concerned Western countries (Ahmed et al., 2007), this study is one of the first academic studies, examining shoppers behavior in malls in an Asian country in general and Indonesia in particular. Secondly, there have been very

1 INTRODUCTION

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few studies on a detailed analysis of shoppers’ movement patterns inside shopping malls. Hence, this study increases our knowledge about the use of shopping malls, which as indicated should be useful for locating stores inside malls and deciding on tenure mix.

1.3 Research Approach

This study will be conducted in Jakarta shopping malls. Although, Jakarta had more than 106 shopping malls by March 2011, unfortunately, the shopping malls’ characteristics vary according to the western mall’s categories. It, therefore, requires collecting data on shopping malls that existed in Jakarta by mid 2011 and classifying these malls. The data, that includes size, number of floors, number of stores, tenant mix, feature of tenants, type of ownership, and years of establishment, is collected through as many resources as possible, e.g. literature, research studies, Indonesia shopping mall associations, shopping mall’s experts in Indonesia, and field study.

This central aim of this thesis is to analyze shopping behavior during pre-shopping decisions and mall-use shopping decisions in Jakarta shopping malls. In this regard, this PhD study will use a combination of a survey questionnaire and a tracking method. The questionnaire is primarily used to collect data on shopper’s motives and evaluation of shopping malls’ attributes. The tracking data are used to analyze the micro behavior of shoppers inside a mall. To investigate the use of floors and comprehensive behavior patterns, tracking is used to unobtrusively observe individuals’ shopping behavior inside the mall. For each shopper, data on the sequence of stops and visits, and route between stop is collected. In order to get better movement behavior data, the study will correspond to multi-story shopping mall, meaning that the building will consist of two or more floors above ground.

1.4 Outline

To achieve the formulated goals and objectives, the thesis is organized into three parts and nine chapters. Part I deals with the theoretical background and introduces the study area. Part II focuses on investigating pre-shopping decisions. Part III examines mall-use shopping decisions.

Following this Introduction, Part 1 starts with Chapter 2, which presents the growth of shopping malls in Jakarta. It derives a classification of shopping malls and reports a comprehensive review of the evolution of these malls to provide a general background

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to this study. Chapter 3 formulates a conceptual framework derived from a literature review that guides subsequent analyses.

Part II covers behavioral analyses of pre-shopping decisions. It starts with Chapter 4, which documents the development of the questionnaire and the data collection. The questionnaire was designed to elicit shoppers’ needs underlying their mall choice including their evaluation or assessments on the attributes of shopping malls. Three types of shopping malls are selected to collect the data. These are obtained from the classification of shopping malls, discussed in Chapter 2. This is followed by an overview of sample characteristics. The significant differences in the datasets are examined in detail according to shares and facets across the malls, and the differences of data characteristics among the malls. Attention in Chapter 5 turns to discussing shoppers’ evaluation (satisfaction) of attributes of shopping malls, which shows the influencing attributes of shopping malls on shoppers’ mall choice. This chapter also examines which attributes significantly contribute to the overall evaluation of the malls.

Part III then shifts the focus of attention to mall-use shopping decisions. This data for the analyses of this part is collected in two ways: the questionnaire and the tracking method. Chapter 6 analyses shoppers’ store visiting behavior in three types of shopping malls from the questionnaire. A cluster analysis is applied to identify shopping styles that describe store visits patterns in the shopping malls. As the data was taken from the questionnaires in this analysis we did not examine sequences and location of stores. Chapter 7 explores shopping behavior pattern through the tracking method. Ideally, we investigate shoppers’ behavior in different types of shopping malls. However, due to lack of time and insufficient resources, we only focused on a specifically selected mall. This chapter analyses shoppers’ stop and movement behavior, including the sequence and the location of their stops. It includes a description of the sample and the results of a descriptive analysis of sequential shopping behavior. Chapter 8 continues by analyzing the tracking data using multidimensional sequence alignment to examine differences and commonalities in sequential shopping behavior. Subsequently, a cluster analysis is applied to identify different sequence patterns. Finally, a logistic regression analysis is used to estimate regularities between sequence patterns and sociodemographics variables.

The final chapter, Chapter 9, draws together the findings of this thesis and concludes it by discussing research implications, limitations, and directions of future research.

PART I INDONESIAN SHOPPING ENVIRONMENT AND THEORETICAL BACKGROUND


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2 EVOLUTIONARY PATTERNS IN JAKARTA SHOPPING MALLS

According to Jakarta’s regulation, a modern private market is any kind of trading place (buying and selling goods or services) that is owned by groups, individuals, companies or cooperatives, in an integrated building or space. A shopping mall as a type of modern private market concerns enclosed buildings with at least 4000m2 (Peraturan Daerah Nomor 2 Tahun 2002). In practice, this definition has found various interpretations; modern private market buildings called “shopping center”, “plaza”, “shopping mall” or “international trade center” do not differ much in form and function as a trading place. The different terminology is used primarily for marketing purposes and is not based on the general terms that are published by the International Council of Shopping Centers (ICSC). Thus, in this study, we do not make any explicit distinction based on this different terminology: all modern private market buildings were included in analyzing the retail structure of Jakarta.

Keeping this in mind, to describe the context of this PhD study, this chapter will first briefly discuss the development of shopping malls in Jakarta, with reference to developments elsewhere in the United States and Europe. Next, attention turns to examining the data of shopping malls that existed in Jakarta from 1961 up to 2010 and investigating the category system. Lastly, we will report the outcomes of a classification of shopping malls in Jakarta using cluster analysis. The clusters of shopping malls describe the retail structure of Jakarta and are used to select specific malls for further studying shopping behavior.

2.1 The Evolution of Shopping Malls

This section describes the evolution of shopping malls in Jakarta. The first sub-section gives a brief account of the historical evolution of shopping malls in the United States and Europe, against which the developments in Jakarta can be positioned. The second sub-section describes in greater detail the growth of Jakarta shopping malls. In addition, background information relating to the characteristics of Jakarta is presented.


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2.1.1 The Historical Evolution of Shopping Malls

In the 1900s, department stores appeared in major cities in the United States and Europe. Mechanical inventions, such as the steel frame and escalator, allowed the expansion of department stores. In 1922, the first unified planned shopping mall was built in Kansas City (Koolhaas, et al., 2002; Coleman, 2006). In the 1940s, the development of new shopping malls responded to the population growth. However, there are differences between the development in the United States and Europe. There was plenty of available land and good road networks and accessibility in the United States that stimulated expansion in demand, manufacture, and suburban living. At the same time, shopping centers rapidly increased with some new stereotypes. These shopping centers were planned on green fields near highways and interconnections to serve suburban residences.

Developments in Europe differed in some important respects. After the world war, many bombed cities were rebuilt to accommodate the increasing population in Europe. Gradually, the pedestrianized area concept was introduced in the city centers. Particularly in the Northern and Mid European countries, retail planning was strongly enforced with the concept of a functional hierarchy as its hallmark. Retail planning restricted car-dependent shopping malls at the edge of cities. Rather, shopping malls expanded along the streets in the center of these cities, where only pedestrians were allowed. Only, more recently, retail planning became more liberal, leading to more peripheral developments, although city centers remained strong.

In 1956, Victor Gruen introduced his first enclosed shopping mall in Detroit. It created a new kind of environment. The mall was a remarkable (almost) copy of downtowns and a magnetic place with its variety of stores (auditoriums, bank, post office, local retailers, supermarket), its individuality, lights, color, and even crowds (Coleman, 1996; Wall, 2005). This was seen as the future shopping mall in the United States. The Europeans started to develop enclosed shopping malls in 1960s. Between 1960 and 1970 mixed-use shopping malls, that were development types of US shopping malls, spread across Europe. In the 1970s, shopping malls became a place not only for shopping but also for socializing by incorporating food courts, cafes, and restaurants. Increasingly, the focus of shopping malls added to their economic role a social role as a communal meeting place (Kingston, 1994). During this period, a covered enclosed shopping mall, which looks like a box, became prominent in the world. The interior design of shopping malls has been more sophisticated than the exterior. Simultaneously, the size of shopping malls was stretching and facilities were added.


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In the 1980s, shopping malls became a popular destination not only for shopping but also for recreation. A new format of shopping mall emerged. Malls with a combination of retail and major leisure, recreational elements, and or an indoor theme park opened both in the US and Europe. In addition, at the end of the 1980s, lifestyle centers were introduced in the US, providing upscale retailing and fine dining in an external environment. During the 1990s, the development of shopping malls still focused on entertainment, although, in this era, shopping malls also started to integrate with new urban retail formats, such as concourses of train stations and airports. Globally, the rapid growth in the number of shopping malls continued, but they became more varied in terms of location, composition, and design.

2.1.2 Jakarta Shopping Malls

A sharp increase in the number of shopping malls can also be witnessed in Indonesia. The first 6-floor building mall was built in 1961. A slight growth happened between 1961 and 1990. However, since 1991, the number of shopping malls drastically increased by 100% every ten years. This trend was not affected, not even when the economic crisis struck in 1997-1998.

These developments should be understood in the context of the characteristics of Jakarta. Jakarta is the most developed city of Indonesia. The capital city of Indonesia, Jakarta, has an area of 704 km2 and 9.6 million people (Badan Pusat Statistik Republik Indonesia/Statistics Indonesia, 2010). The area is divided into five Kota or kotamadya (formerly municipalities), namely North Jakarta, East Jakarta, South Jakarta, West Jakarta and Central Jakarta, and Kepulauan Seribu (thousand islands). North Jakarta is the area that borders the sea. This area includes some exclusive real estate on the waterfront and is mostly occupied by Chinese businesses. West Jakarta has the highest concentration of small-scale industries, as well as the highest population density. The area includes Chinatown, which continues from the North. Originally, South Jakarta was planned as a satellite area. It has an image as a high-class area since colonial times. Although East Jakarta has potential since the area occupies the biggest size in Jakarta, compared to other cities growth is slightly low with some moderate to lower values for industries and real estate. Central Jakarta is the main business area. However, the population in this area is the smallest because almost all of the area belongs to offices and commercial outlets. The development of residences (apartments) in this area has just started in 1999. Finally, Kepulauan Seribu is a collection of 105 small islands located in the Northern part of Jakarta, Java Sea. Figure 1 shows a map of Jakarta and Table 1 shows the population density of Jakarta’s districts in 2010.

http://en.wikipedia.org/wiki/Municipality


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Figure 2.1 The City Map of Jakarta Source: http://en.wikipedia.org/wiki/Jakarta

Table 2.1 Population Density in Jakarta's Districts in 2010

City Area (km2) Population (people) Density

North Jakarta 142,20 1,468,84 10,33/km2

East Jakarta 187,73 2,393,79 12,75/km2

South Jakarta 145,73 1,995,21 13,69/km2

West Jakarta 126,15 2,322,23 18,41/km2

Central Jakarta 47,90 861,53 17,99/km2

Source: BPS Provinsi DKI Jakarta, http://www.jakarta.go.id/jakv1/

Jakarta is a tropical and humid city, with temperatures ranging between of 24C to 34C year round. In order to create a convenience temperature, almost all shopping malls in Jakarta including all stores in the malls apply artificial ventilation.

The debate on the definition of shopping malls also applies to the chaotic labels of shopping malls in Jakarta. Almost impossible to follow the ISCS classification, Indonesian shopping malls are not based on the range of services offered. Following

Central Jakarta West Jakarta South Jakarta East Jakarta North Jakarta

http://en.wikipedia.org/wiki/Jakarta


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the Jakarta Local Regulation (Kadin-Indonesia, 2009), in this dissertation, a shopping mall is defined as an enclosed-single real estate entity with a minimum gross floor area (GFA) of 4000 m2 and at least one anchor tenant. Since our focus is also on getting better movement behavior data, the shopping malls should have at least 2 floors. Due to the fact that each mall can decide optionally to join the Indonesian Shopping Management Association (APPBI) or not, data were compiled from the documentation of Indonesian Shopping Management Association (website APBBI, 2008; Hartono, 2009), documentation from the websites of Indonesian Real Estate, reports and papers from property consultants (Prastiana, 2010). A number of developers was contacted to verify the data.

Period 1 (1961-1970) and Period 2 (1971-1980)

The first shopping mall in Jakarta, named Sarinah, opened in 1961 and had 21,000 m2 of floor space (see the yellow circle in Fig. 2.2). This enclosed shopping mall has 6 floors and it is equipped with an elevator and escalator. There was a long vacuum until 1970, as the next shopping malls were built in 1971. The second phase of development continued and in 1980 there were 6 new shopping malls of different size; three malls between 4000 and 21,000 m2; two malls between 21,001 and 42,000 m2, and one mall between 42,001 and 80,000 m2. The malls located not only in Central Jakarta, but in North Jakarta and South Jakarta as well. Figure 2.2 shows that all malls in this phase were located in the middle of Jakarta and not in the periphery. Some of the malls were close to each other and of the same size.

Period 3 (1981-1990)

In the third phase, the growth of the malls continued. The expansion settled in residential areas or other locations where no malls existed. The malls located in residential areas were between 4000 and 42,000 m2. During this period, a mall with over 80,000 m2 was also developed. Until 1990, there were 18 new shopping malls; eight malls (44.4%) were between 4000 and 21,000 m2, five malls (27.8%) were between 21,001 and 42,000 m2, four malls (22.2%) between 42,001 and 80,000 m2, and one mall (5.6%) was larger than 80,000 m2.

Period 4 (1991-2000)

The growth of shopping malls further increased in the fourth phase, although in 1997 the global economic crisis had kicked in. In this phase, the new malls were not only


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developed close to the previous malls, but also close to highways and the ring road. As the highway built connections to the suburbs, in particular the trade area of services was expanded not only for residences in the Jakarta region, but also in the suburbs nearby. The expansion was not only in size, but also in function. Some malls were integrated with hotels, offices, sport centers, and apartments. In total, there were 29 new shopping malls and the largest percentage of these malls were larger than 80,000 m2. More specifically, 24.1% of the malls were between 4000 and 21,000 m2; 24.1% between 21,001 and 42,000 m2, 24.1% ranged between 42,001 and 80,000 m2, and 27.7% is larger than 80,000 m2. In addition, labeling the shopping malls as “Trade Centre” became popular in this phase.

Period 5 (2001-2010)

In the fifth phase, the growth of the shopping malls extremely increased and the total number of malls doubled. The success of the malls generated more developers to construct even more shopping malls within the same area of the same or even larger size. Some malls were built in the periphery of South Jakarta. Accordingly, a number of malls created clusters and competition within these clusters increased significantly. There were 57 new shopping malls opened in this phase; 33.3% between 4000 and 21,000 m2; 17.5% from 21,001 to 42,000 m2, 24.6% between 42,001 and 80,000 m2, and 24.6% larger than 80,000 m2. The growth of Jakarta shopping malls suggests inadequate planning control from the local government (Pemerintah Daerah Jakarta). As a result, property developers can construct new malls of the same size as existing malls. Moreover, these new malls can be situated adjacent to existing malls offering the same range of services. It is not a surprise that side-by-side shopping malls tend to have a similar tenant mix and compete for the same target market. It led to fierce competition and sometimes a decline of business.

Another aspect that differentiates Indonesian shopping malls from those in the US and Europe is the type of ownership. In the beginning, Indonesian shopping malls followed two types of management systems: single ownership management or real estate investment trusts (REIT) properties management. Single ownership management is a company that manages all mall business, while REIT is a company that not only manages the mall, but also gives tenants' the convenience to lease the units. In 1992, strata-lot-title management was added. A strata-lot-title management is a system of managing the mall in which tenants have the rights to possess their unit strata title. In this type of management the company will manage the mall until all units sold out, and afterwards


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its authority is only maintaining the building. In particular, tenants could own their unit and have a freedom to determine their own decisions with regard to the properties, but at the same time they have to share a joint ownership of common areas (Neo, 2005).

Although all units could be sold out in a very short period, tenants had difficulties in managing the malls, especially in controlling tenant mix (Rahman, 2011). A strata-lot-title system is mostly applied in the International Trade Center (TC). [Note: Similar with malls, Indonesia TC has anchors and stores and it directly sells merchandise to the retailers].

The uncommon business system of renting tenants plays an important role in the development of shopping malls. A number of companies in Indonesia could hold over thirty franchising brands and chain stores or restaurants. As a consequence, once mall management has an agreement with such a company, it may happen that the same company manages 30% of the stores in the mall. Definitely, the company could also take part of the management of the mall (Sjohirin, 2010; Santosa, 2010; Susilo, 2010 Rahman, 2010).

2.2 The Need for Classifying Jakarta Shopping Malls

It is essential to develop a classification of shopping malls in Jakarta to better understand and analyze consumer shopping behavior decisions and patterns. Classification systems can help researchers to compare and contrast empirical findings across a variety of spaces, cultures and time periods (Guy, 1998). Many different approaches have been suggested to classify shopping malls. The International Council of Centers (ICSC) has introduced the most popular and global classification. Generally ISCS’s classification is defined according to the US and Europe shopping malls database. Then later on, some countries developed their own classification by adding more attributes.

http://www.wisegeek.com/what-is-joint-ownership.htm


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In 1999, ICSC has launched eight shopping center types based on criteria such as acreage, number of anchors and type of anchors, anchor ratio and trade service area. The main criteria in classifying a center are concept, which covers the types of goods or services sold, and size of the mall. However, the classification of shopping malls was surrounded by some issues regarding the number of shopping malls formats that exist and the allocation of shopping malls to the various categories (DeLisle, 2007). It was also argued that none of the classification systems are universally applicable, even within the similar geographical area and time period (Guy, 1998).

As mentioned in sub-chapter 2.1.2, Jakarta shopping malls are not based on the geographic range of services. Therefore, it would not be accurate to apply the international classification. Rather, we develop our own classification that is better tailored for this specific study.

2.3 Jakarta Shopping Mall Classification

As mentioned in the beginning of this chapter, Jakarta’s regulation is highly ambiguous. Attempting to describe the evolution of Jakarta shopping malls is hampered by the fact that Indonesia has no classifications of shopping malls. Generally, a shopping mall classification system should meet the criteria of being comprehensive, unambiguous, measurable, reliable, meaningful, robust, implementable, defensible, politically palatable, and auditable (DeLisle, 2009).

With the unique characteristics of Jakarta shopping malls and the absence of the shopping mall classification, three steps are needed to construct the classification. Firstly, the key variables need to be identified in accordance with the particular factors underlying a classification of Indonesian shopping malls. Secondly, the data reflecting these variables need to be collected. Thirdly, the method used for the classification should be selected. Finally, the method should be applied and a descriptive analysis of the resulting clusters should be conducted to profile the clusters.

2.3.1 Identifying the Key Attributes

To provide a framework for classifying Jakarta shopping malls, we refer to the most common classification criteria for shopping malls such as size, design, location, number of anchors and tenants, theme (DeLisle, 2009; Coleman, 2006; Neo, 2005; Guy, 1998), function, physical form, retail offering or trip purpose (Guy, 1998), and tenant mix or


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product orientation (Coleman, 2006). In particular, five attributes, that reflect Indonesian shopping malls, were selected:

- Size: This attribute is applied in almost mall classification systems. In this research study, ground floor area (GFA) was used as a proxy variable of size.

- Design: The variable classifies design features or physical form (DeLisle, 2009; Coleman 2006) such as shape, location of anchors, and number of floors. The empirical data suggest that the typical shopping mall in Jakarta is an enclosed-mall. The number of floors, then, differentiated physical form.

- Features of tenants: In many respects, consumers recognize the assortment of goods and services as a reflection of shopping mall (DeLisle, 2007; Guy, 1998). In this research, features of tenants were analyzed in terms of the price level that tenants represent. Five types were identified: HE-class (high-end) price levels, which consist of international designer brand shops (boutique/haute couture); A-class price levels, which consist of international brand shops (mass production); B-class price levels, which consist of national brand shops; C-class price levels, which consist of local brand shops; Trade Center-class price levels, which consist of wholesalers and distribution outlets.

- Theme: Themes or market positioning strategies classify the orientation of the shopping mall (DeLisle, 2009; Coleman, 2006), but they could also aim at differentiating demographic segments of the market by using price, value and amenities. There are five themes related to the attachment of facilities within the building to understand the market positioning strategy. Firstly, a shopping mall with no facility. Secondly, a lifestyle center, which has many specialty stores, dining, and entertainment. Thirdly, a specialist center, which offers one major category of trade combined with other tenants such as leisure to support. Fourthly, mixed use center, which has some combination of retail, offices, hotels, residences, recreation or other functions. Finally, an entertainment center, which has a special sport area such as basketball hall, bowling alley, swimming pool, or convention hall.

The last attribute is the type of ownership. Since the type of ownership dictates the management of the shopping mall and influences its positioning strategy (Neo, 2006), it is essential to include this attribute in the classification.


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- Types of ownership: There are four types of ownership: single ownership management; strata-title-lot; mixture of single ownership & strata-title-lot ownership; REIT (real estate investment trusts) ownership.

2.3.2 Data Preparation

Data were collected for all shopping malls in Jakarta, which operated and existed between 1960 and 2010. To ensure the accuracy of the data, all shopping malls were investigated in the field in March 2011. A total of 106 shopping malls were identified. A further 17 were excluded from the analysis due to gaps in the data. Therefore, the results presented below thus refer to 89 shopping malls.

2.3.3 Classifying Jakarta Shopping Malls: Hierarchical Cluster Analysis

A hierarchical cluster analysis was applied to find relatively homogeneous clusters of shopping malls among the 89 shopping malls. More specifically, the complete linkage method was used to identify the clusters. Clustering is achieved on the basis of a measure of ‘distance’ or ‘(dis) similarity’ between malls. Typically, in clustering methods, all shopping malls within a cluster are considered to be equally belonging to the cluster. As an output of HCA, a hierarchical tree diagram, called a dendrogram, shows explicitly the process of linkages. The clusters are linked at an increasing level of dissimilarity. The goal of the clustering algorithm is to join objects together into successively larger clusters, using some measure of similarity or distance. With the aid of the dendrogram, the total number of clusters can be recognized, and the hierarchical structure of the data is depicted.

Because the variables were measured on different scales, the data were first standardized. The number of clusters was decided based on a visual inspection of the dendogram.

2.4 Identifying and Labeling the Clusters

The goal of the clustering is to classify the shopping malls in Jakarta. The dendrogram depicted in Figure 4 shows the results of the clustering process using size, number of floors, feature of tenants, themes, and type of ownership as the input. The horizontal axis represents the level of (dis) similarity at which two shopping malls are joined into a cluster. It is also possible that a shopping mall is joined with a cluster formed earlier during the clustering process, or that two clusters are joined to make up a larger cluster. On the vertical axis, It is indicated which objects are joined. The vertical red line shown


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in Figure 2.6 suggests that the classification yielding three clusters might be a good division to describe the variability in the criteria used to differentiate between shopping malls.

Table 2.2 presents the distinctness of the shopping malls within each cluster and the classification criteria; size, number of floors (design), feature of tenants, themes, and types of ownership. Based on the profile of the clusters, the following labels were given to the clusters: classic shopping mall, local shopping mall, and modern shopping mall.

The first cluster is labeled “classic shopping mall” due to the fact that all malls have a single type of ownership and the theme of the majority of the malls (market positioning strategy) is a shopping mall without any facilities. These concepts follow the ICSC definition of shopping mall: a group of retail and other commercial establishments that is planned, developed, owned, and managed as a single property. The majority of the malls provide the A-class price level. In contrast, the second cluster – “local shopping mall” - has the majority of strata title or the long leasing type of ownership, which defines the unique Indonesian’s characteristics of shopping malls. In addition, most of the featured tenants have a Trade-center class price level, which indicates local merchandise. The third cluster is labeled “modern shopping malls”, because it represents new improved designs of classic and local shopping malls. The majority type of ownership is a mixture between single ownership (that is related to classic shopping mall) and strata title (that is related to local shopping mall). In addition, the majority of theme in this cluster operates as mixed-use which combine shopping mall and other facilities, for example residential, rental office, convention hall, etc. The cluster on average has the largest size. The majority of these centers apply the B-class price level.

To obtain a better interpretation on identifying the clusters, Table 2.3 presents additional information about clusters regarding the range of size, range of feature of tenants, range of timeline, and district. The timeline shows time when shopping malls have been operated. The timeline is divided into five time periods: period 1 is between 1961 and1970, period 2 is between 1971 and 1980; period 3 is between 1981 and 1990; period 4 is between 1991 and 2000, while period 5 is between 2001 and 2010. The district indicates the majority location of shopping malls in the clusters.


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Figure 2.6 Dendrogram for Complete Linkage Hierarchical Clustering of Jakarta Shopping Malls

1

3

2


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Table 2.2 Profile Clusters of Jakarta Shopping Malls

Cluster 1 Cluster 2 Cluster 3 Classic shopping mall Local shopping mall Modern shopping mall

N=45 (50.56%) N=25 (28.09%) N=19 (21.35%) Average size (GFA) 65, 233 m2 50,596 m2 98,835 m2 Average number of floors 6 floors 6 floors 9 floors

Type of ownership Single ownership (100%)

Strata title/ long leasing ownership (52.0%)

Mixed ownership (68.4%)

Majority feature of tenants

A-class price level (44.4%)

Trade Center -class price level (68.0%)

B-class price level(63.2%)

Majority theme or market positioning strategy

Shopping mall (51.5%) Shopping mall (48.0%) Mixed-use (31.6%)

Table 2.3 Specific Profiles Clusters of Jakarta Shopping Malls

Classic shopping mall Local shopping mall Modern shopping mall

N=45 (50.56%) N=25 (28.09%) N=19 (21.35%)

Range of size 11,168-239,490 m2 8000-180,608 m2 6122-361,000 m2

Range of feature of tenants HE – A – B price levels B – C – TC price levels A – B – C – TC price

levels Range of timeline

1971-1980 (2.2%) 1961-1970 (5.3%) 1981-1990 (31.1%) 1981-1990 (16%) 1981-1990 (10.5%) 1991-2000 (24.4%) 1991-2000 (40.0%) 1991-2000 (10.5%) 2001-2010 (42.2%) 2001-2010 (44.0%) 2001-2010 (73.7%)

District South Jakarta (42.2%) South Jakarta (36.0%) Central Jakarta (31.6%)

To provide some illustrations on floor plans and building designs of the three clusters, Figure 2.7, Figure 2.8, and Figure 2.9 shows pictures of each cluster.

2.4.1 Profile of Cluster 1: Classic Shopping Malls

The first cluster is the largest in size, representing over 50.56% of the shopping malls in Jakarta (N=45). All malls in this cluster have single ownership and the focus of the market positioning strategy is the shopping mall. The size of the malls in this cluster is between 11,168 m2 and 239,490 m2. While the average size is in between that of the local and modern shopping malls (65,233 m2) with around 6 floors. The cluster has three classes price levels: HE-A-and-B. The A-class price level is the majority in this cluster. In addition, shopping malls in this cluster start to operate between 1971 and 2010, but the majority of shopping malls in this segment start to operate between 2001 and 2010. The shopping malls in this cluster are predominantly located in South Jakarta.


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Figure 2.7 A Sample of Classic Shopping Malls: Mall Puri Indah (1997) – West Jakarta 57,000 m2, 5 floors, A-class price level, theme: shopping mall

The names of the shopping malls in this cluster are: Belleza Shopping Arc La Piazza Pejaten Village Blok M Plaza Mall Ciputra Plaza Cibubur Central Park Mall Kelapa Gading 1 Plaza Indonesia Cilandak Town Square Mall Kelapa Gading 2 Plaza Mebel TC Daan Mogot Mall Mall Kelapa Gading 3 Plaza Senayan D'best Fatmawati Mall Kelapa Gading 5 Pluit Junction Dharmawangsa Square Mall of Indonesia Pluit Village Emporium Pluit Mall Mall Taman Anggrek Pondok Indah Mall I eX Plaza Matahari Puri Mall Pondok Indah Mall II fX Mall Mega Pasaraya Seibu Puri Indah Mall Gandaria City Menteng Huis KF Rasuna Epicentrum Golden Truly Gn Sahari Pacific Place Ratu Plaza Grand Indonesia ST Pasaraya Blok M Senayan City Jakarta Design Centre Pasaraya Grande Setiabudi One Kramat Jati Indah Plaza Pasaraya Manggarai The Arcade @Oakwood


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2.4.2 Profile of Cluster 2: Local Shopping Malls

The local shopping malls represent 28.09% of the shopping malls in Jakarta (N=25). The local shopping mall represents the strata title/long leasing ownership which is very rare in the Western malls. Trade Center-class price level dominates in this cluster although the range of feature of tenants is including also B and C class price levels. Indicating that most of the shopping malls in this cluster focus on selling local brand shops, wholesales, and distribution outlets. Interestingly, this cluster has the smallest average size among the (50,596 m2) with size between 8000 m2 and 180,608 m2. The average number of floors are 6 floors.

These malls tend to have the shopping mall as the core of their market positioning strategy. Shopping malls in this cluster start to operate between 1981 and 2010, while most of the malls operate during 2001-2010. South Jakarta is the area where this cluster is located.

Figure 2.8 A Sample of Local Shopping Malls: Blok M Square (2008) – South Jakarta

110,61 m2, 11 floors, TC-class price level, theme: shopping mall


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The names of the shopping malls in this cluster are:

2.4.3 Profile of Cluster 3: Modern Shopping Malls

With 21.35% of shopping malls in Jakarta (N=19) the modern shopping mall becomes the smallest cluster in Jakarta. The dominant market position strategy of this cluster is mixed-use, that showing a shift from pure shopping malls. Most of malls in this cluster have a mixture ownership between single and strata title ownership. This cluster has the largest range of size (between 6122 m2 and 361,000 m2) and the biggest average size (80,036 m2). It is not surprising that this cluster also has the tallest on average floors (8 floors). This cluster has the widest range of price levels (A-B-C-Trade Center), however, B-class price levels is the focus feature of tenants. The range of timeline in this cluster is between 1961 and 1970 and between 1981 and 2010, but most malls start to operate between 2001 and 2010. This cluster predominantly locates in Central Jakarta.

The names of the shopping malls in this cluster are:

Arion Mall Mega Glodok Kemayoran Sport Mall Kelapa Gading Carrefour Cempaka Putih Metro Tanah Abang STC Senayan Cibubur Junction Plaza Semanggi Sunter Mall Gadjah Mada Plaza Pulogadung Trade Center Tamini Square Lindeteves TC Sarinah Thamrin City Mall Artha Gading Season City Trade Mall Taman Palem WTC Mangga Dua

Atrium Plaza Senen ITC Roxy Mas Mall Blok M Blok M Square Jatinegara Plaza Mall Cilandak Grand ITC Permata Hijau Kalibata Plaza Mall Pasar Festival ITC Cempaka Mas Kelapa Gading Hypermall Mangga Dua Square ITC Fatmawati Kelapa Gading Trade Center Metro Pasar Baru ITC Harco Mas Mangga Dua Kenari Mas Plaza Plaza Glodok ITC Kuningan Koja Plaza Pusat Grosir Cililitan 1 ITC Mangga Dua Mall Ambasador Pusat Grosir Cililitan 2 Slipi Jaya Plaza


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Figure 2.9 A Sample of Modern Shopping Malls: Thamrin City (2006) – Central Jakarta 361,000 m2, 12 floors, TC-class price level, theme: mixed use (shopping mall & apartment)

2.6 Conclusions and Discussion

As a general background to the core of this thesis, this chapter reflected on some basic definitions of shopping malls, and described briefly the evolutional pattern of shopping malls in the US, Europe and Jakarta. It is argued that the general shopping mall classification of ICSC is not appropriate for categorizing Jakarta shopping malls. Thus, Jakarta shopping malls were classified based on a set of specific criteria, such as size, number of floors (design), themes, feature of tenants, and type of ownership. The classification resulted in three types of shopping malls, namely Classic shopping mall, Local shopping mall, and Modern shopping mall.

This classification will serve as the context to collect data about consumer motivations and mall usage. We will examine whether exemplars of the clusters of shopping malls in Jakarta induce discriminatory perception, motivational dispositions, shoppers’ satisfaction and shopping behavior patterns inside the malls. These results will be discussed starting in chapter 4. First, however, in the next chapter we start with a review of the relevant literature to develop a conceptual framework of consumer behavior.

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3 SHOPPING BEHAVIOR: A LITERATURE REVIEW

As described in the introductory chapter, a better understanding of shopping behavior will result in the formulation of better marketing plans and development strategies. Therefore, this study sets out with a conceptual framework of shopping behavior that identifies the various decisions and choices consumers have to make during their shopping trips. These decisions range from the decision to go shopping, through the choice of shopping mall to which stores or tenants to visit, what to buy, where to stop for a rest, and so on. Information on malls’ choice will be useful for practitioners to secure their position in the arena of competing shopping malls. Knowledge about motives underlying mall choice and about shoppers’ preferences and behavior is beneficial in creating a demand-oriented mall environment and the right arrangement of tenants. Understanding movement patterns inside malls may assist decisions regarding stores’ locations in the malls and the spatial (vertical and horizontal) configuration of different types of stores.

Based on the postulated conceptual framework, this chapter summarizes the key literature on shopping behavior with a special focus on shopping malls. Particularly, the literature on shopping motives, the choice of shopping malls, and shopping behavior inside shopping malls will be discussed. This chapter also reviews some approaches in shopping behavior research that related to shopping malls.

This chapter is structured as follows. The first section discusses the conceptual framework. The second section discusses reasons to go to shopping malls. Several studies are reviewed to identify the purposes of people go to a shopping mall. The next section presents a discussion to clarify the decisions on mall choices. The discussion also defines hidden dimensions of shopping malls’ environments that satisfy shoppers and lead shoppers to choose the shopping malls. Previous studies on attributes of shopping malls that significantly appeal to shoppers are discussed. The following section summarizes the literature about behavior inside shopping malls, which includes stop and movement behavior. The chapter ends with conclusions and a discussion of issues that provide guidelines for the subsequent data collection and analyses.


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3.1 Conceptual Framework

Shopping is conducted to satisfy particular needs and desires. These include the needs of survival, such as purchasing food, and the desire of pleasure and comfort, such as to enjoy the shopping experience under comfortable conditions. Examples of other needs and desires would be the needs to maintain relationships and be liked, to socialize, to be accompanied by friends, and to be part of the crowd. Needs and desires are time-dependent. Therefore, after some moment in time, an individual activates the decision to go shopping if the need arises.

When embarking on a shopping trip, individuals have to make several interdependent decisions. First, they need to make some decisions before the actual trip; the so-called pre-shopping decisions. Pre-shopping decisions concern decisions that are made before going to the mall. Generally, individuals decide why they need to go to the mall (purposes). The degree of pre-planning that is involved may differ. Some individuals may prepare a detailed shopping list and plan exactly the sequence in which they intend to visit the stores. Other may not plan at all, except for having a very crude idea what kind of things they wish to do. Then, they choose the shopping mall. In addition, they might consider how they can get there, and who will go with them. The travel party may also influence the choice of mall and what to do there. Previous shopping experiences with malls will influence future choices. The more satisfied an individual is with a mall based on previous shopping experiences, the more likely the individual will choose that mall again. Such reinforcement behavior may be context-dependent.

Next, the actual trip is made. Having arrived at the shopping mall, the shoppers need to decide which stores to visit, in what order and how to move between stores. This list of stores may not be fixed. Particularly in case of comparison shopping, such as for clothes, it may be that a shopper first compares the available merchandise in different stores before buying. The sequence may also depend on the shopping list. A shopper likely avoids having to carry heavy items during the full stay in the mall. It is also likely that shoes will be bought after clothes. Under time pressure, one may assume that the highest priority items are moved forward to ensure that sufficient time can be allocated to the purchasing of these items (e.g. Van der Hagen et al., 1991).

As for the movement pattern, the most efficient tour would be according to a traveller’s salesman problem, which would imply the overall shortest distance. Alternatively, individuals may apply a sequence of shortest paths between successively visited stores. However, more complex patterns may also emerge due to window shopping, imperfect


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knowledge or because the decision is not only made on distance, but also on other factors, such as mall environment. Shopping mall environment can influence shoppers whenever they visualize, employ or sense it. Shoppers are eager to increase their time to linger in a mall that has good atmospherics such as good smells and well-designed décor and interior. If shoppers stay longer, their movement pattern will be more complex and stores have higher chances to be visited. Attractive stores like stores with fascinating window displays or inviting discounts may trigger a shopper to stop for a visit even though the merchandise in that store is not on the shopping list. If this store has excellent services, shoppers may become eager to purchase.

Attractive tenants are also able to drive shoppers to visit them. Vice versa, unremarkable tenants may lead shoppers to ignore their stores and not to follow

Figure 3.1 Conceptual Framework

shopping’s purpose

satisfaction with shopping mall dimensions

e.g. location, price, store variety & merchandise selection, atmospherics, advertising & promotion, social environment, and personal service

store choice sequential shopping pattern

STOP BEHAVIOR

visiting stores

visiting facilities

using public space

purchasing

PRE-SHOPPING DECISIONS MALL-USE SHOPPING DECISIONS

MOVEMENT BEHAVIOR

strolling

sequence

switching floor

shopping-mall choice


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certain paths. Regarding multi-story malls, vertical circulation through elevators, escalators, and stairs, play an important role in channeling shopper flows between floors. In addition, shoppers likely stroll along paths, which are close to vertical circulations and prefer to visit stores located close to the destinations (stores which are on the list). Given these considerations, the mall environment including atmospherics, selection of stores, layout of stores and services become significant in influencing shoppers’ decisions about store choice and the implied sequential shopping patterns. Figure 3.1 gives a conceptual framework of individual’s shopping trip decisions. We will now discuss the finding of empirical studies about the different components of this conceptual framework.

3.2 Motives Underlying Shopping Mall Choice

As explained earlier, shopping malls have turned from retailing only to mixed retail, entertainment, and leisure, complexes to tailor the shifting motivations of consumers. Consequently, as a mirror image, consumers may display different motives when visiting a shopping mall. In the early times, most shoppers had a single purpose to go to a store. However, nowadays, because a shopping mall or shopping center offers a wider set of options, including plenty of stores, variation of products, and other facilities, shoppers are more often involved in multi-purpose shopping (Leszczyc, et al., 2004; Pan and Zinkhan, 2006). The expansion of service facilities in malls such as beauty salons, health-facilities, and banks also changes the purpose to go to a mall. Shoppers visit a mall not only for shopping, but also for service-and-business related activities. In the same way, an attractive mall environment expands the purposes of visiting a shopping mall to include recreation and leisure.

Not only the development of shopping malls, but also other aspects may influence multi-purpose shopping behavior, such as, for example, location. It is argued that shoppers who live far away from the mall and spend a long time travelling from their location to the mall may be more involved in multi-purpose shopping than shoppers who live in the same neighborhood as where the mall is located (e.g. Leszczyc et al., 2004; Arentze. 2005). For them, multi-purpose shopping behavior is a more efficient way of organizing their shopping activities.

The personality of shoppers has been found to be closely related to shopping motives. In addition, differences in shoppers’ profiles tend to be related to shopping motives. For example, the motives of teenagers to visit malls are more likely to hangout or to-see-and-be-seen (Matthews et al., 2000; Vanderbeck and Johnson, 2000), while the


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motives of young couples to malls are more likely to go for a rendezvous. Females’ shopping motives are more likely to shop for fashion while males tend to shop for other reasons (Dholakia, 1999; Anselmsson and Johansson, 2007; Helgesen and Nesset, 2010). It can thus be concluded that sociodemographics, including age, gender, status, and income that constitute a shopper’s profile may be correlated with shopping motives. Besides sociodemographics, some studies found that shoppers travelling with other people may have special reasons for their shopping trips (Haytko and Baker, 2004; Chebat, et al., 2014). For example, shoppers who are accompanied by children may combine shopping with entertainment, while shoppers who are accompanied by colleagues may combine shopping with a visit to a restaurant in the mall.

Shoppers may have multiple reasons to go to a mall, complicating the choice process (Ibrahim and Wee; 2002; Sit et al., 2003; Stoel, et al., 2004; Dennis, 2005; Allard, et al., 2009). The motives may also influence micro-behavior. If a shopper has a utilitarian motive and has pre-planned her shopping trip to the mall, the sequence of stops and the routes chosen are likely more efficient compared to shoppers without a clear shopping list.

3.3 Choice of Shopping Mall

Studies in geography and urban planning have predominantly conceptualized the choice of shopping mall as a trade off between size and distance or travel time. Size is usually considered a proxy for the amount of choice that is available. In addition, sometimes anchor tenants are introduced as they exert an extra pull in attracting consumers to the mall. In contrast, distance or travel time is meant to represent the amount of effort needed to reach the mall. In that sense, travel time is a better representation than distance as it captures any variability in travel times. Prior research has consistently shown that the willingness of people to travel further distances decreases with increasing distance (e.g. Leszczyc et al., 2004). Sometimes, this distance decay concept is replaced by accessibility. In addition to travel time, it may include some variables related to parking such as availability and parking fees.

In contrast to urban planning research, marketing research has tended to identify a more detailed set of factors influencing mall choice. The focus is upon functions and challenges that impact business practices, more specifically in increasing revenues. In retailing, understanding consumer behavior is critical for marketing any product or service successfully. Thus, marketing studies have typically added variables such as

http://www.tandfonline.com/doi/full/10.1080/09593969.2013.801358#cit0050


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price, service, and atmospherics, and retail image as dimensions of shopping mall choice.

The importance of consumers’ shopping experience in the formation and enhancement of retail image has been identified in several studies (e.g. Maclnnis and Price, 1987; Hart et al. 2013). Marketing literature has typically differentiated several shopping malls’ dimensions that are salient to consumer’s shopping experience and subsequently influence mall patronage (Puccinelli et al., 2009). Factors include tangible aspects of shopping mall’s dimensions (e.g. the location of the mall in proximity to home and work, price and promotion, parking spaces, hours of operation, store variety, merchandise selection and quality) and intangible aspects such as atmosphere, services, and social environment. The next section provides details of studies that have examined the interrelationships between shopping mall’s dimensions, shopping experience, and shopping mall choice.

3.3.1 Location and Convenience

The three most important things in retailing are: location, location, and location (e.g. Timothy, D. J., 2005; Coleman, 2006). It is obvious that location decisions are among the most critical aspects of successful retail establishments. Shopping experience starts with the trip to the shopping mall and thus the shopping location becomes part of the experience (Hart et al., 2013). However, shopping opportunities are scattered and scarce. By definition, it means that shopping requires some effort to reach the destination. Basically, location relates to the geographical distance to stores or shopping malls from a consumer’s home. It is assumed that the probability of visiting stores or shopping malls with similar characteristics and the same distance becomes equal for single purpose shopping. The farther the mall location, the lower the probability that it will be visited. In addition, individuals also make multi-stop, multi-purpose or combined purpose trips. They may combine visiting different locations to buy different products or combine the shopping trip with other activity (e.g. Carter and Haloupek, 2002). It means that distance or travel time from home is no longer a good measure of the effort to reach a location. To get a better understanding about consumers’ evaluation of their location shopping experience, we will now discuss some previous empirical studies.

Some studies have operationalized the effort of consumers to reach the shopping mall destination in terms of distance, others in terms of travel times, yet other in terms of the broader notion of accessibility. Traditionally, many studies have provided evidence


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that shoppers consider choosing malls conveniently located and close to their residential or working area (e.g., Jackson et al., 2011; El Hedhli et al., 2013; Singh and Prashar, 2014). Many formal location theories such as central place theory (e.g. Craig et al., 1984) are based on the theoretical assumption that shoppers choose the nearest mall. Central place theory suggested that if consumers have to decide where to shop among similar stores, they will select the nearest available store to get their products (Lösch, 1954; Christaller, 1966). However, central place theory created controversy in nowadays situations, because markets in retailing are overlapping without strict boundaries. Therefore, shoppers may travel a bit further to get a cheaper price and subsequent sales price savings may pay for the additional transport cost. Interestingly, there is no certainty about shoppers’ choice, as there are many aspects that may change their decisions. In addition to distance or travel time, shoppers consider other attributes of shopping malls when deciding where to shop.

Huff (1963) introduced an alternative approach, which is called the spatial interaction model. The model suggested consumers will patronize particular stores or shopping malls not only as a function of distance or travel time to these locations, but also of the attractiveness of locations. For example, Sit et al. (2003) investigated how shoppers choose between regional and sub-regional shopping centers in Australia. In their study, Sit et al. separated between macro accessibility and micro accessibility. Macro accessibility is concerned with closeness to home and the condition of access roads, while micro accessibility is concerned with adequate parking space, easy access to the mall and appropriate trading hours. The study collected data by using mail surveys and a confirmatory factor analysis was employed to evaluate the data. Results demonstrated the importance of micro accessibility in both shopping centers. Results indicated that the expectation of convenience is always high in selecting the shopping center. However, macro accessibility is fairly important only in the sub-regional center, which on average has a half size of the regional center and more limited number of retail outlets than the regional center. These findings suggested that the expectation of distance and travel time to shopping centers becomes higher if the centers have limited offers of the retail outlets. These results indicated that in shopping center choice shoppers perceive location not always as a short distance or a short travel time, they also consider convenience of accessibility in their decisions.

Nicholls et al. (2002) examined changes in shopping behavior patterns between 1993 and 1999, including the travel time pattern between the mall and shoppers’ home and number of store visits. Data was collected through mall-intercept interviews at a mall


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located in Florida. Two-way tabulation data was analyzed to detect any differences between travel time behavior patterns across time. Results demonstrated that the majority of shoppers choose the mall because of its convenient location. Regarding travel times to reach the mall, however, shoppers reported a smaller proportion of willingness to travel short distance and a greater proportion of willingness to travel long distance in the sample of 1999 as opposed to the results in 1993. This may suggest that shoppers have a greater willingness to make more efforts when embarking on a shopping trip. Traditional theory has assumed that consumers may combine shopping purposes to reduce the time and cost of travel (Baker, 2006). Thus, Nicholls’ et al. study indicated that the willingness to travel might be more related to multi-purpose shopping than single-purpose shopping.

With regard to multi-purpose shopping, Leszczyc et al. (2004) and Arentze (2005) discussed the tendencies of shoppers to purchase different groups of products on a single trip. Consumers with multi-purpose shopping do not consider distance as their major decision, but other attributes may come to be dominant in their decisions (e.g. see Leszczyc et al., 2004). This means location is not the only dimension that consumers consider in their mall patronage.

Our discussion about location and convenience above underlines that the choice of mall in many studies has been conceptualized as a trade-off between size (selection) and distance separation. In case of single-purpose shopping, usually some measure of the separation of the mall and home is used. Multi-purpose shopping is more complicated in that more centers are involved. Size is a proxy of choice range. Other, particularly marketing studies, have assumed that aspects such a merchandise, advertising, service quality also play a critical role. Next, we will discuss other mall attributes that are considered in mall choice.

3.3.2 Store Variety, Merchandise Selection and Quality

Although at the level of the choice of mall, we tend to differentiate between shopping, leisure, recreation etc. as different types of activities, at the level of store choice, it is important to realize that many shopping trips involve two or more different types of stores. Similarly, when visiting a store such as a grocery store, more than one item of merchandise is bought. Thus, shopping mall choice is not only based on the least effort to arrive at the mall, but also on the range of choice that is available, store variety and merchandise selection. For example a longitudinal study on out-of-town shoppers’ shopping behavior in Finland by Marjanen (1995) showed that the more diverse the


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choice of products and variety of stores the more attractive the shopping centre is as a place for shopping. A wider choice is important either because there will be a higher chance that a shopper can complete the shopping list at a single mall (one-stop destination) and/or there are more opportunities for comparison shopping. While shopping, very often shoppers do not only visit one store or browse for a product, but they may visit an assortment of stores and browse for a number of products.

In general, there are two types of tenants in shopping malls that are based on the size, namely anchor tenant or anchor store and store tenant. A shopping mall at least has one anchor tenant which typically occupies the largest store space (Coleman, 2006). The number of anchor tenants in a shopping mall may vary depending on the size of the shopping mall, the size and type of anchor stores, and the proximity of the competition (Primo, 1988). Due to its size an anchor tenant is able to have larger assortments of merchandise with a decent price than store tenants, for example department stores or supermarket. For this reason, an anchor tenant could appear as a traffic generator or a magnet store (Halper, 1991; Leszczyc et al., 2004). In other words, an anchor tenant has the ability to attract more shoppers to the mall. In contrast, a store tenant has a smaller size than the anchor tenant. Due to the size, a store tenant usually offers limited assortments of merchandise or only has a certain type of merchandise to sell. For this reason a store tenant is also called a specialty store.

As mentioned above, there are pros and cons of providing anchor tenants in a shopping mall. On the one hand, anchor tenants contribute meaningfully to shoppers’ mall choice. However, on the other hand, anchor tenants may generate problems of tenant mix. The size and variety of products that are provided by anchor tenants may attract more shoppers to visit anchor tenants than to visit specialty stores. According to Kotler and Keller (2006), a high competition between specialty stores and anchor tenants may occur if they are competing for the same customers. Their study argued that anchor tenants have more advantages than specialty stores based on the size, variety of merchandise, and the price. Thus, logically, anchor tenants may attract a higher share of customers compared to specialty stores. There is also the possibility of anchor tenants to become category killers (Gilbert, 2003; Levy and Weitz, 2006). However, in general, the presence of anchor tenants will bring benefits to specialty stores (Konishi and Sandfort, 2002). If shoppers primarily visit the mall because of anchor tenants, they will also visit the other stores. Additionally, it has been argued that anchor tenants transfer some of their own store image to the mall they anchor (e.g., see Finn and Louviere, 1996; Oppewa and Timmermansl, 1999).


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Some studies in retailing suggest the importance of having anchor tenants in shopping malls. For example, Nicholls et al. (2002) examined changes in mall patronage between 1993 and 1999, through mall-intercept interviews at a mall located in Florida. They asked shoppers for their major reasons for selecting the mall. Results demonstrated that merchandise, specialty stores, department stores (an anchor tenant in the survey) are significant reasons for shoppers to select the mall. In particular, shoppers in 1999 considered a high proportion of department stores and merchandise as their major considerations of mall choice compared to shoppers in 1993. These findings showed that along the time anchor tenants are the major features for selecting the mall. The assortments of merchandise appear also as a significant feature and as a viable dimension on shoppers’ mall choice.

Following similar international (Western) consumer behavior research, Ahmed et al. (2007) assessed shoppers’ mall shopping habits in Malaysia. A total of 132 students at the university were asked to evaluate 27 mall attributes representing a variety of possible reasons and benefits shoppers might seek during their mall visits. With the majority of respondents between 22-30 years old, results demonstrated that one of the highest rated dimensions why consumers visit shopping malls is because of certain stores they find in shopping malls. In particular, respondents said that these stores are fun to visit because they sell interesting products. Unfortunately, there is no further explanation either about which stores or which products. This study also asked respondents to answer an open question, which measured their shopping habits, such as number of store visits during a trip to the mall. In terms of shopping habits, results showed that Malaysian students tend to visit a slightly higher number on stores than American shoppers. In this study Malaysian students visit about six stores per trip, while American shoppers (Bloch et al., 1994) visit five stores. The conclusion by Ahmed et al.’s study showed that stores are the aspects which motivate Malaysian customers to visit a mall. Similar result are found in the previous studies from Dawson et al., 1990, Wong et al., 2001, and Nicholls et al. 2002.

El-Adly’s (2007) investigated the attractiveness of UAE shopping malls from the shoppers’ perspective. A survey asked university staff to assess 26 mall attributes on a five-point scale ranging from very important to not important at all. Results of a principal component analysis suggested that diversity and mall essence were the two most important choice dimensions. The diversity factor explains that shoppers go the mall not only for shopping, but also for entertainment. The mall essence factor reflects products quality, plurality and variety of stores, and operational aspects of stores,


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including level of price and availability of after services. El-Adly’s study confirmed that diversity, products quality, variety in types of stores and operational aspects of stores determine why UAE’s shoppers patronage the malls.

Support for the contention that sometimes consumers are more attracted to specialty stores than to anchor stores is provided in Haytko and Baker (2004). Their study examined a group of young girls’ mall experiences in the US. Using a qualitative in-depth interview, a sample of 24 participants assessed the retail mix. Results showed that although the girls favored a specific mall that has two large department stores as anchor tenants, they particularly visit specialty stores rather than the anchor tenants. Results indicate the girls visit the anchor tenants only if they visit the mall with their mother. Regarding activities in the mall, Haytko and Baker (2004) found that browsing is the main activity for these girls. They are willing to spend time and effort to locate just the right product. Here, we can see that variety of stores is significant in attracting young girls to the mall, while anchor tenants may be more attractive to mature women.

Studies that we discussed above did not examine which types of specialty stores shoppers are likely to visit or browse. However, other studies did investigate store preference of shoppers. For example, Brown (1991b; 1992) observed unobtrusively shoppers when they entered stores in a Belfast’s shopping mall. By counting the number of shoppers entering the stores, this study demonstrated that the anchor tenants were visited most frequently, followed by the grocery and drapery store. The most infrequently visited tenants in this study were service providers, such as a bank, an insurance office, a travel agent, and a hairdresser.

It is argued that clothing outfitter or fashion is the most favorite product to find in the mall (De Bruwer, 1997; Yavas, 2001; Yip et al., 2012). For example, Yip et al.’s (2012) study examined the favorite specialty stores of young people in Hong Kong. In their study, Yip et al. classified merchandises into two categories, namely non-durable merchandise for consumer products that has an expected lifespan of less than three years and durable merchandise for consumer products that do not need to be purchased frequently because they are made to last for a long time (usually lasting for three years or more). By using a qualitative study, Yip et al. (2012) interviewed participants who were 15 to 21 years old. Participants were asked to answer the name of their favorite specialty store that they visited the last six months. Findings demonstrated that stores, which sell non-durable merchandises, such as food outlets and fashion stores, are the favorite stores. On the contrary stores sell durable products


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such as electronics, home appliances, are seldom reported as the favorite stores. These findings seem relevant to results from Yavas (2001) who examined products purchase patterns of US shoppers in the shopping mall. Yavas’s study asked respondents to answer where they generally preferred to purchase a variety of products and services and classified them to prefer to buy or not. Similar to Yip et al.’s study, Yavas’s descriptive analysis results demonstrated that women’s clothing, gift items, men’s clothing and shoes are the most frequently mentioned products followed by women’s accessories and children clothing and furniture and garden supplies are the least products shoppers want in a shopping mall. The literature studies above suggest that the variety of tenants or tenant mix strongly influence shoppers to repatronage the mall. Moreover, it is obvious that the most seeking tenant in shopping malls is more related to un-durable merchandise, such as fashion stores, food outlets, supermarket and the less seeking tenant is more related to durable stores’ products.

As we have discussed, in reality most of individuals engage in some amount of multi-purpose shopping. Therefore, when shopping, they need to have variety of cross-category assortments. From multi-purpose shoppers’ perspectives, shopping in a one-stop shopping mall, which offers a wide selection of products, is beneficial as it can reduce the perceived costs and ease the shopping tasks (Pan and Zinkhan, 2006). Marjanen (2000) found that “I can get all I need at the same place” is important to shoppers in rural Finland as well. Thus, it is not surprising that shoppers likely favor malls with more variety of type of stores than malls with less variety of type of stores. In addition, shoppers have the propensity to get the best product or to browse, while shopping by comparing similar products they want to buy in different stores (e.g. Chebat et al., 2010). To do so, shoppers tend to look for alternatives of the products they want to buy in similar type of stores. In particular, some shoppers also consider preferences regarding brands, styles, and quality of products. Given these points, shopping malls should provide stores not only according to variety of cross-category assortment, but also variety of within-category assortments.

The literature on shopping malls frequently identified variety and mixtures of stores, merchandise selection and quality as important dimensions (Marjanen, 1995; Frasquet et al., 2001; Wong et al., 2001; Sit et al., 2003). A number of studies from almost all over the world have reported the importance of quality and variety of stores as criteria for shoppers in selecting malls (e.g. Dawson et al., 1990; Wong et al., 2001; Haytko and Baker, 2004; Ahmed et al., 2007; El-Adly, 2007; Teller and Reutterer, 2008; De Nisco and Warnaby, 2014). To have a variety of stores and products is not the ultimate factors


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that people will buy. Price is one of shoppers’ considerations on purchasing. In the next sub-section, we explore the literature on the price, advertising and promotion.

3.3.3 Price, advertising and promotion

One of the goals while shopping is to purchase. Due to this reason price becomes an important aspect in shopping. Basically, price is related with products that are sold in a store. Thus, it is not surprising that shoppers will consider the price in the store before going to a shopping mall. According to Oppewal (1995), prices at stores in a shopping mall defined the price image of the entire mall. This means that shoppers’ price experiences in stores play a role in their price image of the entire mall.

To understand how shoppers consider price of stores or products, some studies have investigated price related to shoppers’ profiles. For example, Jhamb and Kiran (2012) examined which place Indian consumers prefer to go for shopping according to their income. This study classified three categories of shoppers’ income by differentiating their tax’s income, including no tax payer, low tax payer, and high tax payer. Results indicated that the no tax payers prefer to shop at discount stores and convenience stores which sell cheap products, shoppers in the category of low tax payer prefer to shop at malls and convenience stores which sell moderate price products, and the high tax payer shoppers prefer malls and specialty stores which sell expensive products. Results demonstrated not much difference regarding age; the groups of 18 to 30 years and 31 to 45 years prefer to go to malls and discount stores for their shopping purposes, while shoppers over 45 years old have a large preference for convenience stores and department stores. Findings indicated that young consumers who have not much income prefer to visit the mall for window-shopping, but to purchase products at discount stores which offer low prices.

It is argued that for the same identical products, most shoppers will go to the store, which offers the cheapest price. However, as quality of products differs, shoppers’ appreciation of price becomes more complex. It was found that not every shopper likes a low price, as they associate it with bad quality, old style, and low lifestyle. Some shoppers are willing to pay a high price because it usually relates to good quality, brand product, and in some cases it gives more value to shoppers such as raising their prestige (e.g., Grace and O’Cass, 2005; Lloyd et al., 2011). With this value in mind, the brand of products or the name of stores influence shoppers’ price perception and vice versa. In other words, shoppers may consider the price not only because of the amount of money


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that they pay, but also the perceived value and feelings of shopping experiences that they can get from the product.

High and low price’s perceptions have no standard. For example, Severin et al. (2001) conducted empirical studies in a shopping mall in Canada. They asked random shoppers to identify the mall image attributes they associate with their future mall choice including high and low price. From the data, they estimated models of mall choice based on shoppers’ perceived mall attributes. Results demonstrated that high and low prices are not consistently significant in the separate, yearly models. In this study, Severin et al. found that high and low prices have the smallest impact on shopping mall choices.

In general, sales are part of shopping malls’ advertisement and promotion. It is suggested that advertisement and promotion have the power to present and deliver information to a large number of people (McGoldrick, 2002). With advertisement and promotion, shopping malls are able to build their reputation and subsequently increase the number of visitors (LeHew and Fairhurst, 2000). Stores usually create sales promotions every time the season changes. Findings of several studies demonstrated that sales significantly attract customers (Yavas, 2001, 2003; Parsons, 2003; Chebat et al., 2010; Cachon and Feldman, 2015). However, shoppers derive different value from these sales promotion events. For example, Davis and Hodges (2012) conducted in-depth interviews to identify in-store value dimensions. In their study, in-store shopping value originates from retail elements that create in-store shopping experiences. Sixteen participants were selected from the university and local church in the Southeastern US. Participants revealed product price value, selection value, and shopping efficiency value as the key in-store shopping value they gained at mass merchandisers, while they indicated that product quality value, selection value, in-store services, and shopping environment value are the key in-store values they gain at department stores. Regarding product price, participants claimed that they are very likely to hunt for bargains and sales at department stores because they could find nice products at very good prices when department stores marking down their merchandise during sales and promotions. Therefore, they could gain transaction value at department stores. In contrast, although everyday low price of mass merchandisers is very appealing, participants do not gain the same kind of excitement or pleasure as in department stores when they found a real bargain.

Focusing on discounts, Khare, et al. (2014) investigated Indian shoppers in local stores. They found that discounts are attractive only to the low-income group. The results of


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this study may be influenced by the Indian culture in which small stores provide customized and personalized products and services. In India, the relationship between consumers and retailers is based on trust for a long-term orientation and individualism. Retailers bring assurance to consumers’ feeling that they know each other, which removes the fear of being cheated. In addition, Khare et al.’s study found promotional activity such as free gifts, lucky draws, etc. can please Indian shoppers and generate mall traffics. However, not all shoppers are interested to receive promotion; only older shoppers are attracted in promotions.

Focusing on promotions in shopping malls, Parsons (2003) investigated what kind of promotional activities will increase visits to the shopping mall. He conducted intercept surveys across three regional shopping malls in three New Zealand cities. This study examined ten common malls’ promotional activities, including sale, gift-with-purchase, competition or lottery, discount with minimum sale, general entertainment, market days, voucher, fashion shows, school or community displays, product displays. Results showed that non-price-based promotion stands highly in increasing mall visits. Results also demonstrated general entertainment and entertainment through market days have strong attractions along with sales in increasing mall visits and school or community displays strongly increase the likelihood of visiting. Interestingly, the traditional promotions, such as fashion shows and product displays, show little effect in increasing visits.

In conclusion, many previous studies have been examined the relationship between price and store choice (Jackson et al., 2011), between price and impulse buying (Tendai and Crispen, 2009), between price and repatronage (Chebat, et al., 2010). Most of these studies suggested price is a significant attribute influencing shoppers to visit or re-visit and in subsequent to buy the products. However, the way shoppers perceive price is not always the same. Price has different meanings for consumers. Price is not related to consumers’ affordability (Phillips and Sternthal, 1977).

3.3.4 Atmospherics

Atmospherics are the first things that evoke shoppers’ senses and emotions in shopping experiences when they perceive retailing environments (Wakefield and Baker 1998; Baker et al., 2002; Michon et al., 2005). The term atmospherics is defined as intentional control and structuring of environmental cues (Kotler, 1973). This means that in shopping experiences, a shopper does not consider only the product as the main aspect


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to purchase, but also other aspects such as store’s physical environment that influence the shopper’s decision.

Donovan and Rossiter (1982) were the first to apply the framework provided by Mehrabian and Russell (1974) to investigate the relation between store atmospherics and shopping intentions within the retailing environment. Since the work of Donovan and Rossiter (1982), research studies in atmospherics have largely explored elements of atmospherics, such as music (e.g., Milliman, 1982; Yalch and Spangenberg, 2000; Morin et al., 2007;), color (e.g., Baker, 1986; Bellizzi and Hite, 1992), lighting (e.g., Areni and Kim, 1994), ambient and design factor including, layout and signs, ceiling, pictures and artwork, textures, floor, wall, lighting, color (Baker et al., 1994), odor/scent (e.g., Hirsch, 1995; Spangenberg et al., 1996; Spangenberg et al., 2005), architecture style (e.g., Turley and Milliman, 2000); landscaping and greenery (e.g. Brengman et al., 2012; Mower et al., 2012) that affect shopping decisions. Most of these studies have been conducted at the store level. Studies on store atmospherics have demonstrated the positive impact on shoppers’ shopping experience. Research studies demonstrated that a pleasant store environment increases the time spent in the store (e.g. Donovan et al.’s (1994).

A recent study of Ballantine, et al. (2015) argued that holistic atmospheric cues encountered in a retail environment contribute to the creation of a retail experience. They attempted to investigate in detail the atmospheric cues that influence the whole process of shopping in a store; starting when shoppers perceive a store, what aspects made them enter the store, how comfortable they felt and how much time they spend in the store, and other processes prior to purchasing by using in-depth semi-structured interviews in New Zealand. Their findings demonstrated that floor, interior furnishings, layout, lighting, music, and temperature are the atmospheric cues that comfort them in a store. In particular, participants mention that to have some additional furnishings or displays inside the store creates feelings of comfort and increase their intention to stay in a store. Spacious layout and soft lighting also create a comfortable in-store feeling, whereas cramped stores, and bright lighting made them uncomfortable. Regarding music, too quiet or too loud make participants feel uncomfortable while soft music that is in the middle of the two extremes induces comfort. The absence of music in-store also creates uncomfortable feelings. Some atmospherics cues, such as layout, lighting, product displays, and signage increase their abilities to browse the merchandise. Interestingly, slightly cooler temperature in a store makes participants


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comfortable and bring propensity to try on the merchandise, since they felt the temperature in the mall could become a bit stuffy.

More recently, studies on atmospherics were extended from stores to malls. Wakefield and Baker (1998) can be viewed as authors of seminal work on mall atmospherics. They provided insight about antecedents and consequences of excitement at an enclosed community mall in US. They concluded that the satisfaction of shoppers with the environment inside the mall could induce them to stay longer. Subsequently, this experience leads her re-patronage intentions. In addition, this study explored which elements of the mall environment contribute most to shoppers’ excitement and desire to stay. Using correlation analysis, Wakefield and Baker classified the mall environmental factors into five distinct elements of music, lighting and temperature, layout, architecture design, and interior décor. Findings showed that all elements except lighting and temperature are positively associated either with excitement or desire to stay at the mall, or to both.

Focusing on ambient scent, Chebat and Michon, 2003) conducted two experiments between two groups of shoppers by vaporizing a series of 26 non-offensive odors in the main mall’s corridor. Shoppers were then asked to answer questionnaires about their emotion towards product quality, shopping environment, pleasure, and arousal, as well as their sociodemographics and their spending during the shopping trip. With an assumption that perception influences shoppers’ mood, Chebat and Michon illustrated the cognitive and affective effects of ambient scent by using a structural equation model. They concluded that scent significantly impacts the perception of product quality and the shopping environment. Odors affect shoppers’ perception of products both directly and through their perceptions of the mall. The results also suggested that the effect of shoppers’ mall perception on product quality is very strong.

Trying to find the effects of different aspects of music such as volume, tempo/rhythm, type of music, and familiarity, on shopping experience in shopping malls, several scholars examined these relationships. For example, Oakes, et al. (2013) examined the effect of tempo of music on shopping behavior. Their findings showed that a slow music tempo compared to a fast music tempo enhances positive affective responses from consumers who are waiting to be served. Petruzzelis et al. (2015) investigated the impact of familiarity of music on consumer behavior. They conducted an experiment with 2 playlists of 100 tracks of famous and not famous music in a shopping mall in Italy. During this experiment, shoppers were interviewed to state their perception of the


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mall’s environment including mall image, affective states, mall attitude, their profile and their shopping experiences. Their findings suggested that music significantly affects only on shoppers’ pleasure and slightly arousal in the shopping experience. Results showed that during the shopping activity shoppers feel less pleasure but more aroused towards shopping when listening to familiar (famous) music than non-familiar one.

Since many landscaping features such as green gardens, green walls, and green roofs are considered in building design, including shopping malls, Chan and 陳靄恩(2015) evaluated the effects and impacts of landscaping and greenery to shoppers in Hong Kong shopping malls. Their study showed that landscaping and greenery features may increase customer satisfaction. To a certain extent, for example in food and beverage stores, these green factors contribute to sales. Chan and 陳靄恩 also confirmed that the greenery features lead to a better performance of the shopping malls.

Most mall atmospherics studies referred to shoppers in Western shopping malls. Only little research on mall atmospherics has been conducted in Asia. Replicating studies on mall atmospherics in Western countries, Ahmed et al. (2007) investigated the relation between mall atmospherics and shopping orientation in Malaysia. Although their study only covered young adult university’s students, they found the aesthetic factors, such as interior design of the mall, color and texture of the mall interior, lighting and decorations, receive the highest appreciation. Subsequently, the aesthetic dimension is the strongest motivator for students to patronize the malls. Particularly, the aesthetics of the mall is one out of two strongest motivators that influence them to visit the mall. Ahmed et al. also investigated the relationship between age groups and mall atmospherics. They found that younger respondents are more enthusiastic about exploring elements of the mall than older respondents

When most studies addressed only one or two aspects mall atmospherics, Jackson, et al. (2011) conducted a study to examine the effects of several aspects of mall atmospherics on shoppers’ satisfaction. In this study, mall atmospherics assisted in attitude towards mall hygiene factors, including safety, cleanliness, décor, and atmosphere. By using an intercept technique, shoppers who exited a regional mall in the US evaluated the mall attributes on five-point Likert-type scales. A principal component analysis suggested that good atmospherics increase shoppers’ satisfaction. Jackson et al. also looked at sociodemographics as moderating variables. Their result demonstrated that the older generation has stronger attitudes toward atmospherics than the younger generation and that females evaluate atmospherics higher than


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males. Haytko and Baker (2004) conducted a similar study about the influence of atmospherics on shopping mall choice among young girls. In this study the atmospherics are related to safety, a clean environment, color and light, openness in the entire mall, and music. By investigating young girls’ mall patronage, they found that atmospherics influence girls’ decisions regarding their favorite mall. Additionally, their results showed that young girls also consider facilities and comfort (for example the availability of places to sit down and the comfort of restrooms) in their choice of mall.

Our literature reviews have shown that mall atmospherics affect shoppers’ emotions and bring satisfaction in shopping experiences that subsequently influences shoppers’ mall choice. Some aspects of mall atmospherics including music, scent, atmosphere, cleanliness, safety, lighting, architecture and interior design, facilities, décor, comfort, color, and openness have received some attention. However, as discussed above, there are still some store atmospherics which may bring positive shopping experiences that have not yet been discussed much in mall atmospherics, such as flooring, layout, temperature, design style, green and garden, ceiling, signage, and artwork. We argue that a comprehensive study that includes this wider set of variables may create a better understanding about the influence of this dimension on shopping experience.

3.3.5 Social Environment

Research studies have identified the role of the retail environment as a place for having social interactions. As mentioned by many scholars, for some people shopping is a social activity, where they can interact with family, friends or even other shoppers, can see-and-be-seen especially by the different gender (e.g., Tauber 1972; Knack 2000; Haytko and Baker, 2004; Hu and Jasper, 2004; Woodruffe-Burton and Wakenshaw 2011; Davis and Hodges, 2012). The need for social interactions in shopping varies across shoppers. For example, focusing on teenage girls, Haytko and Baker (2004) confirmed that shoppers want to be in a mall where they can meet and identify other shoppers and where they can see or meet people of the opposite sex. In addition, findings in this study demonstrated that shoppers’ satisfaction with the social environment affect their mall choice.

Several studies have examined the relationship between the social environment and sociodemographics. For example, Cox et al.’s (2005), focusing on females above 17 years old with recreational shopping experiences found that childless and lower-income women enjoy interacting with other shoppers. In particular, they enjoy being with customers with similar tastes or similar people, and meeting men while shopping.


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Chebat et al. (2006) examined how shoppers interact with the social class image of a mall regarding their status: upscale and downscale. Using the concept of self-congruity that is defined as how shoppers see themselves as being the kind of person that the mall is designed to cater to, they concluded that the high socioeconomic class shoppers experience higher levels of self-congruity with upscale malls than low class socioeconomic shoppers. The higher the self-congruity the more likely stores are perceived as having high quality.

Focusing on the age of shoppers, Khare (2011) found that the social implications of malls differ across different age groups. Younger consumers (20–30 years) perceive malls as places where they can stroll with friends, become engaged in window-shopping, watch other shoppers, eat at food courts, and watch movies. A mall is a place where they can meet friends; spend time unrestricted from the watchful eyes of parents. The consumer age group between 30 and 50 years view malls as places, which facilitates social interaction, while for the group of older shoppers a mall is a place that provides the stimuli of watching people purchase products, walking around with friends, and watch new fashions.

Considering gender, Kotzé et al. (2012) Investigated shopping motives as sources of shopping enjoyment. Regarding shopping as a means to socialize, they found that gender comes with different motivations. Females have a higher engagement with shopping to socialize than males.

Kwon et al. (2016) showed that there is a relationship between social environment and shoppers’ satisfaction of the mall. They studied how the social environment, especially social presence with other shoppers, influence shoppers’ satisfaction in US shopping malls. Using an online survey, Kwon et al. asked respondents to evaluate their satisfaction towards other shoppers according to their recent mall’s experiences for apparel shopping. Findings demonstrated that the way customers perceive others’ presence has a direct impact on their satisfaction. In particular, if shoppers surround themselves with similar people, it provokes feelings of pleasure, happiness and excitement and their actual shopping experience exceed their expectation. Furthermore, results in this study demonstrated the presence of other shoppers could influence shoppers to take a look or even to buy impulsively.

The literature about the social environment shows that shoppers consider the social environment in the mall to select the mall. Studies have demonstrated that shoppers perceive a mall’s social environment differently according to sociodemographics such


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as age, gender, status, and income. In addition, satisfaction with the social environment could evoke satisfaction and subsequently provide benefits for retailers. Almost all studies are related with Western shopping mall environments, it will be beneficial to know how the social environment influences Asian shoppers.

3.3.6 Personal Service

Personal service is one of the considerations influencing the choice of store (Tauber, 1972, Westbrook and Black 1985; Kim et al., 2005). Bitner’s (1992) service scape study is one of the most widely cited in this regard. This study suggested that not only environmental factors influence shopping behavior, but also employees who provide the services. When evaluating personal service in a store environment some scholars point at the behavior of salespeople or employees, including personal responsiveness, helpfulness, friendliness, promptness, knowledge, and courtesy (Dabholkar et al., 1995; Baker et al., 2002; Simmers and Keith, 2015). In addition, after purchase service delivery has been investigated (see e.g. Simmers and Keith, 2015).

Results of these and other studies have revealed that shoppers like to get attention and receive personal service in a store environment. Baker et al. (2002) examined undergraduate students using videotapes to measure the impact of retail salespeople’s service provision. Findings in this study demonstrated that the service from retail salespeople influence arousal and the interaction with retail salespeople together with the environment affect shopping pleasure and willingness to buy. Supporting these previous studies, Hartline and Ferrell (1996) confirmed that the interaction between salesperson and customers affect customers’ evaluations of service quality.

Taylor et al. (1997) examined the relation between shoppers’ satisfaction towards personal service and purchase intention of Mexican consumers. They found that service quality perceptions contribute to purchase intentions. In another study, Underhill (1999; 2004) confirmed that people would shop where they feel wanted and will even pay a little more for the privilege. Studies by Jones (1999) and Laroche et al. (2005) revealed that an excellent service may lead to customers’ good mood, stimulate their purchase intention, and ultimately enhance revisiting the store. In contrast, a lack of assisting customers in a store may cause a negative shopping experience, which in turn may trigger customers to discontinue the store visit. In addition, too much help may be intrusive.


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Whereas most of studies outlined thus far addressed the influence of personal service, Reynold and Beatty (1999) examined whether shoppers needs interaction with the salespeople in retail clothing stores. It is surprising that this study found that not every shopper needs social interactions with the employee. Results in this study demonstrated that non-confident shoppers require assistance of salespeople for advice and reassurance. Findings also showed that there is a group of shoppers who like to engage with the personal service that makes them look valuable. In another study, Kim et al. (2005) investigated the needs for personal service of old and lonely shoppers. They found that lonely individuals, mostly older consumers prefer a high intensity to have service and interactions with salespeople. In addition, old consumers prefer to be served by old salespeople. Cox et al.’s (2005) supported this conclusion. They also confirmed that the older the consumer, the greater the desire of being pampered by salespeople.

Shifting from a store to a shopping mall environment, Howard (1997) argued that personal service in a shopping mall is a bit challenging. According to Howard, a shopping mall is a business with no product, containing retailers who are to some extent the same as those on the high street and selling at the same prices as on the high street. Therefore, she argued that service in the mall is similar to a customer-service department, which offers a friendly welcome and where employees aware of all services offered in the mall and facilities.

Following Howard’s concept, Sit et al. (2003) conducted a study about shoppers’ perception towards eleven mall’s image attributes, including personal service. The personal service of the mall in this study is related to the employees who work for mall management, such as public relations and customer services, receptionists at the information desks, safety guards, valets, cleaning services, etc. Sit et al. measured five items including courtesy, prompt service, knowledge of employees, neat uniform of employees at the information desk, and helpfulness or positive attitude. By categorizing shoppers into six distinct market segments, findings in this study demonstrated that the shoppers in four clusters find personal service of high importance.

Other studies about personal service in shopping malls focused specifically on salespeople. For example, Anselmsson and Johansson (2007) and Raajpoot et al. (2008) argued that personal service at the mall level is not very different from personal services at the level of a single store.


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3.4 Behavior inside Shopping Malls

Once arrived at the mall, the consumer should decide which stores to visit, in which sequence, and which routes to select. Based on the way shoppers behave in shopping malls, two distinct behavior namely stopping and movement behavior can be identified. In this section we will take a closer look at studies on shopping behavior inside shopping malls, differentiating between stop behavior and movement patterns.

3.4.1 Stop Behavior

Stop behavior describes the number and kind of stops that shoppers make during their shopping trip. Stop behavior is thus directly related to store visits and facility use. Normally, before going to the mall a shopper develops, either mentally or on paper or in digital format, a list of stores to visit. After arriving at the mall she or he may directly follow the list. However, in a real shopping situation, shoppers may be sensitive to new stimuli, which may lead to unplanned decisions, which is called spontaneous behavior. It may happen that shoppers do not have lists, so their store visit decisions are made instantaneously. It is also possible they do have lists, but in the mall they change their mind and do not strictly follow their plans, for example by adding stops, becoming involved in window-shopping, visit facilities, or stop to take a seat. Therefore, shoppers tend to visit more stores than they had planned (Borgers and Timmermans, 1986a; 1986b; Cobb and Hoyer, 1986; Van der Hagen et al., 1991).

Because shoppers often need to visit multiple stores, they may develop a multi-stop plan. The stores in this plan will be included because they plan to purchase in these stores. However, they may also appear on the list to browse, to update information (e.g. books) or to compare items before making a purchase. The latter behavior is especially relevant for fashion and shoes (e.g. Carter and Haloupek, 2002). According to Dijkstra et al. (2006), shoppers have the tendency to make a multi-stop plan due to the uncertainty of shoppers toward price and quality of the product.

Stop patterns may thus be characterized by impulse buying. Donovan et al.’s (1994) found that pleasant stores atmospheres generate shopping pleasure and are significantly associated with spending extra time in these stores. Beatty and Ferrel (1998) argued that impulse buying is generated for two reasons: first, it starts whenever shoppers have time and second if they still have budget. If a shopper has sufficient time it will drive her to extend the stay in the mall and to browse extra stores and products.


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This condition will increase the enjoyment in the shopping experience. If a shopper still has some budget left, the enjoyment in shopping experience may cause impulse buying.

Shoppers face different time and budget limitations. Consequently, these constraints may influence shopping behavior and decisions. Research studies confirmed that shoppers with ample time have a higher probability of inserting unplanned store visits and becoming involved in window-shopping. Shoppers without to-do-lists tend to shop for fun and a pleasurable shopping experience (Jones, 1999; Kim and Kim, 2008). In contrast, shoppers who have limited time tend to stick to the lists by going directly to the stores that are on the list (Titus and Everett, 1995).

Stop behavior does not only involve store visits, but also purchasing in stores. Many studies have investigated the reasons behind shopper purchasing behavior. For example, Grewal et al. (1998) found that store image and store’s perceived value have a direct relation with purchase intentions. Nicholls et al. (2002) studied customer purchasing behavior patterns in Florida. They confirmed that gender has a significant impact on purchasing decisions. In particular, females are more likely to purchase than males. Findings (e.g. Wakefield and Baker, 1998; Tendai and Crispen, 2009) also suggested that the probability of purchases is higher among shoppers who stay longer than among those stay shorter in the mall. In addition, travel distance has an impact on purchasing behavior. The closer shoppers live to the mall, the less likely they purchase when shopping. It suggests that they may go to the mall for reasons other than purchasing.

Focusing on the mall environment, several studies confirmed that particular dimensions of shopping malls might generate positive spontaneous purchasing decisions. For example, Jarboe and McDaniel (1987) found that window-shopping may increase or decrease the probability of an actual purchase; if a shopper stops at a store and talks to the store assistant who demonstrates much persuasion, it will increase the purchase probability. Khare (2011) found that some shopping mall’s elements such as décor, layout, services, variety of stores, and entertainment facilities enhance purchasing. Similarly, Mehta and Chugan (2013) showed that mall’s elements, such as window display and promotional signage significantly influence impulsive buying behavior. Results also demonstrated that impulsive buying becomes high if shoppers try the items first. Gilboa and Yavetz (2013) demonstrated that music, shelf displays, colors, and pleasant salespersons influence shoppers to buy impulsively. Gilboa and Yavetz (2013) found that the presence of other shoppers trying clothes drives shoppers to buy.


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Due to the fact that shopping is inherently a social experience, several studies have examined how shopping companion affects shopping behavior. For example, according to Beatty and Ferrell (1998) the presence of a companion either a friend or a family member creates a feeling of competition and social comparison, which consequently draws shoppers from their routine shopping activities, and making them more cognitively and emotionally alert. Conversely, this study demonstrated that a shopper who is alone is more likely to reduce their inhibitions and make impulse purchases. While Beatty and Ferrel’s investigated shoppers with a single companion, others focused their attention to groups of companions, and reached completely different conclusions. For example, Nicholls et al.’s (2002) found that a shopper who is a member of a group has a higher probability of making purchases than a shopper who comes alone. Borges et al. (2010) investigated how the presence of others influenced shopping experience in two different regional shopping malls in US. Results demonstrated that shoppers expressed a significantly higher positive affect and enjoyment if they were shopping with friends rather than alone or with a single-family member.

Finally, shoppers’ physical needs may trigger some unplanned stops during shopping. Only a few scholars discussed unplanned behavior that relates to physical needs. For example, shoppers may stop for sitting because of tiredness, stop for a toilet, or stop for feeding a baby. Similarly, Moslem shoppers may stop for praying because there are 4 daily prayer times between 10:00 and 22:00.

3.4.2 Movement Behavior

Turning now to movement behavior, it involves the decisions that relate to movement between the successive visits of stores and/or facilities. Movement behavior in the shopping mall requires complex decisions making; for example, shoppers need to decide which direction to go, which route to take, which sequence to follow, which vertical circulation to choose, etc.

Our main interest in this sub-section concerns the question what factors influence shoppers’ movement patterns. We will first review studies about pedestrian movement in outside shopping areas and then in shopping malls. A substantial number of studies have examined pedestrian route choice. Whereas in many other contexts, the fastest or shortest route is the dominant choice, walking patterns of shopping pedestrian tend to be much more diverse. Borgers and Timmermans (1986a, 1986b) proposed a model to predict pedestrian movement in a downtown shopping area. Although, our focus is not on discussing the model in any detail, they assumed that pedestrians move such as


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to minimize distance between successive pairs of destinations during their shopping trip. The sequel further elaborated the model. An important assumption for the model is that pedestrians tend to enter and leave the area at the same entry/exit point, due to collect their cars or bikes. Consequently, the distance from and to the entry point is an important factor, shaping the area of pedestrian movement.

Van der Hagen et al. (1991) investigated the spatiotemporal sequencing of destination choices. They argued and provided empirical evidence that the commonly made assumption of a distance-minimizing strategy is only one of various possible decision heuristics pedestrians may apply. To allow for different behavior, they differentiated between different heuristics that pedestrians may apply when shopping. Particularly, they differentiated between temporal and spatial heuristics. A heuristic is defined as a rule, which describes some principle underlying the choice behavior of pedestrians. There are three kind of sequence-driven decisions process in temporal heuristics: the local-distance-minimizing heuristic, the total-distance-minimizing heuristic, and the global-distance-minimizing heuristic. The local-distance-minimizing heuristic indicates that the pedestrians choose the shortest route between consecutive stops. Thus, the sequence of store visits only results from the distance-driven decisions heuristics. On the contrary, the total-distance-minimizing heuristic the pedestrians minimize the total distance traveled. This implies that the pedestrians simultaneously decide about the sequencing of the destinations and their route choice behavior. While the global-distance-minimizing heuristic is in-between; the pedestrians still attempt to sequence their visits in some optimal way, the shortest route associated with every alternative sequence would result in longer distance traveled. In other words, the pedestrians do not minimize distance in all segments of their trip even tough their sequences are optimal. In terms of spatial heuristics, a distinction was made between the farthest-destination-oriented heuristic and the nearest-destination-oriented heuristic. The farthest-destination-oriented heuristic indicates that a pedestrian decide to first visit the destination farthest from her or his entry point and then proceed back to the departure point, while the nearest-destination-oriented heuristic is the opposite. The research findings suggest that only a small proportion is engaged in distance-minimizing behavior, and this proportion drops with an increasing complexity of the retail environment.

Many aspects may influence pedestrian movement. For example, according to Dellaert et al. (1995) contextual variables, such as the time of day and weather conditions may impact pedestrian movement. When pedestrians walk in an urban environment,


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Zacharias (2001a) argued they will experience a variety of sensations related to comfort and stimulation, such as climate and crowding, which influence their decisions to remain or leave the space.

Kurose et al. (2001) used a rule-based model to classify stop sequences of pedestrians in a Dutch shopping center. Their main conclusion was that pedestrians globally minimize the distance to walk and tend to select the nearest shop as the first to visit. In another study about pedestrian walking behavior, Cao Xinyu et al. (2006) found that the impact of the built environment on pedestrian behavior depends on the purpose of the trip.

Borgers et al. (2009) simulated pedestrian behavior using a pedestrian destination and link choice model by using data on pedestrians in a segment of a shopping street in Belgium. Findings demonstrate that distance to the destination and supply of shops are important variables in the destination choice model. In addition, results in this study suggest different behavior between genders and group composition. Individual or groups of females appeared to have a higher tendency to visit a shop in general and stay longer in shops than individual females or groups of males. And individual males or mixed groups appeared to be more attracted by non-fashion shops than individual females or groups of females. Borgers et al. also found a decrease in attraction if the pedestrian had already passed along or visited the fashion shop. However, this effect was not found for non-fashion shops. Regarding the link choice model results demonstrate that pedestrians are most likely to walk according to straight lines as much as possible to their destination. Kurose et al. found similar results (2009) while examined several temporal and spatial heuristics in central shopping areas in Japan, Korea and China. Their findings demonstrate that pedestrian background such as age and occupation and street characteristics affect pedestrian behavior. With regard to Kemperman, et al. (2009) familiarity with the environment is one of the factors that affect shopping route choice behavior of tourists in Maastricht’s shopping area.

Studies about pedestrian movement have not only been concerned with open space, but also with enclosed spaces. For example, Brown (1991b, 1992) examined how individuals circulate in a-1-floor suburban district center in the UK using weeklong observation of 250 shoppers regarding their store visits and selection of routes. Findings showed that the composition of stores or tenant mix attracts shoppers exploring the mall and creates traffic inside the mall. Routes of shoppers tend to be limited to a small section of the center. This study suggested that all types of stores can


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have some benefits if they are located close to each other. The benefit is largest for convenience retailers and comparison retailers.

Investigating what attracts the intentions of shoppers on their movement Zacharias (2001b) found that layout or the arrangement of stores, the visual and social environments influence shoppers’ itineraries and their actions. According to Chebat et al. (2005) and Okamoto et al. (2011) familiarity with the mall helps shoppers to easily navigate their movement. In 2006, Zacharias continued the study of movement behavior by observing how shoppers strolled in a shopping mall in Canada. In this study, first Zacharias asked shoppers’ plans via questionnaire before they started shopping and then discretely observing them. Findings demonstrated sometimes shoppers make spontaneous movement decisions. People, including all mentions of individuals, groups, crowds, and flow influence shoppers’ path choice and change their previous plans on the path. For example, a shopper may change her path if she faces a crowd in her way, or a shopper may switch her path when she sees a number of people going to the other path that she had never planned before. Zacharias’ study appeared to explain that in reality shoppers’ movement is very dynamic and unpredictable.

Vertical circulating is also part of movement behavior, however not many studies discuss about movement in the vertical direction. According to Kerr et al. (2001) shoppers’ volume would increase in one shopping center if the position of stairways stands adjacent to escalators. Kerr et al. concluded that the position of vertical circulation influences pedestrian movement. They also confirmed that shoppers most likely stroll close to the floor where the gates are located. Regarding multi-story building, Dolan et al. (2006) found that people’s volume tend to reduce when the height is increase. In other words, the higher the floor the less people volume it has. A recent research by Hirsch, et al. (2016) was conducted in a two-floors German shopping center. They found that the pass ratio declines according to the distance from the central point of the shopping center. The findings related to vertical movement behavior somehow support the previous research from Brown’s (1991b; 1992) study, which confirmed that in a one-floor shopping mall only few shoppers stroll along the whole mall during their visits.

Literature about movement behavior shows that the purpose of individual underlies the walking decision. In addition, when walking people also consider about the distance. In the shopping mall, distance is not always related to the short distance, but also related to directness, clarity, familiarity of the way that were arranged by the


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composition of the stores. Regarding vertical circulation the higher the floor the less people tend to visit the floor. Other people in the mall (e.g. crowd, flow of people) also influence walking decisions.


In this chapter, we discussed a conceptual framework of shopping behavior that is based on different decisions consumer make when shopping at a mall. The proposed framework differentiates two stages: pre-decisions before going to the mall and decisions regarding actual mall-use. Using this framework as the basis, the relevant literature was discussed. It represents the state of the art that allows positioning the present study.

A recurring theme of the literature review is the variety in shopping behavior. Consumers differ in their motivations. These differences may lead to the choice of different shopping centers. In addition, differences in motivation will lead to different use of mall. It implies that analyses should not only address common patterns and averages, but also identify differences in behavior.

PART IIPRE-SHOPPING DECISIONS

PART II PRE-SHOPPING DECISIONS

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4 DATA COLLECTION AND DESCRIPTIVE ANALYSES

The main goal of this dissertation is to understand shopping behavior in shopping malls. Part I of this thesis has explained and motivated the conceptual framework underlying this study and how it relates and is derived from the state of the art in the literature on shopping behavior. Moreover, it reported the selection of shopping malls in Jakarta for this study.

Part II will discuss a series of analyses about pre-shopping decisions. In other words, this chapter will examine shoppers’ behavior before going to the malls. In this regard, we will investigate shoppers’ needs before shopping and their evaluations of mall attributes. The data for these analyses was collected in three selected shopping malls in Jakarta, because we expected that contrasting consumer profiles would be associated with the different malls. Based on our classification of malls in Jakarta, we anticipated that each mall would have its own customer base, differing from one mall to another.

This chapter outlines the design of the data collection. This chapter is divided into seven sections. The first section provides an overview of the survey approach. The following section introduces the design of the questionnaire. Then, the chapter continues with a description of the process or respondent recruitment for collecting the data. Section 4 discusses the three shopping malls that are selected in this study. The next section briefly describes descriptive statistics of the characteristics of the sample. Section 6 presents differences in shoppers’ characteristics between the malls. The final section draws conclusions.

4.1 Overview of the Survey Approach

In order to better analyze shoppers’ behavior before their choice of mall to visit, this study employed a survey approach for collecting various relevant data. It is considered as the most appropriate method since a survey allows for gathering a number of data on variety of topics. In particular, with a survey, we can systematically collect specific information related to the whole shopping process , such as plans what to do in the mall, the decision to choose a specific mall, and perceptions about the shopping mall according to shopping experiences.


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Surveys can be conducted in several different ways. Since the objective of this study is to understand shoppers’ behavior and the attributes that influence such behavior, the appropriate data about their behavior in malls was required. As the data validity and reliability of in-mall shopping behavior likely to increase with a shorter time gap between the interview and the actual process, it was decided to use an on-site field survey. The main advantage of an on-site field survey versus a mail questionnaire or face-to-face home interview is the less dependecy on respondents’ memory recall. The longer the time gap between the behavior of interest and the interview/data collection, the more likely biases will be introduced in the memory recall process. Moreover, on-site surveys have the advantage that evaluative questions about mall attributes are asked while respondents can actually see or experience these attributes and therefore do not need to rely on their memory of these attributes (Wakefield and Baker, 1998; Andreu et al., 2006).

4.2 Questionnaire Design

A set of questions was developed to investigate shopping decisions that were made before and during the shopping events. The questions included shoppers’ key motivations to go to the chosen mall, factors influencing the choice of mall, and shoppers’ preferences and perceptions of the mall environment.

A paper-based questionnaire was written in Bahasa Indonesia, the main language of residents in Jakarta. Regarding pre-shopping decisions, our questionnaire consists of two sections. The first section was to gather sociodemographic information of the respondents. The next section asked for the reasons to go to the mall and factors influencing their choice of mall. This section also asked respondents to evaluate the mall environment.

4.2.1 Sociodemographics

The first part of the questionnaire focused on various personal and household characteristics such as gender, age, race, marital status, occupation, education, place of residence and place of office or school. Because Indonesia has many races (ethnic groups), we categorized these into 6 major islands and 1 for non-Indonesian ethnic groups. To allow investigating the relationship between the choice of shopping mall and residential and job location, the questionnaire also asked respondents to mention


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their place of residence and place of work/school. This locational information was categorized according to 5 districts of Jakarta and 1 district outside of Jakarta.

4.2.2 Shopping Motives and Mall Preference

As discussed in the literature, shoppers may display different motives when visiting a shopping mall. Therefore, the second part of the questionnaire was designed to capture shoppers’ shopping motives and factors influencing mall selection. Since, shoppers may have many motives, respondents were free to write their main reasons for the current shopping trip.

In measuring factors influencing mall preference, two different means of data collection were included in the survey to collect information why respondents select the shopping mall. Firstly, respondents were asked an open question about factors influencing their mall choice. Respondents could provide up to five reasons why they choose to go to the mall of their interest. Answers to this open-ended question were then classified into influential factors. Secondly, respondents were invited to evaluate the malls’ environment. Based on the literature review, a list of mall image dimensions was identified. This list included a set of specific attributes underlying the dimensions of location and convenience, store variety, merchandise selection and quality, price, atmospherics, personal service, advertising and promotion, and social environment. In the evaluation respondents scored on a seven-point scale, anchored by “terrible” and “excellent”. We added the option of “never use” or “unknown” particularly in questions about public facilities, since it may happen that our respondents do not know or have never used the public facilities in the mall.

Following a review of the relevant literature that we have discussed in chapter 3 (e.g. McGoldrick and Thompson, 1992a;1992b; Dennis et al., 2002; Sit et al, 2003), a set of questions was compiled which were thought to sufficiently represent the dimensions of the shopping mall environment. In this part, at the beginning of the questions, we asked respondents to assess their overall evaluations of the mall. After those questions, the questions were divided into several parts asking more details of the respondents’ evaluation of mall image dimensions. In this sub-section, the chosen dimensions are explained and motivated:

The dimension location and convenience was operationalized into accessibility by public and private transport mode, availability of parking space, parking costs, travel-time, and opening hours of the mall. Since shopping malls open from morning to mid


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evening (10 a.m. to 10 p.m.) and Jakarta has severe traffic congestion, we allowed for the possibility that respondents may combine shopping with other activities. Thus, we asked respondents about the place they start before their shopping trip (home, job location, or other). In addition, we also asked respondents which transport mode they use to get to the mall.

Evaluating of price dimension was differentiated into price level of food-and-beverages, price level of fashion stores, price level of supermarkets and presence of particular high-price and low-price image stores in the shopping mall.

Advertising and promotion was a multi-attribute dimension. Promotion focused on the quality, attractiveness, and regularity of how the shopping mall promoted itself, and also covered the characteristics of advertising activity (events, exhibitions) and media (website, brochure, magazine, etc.).

The dimension store variety, merchandise selection and quality was evaluated in terms of number of stores, type and variety of stores, product selections in the stores, variety of major stores, variety of food in the food courts, variety of leisure facilities, quality and variety of banking facilities, quality and variety of food-and-beverage stores, quality and variety of children facilities, quality and variety of entertainment facilities, quality and variety of health facilities, quality and variety of beauty facilities.

Atmospherics is a much more difficult dimension to operationalize. Our review of the literature (Chapter 3.2.4) has clearly indicated that the atmospherics of a shopping mall is made up of many different intangible attributes. The concept itself has little meaning to respondents. Therefore, three more succinct sub-dimensions were identified, namely mall comfort and visual appearance, space arrangement, and quality of facilities. Mall comfort and visual appearance were further operationalized into cleanliness, safety, music, ambient scents, and temperature in the mall’s public space (outside the stores), facade, decor, attractiveness of interior walls and floor color, ceilings and lighting, architecture and interior design, window display, signs and decorations in the public space, visual attractiveness of public space, building style, and quality of lighting, garden and greenery, and artwork. The space arrangement was measured in terms of easiness of layout to find stores, vertical circulation, and service facilities. Quality of facilities was measured in terms of cleanliness and odor of public facilities (toilet, nursing room, praying room, public seats).


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Personal service was evaluated considering helpfulness and friendliness from greeters, receptionists, securities, and customer services.

The final dimension was the social environment. Respondents were asked to evaluate the politeness and social behavior of other shoppers in the mall.

After the questionnaire was designed, it was pre-tested on a pilot sample of thirty-three respondents in a mall in North Jakarta to resolve any issues regarding understanding of the questions and to indicate whether any changes needed to be made. This pre-test allow us the test the wording and the flow of the questionnaire. Suggestions and comments were collected from respondents to identify potential errors regarding the wording, phrasing of questions, which were then corrected. Some changes were made, particularly about layout, and typography. The pre-test data were excluded from further analyses. Appendix 1 shows the complete questionnaire.

4.3 Procedures for Collecting Data

The survey was administered in September and October 2012 in three shopping malls, which represent the local shopping mall, the classic shopping mall, and the modern shopping mall. Due to the fact that we want to understand the shopping decisions process, an intercept technique was implemented to make shoppers able to recall their shopping experiences. By interception, surveyors administered the questionnaire to shoppers who finished shopping in the exit gates of each mall. Those who agreed were seated at a table provided by the mall for data collection and presented with the paper-and-pencil survey. Controls, however, were placed on every fifth shoppers encountered on the spot by each surveyor. Surveyors should only select shoppers who were more than 15 years old. To avoid familiarity bias, shoppers were asked to confirm that they had visited the mall before.

Studies have indicated that consumer behavior may vary depending on the time of the day (Skogster et al., 2008; Kuruvilla et al., 2009). Therefore, to ensure adequate sample diversity, surveys were administered during the operational hours of the malls. An attempt was made to fill approximately half of the surveys during the morning hours (10:00 to 16:00) and the other half in the afternoon and the night (16:00 to 22:00). In order to reduce non-response error, we hired and trained some undergraduate students as surveyors. The surveyors worked in pairs when conducting the survey and they were monitored on-site and coached as needed by the researcher. Additionally, the surveyors were always positioned at different entrances of the malls. Respondents


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spent on average 20 minutes completing the questionnaire and received an IDR 25000 voucher to be used within the mall as compensation.

4.4 The Shopping Malls Case Study

To avoid bias in mall catchment areas, all three malls are located in the South of Jakarta. South Jakarta was selected because it has at least 18 shopping malls in each category of our classification, some of which were recently built. Other considerations underlying the selected of malls were the authorization permits from mall managers to conduct the surveys. In addition, South Jakarta is an area of approximately 141,3km2 with almost 2 million residents in 2010. It is the third most populated of the five cities of Jakarta.

The local shopping mall (LM) was built in 2008. The area is in the busiest commercial zone in Jakarta. There are two other local shopping malls, hundreds of stores and a main bus station located in the same area. The mall features 8-floors with 2277 store tenants and 2 anchor tenants. The ownership of tenants is strata tile or long leasing ownership with the majority of tenants of C-class and Trade Center class price level. The mall also has a variety of stores and a daily open market. However, there is no department store in this mall. In most stores shoppers can bargain. The total retail floor space is approximately 110,610 m2.

The modern shopping mall (MM) is located in the prime street of Jakarta, next to a Business District and a 26,013 m2 university. The mall that was built in 2003 has 9 floors with total retail floor space of 64,515 m2. The mall tenants represent a mixture of ownerships with majority tenants of B-class price level. There are 488 store tenants and 2 anchor tenants. The building is integrated with a convention center for 1800 persons and two office towers.

The classic mall (CM) is located at the junction point which is next to a-middle-class residential area and office district in South Jakarta. The mall was built in 1993, and has been rebuilt in 2008. CM has 6 floors with 2 anchor tenants and 155 store tenants. The mall has a B-class price level. The total retail floor space is approximately 57,948 m2.

4.5 Data Collection

The data were obtained from 686 respondents in three shopping malls that represent three types of malls. After cleaning the data, 670 respondents were useful for analysis.


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4.5.1 Sociodemographic Characteristics

Table 4.1 gives an overview of the respondents’ characteristics. The respondents’ profile shows a higher number of females than males. Several studies of shopping malls have found a larger share of women (Anselmsson, 2006; Chebat, et al., 2009; Cai and Shannon, 2012). According to Bakewell and Mitchell (2004), women enjoy going to a shopping mall and they are shopping more than men. In addition, we found women to be more willing to participate in our study. Respondents were mostly single and over 70% were Javanese. The Sumatrans made up approximately 17% of the sample, and the remainder was from other races. This result may be explained by the fact that Jakarta is located in the Western part of Java Island and Sumatra Island is the closest big island from Jakarta. On average, respondents were 29 years old. The sample had a higher number of employees and students than entrepreneurs, housewives, and retired people. Over 58% of the respondents finished or are going to the university. Respondents reported about 1 person accompanying them to the mall (the average is 1.35). One companion accompanied over 37% of the shoppers. The percentage visiting the mall alone and the percentage that was with more than one companion are virtually the same (32% vs. 31%). The majority of respondents lived or worked in South Jakarta (over 58%). All malls in this study are located in South Jakarta; this finding supports previous study that mentioned shoppers prefer to patronize the closest shopping area which has the ability to fulfill their shopping needs (see e.g. Marjanen, 2000).

4.5.2 Shopping Motives and Factor Influencing Mall Choice

Table 4.2 shows the motives underlying mall choice. Many variations of answers were collected, since respondents responded to an open-ended question to report their motives for choosing the particular mall. Each shopper provided at least one motive. We categorized the answers into eight reasons that motivated shoppers to choose the mall: “to go shopping”, “to use entertainment facilities”, “for leisure”, “to walk around”, “to socialize”, “to work”, “to use service facilities”, and “to pass through”. Answers, which did not belong to these categories, were coded as “others”.

As shown in Table 4.2, the 670 respondents mentioned 765 motives for choosing the mall. This means that some shoppers gave more than one answer. ”To go shopping” or to buy something (as a translation from Bahasa Indonesia) was the most mentioned motive as one would expect. Over 50% of the respondents indicated they went to the mall “to use entertainment facilities”, “for leisure”, “to walk around”, and “to socialize”.


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Work, which included to have a meeting and doing business, was not, mentioned much (1.83%). Table 4.3 shows the distribution of factors influencing mall choice.

Table 4.1 Sociodemographic Characteristics of the Sample (N=670) Variables Sample Gender Male 34.1 %

Female 65.9 % Marital Status Single 61.4 %

Married 38.6 % Race/ ethnicity Sumatra 17.1 %

Java 73.7 % Bali and Nusa Tenggara 0.9 % Borneo 1.3 % Sulawesi 4.0 % Maluku and Papua 1.8 % Non-Indonesian 1.2 %

Average of age in years old 28.86 (9.7) Age <20 years old 11.8 %

20 to 29 years old 52.0 % 30 to 39 years old 22.0 % >39 years old 14.2 %

Occupation Student 29.5 % Employee 44.5 % Entrepreneur 15.7 % Housewife 8.2 % Retired 1.3 %

Education High School 26.5 % Polytechnics 3.6 % Academy 11.1 % University 58.7 %

Average of number of companion 1.35 (1.54) Number of companion None/Alone 32.0 %

With 1 Companion 36.6 % With >1 Companion 31.4 %

Place of residence West Jakarta 6.6 % North Jakarta 3.9 % Central Jakarta 8.8 % East Jakarta 14.5 % South Jakarta 58.2 % Outside Jakarta 8.1 %

Place of office/school West Jakarta 4.4 % North Jakarta 2.7 % Central Jakarta 14.9 % East Jakarta 7.2 % South Jakarta 64.5 % Outside Jakarta 5.7 %

Standard deviations are between the brackets


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As respondents were asked to answer an open question with five empty spaces,we had a variety of answers. In general, shoppers mentioned one to three factors. We identified and categorized all the answers into eight categories which are as follow: “Variety of products and stores”, encompassing answers such as good quality of products, comfort of food-and-beverage, variety of stores and products, variety of fashion stores, complete set of stores or products, and there is a certain store I want to visit. “Activity” concerns the activities shoppers wish to conduct in the mall, for example to hang out, to socialize, to accompany friends or family, for an appointment or for business, to kill time, to take a walk, to relax, etc. “The price” refers to price factors such as cheap price, discount, or sale. Four answers, location, access, easiness of public transportation, availability of parking were identified as “accessibility”. “Entertainment and services” consists of answers such as to go to a certain entertainment place, to eating out, and to watch movie. Answers that mention the friendliness, politeness of the store assistants, the beauty of the store assistants, and good services were categorized as “personal service”,

Table 4.2 Motive to Visit the Mall (N=670) Motive Frequency % To go shopping 344 44.97 To use entertainment facilities 134 17.52 For leisure 96 12.55 To walk around 90 11.76 To socialize 69 9.02 To work 14 1.83 To use service facilities 13 1.70 To pass through 4 0.52 Others 1 0.13

Total number of motives 765 100

Table 4.3 Factors to Choose the Mall (N=670) Variables Frequency % Products & Stores 374 32.27 Activity 242 20.88 Accessibility 218 18.81 Entertainment & Services 192 16.57 Atmospheric 68 5.87 Price 45 3.88 Public facilities 5 0.43 Personal service 4 0.35 Other aspects 11 0.95

Total 1159 100


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while answers that explain quality of toilet or praying room, comfort of praying room, easiness to find toilet, to get Wi-Fi, to go to praying room were classified as “public facilities”. “Atmospherics” relates to answers that express shoppers’ appraisal of the mall environment, for example nice mall, comfort environment, cleanliness, safeness, to cool down, good design, luxury, and good layout. Answers, which did not belong to one of these categories, were identified as “others”. As can be seen in Table 4.3, “variety of products and stores”, “activity”, “accessibility”, and “entertainment and services” made up the largest portion of factors that shoppers considered in selecting the mall (88.5%).

4.6 Shoppers’ Characteristics

This section will discuss the profiles of the shoppers in each of the three shopping malls and examine their distinctiveness among the malls. The following sociodemographic data were collected: gender, marital status, age, race/ethnicity, occupation, education, and place of residence or office.

4.6.1 Sociodemographic Characteristics

First, we compared the three samples in terms of gender. Table 4.4 shows that for each of the samples, the proportion females are significantly higher than the proportion of males. This is an expected outcome. A larger percentage of females are usually found in studies like this because shopping is a gender-related activity (e.g., Kotzé et al., 2012). Although females outnumbered males in each of the samples, nevertheless differences between the three malls can be noted. The percentage females in the local mall (LM) is “only” 57.6%, the percentage females is 63.8% in the modern shopping (MM), while it is as high as 75.8% in the classic mall (CM). Single status in the LM is the highest (64.8%); in the MM it is over 61.9%; while in the CM it is as low as 57.5%. The average age of respondents in the LM is 27.7 years old, in the MM it is 28.9 years old, and in the CM it is as high as 30 years old. In all malls, the majority of the shoppers are between 20 and 29 years old.

The percentage of shoppers between 20 and 29 years old in the LM is 53%, while in the MM it is as high as 57.8 %, and in the CM it is as low as 45.4%. The shoppers younger than 20 years in the LM represent 14.5%, in the MM it is as low as 5.8%, while in the CM it is 14.5%.


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As Table 4.4 demonstrates, each mall has over 70% of the Javanese ethnic group. The CM has the most diverse ethnic group of shoppers, while the LM is most homogeneous in this respect. This result can be explained by the fact that our study area is located on Java Island. Regarding the occupation of respondents, most are employees. In the MM, employees represent over half of the respondents (56.3%). A possible explanation for this might be that the location of the MM is in the central business district, so that many shoppers are employees who work in this area.

Table 4.4 Sociodemographic Characteristics

Variables Local

Shopping Mall N=218

Modern Shopping Mall

N=225

Classic Shopping Mall

N=227 Gender Male 42.40% 36.20% 24.20% Female 57.60% 63.80% 75.80% Marital Status Single 64.80% 61.90% 57.50%

Married 34.50% 38.10% 42.50% Race/ ethnicity Sumatera 21.10% 14.40% 14.50%

Java 70.40% 71.60% 78.90% Bali & Nusa

Tenggara 3.60% 0 0.40%

Borneo 4.50% 1.80% 0.90% Sulawesi 0 6.70% 2.60% Maluku & Papua 0.40% 3.10% 1.30% Non-Indonesian 0 1.80% 1.30%

Average of age in years old 27.65 (8.93) 28.89 (8.8) 30 (11.06) Age <20 years old 14.50% 5.80% 14.50%

20 to 29 years old 53.00% 57.80% 45.40%

30 to 39 years old 22.60% 24.00% 19.40%

>39 years old 9.20% 12.40% 20.70% Occupation Student 33.20% 23.70% 31.70% Employee 39.60% 56.30% 39.60% Entrepreneur 18.40% 12.90% 15.90% Housewife 7.80% 5.80% 11.00% Retired 0.90% 1.30% 1.80% Education High School 41.60% 14.50% 23.00% Polytechnics 6.50% 2.70% 1.80% Academy 11.20% 12.10% 10.20% University 40.70% 70% 65% Average of Number of Companion 1.71 (1.78) 1.3 (1.53) 0.98 (1.18) Number of companion

None/Alone 25.70% 28.40% 41.60%

With 1 Companion 34.90% 39.10% 35.80%

With >1 Companion 39.40% 32.40% 22.60%


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Place of residence

West Jakarta 6.70% 11.10% 2.60%

North Jakarta 3.10% 4.60% 3.10%

Central Jakarta 4.60% 19.10% 2.20%

East Jakarta 8.40% 19.60% 15.00%

South Jakarta 68.00% 35.60% 70.90%

Outside Jakarta 8.40% 9.30% 6.20% Place of office/school West Jakarta 6.70% 6.20% 3.10%

North Jakarta 2.70% 3.10% 2.20%

Central Jakarta 8.40% 28.00% 7.90%

East Jakarta 5.80% 8.40% 7.00%

South Jakarta 68.00% 48.40% 76.70%

Outside Jakarta 8.40% 5.80% 3.10%


Results demonstrate that students dominate the LM (33.2%), while in the CM housewives represent the highest percentage (11%). The LM has shoppers from young age groups, who are single. In contrast, shoppers at the CM tend to be housewives who are older than 39 of aged. According to Table 4.4, the majority of respondents in MM and CM have a university background (70% and 65%), while the majority of respondents in the LM have a high school or university background (41%). Regarding shopping companion, we found that shoppers in the LM have on average the largest travel party (1.7 companions), while shoppers in the MM have on average 1.3 companions, and shoppers in CM have 0.98 companions. Results seem to follow previous studies that mentioned younger people with their friends mainly patronize the mall (e.g., Tootelian and Gaedeke, 1992; Haytko and Baker, 2004; Martin, 2009).

Regarding place of residence and office, Table 4.4 suggests that over 68% of the respondents in the LM and CM live and work in South Jakarta, while less than 15% live in West, North, Central and East Jakarta. Similar to LM and CM, over 48.4% worked in South Jakarta and over 35.6% of MM’s respondents lived in South Jakarta. In addition, results show that MM’s respondents who live in Central Jakarta have a similar percentage as respondents who live in East Jakarta (19%). It seems the location of the MM in the Central Business District brings a larger variety of visitors who lived outside South Jakarta. Shoppers visited the mall because they work in this area. All malls have less than 9.3% respondents who work or live outside of Jakarta. This indicates that the malls in our study performed as regional shopping malls, which serve people inside Jakarta.


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4.6.2 Shopping Motives and Factors Influencing Shopping Mall Choice

Having compared the three sub-samples in terms of their sociodemographics, next we compare these samples in the context of motives for choosing the mall and the factors underlying the choice of shopping mall. As can be seen in Table 4.5, motives such as “to go shopping”, “to use entertainment facilities”, and “for leisure” were frequently mentioned answers across the malls. Our results show that “to go shopping” was the primary motive for all malls. Again, however, there are differences. In case of the LM, 51.6% of the respondents mentioned, “to go shopping” as their primary motive. For the other two malls, this percentage was less than 45%. This difference may indicate that the shopping malls in our study are varied and have quite distinct characteristics from one another. “To use entertainment facilities”, appeared as the shoppers’ second most frequently mentioned motive in MM (23.0%) and CM (15.5%), while shoppers’ second motive in the LM was “for leisure” (16.0%). “To walk around or to browse” ranks third in the LM (14.75%) and in the MM (12.96%), “to socialize” is also often mentioned for the CM (13.2%). These differences are difficult to explain.

By means of the chi-square test, we investigated whether the shopping motives differed across the three types of shopping malls. For each motive, respondents indicated whether it was relevant to them. Significant differences were found between shoppers who selected “to socialize” (p<0.01) and “to go shopping”, “to use entertainment facilities” and “to walk around and to browse” motive (p< 0.05), see Table 4.5.

Regarding factors influencing shopping mall choice, our data demonstrates that in all malls “products and stores” was the major consideration in selecting the mall (see Table 4.6). This seems a logical answer when visiting the malls “to go shopping”.

Table 4.5 Motives to Visit the Mall

Motives To Visit Local

Shopping Mall N=218


N=225


N=227 To go shopping* 51.64% 38.89% 45.02% To use entertainment facilities* 13.52% 22.96% 15.54% For leisure 15.98% 11.11% 10.76% To walk around and to browse* 14.75% 12.96% 7.57% To socialize** 3.69% 10.00% 13.15% To work 0% 2.22% 3.19% To use service or facilities 0.41% 1.11% 3.59% Miscellaneous 0% 0.74% 1.20%

Total 100% 100% 100%

Note: ** significant at .01 level (2-sided); * significant at .05 level (2-sided)


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Table 4.6 Factors Influencing Mall Choice

Factor To Choose The Mall Local

Shopping Mall N=218


N=225


N=227 Products and Stores** 37.27% 28.29% 31.47% Activity** 22.57% 23.08% 16.80% Accessibility** 12.86% 19.60% 24.00% Entertainment and Services 17.06% 18.36% 14.13% Atmospherics* 4.46% 4.22% 9.07% Promotion and Price 4.20% 4.71% 2.67% Public Facilities 0.52% 0.25% 0.53% Personal Service 0% 0.50% 0.53% Miscellaneous 1.05% 0.99% 0.80%

Total 100% 100% 100%

Note: ** significant at .01 level (2-sided); * significant at .05 level (2-sided)

“Accessibility”, “activity”, and “entertainment and services” are the other three factors influencing shopping mall choice which have above 10% in all malls. Interestingly, the percentages are different among the malls. For example, shoppers in the LM listed “products and stores” as the most important factor of mall choice (37.2%), followed by “activity” (22.6%), “entertainment and services” (17.1%), and “accessibility” (12.58%). Respondents in the MM showed that besides “product and stores” (28.3%), “activity” (23.1%), “accessibility” (19.6%), and “entertainment and services” (18.4%) were factors influencing mall choice. Shoppers in the CM showed that besides “products and stores” (31.5%), “accessibility” (24%), “activity” (16.8%), and “entertainment and services” (14.13%) were considered as factors influencing mall choice. These differences indicate that each of the malls has its own characteristics for deciding shoppers to choose that mall.

According to chi-square tests, significant differences in factors influencing mall choice were found for “products and stores”, “activity”, and “accessibility” factor (p<.01), and “atmospherics” factor (p<0.05), see Table 4.6.


A primary objective of the empirical study was to obtain detailed information of pre-shopping decisions. In this chapter, we developed a survey design and procedure to collect the data. The survey aimed at collecting data to understand pre-shopping decisions by examining sociodemographics, and shopping motives. Using a paper based questionnaire, we collected data from 670 respondents who just finished their


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shopping in three types of Indonesian shopping malls in September and October 2012. All shopping malls were selected in South Jakarta and had similar catchment areas.

Results on the analyses of pre-shopping decisions reported in this chapter suggest that respondents differ in their motives of visiting a mall. In addition to shopping, a variety of other motives were mentioned, supporting that the malls serve different purposes. Although women and younger people make up the majority of the clientele of the malls, differences were found which can be largely explained by the specific products and stores and activity of the mall. By and large, these findings seem in line with findings obtained for Western malls, but most of the distributions by age, gender, and motives look more extreme.

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5 SHOPPERS’ EVALUATIONS As discussed in chapter 4, we have selected three shopping malls, which are considered representative of different stages of shopping malls development in Jakarta. Our analyses of three types of shopping malls in the previous chapter demonstrated that each type of mall has its own shoppers’ profiles. To better understand how shoppers evaluate these shopping malls, this chapter discusses the results of analyses of shoppers’ evaluations of mall’s attributes.

The analyses in this chapter covered 670 respondents who either visited the local shopping mall (LM), the modern shopping mall (MM), or the classic shopping mall (CM). Respondents were asked to respond to a set of semi-structured questions which prompted them to evaluate the shopping mall of their visit on mall image dimensions: location and convenience, store variety, merchandise selection and quality, price, atmospherics, personal service, and social environment. The choice of these dimensions is based on the results of the literature review, reported in Chapter 3.

The purpose of this chapter is to describe the malls in terms of shoppers’ evaluation of mall’s dimensions and to examine which attributes significantly contribute to the overall evaluation of the malls. In addition, these results allow comparing whether shopping evaluations show systematic differences between the three selected malls.

This chapter is organized as follows. The first section discusses the measurement of the evaluation of the shopping mall’s dimensions. The next section presents the descriptive results of the evaluations of each mall’s dimensions. The third section discusses the comparison of evaluations among the malls, specifically the differentiation among evaluations’ mean ratings. The fourth section examines the relation between the overall evaluations of mall’s dimensions and attributes of mall’s dimensions for each mall. The last section gives a summary and draws conclusions.

5.1 Measurement of Shopping Mall Dimensions

As discussed in chapter 4.2.2, our questionnaire was designed to evaluate shopping mall’s environments. Consumers were asked to evaluate a set of malls’ dimensions on a seven-point rating scale anchored by “terrible” and “excellent”.


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Table 5.1 Evaluations of Mall Image Dimensions

Evaluation of Mall Image Dimensions Number of

Evaluations/ Attributes 1 Overall evaluations 9 evaluations 2 Location and convenience 8 attributes 3 Price 5 attributes 4 Advertising and promotion 4 attributes 5 Store variety, merchandise selection and quality 18 attributes Atmospherics: 6 - Mall comfort and visual appearance 21 attributes 7 - Space arrangement 8 attributes 8 - Quality of facilities 11 attributes 9 Personal services 6 attributes 10 Social Environment 2 attributes

Shoppers evaluated 83 attributes, which were supposed to represent mall image dimensions: location and convenience, store variety, merchandise selection and quality, price, atmospherics, personal service, advertisement and promotion, and social environment. As mentioned in Chapter 4.2.2, we identified three sub-dimensions to measure the atmospherics dimension, namely mall comfort and visual appearance, space arrangement, and quality of facilities. Additionally, we asked respondents to provide overall summary ratings of the malls on each mall image dimensions. Thus, in evaluating the malls, respondents provided answers to a total of 92 questions. Table 5.1 presents information regarding the evaluations of mall’s dimensions and the number of attributes in the questionnaire.

Besides using a seven-point rating scale, three multiple-choice questions were used to collect detailed information regarding location and convenience and five multiple-choice questions regarding mall comfort and visual appearance. These multiple-choice questions were separately analyzed as they involved a nominal level of measurement. However, all results will be discussed together in this chapter.

5.2 Results of Evaluations: Descriptive Analysis

Our data consists of ratings of the shopping malls on the nine selected dimensions and corresponding attributes for each dimension. The evaluations on each mall were provided by more than 98% of the respondents. However, questions related to “location and convenience”, “quality of facilities”, and “space arrangement” dimensions, specifically on questions about the accessibility of using a bicycle, and quality and space arrangement of the nursing room (comfort, cleanliness, odor, and easiness to find nursing room) were answered by less than 46% of respondents. This

5 SHOPPERS’ EVALUATIONS

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may indicate that the majority of respondents had never used bicycles or the nursing room (see Appendix 2: Figure A2.1b, Figure A2.6, and Figure A2.7). Therefore, questions related to accessibility of using a bicycle and quality and space arrangement of the nursing room were removed from the analyses.

Table 5.2 shows the results of a comparison of the three shopping malls in terms of shoppers’ mean evaluative ratings of the various selected attributes. Appendix 2 displays the underlying frequency distributions for each attribute. Attributes are organized in terms of the underlying dimensions.

5.2.1 Evaluations of Dimensions

Respondents were asked to provide their evaluation for each of nine mall’s dimensions we identified. Thus, they provided evaluative scores on location and convenience, price, store variety, merchandise selection and quality, advertising and promotion, mall comfort and visual appearance, space arrangement, quality of facilities, personal service, and social environment. Results demonstrate that the LM had the highest mean rating for almost all dimensions, following by the CM, and the MM. The CM had the highest ratings regarded to overall evaluation of location and convenience, space arrangement, and personal service. This may demonstrate that in overall evaluations of mall dimensions the LM was better than the other malls, except for location and convenience, space arrangement, and personal service. The highest mean ratings of each mall were different, for example in the LM, the highest rating concerned the store variety, merchandise selection and quality; in the MM it was personal service, whereas in the CM it was space arrangement. Differences in evaluative ratings were also found at the level of the dimensions; the lowest mean rating in the LM was Overall evaluation of social environment, in the MM it was Overall evaluation of space arrangement, and in the CM it was the Overall evaluation of advertising and promotion. This result indicates that according to the shoppers’ judgments, the LM may be strong point in store variety, merchandise selection and quality, but it may have a weak point in the social environment; the MM had a strong point in the personal service, but it had a weak point in the space arrangement, and the CM had a strong point in the space arrangement, but a weak point in advertising and promotion.

5.2.2 Location and Convenience

Table 5.2 presents the mean ratings of the seven attributes selected to represent the location and convenience dimension. These attributes are accessibility by public


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transportation, accessibility by private car, accessibility by private motorcycle, ease of getting a parking space, parking cost, opening hours of the mall, and the trip to the mall. As mentioned above, the evaluations of the location and convenience dimension involved multiple-choice questions to give background information, which may connect to shoppers’ evaluations of this dimension. The multiple-choice questions asked about the place shoppers start before their shopping trip (home, job location, or others); type of transport mode shoppers use to get to the mall (private car, private motorcycle, bicycle, on foot, bus or taxi), and the duration of shopping trip (less than 1 hour, between 1 and 2 hours, between 2 and 3 hours, more than 3 hours). Results from the multiple-choice questions show that the majority of shoppers departed from home before their shopping trip. The majority of shoppers in the LM came by motorcycles (42.8%), and the majority of shoppers in the CM by private cars (38.6%) as in the MM (30.7%). Furthermore, in terms of public transportation over 11.7% of shoppers in CM and MM employed taxis to the malls, while “only” 4.2% of shoppers in the LM came by taxis. Over 31.2% of LM’s shoppers employed buses to the mall, while less than 28% of MM’s and CM’s shoppers came by bus. Shoppers who came on foot in CM and MM were less than in the LM. Regarding the trip to the mall over 62% of shoppers in LM and CM and over 44.2% of shoppers in the MM spent less than one hour on the trip (see Appendix 2 Figure A2.1b, Figure A2.1c, and Figure A2.1d).

Shoppers’ evaluation of attributes pertaining to the location and convenience dimension show that the LM is evaluated better than the other two malls on most location and convenience attributes, except on the trip to the mall’s attribute. In contrast, the MM is evaluated as the lowest on all attributes. It can be seen that shoppers in all three malls evaluated the opening hours of the mall as the highest. This means shoppers in all three malls saw the malls were able to manage good opening hours. In contrast, shoppers in all three malls rated the trip to the mall as the least satisfying attribute. As mentioned, the majority of shoppers spent less than one hour on the trip. This result may indicate that one hour on the trip to go to the mall is inadequate.

Since shoppers used different modes of transportation to the mall it is expected that malls’ accessibility by public transport, accessibility by private vehicle and parking attributes may be evaluated differently. Shoppers tended to give higher ratings according to the transport mode they used on the day of the survey. For example, the majority of shoppers in the LM came with motorcycles to the mall; and they evaluated the accessibility of motorcycle higher than the accessibility of other transport modes.


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Similarly, the majority of shoppers in the MM came with public transportation; and they evaluated accessibility of public transportation higher than accessibility associated with other transport modes. However, the evaluation of accessibility in the CM is different. The majority of shoppers in the CM came by car, but surprisingly they evaluated the accessibility by private car lower than evaluation of the accessibility by public transportation and by motorcycle. This may suggests that in the CM the accessibility by private car was not as good as the accessibility by other transport mode.

5.2.3 Price

Price was evaluated in terms of five attributes, including price level of food and beverages, fashion, supermarket, and the presence of particular high-image and low-image stores in the mall. The LM received the highest mean ratings on all attributes. Shoppers in the MM gave higher mean ratings regarding price level of food and beverages, fashion, and presence of particular low-price image stores than shoppers in the CM. Results show that price level of supermarket had the highest mean rating in all three malls (Table 5.2).

Shoppers in all three malls showed less satisfied with price level of food and beverages and presence of particular high-price image stores by evaluating them as the two of the lowest mean ratings in price dimension.

As mentioned in Chapter 4, each mall had its own specific shoppers’ profiles. In the evaluation of price our results suggested that the supermarket’s prices was favourable to all shoppers and might be one of the reason shoppers visit the malls. Results also indicate that presence of particular high-price was the weakest point in all three malls.


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Table 5.2 Mean Evaluations of Mall Image Dimensions

Mall Image Dimensions LM MM CM

Mean St.Dev. Mean St.Dev. Mean St.Dev.

Overall Evaluation

Overall evaluation of location & convenience

5.03 1.222 4.76 1.258 5.04 1.289

Overall evaluation of price 4.96 1.140 4.74 1.076 4.74 1.031

Overall evaluation of store variety, merchandise selection and quality

5.24 1.071 4.86 1.012 4.99 1.089

Overall evaluation of advertising and promotion

5.00 1.186 4.65 1.175 4.68 1.187

Overall evaluation of mall comfort and visual appearance

5.17 1.124 4.71 1.099 5.09 1.116

Overall evaluation of space arrangement 5.19 1.193 4.62 1.138 5.22 1.080

Overall evaluation of personal service 5.11 1.097 4.92 1.060 5.13 1.121

Overall evaluation of quality of facilities 5.18 1.144 4.77 1.173 5.13 1.112

Overall evaluation of social environment 4.91 1.176 4.80 1.018 4.90 1.147

Location and Convenience

Accessibility by public transportation 5.23 1.284 4.73 1.244 4.99 1.385

Accessibility by private car 4.98 1.277 4.62 1.222 4.86 1.279

Accessibility by private motorcycle 5.30 1.259 4.62 1.342 4.97 1.351

Ease to get parking space 5.13 1.263 4.45 1.125 4.64 1.436

Parking cost 4.88 1.296 4.50 1.068 4.64 1.248

Opening hours 5.35 1.148 5.05 1.098 5.13 1.179

Trip to the mall 4.33 1.889 4.30 1.641 4.40 1.864

Price

Price level of food and beverages 5.02 1.193 4.76 1.001 4.67 1.048

Price level of fashion 5.15 1.224 4.86 1.045 4.81 1.205

Price level of supermarket 5.34 1.117 5.01 1.009 5.19 1.112 Presence of particular high-price image stores 4.78 1.229 4.42 1.017 4.67 1.219

Presence of particular low-price image stores 5.18 1.152 4.79 1.043 4.78 1.287

Store Variety, Merchandise Selection and Quality

Number of stores 5.48 1.035 4.94 1.040 4.89 1.070

Types and variety of stores 5.43 .996 4.92 1.036 4.81 1.134

Product selection in stores 5.37 .978 4.91 .978 4.93 1.117

Variety of major stores 5.34 1.028 4.99 1.084 5.10 1.215

Variety of food in the food court 5.37 1.117 5.21 1.039 4.69 1.291

Variety of leisure facilities 5.31 1.177 4.92 1.092 4.41 1.268


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Quality of banking facilities 5.31 1.232 5.00 1.191 5.44 1.134

Variety of banking facilities 5.33 1.165 5.00 1.113 5.34 1.144

Quality of food and beverage stores 5.46 1.097 5.27 1.020 5.38 1.098

Variety of food and beverage stores 5.52 1.097 5.27 1.076 5.31 1.170

Quality of children facilities 5.30 1.170 4.87 .987 5.01 1.181

Variety of children facilities 5.30 1.147 4.78 .981 4.89 1.171

Quality of entertainment facilities 5.41 1.164 4.97 1.096 5.23 1.287

Variety of entertainment facilities 5.38 1.117 4.89 1.124 5.07 1.279

Quality of health facilities 4.82 1.221 4.52 1.036 4.44 1.305

Variety of health facilities 4.88 1.255 4.43 1.114 4.38 1.308

Quality of beauty facilities 5.06 1.146 4.63 .993 4.97 1.225

Variety of beauty facilities 5.05 1.222 4.65 .980 4.92 1.205

Advertising and Promotion

Quality of activities/events/exhibitions 4.76 1.106 4.42 1.112 4.40 1.178 Attractiveness of activities/events/ exhibitions 4.78 1.164 4.33 1.210 4.34 1.152

Frequency of activities/events/exhibitions 4.75 1.216 4.25 1.138 4.24 1.142

Special sales promotions 4.79 1.167 4.24 1.152 4.35 1.267

Mall comfort and visual appearance

Cleanliness of stores and public spaces 5.17 1.113 4.82 1.046 5.31 1.137

Safety of stores and public spaces 5.21 1.041 4.88 1.050 5.38 1.067

Attractiveness of architecture design 5.20 1.097 4.66 1.053 4.96 1.212

Attractiveness of interior design 5.10 1.107 4.66 1.045 4.98 1.119

Attractiveness of window displays 5.02 1.128 4.70 1.086 4.94 1.095 Attractiveness of interior walls and floor color 5.04 1.058 4.67 1.034 5.00 1.062

Attractiveness of ceiling and lighting 5.09 1.078 4.64 1.048 4.98 1.152

Artwork in the public spaces 4.90 1.268 4.40 1.183 4.43 1.309 Signs and decorations in the public spaces 5.00 1.092 4.47 1.082 4.70 1.225

Visual attractiveness of public spaces 4.93 1.047 4.53 1.061 4.73 1.118

Quality of gardens and greenery 4.65 1.328 4.07 1.310 4.21 1.436

Building style 5.04 1.063 4.61 1.023 4.92 1.188

Public spaces' atmosphere 5.05 1.026 4.74 .939 4.90 1.051

Food court's atmosphere 5.08 1.038 4.85 .941 4.90 1.114

Quality of lighting 5.16 1.040 4.75 .903 5.07 1.060

Type of music 4.89 1.153 4.51 1.142 4.84 1.126

Comfort in music sound 4.78 1.103 4.50 .950 4.81 1.034

Odor in public spaces 5.11 1.083 4.70 1.007 5.19 1.121


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Table 5.2 Mean Evaluations of Mall Image Dimensions (continued)

Mall Image Dimensions LM MM CM

Mean St.Dev. Mean Mean St.Dev. Mean

Odor in elevators 5.06 1.078 4.74 .928 5.08 1.111

Smoke in public spaces 4.98 1.409 4.85 1.155 5.38 1.200

Thermal comfort 4.87 1.112 4.59 1.019 4.86 1.126

Space arrangement

Ease to find stores 4.96 1.145 4.47 1.190 5.07 1.169

Ease to find escalators 5.27 1.117 4.61 1.178 5.38 1.116

Ease to find elevators 5.01 1.472 4.37 1.292 4.59 1.354

Ease to find toilets 4.77 1.240 4.15 1.236 4.93 1.271

Ease to find the praying room 5.18 1.365 4.39 1.378 4.72 1.477

Ease to find ATMs 5.27 1.306 4.99 1.161 5.51 1.182

Ease to find public seats 4.60 1.522 3.87 1.366 4.32 1.545

Quality of facilities

Quality of toilets 4.68 1.281 3.88 1.280 5.06 1.221

Cleanliness of toilets 4.61 1.253 3.80 1.332 4.99 1.290

Odor in toilets 4.48 1.321 3.71 1.321 4.90 1.270

Cleanliness of the praying room 5.49 1.182 4.71 1.292 4.97 1.285

Quality of the praying room 5.50 1.213 4.56 1.338 4.98 1.271

Odor in the praying room 5.19 1.341 4.58 1.244 4.81 1.285

Quality of public seats 4.56 1.483 3.85 1.311 4.44 1.438

Number of public seats 4.38 1.530 3.55 1.298 3.89 1.500

Social Environment

Politeness of other shoppers 4.67 1.260 4.41 1.115 4.69 1.321

Social behavior of other shoppers 4.74 1.199 4.43 1.080 4.64 1.350

Personal Service

Helpfulness of greeters/receptionists 5.02 1.056 5.11 1.023 5.03 1.164

Friendliness of greeters/receptionists 5.12 1.107 5.05 1.051 5.12 1.119

Helpfulness of security services 5.15 1.130 5.08 1.051 5.01 1.147

Friendliness of security services 5.16 1.152 5.09 1.036 5.04 1.152

Helpfulness of customer services 5.11 1.140 4.94 1.068 4.93 1.160

Friendliness of customer services 5.22 1.173 4.99 1.076 5.01 1.212

5.2.4 Store Variety, Merchandise Selection and Quality

Regarding the evaluation of store variety, merchandise selection and quality, shoppers evaluated 18 attributes including number and types and variety of stores, product


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selection in stores, variety of major stores, food in the food court, leisure facilities, quality and variety of banking facilities, food and beverage stores, children facilities, entertainment facilities, health facilities, and beauty facilities. Again the LM received the highest mean ratings for all attributes, except for quality and variety of banking facilities, while the MM received the lowest mean rating for all attributes. Results show some differences in mean ratings among the malls. Shoppers in the LM and the MM evaluated the variety of food and beverage stores attribute highest, while shoppers in the CM evaluated the quality of banking facilities highest. The variety and quality of health facilities in the LM and the MM were evaluated as two of the lowest attributes, while the variety of leisure facilities and variety of health facilities were the lowest rated attributes in the CM. This indicates that health facilities attributes were somehow the weak points in all three malls.

5.2.5 Advertising and Promotion

Four attributes were assessed to evaluate the advertising and promotion dimension. As can be seen, the mean ratings in each mall are quite similar. This indicates that all three malls treated the advertising and promotion dimension in a similar way. Shoppers in the LM rated the special sales promotions attribute as the highest, while shoppers in MM and CM rated quality of activities/events/exhibitions attribute as the highest. The frequency of activities/events/ exhibitions attribute was evaluated as the lowest in the LM and CM, and the special sales promotions attribute was rated the lowest in the MM.

5.2.6 Mall Comfort and Visual Appearance

Twenty-one attributes were selected to represent mall comfort and visual appearance: cleanliness and safety of stores and public spaces, attractiveness of architecture, interior, window displays, interior wall and floor color, and ceiling and lighting, artwork and signs and decorations in the public spaces, visual attractiveness of public spaces, quality of gardens and greenery, building style, public spaces’ and food court’s atmosphere, quality of lighting, type of music and comfort in music sound, odor in public spaces and elevators, smoke in public spaces, and thermal comfort. Results demonstrate that the mean ratings of all attributes in the MM were the lowest while the mean ratings of all attributes in the LM were the highest, except for cleanliness and safety of stores and public spaces, and odor and smoke in public spaces. Similar attributes were evaluated as the most satisfying in all three malls, namely safety of stores and public spaces. This indicates that all three malls in this study were able to induce a secure feeling among their shoppers.


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Shoppers in all three malls rated the same attribute as the lowest, namely quality of garden and greenery. This indicates that the malls in this study may lack attention on putting greenery and plants in the malls. As expected, some differences were found in the evaluations of mall comfort and visual appearance, due to the fact that shopping malls in this study are different. In addition, personal sensibility may influence the evaluations of this dimension.

5.2.7 Space Arrangement

In terms of the space arrangement dimension, respondents evaluated seven attributes, including the ease to find stores, escalators, elevators, toilets, the praying room, ATMs, and public seats. The MM received the lowest mean ratings for all attributes. The highest mean rating in the LM were the easiness to find escalators, the praying room ATMs, and public seats, and in the CM were the easiness to find stores, escalators, and toilets. Results demonstrate that shoppers’ evaluation about the easiness to find ATMs is highest in all three malls. In contrast, shoppers in all three malls expressed their low evaluation cerning the ease of finding public seats.

5.2.8 Quality of Facilities

Eleven attributes were evaluated in assessing the quality of facilities, including quality, cleanliness, odor of toilets, the praying room, and quality and number of public seats. Results show some differences in the evaluation. For example, the LM’s shoppers were mostly satisfied with the quality of the praying room, while the MM’s shoppers with the cleanliness of the praying room, and the CM’s shoppers with the quality of toilets. The lowest average rating from shoppers in the LM and the MM were obtained for odor in toilets and the number of public seats, while shoppers in the CM expressed the lowest rating for quality and number of public seats.

5.2.9 Social Environment

Social environment consist of two attributes, namely politeness of other shoppers and social behavior of other shoppers. The MM received the lowest on both these attributes. Shoppers in the CM gave the highest average rating to politeness of other shoppers, while shoppers in the LM provided the highest average rating to the social behavior of other shoppers.


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5.2.10 Personal Service

There are six attributes representing the personal service dimension, including the helpfulness and the friendliness of greeters/receptionists, security services, and customer services. Results show differences in the evaluation of personal services in all malls. The LM had the highest mean ratings on all attributes, except helpfulness of greeters/receptionists. The MM had higher mean ratings than the CM on all attributes, except the friendliness of greeters/receptionists. The highest mean rating in the LM was for friendliness of customer service; in the MM it was for helpfulness of greeters/ receptionists, while in the CM it was for the friendliness of greeters/ receptionists. Differences were also found for the lowest mean ratings. The lowest evaluation scores in the MM and the CM were expressed for the helpfulness of customer service, while in the LM helpfulness of greeters/receptionists was evaluated lowest on average.

5.3 Comparing Evaluations

Besides analyzing the mean ratings, it is interesting to investigate the differences in these mean ratings between the three selected malls. To investigate the differences in shoppers’ evaluations, independent t-tests were applied.

5.3.1 Method: Independent t-Test

Since we interviewed different respondents in the three shopping malls, independent t-tests were used to analyze statistical differences between the mean evaluations of a particular attribute of each pair of shopping malls. That is, in this study we compared the overall mean evaluations of each attribute for all pairs of malls, i.e. between the LM and the MM, between the LM and the CM, and between the MM and the CM. The conventional 5 per cent significance level was used to detect significant differences between attribute evaluations.

5.3.2 Results

Table 5.3 presents a summary of the independent t-test. Results of independent t-tests of all attributes for each pair of shopping malls are presented in Appendix 3.

In terms of overall evaluation of a particular dimension (Table 5.3), this study found that the differences in these overall evaluations between the LM and the MM were all significant, except the overall evaluation of the personal service and social environment. The mean ratings of the LM were higher compared to those of the MM.


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Five attributes, including overall evaluation of location and convenience, mall comfort and visual appearance, space arrangement, personal service, and quality of facilities had significantly different mean ratings between the MM and the CM, with the mean ratings of the CM being higher than the mean ratings for the MM. The mean rating of overall evaluation of price, store variety, merchandise selection and quality in the LM were significantly different from those for the CM, with the mean ratings for the LM being higher than the mean ratings for the CM. In addition, the overall evaluation of social environment in all pairs of malls was not significantly different.

Table 5.3 Summary of Independent t-Tests

Mall's Dimensions LM-MM LM-CM MM-CM

t Mean Diff. t Mean

Diff t Mean Diff

Overall evaluation

Overall evaluation of location and convenience

2.23 * .26 -.06

-.01 -2.26 * -.27

Overall evaluation of price 2.09 * .22 2.16 * .22 .02

.00

Overall evaluation of store variety, merchandise selection and quality

3.85 * .38 2.42 * .25 -1.35

-.13

Overall evaluation of advertising and promotion

3.09 * .35 2.79 * .31 -.29

-.03

Overall evaluations of mall comfort and visual appearance

4.34 * .46 .72

.08 -3.66 * -.38

Overall evaluation of space arrangement

5.17 * .57 -.28

-.03 -5.78 * -.60

Overall evaluation of personal service

1.81

.19 -.21

-.02 -2.02 * -.21

Overall evaluation of quality of facilities

3.72 * .41 .48

.05 -3.34 * -.36

Overall evaluation of social environment

1.04

.11 .05

.01 -1.01

-.10

Location and Convenience

Accessibility by public transportation

4.14 * .50 1.85

.24 -2.12 * -.27

Accessibility by private car 2.48 * .37 .84

.13 -1.72

-.24

Accessibility by private motorcycle

4.58 * .68 2.20 * .33 -2.20 * -.36

Ease to get parking space 5.55 * .68 3.61 * .49 -1.47

-.19

Parking cost 3.05 * .38 1.85

.24 -1.15

-.14

Opening hours of the mall 2.75 * .30 1.97

.22 -.70

-.08

Trip to the mall 0.22

.04 -.36

-.07 -.62

-.10

Price

Price level of food and beverages 2.47 * .26 3.32 * .35 .98

.09


87

Price level of fashion 2.63 * .28 2.88 * .33 .45

.05

Price level of supermarket 3.27 * .33 1.38

.15 -1.85

-.18

Presence of particular high-price image stores

3.31 * .36 .87

.10 -2.41 * -.25

Presence of particular low-price image stores

3.73 * .39 3.45 * .40 .10

.01

Store Variety, Merchandise Selection and Quality

Number of stores 5.43 * .53 5.84 * .58 .49

.05

Types and variety of stores 5.24 * .51 6.08 * .62 1.07

.11

Product selection in stores 4.97 * .46 4.49 * .45 -.14

-.01

Variety of major stores 3.43 * .35 2.24 * .24 -.98

-.11

Variety of food in the food court 1.58

.16 5.91 * .68 4.66 * .51

Variety of leisure facilities 3.59 * .39 7.71 * .90 4.55 * .51

Quality of banking facilities 2.59 * .31 -1.10

-.13 -3.87 * -.44

Variety of banking facilities 2.99 * .33 -.08

-.01 -3.13 * -.34

Quality of food and beverage stores

1.91

.19 .73

.08 -1.16

-.12

Variety of food and beverage stores

2.40 * .25 1.91

.21 -.39

-.04

Quality of children facilities 4.04 * .43 2.49 * .29 -1.32

-.14

Variety of children facilities 4.96 * .52 3.59 * .41 -1.05

-.11

Quality of entertainment facilities 3.98 * .44 1.53

.18 -2.21 * -.25

Variety of entertainment facilities 4.56 * .50 2.66 * .31 -1.59

-.18

Quality of health facilities 2.52 * .30 2.85 * .38 .69

.09

Variety of health facilities 3.56 * .44 3.64 * .50 .45

.06

Quality of beauty facilities 3.86 * .43 .75

.09 -2.91 * -.34

Variety of beauty facilities 3.46 * .40 1.04

.13 -2.32 * -.27

Advertising and Promotion

Quality of activities/events/exhibitions

3.22 * .34 3.35 * .36 .22

.02

Attractiveness of activities/ events/exhibitions

3.92 * .44 3.95 * .43 -.07

-.01

Frequency of activities/ events/exhibitions

4.46 * .50 4.50 * .50 .05

.01

Special sales promotions 5.02 * .55 3.84 * .44 -.96

-.11

Mall Comfort and Visual Appearance

Cleanliness of stores and public spaces

3.39 * .35 -1.30

-.14 -4.73 * -.49

Safety of stores and public spaces 3.29 * .33 -1.67

-.17 -4.96 * -.49

Attractiveness of architecture design

5.28 * .54 2.16 * .24 -2.83 * -.30

t-test * means significant with p-value <0.05


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Table 5.3 Summary of Independent t-Tests (continued)

Mall's Dimensions LM-MM LM-CM MM-CM

t Mean Diff. t Mean

Diff t Mean Diff

Attractiveness of interior design 4.33 * .44 1.16

.12 -3.14 * -.32

Attractiveness of window display 3.02 * .32 .76 .08 -2.31 * -.24

Attractiveness of interior wall and floor color

3.72 * .37 .46

.05 -3.29 * -.32

Attractiveness of ceiling and lighting

4.47 * .45 1.03

.11 -3.30 * -.34

Artwork in the public space 4.25 * .50 3.79 * .46 -.27

-.03

Signs and decorations in the public space

5.16 * .53 2.77 * .31 -2.10 * -.23

Visual attractiveness of public space

3.97 * .40 2.00 * .21 -1.87

-.19

Quality of gardens and greenery 4.58 * .58 3.31 * .44 -1.09

-.14

Building style 4.32 * .43 1.09

.12 -3.00 * -.31

Public spaces’ atmosphere 3.35 * .31 1.50

.15 -1.76

-.17

Food court's atmosphere 2.39 * .22 1.75

.18 -.47

-.05

Quality of lighting 4.38 * .41 .81

.08 -3.50 * -.32

Type of music 3.52 * .38 .49

.05 -3.10 * -.33

Comfort in music sound 2.84 * .28 -.31

-.03 -3.30 * -.31

Odor in public spaces 4.11 * .41 -.71

-.07 -4.82 * -.48

Odor in elevators 3.32 * .32 -.15

-.02 -3.45 * -.33

Smoke in public spaces 1.04

.13 -3.24 * -.40 -4.78 * -.53

Thermal comfort 2.76 * .28 .03

.00 -2.74 * -.28

Space arrangement

Ease to find stores 4.37 * .49 -1.02

-.11 -5.38 * -.60

Ease to find escalators 6.03 * .66 -1.07

-.11 -7.14 * -.77

Ease to find elevators 4.89 * .64 3.11 * .42 -1.80

-.23

Ease to find toilets 5.28 * .62 -1.33

-.16 -6.60 * -.78

Ease to find the praying room 5.88 * .79 3.25 * .46 -2.34 * -.34

Ease to find ATM 2.30 * .28 -1.95

-.24 -4.59 * -.52

Ease to find public seats 5.21 * .72 1.91

.28 -3.20 * -.44

Quality of facilities

Quality of toilets 6.56 * .80 -3.16 * -.38 -9.94 * -1.18

Cleanliness of toilets 6.58 * .81 -3.12 * -.38 -9.58 * -1.19

Odor in toilets 6.08 * .77 -3.41 * -.42 -9.69 * -1.19

Cleanliness of the praying room 6.37 * .78 4.15 * .51 -2.03 * -.27

Quality of the praying room 7.44 * .94 4.20 * .53 -3.13 * -.41


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Odor in the praying room 4.65 * .60 2.83 * .38 -1.74

-.22

Quality of public seats 5.31 * .71 .88

.12 -4.52 * -.59

Number of public seats 6.10 * .83 3.39 * .49 -2.55 * -.34

Social Environment

Politeness of other shoppers 2.27 * .26 -.17

-.02 -2.41 * -.28

Social behavior of other shoppers 2.88 * .31 .84

.10 -1.83

-.21

Personal Service

Helpfulness of greeters/receptionists

-.89

-.09 -.08

-.01 .78

.08

Friendliness of greeters/receptionists

0.65

.07 .00

.00 -.65

-.07

Helpfulness of security services 0.65

.07 1.32

.14 .73

.08

Friendliness of security services 0.65

.07 1.07

.12 .48

.05

Helpfulness of customer services 1.56

.16 1.75

.19 .29

.03

Friendliness of customer services 2.15 * .23 1.98 * .23 .00

.00

t-test * means significant with p-value <0.05

The summary of independent t-tests (Table 5.3) indicates that accessibility by private motorcycle is significantly different between all pairs of malls. The evaluation of five attributes including accessibility by public transportation and private car, ease to get parking space, parking cost, and opening hours of shopping center in the LM significantly differed from the evaluation of these attributes in the MM. More specifically, the mean ratings in the LM were higher than in the MM. The ease to get parking space was significantly different between the LM and the CM, and the mean rating of the LM was higher than the mean rating of the CM. The accessibility by public transportation was found statistically significant between the MM and the CM. More specifically, the mean rating in the CM was higher than in the MM. The evaluation of trip to the mall had no statistically significance in all malls. This indicates shoppers in all malls did not evaluate different about trip to the mall.

Regard to the price of all attributes the independent t-test shows significant differences between evaluations from shoppers in the LM and in the MM. However, three attributes including price level of food and beverages, fashion, and presence of particular low-price image stores had significant differences between the LM and the CM. Additionally, the mean ratings in the LM were higher than in the MM or in the CM. The MM’s evaluations of the presence of particular high-price image stores statistically differed from the CM. Specifically, the mean rating in the CM was higher than in the MM.


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Results demonstrated that in evaluating store variety, merchandise selection and quality one attribute, namely the variety of leisure facilities, is statistically different in all malls. Specifically, shoppers in the LM evaluated this attribute significantly higher than shoppers in the MM and CM. Shoppers in the MM evaluated the attribute significantly higher than the shoppers in the CM. However, no significant differences were found in the quality of food-beverages between the malls. This indicates that shoppers in all malls evaluated significant the variety of leisure facilities and did not evaluate differently the quality of food-beverages. All attributes, except variety of food in the food court and quality of food-beverages stores, were evaluated significantly different between the LM and in the MM. Specifically, all mean ratings in the LM were higher than those in the MM. Significant differences in evaluations of number of stores, type and variety of stores, variety of major stores, variety of food in the food court, quality and variety of children facilities, variety of entertainment facilities, quality and variety of health facilities were found between the LM and the CM. Particularly, the mean ratings in the LM were higher than in the CM. The mean ratings from the MM and the CM were significantly different in evaluating variety of food, quality and variety of banking facilities, quality of entertainment facilities, and quality and variety of beauty facilities. Specifically, the mean ratings in the CM were higher than in the MM, except in evaluating variety of food in the food court. In addition, quality of food-beverages failed to reveal statistically difference in all malls. In other words, shoppers in all malls did not evaluate different about this attribute.

Results of independent t-test indicate statistically significant different of evaluations of all attributes in advertising and promotion between the LM and in the MM and between the LM and in the CM. Particularly, in all attributes of advertising and promotion the LM had higher mean ratings than the MM and the CM.

In terms of mall comfort and visual appearance, two attributes, namely attractiveness of architecture design and signs and decorations in the public space, were evaluated statistically different in all malls. This indicates that shoppers in all malls evaluated significantly different the attractiveness of architecture design and signs and decorations in the public space. In addition, 13 attributes regarding cleanliness and safety of stores and public spaces, attractiveness of interior design, window display, interior wall and floor color, ceiling and lighting, building style, quality of lighting, type of music and comfort in music sound, odor in public spaces and in elevators, and thermal comfort appeared statistically significant difference between the LM and the MM and between the MM and the CM. Specifically, the mean ratings in the LM were


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always higher compared to the MM; and the mean ratings in the CM were higher than the MM. While 3 attributes including, artwork in the public spaces, visual attractiveness of public spaces, quality of gardens and greenery showed significant differences between the LM and the MM and between the LM and the CM. Particularly, the mean ratings in the LM were higher than the MM and the CM. Two attributes in the LM namely public spaces’ atmosphere and food court’s atmosphere were found to show statistically significant difference from the MM. The evaluation of smoke in public spaces was statistically significant difference between the LM and the CM and between the MM and the CM. Particularly, the mean ratings in the CM was higher than the LM and the MM.

In evaluating space arrangement, the evaluations of most attributes showed significant differences between the LM and the MM, and between the MM and the CM.

Statistically significant differences were also found between all pairs of malls regarding the quality of facilities, except odor in the praying room and quality of public seats. This indicates that quality, cleanliness, odor of toilets, cleanliness and quality of the praying room, and the number of public seats were evaluated significantly different by shoppers in all malls. The difference in the evaluation of odor in the praying room is statistically significant between the LM and the MM and between the LM and the CM. Meanwhile, the evaluation of quality of public seats is statistically significant different between the LM and the MM and between the MM and the CM. In all cases, significant different mean ratings were found among the malls; the mean ratings of the LM were higher than those of the MM and the CM, while the mean ratings of the CM were higher than those of the MM.

Regarding the social environment, significant differences were found in all attributes (politeness of other shopper and social behavior of other shoppers) between the LM and the MM. A significant difference was also found in politeness of other shoppers between the MM and the CM. The mean ratings of the LM were higher than MM and the mean rating of the MM was less than the CM. No significant difference was found between pairs of malls in terms of all attributes of personal service dimension, except for friendliness of customer services. The friendliness of customer services had significantly different mean ratings between the LM and the MM and between the LM and the CM. More specifically, the mean rating of the LM was higher than the MM and the CM.


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Overall, then, the evaluation of eleven attributes showed statistically significant different between malls, including accessibility by private motorcycle, variety of leisure facilities, attractiveness of architecture design, signs and decorations in public space, easiness to find praying room, cleanliness and quality of praying room, quality, cleanliness, and odor in the toilets, and number of public seats. In contrast, eight attributes (overall evaluation of social environment, trip to the mall, quality of food and beverage stores, helpfulness and friendliness of greeters/receptionists, helpfulness and friendliness of security services, and helpfulness of customer service) did not show statistical differences between all pairs.

5.4 Understanding Overall Evaluations of Dimensions

In the previous sections, we have reported the overall evaluations of the various dimensions of malls and compared the three malls in this regard. Respondents arrive at these evaluations by cognitively integrating their evaluations of the attributes of the mall representing the underlying dimensions. Although this integration can take on different forms, we assume that respondents use a compensatory process, which can mathematically be represented by a linear additive model. Thus, in this section, for each mall separately we examine the relationship between the overall evaluations of each mall’s dimension and the evaluation of attributes that define the dimension

5.4.1 Method: A Stepwise Regression

We apply a stepwise regression model by using the overall evaluation of a mall’s dimension as the dependent variable and the evaluations of the attributes of the malls as independent variables. To clarify the evaluation we discussed shoppers’ subjective assessment of 83 attributes of the malls regarding 9 dimensions. The stepwise regression model is applied on each mall to analyze which attribute evaluations of each dimension significantly contribute to the overall evaluation of that dimension; for example which attributes’ evaluation of “location and convenience” have the most significant impact on overall evaluation of “location and convenience” dimension, and so on. In stepwise regression after each step in which an attribute is added all candidate attributes in the model are checked to see if their significance has been reduced below some specified tolerance level. Only a significant variable is added to the model. In this study, nine rounds of stepwise regression, including location and convenience, price, store variety, merchandise selection and quality, advertising and promotion, mall comfort and visual appearance, space arrangement, quality of facilities, social environment, and personal service, were separately conducted on each mall.


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5.4.2 Results

The results of the stepwise regression analyses are shown in Table 5.4. The constants entered in the model are all significant at p < 0.01. As seen, all attributes, that are significant, have positive coefficients, which means that the overall evaluation of each mall’s dimension changes in the same direction as the evaluation of the constituent attributes.

In terms of “location and convenience” dimension, the independent variables were 7 attributes including evaluation of accessibility by public transportation, evaluation of accessibility by private car, evaluation of accessibility by private motorcycle, evaluation of ease to get parking space, evaluation of parking cost, evaluation of opening hours of the mall, and evaluation of the trip to the mall. The MM has the highest constant (1.58), which is two times higher than the constant for LM (0.75). The constant of the CM is 1.37. Results demonstrate that evaluation of “accessibility by public transportation” and evaluation of “opening hours of the mall” appear in the final regression models. Evaluation of “accessibility by public transportation” has higher impact on the overall shoppers’ evaluation of the location and convenience dimension than evaluation of “opening hours of the mall” for all malls. However, small differences between them are found. The third significant attribute that has the lowest impact on the overall evaluation of the location and convenience dimension in case of the LM and MM is the evaluation of “the trip to the mall”, while in case of the CM it is evaluation of “the ease to get parking space”. The differences occur perhaps because shoppers in the LM and MM take longer to reach the mall than shoppers in the CM (see Appendix 2, Figure A2.1c). The reason for the evaluation of “to get the parking space” in the CM became significant may have something to do with the transport mode respondents used. Over 63.5% of CM’s respondents used their private vehicle, which also scored as the highest percentage among the malls. This may cause respondents in the CM considered the ease of parking important while assessing their overall evaluation of location and convenience.

The significant attributes’ evaluation of “location and convenience” dimension in the LM explains 44.1% of the variance in the data. This is the highest percentage explained variance in the overall evaluation of “location and convenience” dimension of all three selected malls, following by the CM with 36.6% (R2 =0.366), and the MM with 30.8% (R2 =0.308).


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Regarding the overall evaluation of “price” dimension, results indicate that the evaluation of the “presence of a particular low-price image stores” and the evaluation of the “price level of food and beverages” are the most important explanatory variables. This suggests that when assessing overall evaluation of price, Indonesian’s shoppers considered rating of the “presence of a particular low-price image stores” and the “price level of food and beverages” most important. Some differences occur in the value of the coefficients of the attributes’ evaluation between the malls. The rating of the “presence of a particular low-price image stores” has a higher coefficient than the rating of the “price level of food and beverages” in case of the LM and the MM. Meanwhile, in case of the CM, the rating of “the price level of food and beverages” has a higher coefficient than the rating of “the presence of a particular low-price image stores”. In addition to these attributes’ evaluation, in case of the CM, the rating of the “presence of a particular high-price image stores” is another significant attribute, although with a lower coefficient.

For all malls, the significant attributes’ evaluation of “price” dimension explain more than 30% of the variance in the overall evaluation of the “price” dimension (for the LM R2=0.307, while for MM it is 0.353, and for the CM it is 0.357).

Regarding “store variety, merchandise selection and quality” dimension, the dependent variable was overall evaluation of “store variety, merchandise selection and quality” and the independent variables were 18 attributes including number of stores, types and variety of stores, product selection in the stores, variety of major stores, variety of food in the food court, variety of leisure facilities, quality and variety of banking facilities, food and beverage stores, children facilities, entertainment facilities, health facilities, and beauty facilities. According to “store variety, merchandise selection and quality” dimension results demonstrated each mall has a significant constant and different significant attributes to asses overall evaluation of this dimension. On the following analyses we will mention the significant independent attributes’ evaluation which has the highest coefficient to the lowest one.

The “variety of entertainment” and the “variety of leisure facilities” are significant explanatory variables for the LM. The “variety of entertainment” evaluation brings more influence towards overall evaluations than the “variety of leisure facilities”. The significant attributes’ evaluation in the LM explains more than 32% of the variance in the overall evaluation of store variety, merchandise selection and quality (R2=0.323). There are three independent attributes which influence overall evaluation in the MM,


95

namely “types and variety of stores”, “variety of health facilities”, and “variety of leisure facilities”. The significant attributes in the MM explain more than 29% of the variance in the overall evaluation of store variety, merchandise selection and quality (R2=0.298). Four significant attributes, “variety of children’s facilities”, “product selection in the stores”, “variety of major stores”, and “variety of food in the food court” were found significant in the CM with R2=0.349.

Since the rating in evaluating entertainment, leisure, and health facilities were also higher in the LM and the MM than the CM, this may somehow be related with shoppers’ status in the malls (see table 4.4). The LM and the MM have a higher number of single status shoppers than in the CM. The results suggest that single shoppers considered entertainment, leisure, and health facilities in their overall evaluation of “store variety, merchandise selection and quality” dimensions as important. With the same dimension, the CM which has a majority of married status shoppers, results show that “variety of children facilities” has the highest rating impact on overall evaluations. Results may suggest when visiting a mall married status shoppers look for a place where they can do both shopping and entertain their children.

With regard to advertising and promotion dimension, the dependent variable was the overall evaluation of advertising and promotion dimension and the independent variables were evaluation of quality, attractiveness, and frequency of mall’s activities/events/exhibitions, and special sales promotions. Results presented the evaluation of “special sales promotions” had significant relation in predicting the overall evaluation of advertising and promotions dimension in all malls. Particularly, this attribute brings the highest influence on overall evaluation of advertising and promotion dimension in MM and CM. Results suggest that evaluation of special sales promotion may significantly contribute to predict shoppers’ overall evaluation of advertising and promotion in Jakarta shopping malls. In addition, LM and CM had another significant attribute namely evaluation of the “quality of activities/events/exhibitions” which contributes in predicting the overall evaluation of advertising and promotion dimension. The significant attributes associated with 26.3% of the variance in the overall evaluation of price in the LM, 28.4% in the MM, and 31.6% in the CM. This means our results could explain the evaluation of “advertising and promotion” dimension for at least 26%.

There were twenty-one attributes in the “mall comfort and visual appearance” dimension. These include independent variables: evaluation of cleanliness and safety


96

of stores and public spaces, attractiveness of architecture, interior, window display, interior wall and floor color, and ceiling and lighting, artwork and signs and decorations in the public space, visual attractiveness of public spaces, quality of gardens and greenery, building style, public spaces’ atmosphere and food court’s atmosphere, quality of lighting, type of music and comfort in music sound, odor in public spaces and elevators, smoke in public spaces, and thermal comfort. The dependent variable was the overall evaluation of mall comfort and visual appearance dimension. On the following analyses we will mention the significant independent attribute which has the highest coefficeint to the lowest one. Results in the LM demonstrated evaluation of “building style”, evaluation of “attractiveness of window display”, evaluation of “signs and decorations in the public space”, and evaluation of “quality of gardens and greenery” significantly contributed to predict overall evaluation of mall comfort and visual appearance dimension (R2= 0.442). Results in the MM showed that evaluation of “attractiveness of interior wall and floor color”, evaluation of “odor in public spaces”, and evaluation of “artwork in the public spaces” significantly contributed to predict the overall evaluation of mall comfort and visual appearance dimension (R2=0.333). Results in the CM presented evaluation of “attractiveness of interior wall and floor color”, evaluation of “safety in stores and public spaces”, evaluation of “building style”, and evaluation of “attractiveness of window display” gave significantly contribution to explain overall evaluation of mall comfort and visual appearance dimension (R2=0.542). The differences of significant attributes’ evaluation among the malls arise may be due to all malls have their own great potential attributes of mall comfort and visual appearance dimension. In addition, it is possible that shoppers on each mall have different sensibility towards mall comfort and visual appearance dimension.

Regarding space arrangement dimension we applied the overall evaluation of space arrangement dimension as the dependent variable and attributes including the easiness to find public seats, to find ATMs, to find the praying room, to find restrooms, to find the elevators, to find escalators and to find stores as the independent variables. Results presented the most significant attributes contributed in considering the overall evaluation of space arrangement in all malls, namely the “easiness to find ATMs”. We can interpret that the easiness to find ATMs is a significant attribute for shoppers in Indonesia when assessing the overall evaluation of space arrangement dimension. In addition, results in the LM and the CM presented two attributes significantly contributed in the overall evaluation of space arrangement dimension in LM and CM, namely evaluation of “ease to find elevator” and evaluation of “ease to find escalators” (R2= 0.231 and R2=0.238). While in the MM the other two significant attributes were


97

evaluation of “ease to find stores” and evaluation of “ease to find public seats” (R2= 0.275). Findings suggest evaluation of vertical accessibilities is important in the LM and the CM. The reason of this result may have something to do with shoppers’ motivations visit the mall (see Table 4.5). As it is mentioned before that shoppers in the LM and the CM mostly like to walk around or to have recreation in the mall, thus the accessibility to reach other floors became important in considering overall evaluation of space arrangement dimension. While shoppers in the MM mostly like to go shopping (visiting the stores) thus the easiness to find stores became significant attributes on overall evaluation of space arrangement dimension. It is difficult to explain the reason why the easiness to find public seats became a significant attribute in this mall. However, it is noted that evaluation of the easiness to find public seats in the MM considered as important to shoppers’ overall evaluation with space arrangement dimension.

With regard to “quality of facilities” dimension the dependent variable was overall evaluation of quality of facilities dimension and the independent variables were eight attributes including the quality, cleanliness, and odor of toilets and praying room and quality and number of public seats. Results demonstrated evaluation of “quality of toilets” as a significant attribute that contributes assessing the overall evaluation of quality of facilities dimension in all malls. In particular, in the LM and the CM evaluation of “quality of toilets” gave the highest influence on overall evaluation quality of facilities dimension, while in the MM this attribute gave the lowest significant influence. The evaluation of “cleanliness of praying room” also appeared to be significant attribute on overall evaluation of quality of facilities dimension in the LM. Besides the evaluation of “quality of toilets” respondents in MM considered evaluation “quality of praying room” and evaluation of “number of public seats” in the MM on overall evaluation of quality of facilities dimension. In addition, evaluation of “quality of praying room” contributed the highest influence on overall evaluation in this dimension. The second attribute in the CM that gave influence on judging the overall evaluation quality of facilities dimension was evaluation of “quality of public seats”. In all malls the significant attributes are associated with above 20% of the overall evaluation of quality of facilities dimension (the LM with R2=0.205, the MM with R2=0.261, the CM with R2=0.255). We can interpret that the evaluation of “quality of toilets” is a significant attributes in judging the overall evaluation quality of facilities. The evaluation of “cleanliness or quality of praying room” occurred to be significant attributes in the LM and MM. This may relate to the fact that these malls had the biggest number of shoppers who ever used the praying room (see Figure A2.6 in Appendix 2). It is difficult to explain why evaluation of number or quality of public seats was significant attributes in the MM and


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Tabl

e 5.

4 Re

sults

of E

valu

atio

ns

Mal

l's

dim

ensio

ns

LM (N

=218

) M

M (N

=225

) CM

(N=2

27)

Attr

ibut

es

B Si

g R2

Attr

ibut

es

B Si

g R2

Attr

ibut

es

B Si

g R2

Loca

tion

and

conv

enie

nce

(8 a

ttrib

utes

)

Co

nsta

nt

0.76

0.

03

Cons

tant

1.

58

0.00

Co

nsta

nt

1.37

0.

00

1

Acce

ssib

ility

by

publ

ic tr

ansp

orta

tion

0.39

0.

00

1

Acce

ssib

ility

by

publ

ic

tran

spor

tatio

n

0.37

0.

00

1

Acce

ssib

ility

by

publ

ic tr

ansp

orta

tion

0.41

0.

00

2

Ope

ning

hou

rs o

f th

e m

all

0.35

0.

00

2

Ope

ning

hou

rs o

f th

e m

all

0.16

0.

03

2

Ope

ning

hou

rs o

f th

e m

all

0.15

0.

03

3

Trip

to th

e m

all

0.08

0.

04

3

Trip

to th

e m

all

0.15

0.

01

3

Ease

to g

et p

arki

ng

spac

e 0.

18

0.00

0.

44

0.31

0.

37

Pric

e (5

att

ribut

es)

Co

nsta

nt

1.85

0.

00

Cons

tant

1.

24

0.00

Co

nsta

nt

1.61

0.

00

1

Pres

ence

of

part

icul

ar lo

w-p

rice

imag

e st

ores

0.36

0.

00

1

Pres

ence

of

part

icul

ar lo

w-p

rice

imag

e st

ores

0.37

0.

00

1

Pres

ence

of

part

icul

ar lo

w-p

rice

imag

e st

ores

0.22

0.

00

2

Pric

e le

vel o

f foo

d an

d be

vera

ges

0.25

0.

00

2

Pric

e le

vel o

f foo

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100

CM. However, these attributes were critical to shoppers’ overall evaluation of quality of facilities in the MM and CM.

Regarding social environment dimension we applied two independent variables namely “politeness of other customers” and “social behavior of other customers” and the dependent variables namely the overall evaluation of social environment. Results demonstrated that evaluation of “politeness of other shoppers” in the LM and the CM and evaluation of “social behavior of other shoppers” in MM were the significant attributes to overall evaluation of social environment. The explanation to these differences may due to the sociodemographic characteristics of respondents, particularly in age and occupation. Respondents in the LM and the CM had higher number of respondents who were below 20 years old with student background than respondent in the MM. Due to their age, young shoppers tended to see other shoppers and mostly like to have friendly shoppers (see e.g. Haytko, et al. 2004). On the contrary, the majority of respondents in the MM who were more mature than respondents in the other malls considered “social behavior of other shoppers” significantly contributed to judge the overall evaluation of social environment. The social environment dimension among other eight dimensions explained the lowest percentage of variance for the dependent variable; in the LM R2=0.19, in the CM R2=0.17, and in the MM R2=0.06. This may be due to the fact that we only have two independent attributes in this dimension.

With regard to personal service the main dependent variable was the overall evaluation of personal service and the independent variables were helpfulness and friendliness of staff, security, and customer service. Each of malls had two attributes which influence the overall evaluation of personal service. Results showed that evaluation of “friendliness of greeters/receptionists was the highest influence on overall evaluation of personal service dimension in LM and CM. Interestingly, evaluation of “friendliness of customer service” which was the lowest significant influence on overall evaluation of personal service dimension in the LM became the highest in the MM. Other attribute that influence on overall evaluation of personal service dimension in the MM was evaluation of “helpfulness of greeters/receptionists” and in CM of “helpfulness of security”. The significant attributes are associated with 26% of the variance in the overall evaluation of personal service dimension in the LM, 34% in the MM, and 17% in the CM. We can interpret that friendliness is the most important for shoppers who were young, and helpfulness somewhat important for shopper who were older because they may need some help to bring the shopping bags. In our case the CM has the highest


101

percentage of shoppers who were older than 39 years old with housewife and retired as occupation background. In addition, LM’s and MM’s shoppers came with more companions compare to CM’s shoppers; companions may help shoppers to bring the stuff.

5.5 Summary and Conclusions

This chapter has presented a detailed comparison of shoppers’ evaluations of nine mall’s dimensions across three types of shopping malls. As it is expected, the mean ratings evaluations of mall’s dimensions among the malls varied. This indicates the three malls selected in this study have different characteristics. Due to our respondents in each mall were different, it seems possible if their standard in justification may be unalike especially with the LM which received the highest mean ratings in most of the attributes and the MM which received the lowest almost in all attributes. In other words, shoppers in the LM were easy to tolerate the mall’s environment, while shoppers in the MM were more critical to the mall’s environment than shoppers in the LM. This may confirm that shoppers in each mall have different preferences of mall’s dimensions while selecting the malls.

A paired samples t-test was conducted to evaluate the assessment of shoppers for nine mall’s dimensions. Findings demonstrate that malls were developed by different approaches regarding attributes; the LM has mostly different characteristics from the MM, while a few different characteristics occur between the LM and the CM and between the MM and the CM. The attributes of mall’s dimensions serves as an important role in the shopper evaluation. This study empirically investigates the relationship between the overall evaluations of each mall’s dimension and evaluation of attributes that define the dimension for each mall. This aims to reveal how the variations of the overall evaluation of each mall’s dimension can be attributed to variations in the evaluation of each mall’s dimension. Findings demonstrate that shoppers’ in each mall have their own significant attributes in judging the overall evaluation of mall’s dimensions. In other words, the significant attributes in evaluating mall’s dimensions are not universal applies to every types of malls. Results reveal that sociodemographics and conditions of the mall (e.g. location and environment) may influence shoppers’ considerations on the evaluation.

PART III MALL-USE SHOPPING DECISIONS


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6 SHOPPING BEHAVIOR INSIDE THE MALLS This dissertation aims to increase our understanding of the entire process of shopping behavior in shopping malls. We have examined pre-shopping decisions in Part II, which focused on shopping mall choice, conditional on shopping purposes and the evaluations on shopping malls’ dimensions. In this chapter, we shift our attention to the mall-use shopping decisions, which embrace all the decisions that shoppers make in moving around the mall and choosing stores to buy particular merchandise.

As before, data for the analysis of this behavior were collected using a questionnaire and tracking. This chapter explores shopping behavior inside the mall using the survey data. The purpose of this chapter is to investigate the shopping styles of shoppers that relate to store visits. Because the surveys were administered in three types of shopping malls, this chapter also examines the relationship between shopping style and shopping mall.

The survey asked respondents to mention which stores they entered during their visit of the shopping mall. Stores were classified into eight types. Descriptive analyses were conducted to describe the behavioral characteristics of the data, in general and for each mall. A hierarchical cluster analysis is employed to identify the shopping styles.

This chapter is composed of eight sections and organized as follows. In the next section, we will discuss the part of the questionnaire that solicited respondents to provide information regarding their use of the mall. The second section presents the sample data, followed by a comparison of behavioral characteristics of the sample data across the malls. Section 4 then provides introduction about shopping styles and store visits behavior. The next section reviews shoppers’ store visits behavior by means of hierarchical cluster analysis method. It follows with the results of the analyses and the identification of the shopping styles on each mall. Four types of shopping styles are identified in this section. Section 7 describes the shopping styles regarding shopping behavior characteristics and its sociodemographic profiles. The chapter is completed with a summary and discussion of managerial implications of the main findings.

6 SHOPPING BEHAVIOR INSIDE THE MALLS

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6.1 Questionnaire Design for Shopping Data

The questionnaire described in Chapter 4 was also used to collect data on shopping behavior inside the malls. Respondents were invited to indicate the total time they spent at the mall, the number of stops made, and the total number of purchases. Respondents reported their shopping behavior after completing their trip to the mall. It means that compared to the tracking data, this data set depends strongly on respondents’ ability to retrieve the requested information from their memory.

To obtain information about what shoppers’ did inside the mall, the survey collected shoppers’ shopping behavior and their activities during visiting the malls. Since shopping behavior has a wide range of possibilities, most of the questions were open questions or questions with some guidance. To record shopping decisions inside the mall, we asked respondents about various facets of shopping behavior: the time and duration of mall visit, the number of stores visited and the names of stores, and the type of facilities visited.

In addition to the information about store visits, respondents were invited to indicate how much they spent on food-and-beverage and how much on non-food-and-beverage items. In addition, we asked respondents to answer what factors affected a fulfilling shopping experience and whether respondents had the intention to come back again to the mall.

6.2 Descriptive Analyses

Table 6.1 presents the behavioral characteristics of the respondents. In particular, it reports the average duration of mall visits, the average number of stores or facilities visited, and average expenditures. On average, respondents reported that they spent approximately 122 minutes or 2 hours and 2 minutes in the mall. In more detail, over 40% of the respondents spent between 31 minutes and 1.5 hours, while more than 36% of the respondents spent more than 2 hours in the mall. Comparing these results to Ooi and Sim (2007), who found that shoppers in Singapore suburban shopping malls on average also spent about 2 hours in the mall, indicating that the average of 2 hours is indicative of the time spent in malls in this part of the world during a single visit.

On average, respondents made 3.9 stops (St. dev. 1.88). The largest percentage (44%), made 4 to 6 stops following by a group of respondents who made 2 or 3 stops (39.7%).


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Table 6.1 Behavioral Characteristics (N=670)

Variables Sample

Average of duration of mall visit in minutes 121.85 (79.13)

Duration of mall visit <30 minutes 6.3 %

>30 minutes - 1 hour 21.7 %

>1 to 1.5 hours 18.5 %

>1.5 to 2 hours 17.6 %

>2 hours 35.9 %

Average of total number of stop visits 3.92 (1.88)

Total number of stop visits 1 stop 6.9 %

2 - 3 stops 39.7 %

4 - 6 stops 44 %

>6 stops 9.4 %

Average of expenses for Food-and-Beverage in IDR 86,755.99 (192,836.4)

Expenses for FB 0 5.8 %

IDR 2,000 – 75,000 64.1 %

IDR 75,100 – 150,000 19.2 %

IDR 150,100 – 300,000 7.9 %

>IDR 300,000 3 %

Average of expenses for Non-Food-Beverage in IDR 186,261.98 (377,967.43)

Expenses for NFB 0 11.2 %

IDR 2,000 – 75,000 36.2 %

IDR 75,100 – 150,000 21.3 %

IDR 150,100 – 300,000 19.8 %

>IDR 300,000 11.5 %

Time of mall visit morning 12.2 %

morning to afternoon 13.0 %

morning to evening 0.3 %

afternoon 46.5 %

afternoon to evening 15.4 %

evening 12.5 %

Day of visit weekdays 57.3 %

weekends 42.7 %


The number of respondents who made more than 6 stops was relatively small (9.4%). Single stop behavior applied to 6.9% of the respondents.

Respondents reported that the average expenditures on food-and-beverage were about half of the expenditures on non-food-and-beverage. Results show that most of


107

the respondents spent between IDR 2,000-75,000 on both types of expenditures. About two-third of the respondents spent between IDR 2,000-75,000 on food-and-beverage, while 5.8% reported to have spent nothing on food-and-beverage.

Over 46.5% of the respondents visited the mall in the afternoon (between 12:01 and 18:00). Respondents who visited in the morning (10:00 to 12:00), from morning to afternoon (up to 18:00), from afternoon to evening (12:01 and 22:00), and in the evening (between 18:01 and 22:00) were between 12.2.0% and 15.4%. Only 0.3% of the respondents spent in the mall from morning to evening (10:00 to 22:00). Furthremore, the respondents in the survey who visited the mall in the weekdays was 57.3%.

As discussed, respondents were also requested to indicate the number of stores and facilities they visited, and the names of the stores they visited. Because Indonesia has no official store classification system, in this study we suggested a new classification that was adopted from the shop classification (SIC codes) of the Urban Land Institute (2004). Our store classification consists of 8 categories, based on the products that the stores offer, as shown in Table 6.2. It differentiates between food and non-food and between general and specialized stores. The classification is exhaustive and exclusive, although the categories differ in their degree of homogeneity. This should be kept in mind when reflecting on the results of our analyses. Some degree of aggregation is required to avoid idiosyncratic behavior.

Table 6.2 Store Classification

Type of stores Description

General-merchandise Department store

Food Supermarket, bakery, delicatessen, dairy products

Eating-Places Food and beverage

Apparel-and-accessories Fashion, special clothing, costume jewelry, watches, footwear/shoes

Furnishing Home appliances and furniture, audio/video, phones/gadget, computer, electronics

Media-and-special-interest Books, newspapers/magazines, religious products, arts & crafts, toys, pet shop, records & tapes, gift

Entertainment-and-education Cinema, children’s playground, karaoke, learning center

Health-beauty (and personal services)

Cosmetics, optical stores, beauty salon, spa, massages/reflexology


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Table 6.3 Type of Stores and Facilities Visited (N=670)

Type of stores Number of visits %

Apparel-and-accessories 472 24.04

Media-and-special interest 226 11.51

Health-beauty 96 4.89

General-merchandise* 177 9.02

Food 308 15.69

Furnishing 67 3.41

Eating-places 479 24.4 Entertainment-and-education 138 7.03

Size 1963 100

Type of facilities Number of visits %

Financial 180 27.03

Public service 486 72.97

* LM has no general-merchandise stores

Table 6.3 shows the distribution of the store visits by type of store and facilities. As seen from the table that two types of stores, namely eating-places and apparel-and-accessories, had almost the same percentage of shoppers’ visits (24%) and were visited most frequently. De Bruwer (1997) who also found that fashion stores and restaurants are the most frequently visited stores by shoppers in South-Africa. Relatively few respondents reported visiting health-beauty stores (4.9%). This may be a similar finding as reported by Brown (1991, 1992) who concluded that the hairdresser was the least visited store. In our study, furnishing stores were the least visited (3.4%). It reflects the fact that furnishing is a durable product, which is purchased less frequently.

In addition to asking respondents about their store visits in the mall, they were also asked what kind of mall facilities they used on that day. Results show that financial facilities (ATM) are mostly used, followed by public facilities (toilet, praying room, nursing room, and parking area).

6.3 Comparison of Behavioral Characteristics Between Malls

To better understand differences in shopping behavior of respondents between the three types of malls, this section discusses the results of a comparison of respondents’ shopping behavior with respect to the duration of the mall visit, the number of stops made, expenditure in the mall, and type of shops visited. Table 6.4 summarizes these behavioral characteristics for each of the three selected malls.


109

In terms of the duration of the mall visit, respondents in the MM spent on average the longest time in the mall (141 minutes). In contrast, respondents in the CM spent on average 34% less time (94 minutes) than those in the MM. This was the shortest time observed to spend in the mall. LM’s shoppers spent on average 131 minutes. The percentage of shoppers spending more than 2 hours in the LM is 42.3%, while in the MM it is 46.4%. This is quite high compared to shoppers who visit the CM. Here, only 19.5% spends more than 2 hours in the mall. CM’s shoppers spent between 30 minutes and 1 hour in the mall (31%). As we know, the LM has the largest number of stores (2277 stores), following by the MM (488 stores), and the CM (144 stores). In addition, only the LM offers bargains in almost all its stores. The fact that LM’s shoppers spent less shopping time compared to MM’s shoppers may suggest that the duration of shopping time had no strong relationship with the number of stores.

Our findings demonstrate that respondents made between 1 and 11 stops in all shopping malls including store visits, product-service facility visits (e.g. banks, ATM, offices, etc.), and public service facility visits (e.g. toilet, praying room). We found one respondent in the CM who did not make any stops. There was not a big difference in the average total number of stops among the malls. Shoppers in the MM on average made 4.67 stops while shoppers in the LM and CM made on average 4, respectively 4.1 stops. As seen on Table 6.4 the majority of the LM’s shoppers made between 4 and 6 stops, (52.8%), the MM’s shoppers made between 2 and 3 stops (47.6%), while the CM’s shoppers made between 2 and 3 stops (37%) and 4 and 6 stops (37%). Regarding the number of stops our findings show that duration of time shoppers spent in the mall had no relationship with the number of stops. A possible explanation for this fact might be that the need to spend time for each stop is not the same; for example to go to the toilet may require less time than to go to the restaurant. On average, expenses for food-and-beverage (FB) and non-food-and-beverage (NFB) of shoppers in the MM were the highest, following by the CM, and the LM. For both of expenses the majority of shoppers spent between IDR 2,000 and 75,000. The former value is the least expenditure that we observed. Shoppers in the MM who spent the longest time in the mall made the highest expenses. However, shoppers in the LM who spent longer time than shoppers in the CM, made the lowest expenses. This result indicates that the longer duration of mall visit does not always coincide with the higher expenses. The explanation may be related to the age of the shoppers and their financial capability: young shoppers in the LM do not spend much. This is congruent with previous studies that argue youngsters are not potential customers (for example Chang et al., 2004).


110

Table 6.4 Description of Behavior

Variables Local shopping

mall N=218

Modern shopping mall

N=225

Classic shopping mall

N=227

Average of duration of mall visit in minutes 130.5 (77.93) 141.3 (81.92) 94.2 (69.54)

Duration of mall visit

<30 minutes 2.3% 5.4% 11.1%

30 minutes - 1 hours 20.5% 12.5% 31.9%

>1 to 1.5 hours 17.20% 17.0% 21.2%

>1.5 to 2 hours 17.7% 18.8% 16.4%

>2 hours 42.3% 46.4% 19.5%

Average of total number of stops 4.0 (1.69) 3.7(1.61) 4.1 (2.26) Total number of stops

1 stop 5.5% 4.9% 10.1%*

2 - 3 stops 39.9% 47.6% 37.0%

4 - 6 stops 52.8% 42.7% 37.0%

>6 stops 7.3% 4.9% 15.9% Average of expenses for food-and-beverage in IDR

52,857.8 (62,917.91)

110,342 (271,778.08)

96,013.3 (176,822.29)

Expenses for FB 0 6.9% 2.7% 8.0%

IDR 2,000 – 75,000 78.0% 59.0% 55.6%

IDR 75,100 – 150,000 9.6% 24.0% 23.6%

IDR 150,100 – 300,000 4.60% 10.7% 8.4%

>IDR 300,000 0.90% 3.6% 4.4% Average of expenses for non-food-beverage in IDR

165,011.4 (307,708.73)

218,913.3 (464,471.72)

174,200.0 (340,988.38)

Expenses for NFB 0 7.30% 10.70% 15.60% IDR 2,000 – 75,000 37.60% 32.40% 38.70% IDR 75,100 – 150,000 24.80% 22.20% 16.90% IDR 150,100 –

300,000 22.50% 20.00% 16.90%

>IDR 300,000 7.80% 14.70% 12.00% Time of mall visit morning 20.47% 7.59% 8.97%

morning to afternoon 14.88% 16.96% 7.17%

morning to evening 0.47% 0.00% 0.45%

afternoon 37.21% 44.64% 57.40%

afternoon to evening 16.28% 18.75% 11.21%

evening 10.70% 12.05% 14.80%

Day of time weekdays 59.5% 59.4% 53.1%

weekends 40.5% 40.6% 46.9%

*one respondent did not make a shop visits Standard deviations are between the brackets


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The expenses for non-food-and-beverage were higher than the expenses for food-and-beverage in all malls. Results demonstrated that shoppers tended to spend about two times more for NFB than for FB expenses. Regarding time of mall visit the majority of shoppers in all malls visited the mall in the afternoon between 12:01 and 18:00. However, each mall has different shares of time of mall visit. We argue, some results may be related to the location and malls’ activities. For example, the LM that had the highest percentage of shoppers who visited the mall in the morning. This may be due to the fact that this mall has a daily open market. The MM that had the highest percentage of shoppers visiting the mall from morning to afternoon and afternoon to evening may be due to the mall’s location. As mentioned in chapter 4.4 the MM locates in the business district area and next to a university. In addition, most of shoppers in the MM do not live in South Jakarta (see table 4.4) . Therefore, they may visit the MM as a transfer area before and after their activities (working or studying). The CM had the highest percentage of shoppers who visited the mall in the afternoon or in the evening. As mentioned in chapter 4.4 the CM locates close to a residential area. In addition to this, housewives and retired were the highest percentage in this mall. It might be the housewives visiting the mall to kill the time in the afternoon or do shopping with the family in the evening, as the mall’s location is in their neighborhood. In this survey the shoppers who visited the mall in weekdays were 59.5% in the LM, 59.4% in MM, and 53.1% in CM.

To find out which stores shoppers visited, Table 6.5 shows which types of stores shoppers visited. It should be noted that there is no “general merchandise” in the LM. Results show that “eating-places”, “food”, and “apparel-and-accessories” are the three types of stores that are most frequently visited across the malls. However, differences among the malls were found; for example, in the LM, the largest percentage of store visits was made to ”apparel-and-accessories” (35%), followed by “eating-places” (21%), while in the MM and CM the highest percentage of store visits was for “eating-places” (31% and 22%), followed by “apparel-and-accessories” (19% and 20%). Similar results have been found in previous studies (e.g. see de W. Bruwer, 1997; Yuo, et al. 2004).

In general, the percentage store visits at “media-and-special-interest” stores in all malls was not much different. Regarding “food” type of stores, over 16% of shoppers in the LM and over 18% of shoppers in the CM visited this store type and this was higher than shoppers in the MM (13%). The reason for this result is not clear but it may have something to do with the number of housewives in the sample. Housewives are in


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Table 6.5 Store Visits Behavior

Type of store

Local Mall N=218

Modern Mall N=225

Classic Mall N=227

number of visits % number

of visits % number of visits %

Apparel-and-accessories 210 35% 129 19% 136 20%

Media-and- special-interest 59 10% 79 12% 90 13%

Health-beauty 32 5% 21 3% 43 6%

General-merchandise* 0 0% 80 12% 77 11%

Food 98 16% 91 13% 120 18%

Furnishing 28 5% 19 3% 20 3%

Eating-places 126 21% 211 31% 147 22% Entertainment-and-education 52 9% 46 7% 40 6%

Total 605 100% 676 100% 673 100%

Average of store visits 2.78 stores 3.00 Stores 2.96 stores

*there is no general-merchandise in this mall

charge of grocery shopping in most Indonesian families. In this study, the CM has the highest number of housewives, following by the LM, and the MM.

“Furnishing”, “entertainment-and-education”, and “health-and beauty” were the least visited store types. Previous studies also found that furniture stores and hairdresser have infrequent visits in the mall (e.g. Brown, 1991; 1992; Yip et al., 2012). However, none of the previous studies discussed entertainment-and-education as a non-popular type of stores in the mall.

In terms of average store visits during shopping time, shoppers in the MM made 3 store visits and scored as the highest store visits among the malls. The average store visits of shoppers in the CM were 2.96 store visits, and shoppers in the LM were 2.78 store visits.

6.4 Shopping Styles and Store Visits Behavior

Shopping style is defined as a shopper’s action of choosing stores in the mall that describes store visits pattern inside the malls. In particular, shopping styles are shoppers’ decisions towards selecting and visiting stores in the mall. Shopping styles provide shoppers’ decisions on store visits that are important in understanding about consumer behavior (Sprotles and Kendall, 1986). Profiling shoppers through their behavior patterns provide more adequate information than profiling through sociodemographics (e.g. gender, age, education background, etc.) because different segments may have different shopping styles (for example see Bloch et al., 1994;


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McGoldrick, 2002; Morrin and Chebat, 2005; Sinha and Uniyal, 2005). In this study we conduct a detailed investigation for differences in shopping styles based on shoppers’ behavior patterns, especially on their store visits to reach different market segments in the best way.

6.5 Deriving Shopping Style

Since shoppers would be classified based on their responses to their selection of store visits, Ward’s method from Hierarchical Cluster Analysis (HCA) was applied.

According to Field (2005) Ward’s method can be explained as follows: Ward’s method aims to join cases into clusters such that the variance within a cluster is minimized. To do this, each case begins as its own cluster. Clusters are then merged in such a way as to reduce the variability within a cluster. To be more precise, two clusters are merged if this merger results in the minimum increase in the error sum of squares. Basically, this means that at each stage the average similarity of the cluster is measured. The difference between each case within a cluster and that average similarity is calculated and squared (just like calculating a standard deviation). The sum of squared deviations is used as a measure of error within a cluster. A case is selected to enter the cluster if it is the case whose inclusion in the cluster produces the least increase in the error (as measured by the sum of squared deviations).

The method starts by processing each shopper as its own cluster and merging in such a way as to reduce the variability within a cluster. The dendrogram connects homogeneous cases into larger and larger segments. The input in this analysis was frequencies of visits to stores which directly contribute to purchase.

By open questions we asked respondents to put the name of every stores they visited on the day they administered the questionnaires. All stores’ names on each mall were categorized into eight types of stores (see Table 6.2). In total the data was collected from 670 respondents, including 218 shoppers on the Local Mall (LM), 225 shoppers on the Modern Mall (MM), and 227 shoppers on the Classic Mall (CM).

6.6 Shopping Style Results

A Hierarchical Cluster Analysis of Ward’s was used to identify the number of clusters that specifically explain the shopping styles according to shoppers’ selection of store visits. We found the optimum of four different clusters for all types of malls differing in store visits patterns based on the dendrogram. Table 6.6, Table 6.7, and Table 6.8


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present shopping style clusters of three malls. Given the information in those three Tables, we provide an interpretation of the clusters in section 6.7.

As it was expected, differences among the store visits pattern in each cluster and clusters’ composition membership were found in each type of malls. Each of clusters is briefly defined in terms of store visits’ pattern.

6.6.1 Shopping Style in the Local Shopping Mall (LM)

Table 6.6 provides further insight into the extent of frequencies of store visits and percentages for each cluster in LM. The first cluster, representing 15.6% of the sample (N=34), is the cluster who made the smallest number of store visits. Shoppers in this cluster only visited two stores, namely food and eating-places. Specifically over 78.6% of shoppers in this cluster visited food store.

The second cluster, comprising 38.1% of the sample (N=83), is the largest cluster in LM. Shoppers in this cluster visited all types of stores. The apparel-and-accessories was the highest type of store visits (38.5%) by the shoppers in this cluster. The media-and-special-interest store visits (17.3%) and the furnishing store visits (8.9%) scored the highest number of visits among the clusters.

The third cluster, making up 32.1% of the LM sample, visited all types of stores (N=70). The significant difference from the previous cluster is shoppers in this cluster scored the highest on visiting eating-places (37.9%). The eating-places store visits and

Table 6.6 Shopping Style Clusters in the Local Shopping Mall

Type of stores cluster 1 N=34 cluster 2 N=83 cluster 3 N=70 cluster 4 N=31 no of visits % no of

visits % no of visits % no of

visits %

Apparel-and-accessories 0 0 87 38.50 30 15.15 93 74.40

Media-and-special-interest 0 0 39 17.26 16 8.08 4 3.20

Health-beauty 0 0 8 3.54 21 10.61 3 2.40

General-merchandise* 0 0 0 0 0 0 0 0

Food 44 78.57 32 14.16 16 8.08 6 4.80

Furnishing 0 0 20 8.85 1 0.51 7 5.60

Eating-Places 12 21.43 28 12.39 75 37.88 11 8.80

Entertainment-and-education 0 0 12 5.31 39 19.70 1 0.80

Size 56 100 226 100 198 100 125 100

*there is no general-merchandise in this mall


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entertainment-and-education (19.7%) scored the highest among the clusters.

The last cluster, representing 14.2% of the sample (N=31), is the smallest. Shoppers in this cluster also visited all types of stores. Over 74.4% of shoppers in this cluster made visits on apparel-and-accessories, this also scored as the highest across the clusters. However, other stores were visited less than 10%.

6.6.2 Shopping Style in the Modern Shopping Mall (MM)

Table 6.7 plots the percentage of shoppers’ MM visiting types of stores in clusters. The first cluster membership comprises 26.7% of the sample (N=60). Shoppers in this cluster visited all type of stores. None of them was scored the highest nor the lowest number of visits among the clusters, except apparel-and accessories stores (48.15%). Visiting to apparel-and-accessories stores also scored as the highest among the clusters.

Not much different size from the first cluster, the second cluster represents 27.6% of the sample (N=62). None of shoppers in this cluster visited furnishing and entertainment-and-education stores. The majority of shoppers in this cluster visited eating-places (39.6%). Over 29% of shoppers visited food which scored the highest among the clusters.

The third cluster has 30.2% of the sample and performs as the largest cluster (N=68). Shoppers in this cluster made the highest visits among clusters in two types of stores, namely media-and-special-interest (25%) and entertainment-and-education (18.9%). Over 21.7% of shoppers also visited eating-places.

Table 6.7 Shopping Style Clusters in the Modern Shopping Mall

Type of stores

cluster 1 N=60

cluster 2 N=62

cluster 3 N=68

cluster 4 N=35

no of visits % no of


visits %

Apparel-and-accessories 104 48.15 2 1.53 5 2.78 18 12.08

Media-and-special-interest 12 5.56 1 0.76 45 25.00 21 14.09

Health-beauty 4 1.85 16 12.21 0 0.00 1 0.67

General-merchandise 30 13.89 22 16.79 17 9.44 11 7.38

Food 17 7.87 38 29.01 25 13.89 11 7.38

Furnishing 3 1.39 0 0.00 15 8.33 1 0.67

Eating-places 45 20.83 52 39.69 39 21.67 75 50.34

Entertainment-and-education 1 0.46 0 0.00 34 18.89 11 7.38

Size 216 100 131 100 180 100 149 100


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The last cluster comprising 15.6% shoppers (N=35) is the smallest. Interestingly, shoppers in this cluster visited all types of stores. Over 50.3% of shoppers stopped at eating-places and this scored as the highest among clusters.

6.6.3 Shopping Style in the Classic Shopping Mall (CM)

Table 6.8 presents the shopping style’s clusters in CM. The first cluster in CM comprises 19.4% of the sample (N=44). The most salient characteristics of this cluster is the number of visited food stores which is over 53.8%. Shoppers in this cluster scored the highest visits among clusters to health-beauty (15%). Shoppers also visited other types of stores, except furnishing and entertainment-and-education.

The second cluster, the smallest among the clusters, presents 18.5% of the sample (N=42). A big difference between this cluster and the first cluster is that this cluster only made three types store visits: eating-places (66.7%), food (32%), and media-and-special-interest (1.3%), while in the previous cluster shoppers almost visited all types of stores. This cluster scored the highest number of visits at eating places among clusters.

The third cluster comprises 25.6% of the sample (N=58). Shoppers in this cluster visited all types of stores. Over 38.1% of shoppers visited media-and-special-interest which scored the highest visits either in this cluster or among the clusters. Shoppers also made the highest scores among the cluster on visiting furnishing (7.48%).The last cluster is the largest cluster in CM composing 36.6% of the sample (N=83). The significant store visits in this cluster is apparel-and-accessories (33.7%) which scored the highest visits

Table 6.8 Shopping Style Clusters in the Classic Shopping Mall

Type of stores

cluster 1 N=44

cluster 2 N=42

cluster 3 N=58

cluster 4 N=83

no of visits % no of


visits %

Apparel-and-accessories 2 2.50 0 0.00 3 2.04 128 33.68

Media-and-special-interest 1 1.25 1 1.33 56 38.10 30 7.89

Health-beauty 12 15.00 0 0.00 1 0.68 30 7.89

General-merchandise 12 15.00 0 0.00 18 12.24 67 17.63

Food 43 53.75 24 32.00 31 21.09 21 5.53

Furnishing 0 0.00 0 0.00 11 7.48 9 2.37

Eating-Places 10 12.50 50 66.67 17 11.56 65 17.11

Entertainment-and-education 0 0.00 0 0.00 10 6.80 30 7.89

Size 80 100 75 100 147 100 380 100


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among clusters. Shoppers in this cluster visited all type of mall, including general-merchandise (17.6%) which also scored the highest among clusters in CM.

6.6.4 Labeling of Shopping Style

Results above show that shoppers’ store visits formed four clusters in all types of mall. We can see that the pattern in visiting food, apparel-and-accessories, eating-places, and media-and-special-interest presented significant differences of shopping styles. Due to this fact, the labels of shopping styles will be defined in terms of these types of stores. Shoppers in the cluster who mainly visited food (for example cluster 1 in LM, cluster 2 in MM, and cluster 1 in CM), we therefore denote them as Grocery Shoppers. The cluster that has majority of shoppers visiting apparel-and-accessories, such as cluster 4 in LM, cluster 1 in MM, and cluster 4 in CM will be called Fashion Shoppers. Shoppers who mainly visited eating-places (for example cluster 3 in LM, cluster 4 in MM, and cluster 2 in CM) are termed Social Shoppers. The cluster with the highest number of visits to media-and-special-interest with different combinations of store visits, such as cluster 2 in LM, cluster 3 in MM, and cluster 3 in CM, will be named Recreational Shoppers.

Although the group of shoppers were defined by the cluster analysis, we need to examine whether shoppers differ according to their sociodemographic characteristics (gender, marital status, races, age, occupation, education, place of residence and office) and their shopping behavior (duration of mall visit, number of store visits, number of accompanying persons, total expenses, expenses for food-and-beverage, and expenses for non-food-beverage, time of visit and day of visit). The identification of differences among the clusters can create principle explanation and show better understanding about shoppers’ decisions on store visits and their profiles and behavior (Gilboa, 2009; Gilboa and Vilnai-Yavetz 2010). We will discuss this in the next section

6.7 Shopping Styles’ Characteristics

In this study shoppers in three types of Indonesian shopping malls had four shopping styles, namely grocery shoppers, fashion shoppers, social shoppers, and recreational shoppers. In this section, each shopping style is compared across the malls to illustrate the association with sociodemographics and shopping behavior characteristics.


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6.7.1 Grocery Shoppers

In this study grocery shoppers belong to clusters which had more shoppers on “food” type of stores, such as supermarket, bakery or diary fresh stores. In more detail figure 6.1 presents the store visits behavior of grocery shoppers. It can be seen that shoppers mainly combined their “food” store visit with “eating-places” store visit. None of shoppers stopped either at “entertainment-and-education” or “furnishing”. Below 17% of grocery shoppers in MM and CM visited also “general-merchandise”, “health-beauty”, “media-and-special-interest”, and “apparel-and-accessories”.

Grocery shoppers shared over 27.6% in MM and about two times higher than those in LM (15.6%). Over 19.4% shoppers in CM were this style. Grocery shoppers in each mall had their own characteristics regarding their shopping behavior and profiles.

Figure 6.1 Store Visits Behavior of Grocery Shoppers

Shopping Behavior Table 6.9 presents an overview of grocery shoppers’ behavior in the malls. The majority of grocery shoppers in LM and CM spent more than 30 minutes up to 1.5 hours, while the majority of this shoppers in MM spent more than 1.5 hours. On average grocery shoppers made about 2 store visits. In particular most of them made 2-3 stops. On average grocery shoppers spent the same amount of their expenses for

2.50

1.25

15.00

15.00

53.75

0

12.50

0

1.53

0.76

12.21

16.79

29.01

0

39.69

0

0

0

0

0

78.57

0

21.43

0

0 10 20 30 40 50 60 70 80

Apparel-and-accessories

Media-and-special-interest

Health-beauty

General-merchandise

Food

Furnishing

Eating-Places

Entertainment-and-education

Local Mall Modern Mall Classic Mall


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Table 6.9 Behavior Characteristics of Grocery Shoppers

Local Mall (15.6%)

Modern Mall (27.6%)

Classic Mall (19.4%)

cluster 1 cluster 2 cluster 1 Average duration of mall visit in minutes 86.9 (55.90) 112.8 (63.76) 73.7 (60.99) Duration of mall visit <30 minutes 8.82 % 9.68 % 18.18 %

>30 minutes - 1.5 hours 64.71 % 37.10 % 59.09 %

>1.5 hours 26.47 % 53.23 % 22.73 % Average number of store visits 1.6 (0.77) 2.1 (1.10) 1.8 (0.95) Total number of store visits

1 store 50.00 % 30.65 % 47.73 % 2 - 3 stores 47.06 % 59.68 % 45.45 %

4 - 6 stores 2.94 % 9.68 % 6.82 % >6 stores 0.00 % 0.00 % 0.00 %

Average total expenses in IDR 141397 (115112.4)

273516 (573291.7)

274977 (296770.4)

Average expenses for food-and-beverage in IDR 80456 (82392.7)

138000 (340755.0)

83614 (115310.5)

Average expenses for non-food-beverage in IDR 60941 (72996.4)

135516 (263295.0)

191364 (266357.8)

Time of visit Morning 38.24 % 6.45 % 16.28 % Morning to afternoon 8.82 % 19.35 % 11.63 % Morning to evening 0 0 0 Afternoon 23.53 % 53.23 % 53.49 % Afternoon to evening 17.65 % 11.29 % 6.98 % Evening 11.76 % 9.68 % 11.63 % Day of visit Weekdays 50.00% 62.90% 70.45% Weekends 50.00% 37.10% 29.55%


FB and NFB, except grocery shoppers in CM who only spent 30% of their expenses for FB. This means only in CM the grocery shoppers spent more on buying non-food-beverage than food-and-beverage products. There was 38.24% of grocery shoppers in LM who visited the mall in the morning and scored as the highest among the mall. It follows that an equal size of shoppers visited the mall between weekdays and weekends. This might be related to the fact the LM has a daily open market. The majority of grocery shoppers in MM and CM visited the mall in the afternoon and the weekdays. This may indicates that the grocery shoppers in MM and CM visited the mall as part of the working activities.

Shoppers’ Profile Table 6.10 describes the configuration of grocery shoppers in terms of sociodemographics. Females were the majority of grocery shoppers (70%) in all malls. According to the status, the majority of grocery shoppers who were single were only found in MM. Grocery shoppers in LM and CM mostly were married. Grocery shoppers indicated to have the highest average age (over 30 years old) among the


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shopping styles. In particular, only less than 12% were under 20 years old. Therefore, it was not surprising that over 50% of this shopping style were employee or entrepreneur in all malls. Additionally, grocery shoppers in CM shared the highest number of housewives among shopping styles. This group shops mostly alone. Regarding location, grocery shoppers in LM and CM mostly lived or worked in South Jakarta. In the MM, however, this group were differs between South and Central Jakarta. Interestingly, CM had the highest grocery shoppers who lived outside of Jakarta. It seems possible that this result is due to CM’s location is nearby the ring road and the food store in this mall is acceptable to shoppers. In other words, the food store in CM drew grocery shoppers outside of Jakarta to visit the mall.

Table 6.10 Grocery Shoppers’ Profiles Local Mall

(15.6%) Modern Mall

(27.6%) Classic Mall

(19.4%) All mall (100%)

cluster 1 cluster 2 cluster 1

Gender Male 29.41% 27.42% 22.73% 34.10% Female 70.59% 72.58% 77.27% 65.90% Marital Status Single 38.24% 56.45% 36.36% 61.40%

Married 61.76% 43.55% 63.64% 38.60% Average age in years old 33.4 (11.56) 29.6 (9.62) 30.3 (11.98) 28.86 (9.7) < 20 years old 2.94% 8.06% 11.36% 11.80% 20 - 29 years old 47.06% 48.39% 50.00% 52.00% 30 - 39 years old 26.47% 29.03% 18.18% 22.00% >39 years old 23.53% 14.52% 20.45% 14.20% Occupation Student 8.82% 22.95% 22.73% 29.50% Employee 41.18% 55.74% 34.09% 44.50% Entrepreneur 38.24% 14.75% 15.91% 15.70% Housewife 5.88% 4.92% 20.45% 8.20% Retired 5.88% 1.64% 6.82% 1.30% Education High School 52.94% 22.95% 20.45% 26.50%

Polytechnics 0.00% 3.28% 6.82% 3.60%

Academy 2.94% 18.03% 15.91% 11.10%

University 44.12% 55.74% 56.82% 58.70%

Average Number of Accompany 0.9 (1.29) 0.9 (1.14) 0.7 (1.05) 1.35 (1.54) Number of accompany None/alone 38.24% 41.94% 55.81% 32.00%

With 1 accompany 50.00% 41.94% 25.58% 36.60% With >1 accompany 11.76% 16.13% 18.60% 31.40% Place of Residence West Jakarta 0.00% 9.68% 4.55% 6.60%

North Jakarta 0.00% 6.45% 4.55% 3.90% Central Jakarta 8.82% 22.58% 0.00% 8.80% East Jakarta 8.82% 9.68% 11.36% 14.50% South Jakarta 82.35% 40.32% 68.18% 58.20% Outside Jakarta 0.00% 11.29% 11.36% 8.10% Place of Office/school West Jakarta 0.00% 1.61% 4.55% 4.40%

North Jakarta 0.00% 0.00% 6.82% 2.70%


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Central Jakarta 8.82% 33.87% 11.36% 14.90%

East Jakarta 8.82% 4.84% 6.82% 7.20%

South Jakarta 79.41% 54.84% 68.18% 64.50%

Outside Jakarta 2.94% 4.84% 2.27% 5.70%


6.7.2 Fashion Shoppers

Fashion shoppers differ from other shoppers in their highly interest on “apparel-and-accessories” type of stores. Figure 6.2 shows fashion shoppers’ behavior on visiting the stores. It can be seen that although fashion shoppers made the highest on visiting “apparel-and-accessories”, they also combined their visits with other types of store.

Figure 6.2 Store Visits Behavior of Fashion Shoppers

Fashion shoppers were the smallest shoppers in the LM (14.2%), in contrast they were the largest shoppers in the CM (36.6%). Shopping behavior characteristics and profiles of fashion shoppers in each mall are as follows:

Shopping behavior Table 6.11 gives an overview of fashion shoppers’ behavior characteristics in the malls. The average duration of time fashion shoppers spent in malls were above 1.5 hours. Fashion shoppers visited on average above 3.6 stores with

33.68

7.89

7.89

17.63

5.53

2.37

17.11

7.89

48.15

5.56

1.85

13.89

7.87

1.39

20.83

0.46

74.40

3.20

2.40

0

4.80

5.60

8.80

0.80

0 10 20 30 40 50 60 70 80



Health-beauty

General-merchandise

Food

Furnishing

Eating-Places




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Table 6.11 Behavior Characteristics of Fashion Shoppers

Local Mall Modern Mall Classic Mall (14.2%) (26.7%) (36.6%)

cluster 4 cluster 1 cluster 4 Duration of mall visit in minutes 143.7 (69.8) 139.6 (75.19) 116.1 (80.20) Duration of mall visit <30 minutes 0.00% 1.69% 7.32% >30 minutes - 1.5 hours 25.81% 32.20% 41.46% >1.5 hours 74.19% 66.10% 51.22% Average number of Store visits 4.0 (1.22) 3.6 (1.51) 4.6 (1.97) Total number of store visits

1 stores 0.00% 6.67% 4.82% 2 - 3 stores 38.71% 41.67% 24.10% 4 - 6 stores 58.06% 51.67% 55.42% >6 stores 3.23% 0.00% 15.66%


342300 (768973.7)

255159 (376074.9)

Average expenses for Food-and-Beverage in IDR 34129 (22312.5)

109033 (381986.4)

84171 (84374.7)

Average expenses for Non-Food-Beverage in IDR 259581 (574651.1)

233267 (402303.4)

170988 (328296.7)

Time of visit Morning 9.68% 8.47% 6.10% Morning to afternoon 25.81% 16.95% 6.10% Morning to evening 0 0 0 Afternoon 41.94% 37.29% 57.32% Afternoon to evening 16.13% 20.34% 15.85% Evening 6.45% 16.95% 14.63% Day of visit Weekdays 64.52% 59.32% 53.10% Weekends 35.48% 40.68% 46.90%

Standard deviations are between the brackets the highest between 4 and 6 store visits. Regarding expenses fashion shoppers spent above 67% of total expenses for NFB, and the rest for FB. It seems that fashion shoppers focused on what they were looking for. Fashion shoppers in all malls visited the mall mostly in the afternoon and rarely in the morning. Fashion shoppers were most likely to visit the mall on weekdays, although more than 35% of the shoppers visited the mall on weekends.

Shoppers’ Profiles Fashion shoppers were the youngest among shopping styles. As seen in Table 6.12 over 67% fashion shoppers in all malls were female and over 61% were single. It follows that in all malls over 60% fashion shoppers were below 30 years old. It is not surprise that fashion shoppers mostly were employee and student with university backgrounds. Almost none of fashion shoppers were retired persons and below 6% were housewives. Interestingly, there are some varieties in the number of accompanying persons among the malls; in LM fashion shoppers were with more than one accompany, in MM with one accompany and in CM they were alone. It is difficult


123

to interpret this, but in our opinion results were related to the education level. Shoppers with the highest high school background seem to go with companions to the malls. Regarding location, South Jakarta was shared the most as the place of residence and office for fashion shoppers in LM and CM. Though the majority of fashion shoppers in MM worked in South Jakarta (50%), only 35% of them lived in South.

Table 6.12 Fashion Shoppers’ Profiles

Local Mall (14.2%)

Modern Mall (26.7%)

Classic Mall (36.6%)

All mall (100%)


Gender Male 29.03% 32.2% 16.87% 34.10%

Female 70.97% 67.8% 83.13% 65.90%

Marital Status Single 67.74% 66.1% 61.45% 61.40%

Married 32.26% 33.9% 38.55% 38.60%

Average age in years old 26.2 (7.66) 28.1 (8.37) 29.8 (10.99) 28.86 (9.7)

< 20 years old 16.13% 3.33% 14.46% 11.80%

20 - 29 years old 58.06% 68.33% 46.99% 52.00%

30 - 39 years old 22.58% 16.67% 14.46% 22.00%

>39 years old 3.23% 11.67% 24.10% 14.20%

Occupation Student 32.26% 23.33% 36.14% 29.50%

Employee 38.71% 61.67% 39.76% 44.50%

Entrepreneur 25.81% 11.67% 18.07% 15.70%

Housewife 3.23% 1.67% 6.02% 8.20%

Retired 0 1.67% 0 1.30%

Education High school 41.94% 16.67% 18.07% 26.50%

Polytechnics 3.23% 1.67% 0 3.60%

Academy 9.68% 10% 13.25% 11.10%

University 45.16% 71.67% 68.67% 58.70%

Average number of accompany 2.2 (2.18) 1.4 (1.26) 0.9 (1.06) 1.35 (1.54)

Number of accompany None/alone 22.58% 23.33% 40.96% 32.00%

With 1 accompany 32.26% 43.33% 38.55%% 36.60%

With >1 accompany 45.16% 33.33% 20.48% 31.40%

Place of Residence West Jakarta 16.13% 15% 3.61% 6.60%

North jakarta 3.23% 5% 3.61% 3.90%

Central jakarta 3.23% 15% 3.61% 8.80%

East jakarta 0 25% 13.25% 14.50%

South jakarta 67.74% 35% 71.08% 58.20%

Outside Jakarta 9.68% 5% 4.82% 8.10%

Place of Office/school West jakarta 6.45% 6.67% 3.61% 4.40%

North jakarta 0 3.33% 1.2% 2.70%

Central jakarta 3.23% 26.67% 9.64% 14.90%

East jakarta 0 6.67% 3.61% 7.20%

South jakarta 77.42% 50% 75.9% 64.50%

Outside Jakarta 12.9% 6.67% 6.02% 5.70%



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6.7.3 Social Shoppers

Social shoppers were most likely to visit “eating-places” in the mall. Figure 6.3 shows the share of each type of stores. As seen in figure 6.3 the highest percentage of social shoppers in all malls were at “eating-places” stores. Social shoppers showed little interests (below 20%) on visiting other types of stores, except social shoppers in CM who also made 32% on visiting “food”.

Figure 6.3 Store Visits Behavior of Social Shoppers

Social shoppers were the smallest clusters in MM (15.6%) and CM (18.5%), but not in LM (32.1%). Regarding shopping behavior and profiles, social shoppers in each mall had their own characteristics as follows:

Shopping Behavior Table 6.13 presents an overview of social shoppers’ behavior in the mall. As seen in the table social shoppers differ in spending their time among the malls. Social shoppers in LM and MM on average spent over 140 minutes in the malls, while social shoppers in CM on average only spent about 80 minutes. In particular, over 68% of social shoppers in LM and MM spent above 1.5 hours, while over 69% of social shoppers in CM spent between 31 minutes and 1.5 hours. It follows that differences were also shown in the number of store visits. Social shoppers in MM visited on average

12.08

14.09

0.67

7.38

7.38

0.67

50.34

7.38

0

1.33

0

0

32.00

0

66.67

0

15.15

8.08

10.61

0

8.08

0.51

37.88

19.70

0 10 20 30 40 50 60 70 80



Health-beauty

General-merchandise

Food

Furnishing

Eating-Places


Local Mall Classic Mall Modern Mall


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Table 6.13 Behavior Characteristics of Social Shoppers


(32.1%) (15.6%) (18.5%) cluster 3 cluster 4 cluster 2 Duration of mall visit in minutes 153.0 (91.33) 139.9 (71.23) 80.0 (54.19) Duration of mall visit <30 minutes 0.00% 2.86% 9.52%

>30 minutes - 1.5 hours 31.34% 28.57% 69.05%

>1.5 hours 68.66% 68.57% 21.43% Average number of Store visits 2.8 (1.05) 4.3 (0.98) 1.8 (1.00) Total number of store visits

1 stores 5.71% 0.00% 45.24% 2 - 3 stores 71.43% 25.71% 47.62% 4 - 6 stores 22.86% 74.29% 7.14% >6 stores 0.00% 0.00% 0.00%


268114 (212238.5)

187146 (192347.2)


95029 (61146.2)

66878 (90860.7)


173086 (177695.3)

120268 (161330.7)

Time of visit Morning 13.43% 8.57% 12.20% Morning to afternoon 13.43% 20.00% 7.32% Morning to evening 1.49% 0 2.44% Afternoon 41.79% 40.00% 53.66% Afternoon to evening 17.91% 17.14% 9.76% Evening 11.94% 14.29% 14.63% Day of visit Weekdays 61.19% 54.29% 66.67% Weekends 38.81% 45.71% 33.33%


4.3 stores and scored as the highest average. Social shoppers in LM visited on average 2.8 stores, and in CM visited on average 1.8 stores. Although they visited “eating-places” according to expenses these shoppers only made below 36% of total expenses on FB. In addition, social shoppers in MM and CM spent the smallest expenses among the shopping styles. Over 40% of social shoppers visited the mall in the afternoon (12:01 to 18:00). The majority of respondents visited the mall on weekedays. As the social shoppers visited mainly for eating, a possible explanation for this might be that the shoppers visited the mall for lunch.

Shoppers’ profiles Table 6.14 presents an overview of social shoppers’ sociodemographics. Social shoppers’ gender in LM and MM were almost even between females and males, while in CM females were over 66%. The majority of social shoppers were single, but interestingly this group visited the mall with companions. The average number of company was the highest among clusters. In terms of age these shoppers in


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all types of mall were mostly below 30 years. Thus, it is not surprise if in general they had university educational backgrounds. Social shoppers in all malls were high in combination of employee and student. Over 68% social shoppers in LM and CM lived and worked in South Jakarta. In contrast only over 37% shoppers in MM lived and worked in South Jakarta.

Table 6.14 Social Shoppers’ Profiles Local Mall

(32.1%) Modern Mall


(18.5%) All mall (100%)


Gender Male 46.38% 42.86% 33.33% 34.10% Female 53.62% 57.14% 66.67% 65.90%

Marital Status Single 76.81% 68.57% 65.85% 61.40% Married 23.19% 31.43% 34.15% 38.60%

Average age in years old 26.4 (6.88) 28.4 (7.63) 29.8 (9.81) 28.86 (9.7) < 20 years old 14.49% 2.86% 14.29% 11.80% 20 - 29 years old 59.42% 54.29% 45.24% 52.00% 30 - 39 years old 18.84% 37.14% 21.43% 22.00% >39 years old 7.25% 5.71% 19.05% 14.20%

Occupation Student 42.03% 17.14% 38.10% 29.50% Employee 42.03% 62.86% 38.10% 44.50% Entrepreneur 10.14% 11.43% 14.29% 15.70% Housewife 5.8% 8.57% 9.52% 8.20% Retired 0 0 0 1.30%

Education High school 40.58% 11.76% 38.10% 26.50% Polytechnics 7.25% 0 0 3.60% Academy 8.7% 14.71% 4.76% 11.10% University 43.48% 73.53% 57.14% 58.70%


Number of accompany None/alone 18.57% 5.71% 23.81% 32.00% With 1 accompany 28.57% 34.29% 50.00% 36.60% With >1 accompany 52.86% 60.00% 26.19% 31.40%

Place of Residence West jakarta 5.71% 8.57% 0 6.60%

North jakarta 1.43% 0 4.76% 3.90% Central jakarta 1.43% 22.86% 2.38% 8.80% East jakarta 5.71% 17.14% 14.29% 14.50% South jakarta 74.29% 37.14% 71.43% 58.20% Outside Jakarta 11.43% 14.29% 7.14% 8.10%


North jakarta 4.29% 2.86% 2.38% 2.70% Central jakarta 2.86% 31.43% 2.38% 14.90% East jakarta 5.71% 5.71% 16.67% 7.20% South jakarta 68.57% 40.00% 76.19% 64.50% Outside Jakarta 7.14% 8.57% 0 5.70%



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6.7.4 Recreational Shoppers

Recreational shoppers are identified for clusters that scored the highest visits to “media-and-special-interest” with different combination of store visits. Figure 6.4 presents recreational shoppers’ behavior related to their store visits. It should be kept in mind, although 17.3% recreational shoppers in LM visited “media-and-special-interest”, this percentage was the highest visited among clusters in this mall.

Recreational shoppers are unique, as it is difficult to find similarity on their store visits’ behavior. For example recreational shoppers in LM made a higher visits to apparel-and-accessories” (38.5%) than to “media-and-special-interest”. While in CM and MM recreational shoppers who visited “apparel-and-accessories” were below 3%. The combination of store visits that is above 10% in CM was “eating-places” (11.6%), “food” (21.9%) and “general-merchandise” (12.2%), while in MM was “entertainment-and-education” (18.9%) and “eating-places” (21.7%).

Recreational shoppers shared between 25.6% and 38.1% of shoppers in the malls. LM and MM had the highest number of recreational shoppers. To have further information

Figure 6.4 Store Visits Behavior of Recreational Shoppers

2.04

38.10

0.68

12.24

21.09

7.48

11.56

6.80

2.78

25.00

0

9.44

13.89

8.33

21.67

18.89

38.50

17.26

3.54

0

14.16

8.85

12.39

5.31

0 10 20 30 40 50 60 70 80



Health-beauty

General-merchandise

Food

Furnishing

Eating-Places




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about recreational shoppers Table 6.15 describes behavior characteristics of this shopping style.

Shopping Behavior Refer to Table 6.15 recreational shoppers spent on average between 89.3 and 169.5 minutes in the mall. The majority of recreational shoppers in LM and MM spent more than 1.5 hours (61.5% and 73.5%), while in CM only 34.5% who spent more than 1.5 hours. Among the malls these shoppers on average visited 2 or 3 stores and they made not more than 6 stops. On average they spent less than 41% of the total expenses for FB in all malls. The total expenses of recreational shoppers in MM and CM made the highest average among the shopping styles, but not in LM. In general most of recreational shoppers in all malls visited the mall in the afternoon. However, there were some differences among the malls related to day of visit. The majority of recreational shoppers in the LM and MM visited the mall on the weekday, while in the CM in weekends. It seems possible that these results are due to the fact that among the

Table 6.15 Behavior Characteristics of Recreational Shoppers

Local Mall Modern Mall Classic Mall (38.1%) (30.2%) (25.6%)

cluster 2 cluster 3 cluster 3 Duration of mall visit in minutes 125.5 (69.33) 169.5 (97.97) 89.3 (62.00) Duration of mall visit <30 minutes 2.41% 5.88% 12.07%

>30 minutes - 1.5 hours 36.14% 20.59% 53.45%

>1.5 hours 61.45% 73.53% 34.48% Average number of store visits 2.7 (1.21) 2.6 (1.09) 2.5 (1.44) Total number of store visits

1 store 18.07% 11.76% 22.41% 2 - 3 stores 62.65% 67.65% 56.90% 4 - 6 stores 19.28% 20.59% 20.69% >6 stores 0% 0% 0%


400037 (749827.4)

346603 (583423.4)


94162 (98713.0)

142759 (306227.3)


305875 (695848.1)

203845 (478287.4)

Time of visit Morning 22.89% 7.35% 5.26% Morning to afternoon 14.46% 13.24% 5.26% Morning to evening 0 0 0 Afternoon 37.35% 45.59% 63.16% Afternoon to evening 14.46% 25.00% 8.77% Evening 10.84% 8.82% 17.54% Day of visit Weekdays 60.24% 58.82% 44.83% Weekends 39.76% 41.18% 55.17%



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malls only the CM is located next to a residential area.Thus, people who live nearby the mall came during the weekends to spend time in the mall.

Shoppers’ Profiles Table 6.16 presents the profiles of recreational shoppers. Gender in recreational shoppers was quite equal percentage between male and female in LM (49.4% and 50.6%) and MM (44.1% and 55.9%), yet female were the highest in CM

Table 6.16 Recreational Shoppers’ Profiles Local Mall

(38.1%) Modern Mall


(25.6%) All mall (100%)


Gender Male 49.40% 44.12% 29.31% 34.10%

Female 50.60% 55.88% 70.69% 65.90%

Marital Status Single 64.63% 59.70% 62.07% 61.40%

Married 35.37% 40.30% 37.93% 38.60%

Average age in years old 26.9 (8.91) 29.2 (9.07) 30.1 (11.56) 28.86 (9.7)

< 20 years old 20.48% 7.35% 17.24% 11.80%

20 - 29 years old 48.19% 58.82% 39.66% 52.00%

30 - 39 years old 24.10% 19.12% 25.86% 22.00%

>39 years old 7.23% 14.71% 17.24% 14.20%

Occupation Student 36.14% 27.94% 27.59% 29.50%

Employee 37.35% 48.53% 44.83% 44.50%

Entrepreneur 14.46% 13.24% 13.79% 15.70%

Housewife 12.05% 8.82% 12.07% 8.20%

Retired 0 1.47% 1.72% 1.30%

Education High school 37.50% 8.82% 22.41% 26.50%

Polytechnics 10.00% 4.41% 1.72% 3.60%

Academy 17.50% 7.35% 5.17% 11.10%

University 35.00% 79.41% 70.69% 58.70%


Number of accompany None/alone 27.71% 32.35% 44.83% 32.00%

With 1 accompany 34.94% 35.29% 29.31% 36.60%

With >1 accompany 37.35% 32.35% 25.86% 31.40%

Place of Residence West jakarta 4.82% 10.29% 1.72% 6.60%

North jakarta 6.02% 7.35% 0 3.90%

Central jakarta 7.23% 17.65% 1.72% 8.80%

East jakarta 14.46% 25.00% 20.69% 14.50%

South jakarta 57.83% 30.88% 72.41% 58.20%

Outside Jakarta 9.64% 8.82% 3.45% 8.10%


North jakarta 3.61% 5.88% 0 2.70%

Central jakarta 15.66% 22.06% 6.90% 14.90%

East jakarta 7.23% 14.71% 5.17% 7.20%

South jakarta 60.24% 45.59% 84.48% 64.50%

Outside Jakarta 9.64% 4.41% 1.72% 5.70%



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(70.7%). In all malls the majority of recreational shoppers were single with employee and student as their occupation. We found less percentage (below 2%) of retired in this shopping style, and below 15% of entrepreneur and housewife. The majority of shoppers’ education were high school (37.5%) or university (35%) in LM, however over 70% of shoppers in MM and CM had university background. As expected recreational shoppers in LM and MM mostly came with more than one accompany, and in CM only with one accompany.According to place of residence and office, recreational shoppers from LM and CM were mostly from South Jakarta, again this group in MM were from varied locations. In particular, most of them lived in South (30.9%) or East Jakarta (25%) but most of them worked in Central (22.1%) or South Jakarta (45.6%).


In this chapter we presented data about shopping behavior inside the shopping malls which were collected via questionnaire to capture behavior patterns in the shopping malls. The data that was taken in three types of shopping malls demonstrated that each type of malls had its own shoppers’ profiles according to shopping behavior pattern inside the malls.

Our findings suggest that the number of stores in the mall had no relation with the duration of shopping time shoppers spent in the mall. Additionally, duration of time shoppers spent in the mall had also no relation with the number of store visits. A possible explanation is that types of stores may require different duration of time to spend; for example visiting a restaurant may consume longer time than visiting a fashion store.

This chapter demonstrated that shoppers’ shopping behavior inside the malls had patterns specifically on store visits behavior that is called the shopping style. In this regards, a wide set of store visits behavior were investigated and were tailored to the shopping style. A hierarchical clustering analysis was employed to identify different shoppers’ store visits patterns; a descriptive analysis was used to describe how the clusters related with shopping behavior and sociodemographics measures. Results identified four shopping styles across the malls, namely grocery shoppers, recreational shoppers, fashion shoppers, and social shoppers. Each shopping style in each type of malls, however, has its own unique characteristics. Various differences in shopping styles of shoppers can be explained regarding their shopping behavior and profiles. This may clarify that each type of malls has its own market. The pattern of store visits suggests the most frequently visited stores in the mall and vice versa.


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According to business practitioners the success of malls reflect on how big is the transactions within the stores. In other words, how much money shoppers purchased when they were in the shopping malls. Our results suggest a valuable input in understanding shoppers’ stores choice to formulate target market in shopping malls.

For example, the LM has the biggest share of recreational shoppers and the smallest share of fashion shoppers. However, the fashion shoppers in this mall spent on average the highest among the shopping styles. The recreational shoppers who spent on average a bit lower amount of money than fashion shoppers tend to visit “apparel-and-accessories” stores. This means “apparel-and-accessories” types of store have the potential for growth in LM.

The MM has the biggest share of recreational shoppers who spent on average the highest among the shopping styles. In this mall social shoppers had the smallest share of consumers who spent on average the least among the shopping styles. It seems MM has already provided a good variety of stores for their recreational shoppers due to the fact that this group also spent the most.

The CM has the biggest share of fashion shoppers and the smallest share of social shoppers. Interestingly consumers who spent on average the highest were recreational shoppers. CM’s mall managers should provide more variety in stores especially with regard to fashion. On the other hand CM should pay more attention to attract and increase the share of recreational shoppers who were their most potential consumers.

Shopping styles could show the interest of shoppers in visiting stores. However, there are still some questions related to the multi-story shopping mall, such as how do shoppers choose the stores; do they stay on the same floor, or go in a specific order to visit the stores? In the next chapter we will include such sequential store visits measures to better understand shopping behavior.


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7 STOP BEHAVIOR AND MOVEMENT PATTERNS

The previous chapter has reported the results of shopping behavior styles inside the three malls. These styles were based on data that respondents provided as part of the survey. Shopping styles were based on types of stores, which shoppers visited.

In this chapter, we shift the focus of attention to a more detailed level of shopping behavior inside malls. That is, in the remainder of this thesis, we will examine the sequence of stops made. Such analysis will provide better insight into the effects of relative location of different types of stores in malls on the probability they will be chosen.

This analysis also differs in terms of the data used. Whereas the previous analysis pertained to all three shopping malls and was based on the survey data, this analysis is concerned with one mall only and was based on collecting observations of where shoppers enter the mall, the sequence of stores they visit, and the route they use in visiting the stores inside the mall through tracking.

We observed during a full day the shopping behavior of 210 shoppers capturing their activities inside the mall. Of these, 166 shopping patterns were used for further analyses. Because details of shopping behavior are difficult to retrieve from memory, leading to less reliable answers, unobtrusive tracking were made to collect accurate measurements of shoppers’ stopping and movement behavior. That is, shoppers were followed inside the mall by an observer, who carefully recorded the shopping patterns of the shopper. A classic shopping mall in South Jakarta was selected to collect the data.

This chapter presents a detailed description of the method used to collect the data, the composition of tenants and facilities, and descriptive statistics of shopping behavior in the mall.

The chapter includes seven sections. The first section of the chapter presents a general overview of the observation method. The second section provides a discussion of the procedures used to collect the data. The next section is a description and illustration of the mall in this study. This is followed by a discussion of the results of respondents’ profiles and time spent in the mall. The fifth section describes the results of the analyses


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regarding stop and movement behavior including the influences from the setting of stores and facilities in the shopping mall. The sixth section describes the composition of stores and facilities among the floors. It follows with examining the impact of composition of stores and facilities in the mall on shoppers’ behavior. In the final section, we draw some conclusions

7.1 Overview of the Obsevation

From the literature review many factors may influence shopping behavior inside the shopping malls, particularly in stopping and movement behavior. Spontaneous stop behavior often happens during shopping (see e.g. Graves, 2013) as well as impulse buying and for those reasons to examining the “shopping list” may not accurate. Moreover, the way pedestrian choose the routes is unpredictable and be largely subconscious (see e.g. Hill, 1982; Zacharias, 2006). Given this propensity of shoppers to engage in unplanned or impulsive shopping behavior (e.g. Zacharias, 2002; Morrin and Chebat, 2005; Borgers et al., 2009), and the difficulty of retrieving information from memory (Tan et al., 2006) the way to collect a-real-situation of shopping behavior in a shopping mall is challenging.

Based on the literature there are several techniques in collecting complex behavior of shoppers or pedestrians. Although, surveys or personal diaries were frequently used to obtain movement behavior data, however, it was claimed that retrospective surveys and personal diaries, have endemic problems (e.g. Gärling et al., 1998, Zacharias and Schinazi, 2003). The possibilities of imprecise or spatially distorted routes are high, due to this approach relying on the power of respondents’ memory. Furthermore, using a set of questionnaires to collect movement behavior data requires simplification of the real observed situation to make respondents easy to understand.

Since by questionnaire it is difficult to obtain individual movement, Zacharias and Schinazi (2003) combined questionnaire and tracking approaches to collect the shopping behavior in a shopping mall. In this study the questionnaire focused on spatial and temporal context of shoppers’ visit which was impossible to get through direct observation. Tracking in this study referred more specifically to recording in a detailed shoppers’ various flows, visits and activities inside the shopping mall. Zacharias and Schinazi used maps to record the walking speed, times in stores and other locations, window-shopping along with some obvious personal characteristics. The field data were then entered to a Geographical Information System (GIS) to be able to read the spatial layout effectively. Unfortunately, the tracking of Zacharias and Schinazi (2003)


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stopped if shoppers exited or spent more than 10 minutes inside a store or service. Therefore, the data in this study could not describe the whole shopping trips of shoppers.

A study from Saarloos et al. (2010) collected pedestrian shopping diaries data in the CBD through a questionnaire survey. Respondents were requested to complete the questionnaire back home and returned it by mail. The questionnaire asked respondents for several personal characteristics and descriptions of their visits to the CBD in terms of the transport mode(s) they used to come to the area, the objective of their visit, and the stops they made at facilities in sequential order. Respondents also required to inform type of facilities, whether stops were planned or not, and the amount of time and money spent. In addition, they were also asked to draw the location of each stop and the complete route walked from arrival point to departure point on a map.

To get the global picture of movement behavior, Millonig and Gartner (2011) conducted an unobtrusive observation or shadowing to identify movement patterns. Data was collected in two different shopping environments: indoor (shopping mall) and outdoor (shopping street). The tracking surveys collected the speed of respondents and the location of stops. In addition to the trajectories, main visible attributes (e.g. gender, approximated age, fashion style) have been annotated for each respondent.

As we can see questionnaire and interview approaches offer the chance to collect background information about motives, intentions, expenditures, etc. Though these approaches are able to collect activities conducted during shopping, some scholars (see e.g. Brown, 1992, Borgers, et al., 2008) found that most of respondents have difficulties to report all their activities in details, especially if they had so many activities. Tracking or observation approach offers better actual reliability of data and a complete picture of shoppers’ behaviors (excursion) than questionnaire and interview approaches. However, no additional information related to motives, intentions, expenditures, etc. can be collected. Tracking or observation approach not only requires time, but also poses some human errors. If the area of observation is too crowded, observers might lose their respondents while collecting the data. While if we do an unobtrusive tracking, it has an ethical issue regarding that data is taken without permission.

Collecting empirical data also requires a strategy with regard to which respondents to select, the time of the data collection, and the location points of getting the respondents. Borgers et al. (2008) examined collecting pedestrian behavior data in seven cities’ centers. Their findings demonstrate that the best way to get


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representativeness of the sample is by using a random sample. They suggest that to get the complete information about respondents’ activities is after respondents have completed their tours. The time to get the data collection depends on the purpose of study. If the purpose is to get insight into shopping behavior in shopping areas in general, then a more varied set of days of the week could be selected, instead the special season such as year-end-sale.

To avoid human error, some studies suggested to apply high technology instruments on the observation, for example using high technology devices (satellite-based or land-based) such as smartphones (Lee et al., 2013), Bluetooth, RFID or GPS (Teknomo, 2002; Tanaka and Shibasaki, 2005; Isaacson and Shoval, 2006), LRF or laser range finder (Okamoto et al., 2011). We can get accurate data by using instruments. However, the use of high technology instruments has also a weakness (Borgers et al., 2009). By using an instrument, researchers need to invite shoppers to employ the instrument. When shoppers notice they are being observed, they would behave differently. Studies about shopping behavior that used high technology instruments on collecting data were taken in outdoor shopping context (Tanaka and Shibasaki, 2005; Isaacson and Shoval, 2006; Borgers et al., 2009; Saarloos et al., 2010; Millonig and Gartner, 2011). Collecting data via high-technology devices in outdoor shopping context, is possible because the connection to the satellite is easy to access. However, the data covers only the movement behavior (routes of shoppers) without any accurate information related to the stores that shoppers enter (e.g. see Millonig and Gartner, 2007). Collecting data in indoor multi-story shopping context by using such a high technology instruments is a bit complicated, unless to set up infrared installation (e.g. if using RFID), or QR codes (e.g. if using smartphones) inside the building. Additionally, we argued that the high technology instrument is only able to collect the stopping and movement of shoppers, including the routes, but without additional information about the actions of shoppers. The high technology instrument is not able to gather information, whether shoppers are purchasing, or chatting with other shoppers, or window-shopping, etc.

The study in this part covers all decisions that shoppers made after arriving at a mall until they successfully completed their visits to the mall. Thus, the key decisions will include which stores to visit, in which sequence to visit the stores and the route to take including whether the purchasing has been made or not. Therefore, we decided to use a tracking or a direct observation method to collect the data on shoppers’ stopping behavior and movement patterns. A fundamental threat of this method is that shoppers change the way they act, if they realize that they are being observed (Zeisel, 2006). For


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this reason, an unobtrusive tracking was chosen as a means of collecting the required information. It means that shoppers from the moment they enter the mall until they leave the mall without any interaction or communication. The observers tried to avoid that shoppers became aware of them. They recorded the behavior of the shoppers they followed discretely, using paper-and-pencil to record the observations. Based on Borgers et al. (2009), who studied pedestrian shopping behavior in city centers, and Bloch et al. (1994) who examined behavior inside retail stores, we used the following definitions and conventions:

(1) Stop behavior refers to respondents who stay at a particular position for a certain reason. We distinguished between two types of stopping behavior: visiting a store or a facility and using public space.

(a) Visiting a store or a facility is an event that occurs when a respondent enters a store or a facility. It is marked by a shopper crossing the line between public and private space, defining the store or the facility. In that case, the observer writes down a number representing the sequence number of the stop at the right location on a map. Especially if a respondent enters a store the observer adds the character (Y) or (N) to indicate whether the store visit led to purchasing or not, based on observations outside the store.

(b) Using public space. Four activities were included in this event, namely socializing, window-shopping, sitting down, and to-see-and-be-seen. Socializing is an event that occurs when a respondent meets with people. It was coded by a star (*) followed with letter O if with dine-in and a star (*) followed with letter X if without dine-in. Window-shopping occurs when a shopper examines the merchandise displayed in a store window for 30 seconds or more. This activity was coded by a small dot symbol (•) followed by the letter A on the map where the respondent stopped. Sitting-down occurs when a shopper sits down for 30 seconds or more. This activity was coded by a small dot symbol (•) followed by the letter B. To-see-and-be-seen occurs when a shoppers stands in a public space and looks around for 30 seconds or more. A small dot symbol (•) followed by the letter C was used to code this event.

(2) Movement behavior refers to a change of position, and is further differentiated between strolling and circulating.


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(a) Strolling is connected with walking. The observer should illustrate the route of respondents’ strolling by drawing a continuous line and the length of duration within the brackets on the map. The line ends at the place where the shopper becomes involved in activities. The line has arrows to indicate the direction and numbers to indicate the sequence.

(b) Circulating indicates circulation movement by using elevators, stairs, escalators, and ramps. Those vertical circulations were clear on the map, therefore the observer put a letter N (from Naik in bahasa Indonesia means to go upstairs) to indicate going to the higher level or a letter T (from Turun in Bahasa Indonesia means to go downstairs) going to the lower level. The observer put an arrow on the gates pointing to the inside of the building to indicate entering the mall, and an arrow on the gates pointing to the outside of building exiting the mall.

The analyses also need data on the sociodemographic profiles of the shoppers. Because the measurements were unobtrusive, logically the observers had to subjectively assess profile elements such as age and gender. In addition, information on the size and composition of the group of accompanying persons was collected. To allow analyzing whether situational variables influence shopping behavior, days (weekdays or weekends), weather, and any events that happened during the survey were also recorded.

To avoid any human errors, observers were coached and each observer was accompanied while following the first respondent. Furthermore, observers were provided instructions that were attached to the observation sheets.

7.2 Procedures for Collecting Data

Several final year students in Architecture who studied in Jakarta and Bandung were selected as observers over a 2-week period in September 2012. Each observer prepared a set of observation sheets for each respondent and a set of stationary (a colorful-pen, a correction-fluid-pen, and sticky-notes). The observation sheets consisted of seven pages, including a page for the profile of respondent and the instructional guidelines and six pages of layout plans (see Appendix 4). Observers were positioned at mall gateways to select every fifth shopper entering the mall, who seems at least 15 years old. After the shopper was selected, observers started to record time with their mobile phones and traced the respondent’s stop and movement behavior on the layout plans.


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An observer only followed one shopper even if she came together with other persons. Observed profiles of respondents with situational variables were put on the first page of the observation sheets. During the observation, observers stayed outside the stores and facilities to await the respondent, except the department store. The observers followed the respondent inside the department store, because the department store in this mall has a private gate and four floors. They did, however, keep an eye on the respondent especially to record any purchasing activity. If the respondent entered a cinema, observers also entered to avoid missing observations. There was no limitation to the time respondents were observed. Since the duration of stay in the shopping mall varied from 10 minutes to more than 4 hours, an observer could only follow 3 to 5 respondents on a single day. Observers were instructed to terminate the observations when one of the following criteria was met (a) the respondent obviously notices the observer; (b) the observer loses sight of the respondent for more than 15 minutes. Similar methods have been employed successfully elsewhere (e.g. Zacharias and Schinazi, 2003, Borgers et al., 2009).

We understand that tracking shoppers involves some ethical issues (see e.g. Finn, 1989; Brown, 1992). To limit ethical complications, the tracking in this study had the full permission of the mall managers. Information about the data collection activity was provided to the customer services counter and to all security staff in the mall during the observation.

7.3 The Study

The study was conducted in a classic shopping mall in South Jakarta. This is the same classic mall that was surveyed in part II. The mall was rebuilt in 2008 after it was first opened in 2003. The mall is an enclosed building with air-conditioning and with a total area of retail floor space of 57,948 m2. It has 6 floors for retailing and 2 floors for 2000 car parking spaces and 2100 for motorcycles. This is occupied by 2 anchor stores, including a supermarket and a-4-floors department store, 155 specialty stores comprising 8 types of stores: apparel-and-accessories, media-and-special-interest, health-beauty, general-merchandise, food, furnishing, eating-places, entertainment-and-education. This shopping mall has two types of facilities; those rented for financial services and offices by other parties that we call product-service facilities, such as bank, ATM, church, travel agent, and public library; those managed by mall management which we call public services, such as toilet, nursing room, and praying room. In total, this mall has 18 facilities distributed across the floors. The mall is open daily from 10.00 am to 10.00 pm.


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Figure 7.1 illustrates the layout of the 6-floors. The facilities, such as toilets, nursing room, and praying room, managed by mall management, are presented only in numbers, since the data about size was not available. On each floor, the mall provides at least two benches for the shoppers, located in the public space. Employees are not allowed to use the benches. Full descriptions of the mall’s stores and facilities are presented in Appendix 5.

7.4 Data Collection

A total of 210 shoppers were traced during 12 weekdays and weekends during the opening hours. Of the 210 shoppers observed, two were not in the mall for shopping as they were employees of the stores, ten shoppers were lost during the observation, and a further 32 shoppers had incomplete observation records. These groups were removed from the analysis and thus, the results presented here pertain to 166 respondents. No special event happened during the observation. Shoppers in this data were observed mostly on weekdays (84.3%) and between 16:01 to 22:00 (53.6%).

7.4.1 Respondents’ Profiles

The observation were made without any interaction with respondents who looked at least 15 years old. Table 7.1 provides some basic characteristics of the sample. The majority of the sample is female (66.9%). Based on the assessments of the observers, the 20 and 29 age group is the largest group (45.2%). The majority of shoppers visited the mall alone and did not meet anyone else (34.9%).

The group of shoppers who arrived alone, but then met with friends in the mall represents 18% of the sample. Shoppers who were accompanied by one person made up 30.7% of the sample while those who were in a group represent 16.3%. This suggests that shoppers went to this shopping mall not only for shopping, but also for socializing or for gathering with others.


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Lower ground: The lower ground is located in the basement. A supermarket as an anchor store covers over 81.83% of the floor (7033m2). A product-service facility and twenty-four specialty stores occupy the rest of the space with a praying room with toilet. There is a gate that connects this floor directly to the basement’s parking area. Elevators, escalators, and travelators are used as vertical means of transportation

Ground floor: The total tenant space on the ground floor is the smallest among the floors. Three main gateways locate on this floor facing the front mall and a gate locate inside the anchor store facing the backside of the mall. This floor has 1 anchor store, 30 specialty stores, 3 product-service facilities, and 2 toilets. Four escalators and two elevators serve as vertical means of transportation.

Figure 7.1 Layout of the Shopping Mall


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Upper ground floor: Anchor store covers 40.46% of the tenant space (2818m2), 24 specialty stores, and 1 toilet are located on the upper ground floor. A gate is located at the backside of the anchor store connecting this floor and the parking area. Four escalators and two elevators serve as vertical means of transportation.

1st floor: An anchor store occupies 28.6% of tenant space, which is the largest on the floor. This floor has 28 specialty stores and 4 facilities, including 3 product-service facilities and 1 toilet. Four escalators and two elevators work as vertical means of transportation

Figure 7.1 Layout of the Shopping Mall (continued)


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2nd floor: Over 55.79% of tenant space is covered by 22 specialty stores and over 44.21% by an anchor store. There are also 2 product-service facilities, 1 toilet, 4 escalators and 2 elevators on this floor.

3rd floor: The 3rd floor is located at the top level of the building. During the survey, there was an under construction-fitness area which was not included in this study. There were also some empty rental spaces. The 3rd floor has 26 specialty stores and a church. Two specialty stores, cinema and food court, have toilets inside the stores. In addition, there are a public service toilet, 4 escalators, and 2 elevators on this floor.

Figure 7.1 Layout of the Shopping Mall (continued)


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Table 7.1 Profiles of Respondents (N=166)

Gender Male 33.1 %

Female 66.9 %

Visual age <20 years old 7.8 %

20-29 years old 45.2 %

30-39 years old 22.3 %

>39 years old 24.7 %

Number of accompanying person None (alone) 34.9 %

With 1 accompany 30.7 %

With > 1 accompany 16.3 %

Alone and meet friends at the mall 18 %

7.4.2 Length of Stay in the Mall

As mentioned in section 7.2, our observers recorded the time shoppers spent in the mall. Table 7.2 demonstrates that the amount of time spent in the mall ranged from as little as four minutes to eight hours, with an overall average of 85.17 minutes. This average is in the same order of magnitude as reported for other malls. For example, Othman and Lim (1997) reported that Malaysian shoppers (inclusive of students and working people) spent on average 96 minutes. Bloch et al. (1994) found that American shoppers spent about 78 minutes in malls. This indicates that Indonesian shoppers appear to spend slightly more time in the mall compared to the Western shoppers, and almost the same amount of time as Malaysian shoppers do. Slightly more than half (55.4%) visited the mall between 30 minutes to 1.5 hours. Less than 12% of the respondents spent less than 30 minutes. Some differences in length of stay were found in relation to the number of accompanying persons. When shoppers came with one companion they tend to spend 34% more time in the mall than those who came alone. When shoppers came with a group or shoppers met others in the mall the time spent in the mall increased between 1.5 to 2 times more than if they were alone.

7.5 Results of Stop Behavior and Movement Patterns

This section presents the results of the descriptive analyses on stop behavior and movement patterns.

7.5.1 Stop Behavior

Table 7.3 shows the results of stop behavior based on store and facility visits. Results regarding store visits show that the number of store visits varied from none to eleven


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Table 7.2 Length of Stay in the Mall (N=166)

Average time of mall visit 84.17 minutes (66.62)

Average time of mall visit when visiting alone 50.79 minutes (47.15)

Average time of mall visit when accompanied by 1 companion 68.08 minutes (48.65)

Average time of mall visit when accompanied by >1 person 77.20 minutes (54.31)

Average time of mall visit when meet friends 105.93 minutes (91.18)

Duration of mall visit <30 minutes 12.05 % Min = 4 minutes and max= 480 minutes

30 minutes - 1 hour 31.33 %

>1 - 2 hours 39.16 %

>2 hours 17.5 %


stores, with 32.5% of the visits involving only one store. Three respondents (1.81%) did not make any store visit: two respondents visited only the toilets and one respondent strolled in the public space for four minutes. This means that the majority of shoppers went to the mall to visit stores.

Observers carefully watched shoppers from public spaces (outside the store) if they stopped at the cashiers for paying and whether or not they brought a bag after they came out of the stores. However, sometimes observers experienced some difficulties to detect whether shoppers did purchase or not, especially if they bought small things that were not visible. Our data showed that store visits did not always result in a purchase. Over 24.7% of the respondents purchased every time they visited a store. The proportion of respondents who visited stores without any purchase is relatively small (12%). Respondents whose purchase ratio is between 26% and 50% represent the largest group (32.5%). In other words, the majority of respondents purchased something in between one-fourth and half of the stores they visited (see Table 7.3).

Table 7.3 Stop Behavior: Visits Activity (N=166)

Total number of tenant visits No store 1.81 % Min = 0 store and max = 11 stores One store 32.53 % Two stores 20.48 % Three stores 10.84 % Four stores 12.65 % Five stores 9.04 % Six stores 4.82 % Seven stores 3.01 % Eight stores 3.01 % Nine stores 0.60 % Ten stores 0.60 % Eleven stores 0.60 %


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Ratio of store visits with purchasing to total of store visits

0% 12.00 %

1-25% 14.50 %

26-50% 32.50 %

51-99% 16.30 %

100% 24.70 %

Total number of facility visits Min = 0 facility and max 5 facilities

No visit 55.42 %

Product-service visits 18.67 %

Public service visits 20.48 %

Both facilities visits 5.42 %

Table 7.3 also shows the number of respondents who made facility visits. In this table, we differentiated between the visits to product-service facilities and to public services. As expected, the number of stops at a facility was low. Particularly, shoppers who did not make any facility visits were over 55.42%. The number of shoppers who visited public services was slightly higher than the number of shoppers who visited product-services (20.5% and 18.7%). During the observations, none of shoppers visited the nursing room or the church.

Out of 166 respondents, we found that the public space in the mall was engaged 79 times, for example by socializing, window-shopping, sitting down, and to-see-and-be-seen. As seen in Table 7.4, shoppers made unequal use of the public space on each floor. The public space on the ground floor was the most frequently used space (49.37%). This may happen due to the fact that the main gateways in this mall are connected to the ground floor.

Results demonstrate differences in terms of the length of time of stops. In part, length of time of stop behavior is related to the nature of the activity involved in the sense that some shopping activities inherently take longer than others. However, there are also inter-personal differences.

Table 7.4 Stop Behavior: Use of Public Space (N=166)

Location Frequency %

Lower ground floor 4 5.06

Ground floor 39 49.37

Upper ground floor 19 24.05

1st floor 10 12.66

2nd floor 6 7.59

3rd floor 1 1.27

Total 79 100


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Table 7.5 Length of Time of Visits Activity and Use of Public Space (N=166) Category N visits Average time duration

Store visits Anchor store:

Supermarket 59 35.61 Minutes

Department store 89 14.24 Minutes

Specialty store

Apparel-and-accessories 103 8.42 Minutes

Media-and-special interest 66 16.67 Minutes

Health-beauty 27 18.96 Minutes

General-merchandise 0 0 Minutes

Food 3 3 Minutes

Furnishing 16 9.91 Minutes

Eating-places 91 47.34 Minutes

Entertainment-and-education 33 26.42 Minutes

Facility visits Product-service 43 5.17 Minutes

Public service 50 7.2 Minutes Use of Public Space

Socializing, sitting down, window shopping, to-see-and-be-seen 79

As seen in Table 7.5, shoppers spent on average 47.3 minutes in “eating-places”, which is the longest time spent on visits. This result is as expected as eating may take quite a time. Shoppers spent over 35.6 minutes in the “supermarket” and 8.4 minutes in “apparel-and-accessories” stores. This is lower than visiting the “department store” (14.2 minutes) and even “furniture” store (9.9 minutes)

Because completing the purchase of furniture takes considerable time, this finding suggests that many store visitors just had a stroll in these stores or buy some electronic accessories. Results also demonstrate that shoppers spent on average 26.4 minutes on “entertainment-and-education”. Regarding facility visits, shoppers spent more time in the public services (7.2 minutes) than in product-service facilities (5.2 minutes).

7.5.2 Movement Patterns

These movement patterns are described in terms of two-dimensional sequences. The sequence describes the ordered set of stores/facilities visited, augmented with the entrance and exit. Each segment of the sequence defines the store or facility type visited and the floor number. Sequence length equals the different number of stores and facilities visited plus 2 (a code for entering and leaving the mall).

Consider the following example


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Sequence Length Activity Different

Floor Visited Location

1 Enter the mall 1 Ground floor 2 Visit a store type A Ground floor Switch floor (up) Upper ground floor and 1st floor

3 Visit a store type B 2 1st floor Switch floor (down) Upper ground floor

4 Use of public space 3 Upper ground floor Switch floor (down) Ground floor

5 Exit the mall 4 Ground floor

After entering the mall via the gate on the ground floor a shopper strolled directly to a store type A at the same floor and made a visit, then went to the upper floor and directly continued to the 1st floor, where she stopped at store type B. Next, she went down to the upper ground floor and stopped for a while using public space, and finally she went back to the ground floor and left the mall. The sequence length of this shopper is 5. This movement pattern was coded as:

2Xi – 2A – 4B - 3PS – 2Xo The number represents the floor number (basement=1; ground floor=2, upper ground floor= 3, 1st floor= 4, 2nd floor= 5, 3rd floor=6), the letter code represents activities (Xi= entrance; Xo= exit; A, B, PS, etc. type of store/facilities/ public space).

Table 7.6 Movement Patterns (N=166)

Sequence Length Frequency Different Floor Visited Frequency

sequence length 3 16.27% 1 floor 9.04%

sequence length 4 19.88% 2 floors 40.36%

sequence length5 20.48% 3 floors 27.11%




sequence length 9 6.02%




sequence length13 0.60%



sequence length 16 0%



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Table 7.6 presents the frequency distribution of movement patterns according to their length and the number of floors visited. It can be seen that sequence length varies between 3 and 17, which includes entering and exiting the mall. The majority of shoppers (20.5%) made 5 sequence lengths, followed by sequence length 4 (19.9%). Shoppers who made more than 10 stops represent only 6.62%. The majority of shoppers visited 2 different floors (40.4%). Interestingly, only 2.4% of the respondents visited all floors.

7.6 Composition of Stores and Facilities in the Mall and Shoppers’ Behavior

The composition of stores and facilities in a shopping mall may influence shoppers’ behavior. Each floor has a different composition of stores and facilities. In this section, we will examine the relationship between the composition of stores and facilities at the different floors in the mall and visit frequencies. To do so, first, we figure out the composition of stores and facilities for the various floors in the mall. Next, we present the number of store and facilities visits on each floor and examine the relationship between the number of stores and facilities and the number of stores and facilities visits on each floor.

The detailed results of this analysis can be found in Appendix 5. The results demonstrate that not all stores and facilities were visited. This is an effect of sample size. To avoid zero visits at stores or facilities, our analysis is conducted at the level of 12 types of stores/facilities including anchor stores, specialty stores, and facilities. The anchor stores are supermarkets and department stores; the specialty stores are apparel-and-accessories stores, media-and-special interest stores, health-beauty stores, general-merchandise, food stores, furniture stores, entertainment-and-education stores, while the facilities are product-service, and public service.

Figure 7.2 presents the distributions of type of stores and facilities in the mall according to floor. Distributions of store types and facilities in the mall by floor show that each floor has a varying set of store types and facilities and a varying total number of stores and facilities. For example, there are three floors, which have the highest number of eating-places type of stores: i.e. lower ground floor, 1st floor, and 3rd floor. The apparel-and-accessories type of stores dominate the ground floor and the upper ground floor, while on the 2nd floor the number of health-beauty type of stores is highest.


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Regarding the number of stores and facilities, the ground floor has the highest total number of stores and facilities. The lower ground floor has only 25 stores and 2 facilities; however, it has 9 different types of stores and facilities.

Slightly similar to the lower ground floor, the upper ground also has 25 stores and 1 facility. However, this floor has only 7 different types of stores and facilities. The 1st floor has 29 stores and 4 facilities. Moreover, this floor has the widest variety in the mall; it offers 10 different types of stores. The 2nd floor has the smallest number of stores (24 stores) with 8 different types of store and facilities. The 3rd floor has 26 stores with 6 types of stores and facilities. The anchor stores, the supermarket is located on the lower ground floor, while the department stores are located on the ground floor to the 2nd floor.

Figure 7.2 shows that every floor has at least one product service and one public service facility, except the upper ground floor which has only one public service facility. The ground floor has the highest number for both types of facilities.

Figure 7.3 presents the frequency of shoppers’ stores and facilities visits on each floor. In this figure, each type of stores has six bars that represent the six floors. The length of the bar indicates the frequency of visits. While collecting the data, we counted whether a shopper entered a store or facility. Thus, if a shopper went back to the same store, it was counted twice, and so on.

As it mentioned earlier we separated anchor stores to supermarket and department store due to the fact that these types of stores are located on different floors. As for the distribution of visits on each floor, results demonstrate that the supermarket had the highest visits (10.2%) on the lower ground floor.

Our results on the department store visits demonstrate that the higher the floor, the lower the number of visits. In other words the department store on the ground floor has more visits than the department store on the upper floor. In total department stores had 15.43% visits in this mall.

Apparel-and-accessories stores are located on each floor, except on the lower ground floor. As seen on figure 7.2 ground floor and upper ground floor have the highest number of apparel-and-accessories stores (15 stores) and the 2nd floor has the lowest (4 stores). Our findings demonstrate that this type of stores had the highest number of visits in the mall. However, the distributions of floor visits of apparel-and-accessories


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stores were not equal on each floor. The majority of shoppers visited apparel-and-accessories stores which are located at upper ground floor (6.72%) and this scored as the highest visits on the upper ground floor. Results may suggest that the apparel-and-accessories stores located on upper ground floor influenced shoppers to visit the floor.

* The percentage behind store tenants shows the portion of type of store out of 155 stores

Figure 7.2 The Composition of Stores and Facilities in the Shopping Mall

1 1 1 1 1

15 15

64

84

25

5

3

3

3

3

1

27

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612

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10

15

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30

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lower groundfloor (25 stores

+ 2 facilities)

ground floor(31 stores + 5

facilities)

upper groundfloor (25 stores

+ 1 facility)

1st floor(29 stores + 4

facilities)

2nd floor(24 stores + 3

facilities)

3rd floor(26 stores + 2

facilities)

facility public service facility product service

specialty store entertainment-and-education (9.68%) specialty store eating-places (27.10%)

specialty store furnishing (5.81%) specialty store food (1.29%)

specialty store general-merchandise (0.65%) specialty store health-beauty (10.32%)

specialty store media-and-special interest (14.19%) specialty store apparel-and-accessories (30.97%)

anchor store department store anchor store supermarket


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Media-and-special interest stores are spread on all floors. The highest number of this store is located on the upper ground floor and 1st floor (with 5 stores on each floor). As seen in Figure 7.3, in total above 11.38% of shoppers visited media-and-special interest stores in this mall. The highest number of visits to media-and-special interest stores was on the 1st floor (6.72%), while on the other floors the visits were less than 2%. The reason for this result could be that the media-and-special interest stores on the 1st floor was more attractive than the similar type of stores in other floors. In addition, as seen in Table A5.4 of Appendix 5 among media-and-special-interest stores in the mall, the stores on the 1st floor occupies the biggest area (1447.7 m2).

In this mall, every floor except the 3rd floor has at least one health-beauty store. The health-beauty stores are composed of not only stores which sell products, but also stores, which provide services. The total number of shoppers who made health-beauty visits in this mall was below 4.66%. Interestingly, our finding in figure 7.3 demonstrates that shoppers made the highest health-beauty store visits on the ground floor, following by the lower ground floor. All health-beauty stores on the ground floor sell only products (3 stores), while the health-beauty stores on the lower ground floor provide service or sell products (3 stores). The 2nd floor has the highest number of health-beauty stores (7 stores) and all the stores provide only health-beauty services (see the descriptions of stores in Appendix 5). Therefore, results may suggest that shoppers who visited health-beauty store seemed to come for health-beauty products more than to have health-beauty services.

There is only one general-merchandise store in this mall that located on the upper ground floor. The general-merchandise store is a store that provides fashions, local souvenirs, traditional snacks, and house accessories. From the observation we could not find any shoppers who visited this store. This may indicate that the general-merchandise store in this mall was not attractive.

The food store in this mall concerns a fresh-diary store, which has no seats, and sweets-and-chips’ store. There are two food stores in the mall, first that is located on the lower ground floor and second that is located on the 1st floor (see figure 7.2). It can be seen from figure 7.3 that the total number of food stores visited is one of the smallest among types of store visits in this mall (0.52%). This suggests that food store could not compete with the supermarket that has similar products but in a wider variety. Regarding the distribution of visits at the food stores the ground floor had more visits than the 1st floor.


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* The percentage behind stores shows the portion of total number of type of store visits ** The percentage behind the floor shows the portion of total store visits *** For the detail number of stop visits on each of stores see Appendix 5

Figure 7.3 Frequency Distributions of Store Visits Behavior in the Mall

Furnishing stores only locate on lower ground floor, 1st floor, and 2nd floor. In total shoppers who visited a furnishing store in this mall was lower than 2.76%. This may suggest that furnishing store was not the type of stores shoppers wanted to visit in the mall. Figure 7.3 shows that no shoppers visited furniture stores on the lower ground

0.34%

0

3.28%

1.90%

0

0

0

0.52%

5.00%

0

1.03%

0.17%

0

2.24%

2.41%

0

0.52%

1.03%

0.34%

0.86%

0.69%

6.21%

0.52%

2.24%

0.34%

0.17%

0.52%

6.72%

0.52%

2.76%

0.17%

0

1.03%

0

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0

0

0.34%

0.69%

6.72%

5.34%

4.14%

0.86%

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0

0

2.24%

1.03%

5.17%

6.38%

2.24%

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0.52%

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0

0.34%

1.03%

1.38%

0

10.17%

0% 2% 4% 6% 8% 10% 12%

public service (8.62%)

product services (7.41%)

entertainment-and-education (5.69%)

eating-places (15.69%)

furnishing (2.76%)

food (0.52%)

general-merchandise (0%)

health-beauty (4.66%)

media-and-special interest (11.38%)

apparel-and-accessories (17.76%)

supermarket (10.17%) and department store (15.34%)

lower ground floor (total visits 18.97%) ground floor (total visits 26.38%)upper ground floor (total visits 14.31%) 1st floor (total visits 20.69%)2nd floor (total visits 8.62%) 3rd floor (total visits 11.03%)


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floor. The highest number of vistis at furnishing stores was on the 2nd floor which has the highest number of furnishing stores (6 stores).

Eating-places are located on all floors, except the upper ground floor. As can be seen in figure 7.2, the highest number of eating-places is located on the lower ground floor (12 stores) and the lowest on the 2nd floor (3 stores). In total, over 15.7% of the shoppers visited eating-places. Figure 7.3 indicates that the most frequently visited eating-places visit were on the ground floor, which has only 8 eating-places (6.2%).

Each floor has entertainment-and-education stores, except the 2nd floor. In total, the percentage of shoppers who visited entertainment-and-education stores was lower than 5.7%. The 3rd floor has the highest number of entertainment-and-education stores with 6 stores (see Figure 7.2). Figure 7.3 shows that this floor attracted most visits to entertainment-and-education stores. In total, over 7.4% of the shoppers visited the product-service in this mall. Out of this percentage the highest product-service visits occurred on the 1st floor (6.2%) to the ATM, while the similar visits on other floors had less than 1% of visits. The high traffic to the ATM may be caused by the fact that purchasing through the ATM card was not always available at the moment when we collected the data. Although there are also ATMs on the ground floor, it seems shoppers prefer to visit the ATMs, which are located on the 1st floor (see Figure 7.3). In terms of public service, more than 8.6% of the shoppers used this facility in this mall. It can be seen from figure 7.3 that the lower ground floor had the highest number of visits to public service facilities from all floors. In addition, results show that the higher the floor, the less shoppers used the public service.


This chapter discussed the key underlying principles and operational decisions in a data collection effort to capture the store visit, purchasing and movement patterns of a sample of shoppers in classic shopping mall in Jakarta, Indonesia. Because shopping behavior inside a mall may involve a large number of inter-dependent decisions, we decided to use personal observations to collect the required data in the most accurate possible way. Although personal observation is also not perfect, we argue that the number and kind of errors are smaller than asking respondents to retrieve information about their shopping patterns from memory. To ask respondent to recall their memory about the sequence of activities they conducted inside the mall, the timing and duration of these activities and the path choices, the store and facility visits is impractical and likely leads to substantial bias. By tracking we can reduce the bias. However, this


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method, especially unobtrusive tracking, may be at the border of ethical behavior. We therefore ensured that intrusion was limited, while in addition all precautionary measures were taken to act appropriately should things go wrong. As evidenced by the experiences obtained with this sample, this method of data collection offers the potential advantage of accurately recording data about shopping and movement patterns that is difficult to collect using interviews or self-reports. However, this method does not allow to easily collecting detailed sociodemographics data, except gender. Necessarily, the other sociodemographics data should be discretionized in larger categories and errors around the borders of these categories can be easily made.

This chapter has presented results of descriptive analyses on 166 shoppers concerning their sequential shopping behavior in order to understand where shoppers mostly stroll in a multi-story shopping mall. Results show that there is a relation between the time spent and the number of accompanying persons. The more number of companions a shopper had, the longer she or he spent time in the mall. The mall in this study has 6 floors, however, our data shows that the majority of shoppers explored 2 floors while only a limited number of shoppers explored one floor or the entire shopping mall. Furthermore, findings demonstrate that the public space located on the ground floor had the most frequent visits. It seems possible that these results are due to the fact that the main gates in the mall locate on the ground floor. According to study by Hirsch, et al. (2016) in a multi-story mall the pass ratio declines according to the distance from the central point of the shopping mall. Thus the further the place to the central point is, the smaller number of shoppers will pass by.

Results about store visits do not show much difference with our analysis in chapter 6. In this study, our findings demonstrate that shoppers made the highest number of apparel-and-accessories store visits, following with eating-places visits. Moreover, this study has shown that the number of stores on each floor had no relation with the number of visits. This result may be explained by the fact that stores provided different products and different attractiveness.

In the next chapter we will continue examining the sequential shopping behavior patterns and how these sequential shopping patterns were affected by shoppers’ sociodemographics.

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8 UNDERSTANDING SEQUENTIAL SHOPPING PATTERNS

In the previous chapter, we described our data collection approach to capture stop and movement behavior. The aim of this chapter is to identify sequential shopping patterns and understand the effect of shoppers’ profiles and behavioral factors on sequential shopping patterns. The chapter describes the method of analysis and results based on the unobtrusive tracking pertaining to the set of 166 shoppers presented in chapter 7.

The structure of this chapter is as follows. In order to identify sequential shopping pattern, a sequential alignment analysis is conducted. This analysis explores shoppers’ sequential shopping behavior of the data. Therefore, the first section will discuss concept of sequence, explanation of the method of analysis, and data preparation. The section will also discuss the common sub-patterns in sequential shopping patterns that were found in the data. Next, section 8.2 aims to explore similar patterns in sequential shopping behavior. In this regards, a cluster analysis is conducted in order to group the shoppers. Then, a descriptive analysis is applied to examine the shoppers’ characteristics. The following section aims to understand whether sequences in shopping patterns are significantly related to shopper characteristics. A logit model is employed on clusters to understand the effect of sociodemographics variables on the membership of clusters. Finally, the chapter is completed with a summary of the results and a discussion of managerial implications.

8.1 Sequential Shopping Patterns

Shopping patterns in a shopping mall may be complex. The complexity comes from interrelated decisions regarding stop and movement behavior as well as their sequence or temporal order. A shopping pattern can be defined as a sequence of shopping activities in the mall. Each element of this sequence has a multidimensional profile, defining the type of store visited, the duration of the visit, the floor where the store is located, whether merchandise was bought, etc.

Shopping sequences can be segmented based on their similarity between these multidimensional ordered profiles. A problem of conventional classification methods, such as cluster analysis, is that common similarity measures used as the basis of the classification do not take the sequence into account. While patterns belonging to the


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same cluster may have in common exactly the same set of stores, there is no guarantee these stores were visited in the same order. Consequently, this important information is not considered in the classification of shopping patterns when classis methods are used.

Therefore, in this study, the segmentation of shopping activities is based on multidimensional sequence alignment methods (Joh et al., 2002). The identification of shopping patterns then involves the following steps. First, the multidimensional sequence alignment method (MDSAM) is applied to calculate the degree of (dis)similarity between pairs of shopping sequences. Next, a K-means cluster analysis is applied to classify sequential shopping patterns into the specified number of clusters. Finally, descriptive analyses are used to examine the clusters’ profiles.

8.1.1 Sequence Alignment Methods

Shopping patterns can be encoded as a string of characters, each representing a specific shopping activity. This shopping activity will count not only the particular type of stop (visiting stores or facilities), but also the sequential order in which the stops are made. Thus, the shopping pattern string contains two kinds of information:

(1) Cross-sectional or compositional information which identifies the number of stops, the number of particular stops, the number of different kinds of stops, and so on. For example, a string could consist of three items (or letters) of three types of store visits such as [retail store–restaurant–supermarket], where each item denotes an uninterrupted use of a certain type of store. Conventional statistical analysis techniques can be used to retrieve the desired cross-sectional information (see chapter 7).

(2) Sequential information represents the sequence (order) of stop activities. A particular order of stops may imply particular personal characteristics and/or a particular decision context (Joh, 2004; Saarloos et al., 2010). As an example, a shopper who makes three stops at respectively a retail store, restaurant, and supermarket, may visit these establishments in different order. For instance, the order of the restaurant may depend on the timing of the shopping trip; if many or heavy items are bought at the supermarket, it is more likely the last in the sequence.

Originally developed in molecular biology, a dynamic programming technique called ‘sequence alignment’ enables measuring the difference between two strings of letters


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with regard to both kinds of information, cross-sectional and sequential (Sankoff and Kruskal, 1983). This approach defines the difference between two strings as the minimum number of changes, such as insertions, deletions, and substitutions, of letters needed to make the first string equal to the second string (or vice versa). This metric, called Levenshtein distance, suggests that the more edits are needed the more dissimilar two sequences are.

For example, consider two strings s = [A D B C] and g = [A B C D E]. To make s equal to g a minimum of three operations is needed:

Step Operation Resulting s

1 Delete D from second position [A B C]

2 Insert D at the end [A B C D]

3 Insert E at the end [A B C D E]

The original version of sequence alignment developed for biological applications is limited as it can only handle uni-dimensional strings. However, the structural information embedded in the shopping patterns is multidimensional. The shopping pattern is not only characterized by type of stops and sequences, but also by attributes (dimensions) such as location of the stop, whether the stop was planned, whether a purchase was made, and so on. In our case, the floor is another dimension.

The multidimensional sequence alignment method (MDSAM) developed by Joh et al. (2002) incorporates the interrelationships between different attributes. In other words, the method is proposed to allow one to include cross-sectional and sequential information in the comparison of shopping patterns and take interdependencies between the attributes into account (here store type/floor). The alignment employs a hybrid method of dynamic programming and genetic algorithms to find the minimum changes needed to equalize one pattern with another pattern in the non-algebraic solution space (Joh et al., 2001). The measure provides a better basis for analyzing movement patterns in multi-story malls where sequential information and interdependency are of great importance.

The key principle underlying MDSAM is that sequence alignment is applied to each of the dimensions, with the addition that a set of elements occupying the same position across the attribute sequences can be aligned as if it were one element. For example, assume we have two attributes (dimensions) of shopping activity; first attribute composes of element ABCDE, and the second attribute composes of element 0 and 1.


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And we have two shopping patterns with p= �A0D1B0C0� and q= �A0

B0C1D1E0�. To make p

equal to q a minimum of four operations are needed:

Step Operation Resulting p

1 Delete “D and 1” from second position �A0

B0

C0�

2 Substitute 0 with 1 at third position �A0

B0

C1�

3 Insert “D and 1” at the end �A0

B0

C1

D1�

4 Insert “E and 0” at the end �A0

B0

C1

D1

E0�

As a result, the program constructs a “distance” matrix of all-to-all comparisons by comparing each shopping pattern against every other pattern. Distance is thus defined as the Levenshtein distance, which equals the number of operators needed to make two shopping sequences the same. This matrix was used as input to a cluster analysis aimed at identifying homogeneous groups of shopping sequences based on this distance measure.

By using MDSAM, sequential shopping patterns were examined by using two equally weighted attributes, functional attribute and a spatial attribute. The functional attribute relates to the type of store/facility visited. The spatial attribute represents the floor where the store is located. As for the functional attribute, 7 different types of stops were distinguished: (1) anchor stores stops which represent stops at anchor stores, including supermarkets and department stores; (2) retail stores stops which represent stops at specialty stores that sell products, including apparel-and-accessories, media-and-special interest, health-beauty, food, and furnishing; (3) food-and-beverages stops which represent stops at specialty stores that provide dine-in as their major activity; (4) entertainment stops which represent stops at specialty stores that provide entertaining services. In addition to these, we also took into account (5) facilities stops that relate to stops at product-services and public services; (6) public spaces stops that involve stops at public spaces for socializing, sitting down, window shopping, or to-see-and-be-seen; (7) gates stops that concern stops at or pass through the gates to enter or exit. As indicated, in addition to the store type, we defined at which floor the store is visited. Because the mall has six floors, the values of this dimension range from 1 to 6.

https://www.google.nl/search?espv=2&biw=1680&bih=866&q=levenshtein&spell=1&sa=X&ved=0ahUKEwiV8p_P4-nOAhWFWxQKHTwGDFIQvwUIGSgA


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8.1.2 Data Preparation

Table 8.1 cross-tabulates the 166 observed shopping patterns in terms of store type and floor. It indicates there are no stops at anchor stores on the 3rd floor, no stops at food-and-beverage stores on the upper ground floor, no stops at entertainment stores on the 2nd floor, and no gates stop on the 1st, 2nd and 3rd floors due to the fact that the features did not exist on those floors. It also shows that most stops are made on the ground floor, while the least number of stops are made at the 2nd floor. Regarding type of stops, most stops are made at retail stores and anchor stores. As expected the majority of shoppers entered and exited through the main gate on the ground floor.

Table 8.2 provides shoppers’ switch floor patterns to go up or to go down by using the vertical circulations. The switch floor patterns include the using of vertical circulations inside the department stores. It is possible that the frequency of go up and go down are not similar. However, in this study the number of go up and go down patterns are similar. The majority of shoppers switched the floor from ground floor to upper ground

Table 8.1 The Frequency of Stop Type by Floor (N=166)

lower

ground floor (1)

ground floor (2)

upper ground floor

(3)

1st floor (4)

2nd floor (5)

3rd floor

(6) total

anchor stores stop 59 37 31 16 5 0 148

retail stores stop 16 49 45 48 25 32 215

food-and-beverage stop

18 36 0 13 13 11 91

entertainment stop 3 2 6 3 0 19 33

facilities stop 14 29 1 40 7 2 93

public space stop 4 39 19 10 6 1 79

gates stop 10 318 4 0 0 0 332

Total 124 510 106 130 56 65

Table 8.2 Switch Floor Patterns (N=166)

Go up Frequency Go down Frequency

lower ground fl. to ground fl. 78 ground fl. to lower ground fl. 78

ground fl. to upper ground fl. 130 upper ground fl. to ground fl. 130

upper ground fl. to 1st fl. 116 1st fl. to upper ground fl. 116

1st fl. to 2nd fl. 63 2nd fl. to upper ground fl. 63

2nd fl. to 3rd fl. 30 3rd fl. to 2nd fl. 30


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floor and vice versa. As expected, the higher the floor was, the smaller the number of switches that shoppers made.

8.1.3 Common Sub-patterns in Sequential Shopping Patterns

Comparing seven types of stops (anchor stores stop, retail stores stop, food-and-beverage stop, entertainment stop, facilities stop, public space stop, and gates stop) and six floors (lower ground floor, ground floor, upper ground floor, 1st floor, 2nd floor, and 3rd floor) allows us to identify the common sub-patterns in sequential shopping patterns. As mentioned in Chapter 7.4.3, the sequential shopping patterns of each shopper is characterized in terms of a sequence of two-dimensional profiles of type of stores and floor number.

The 166 shoppers demonstrated 991 sequential shopping patterns. However, the chance of shoppers to have identical sub-patterns related to type of stores and floor number in the sequence is small. The most important limitation lies in the fact that the number of shoppers who had sequence length between 8 and 17 was small. With this limitation to anlyze the common sub-patterns in sequential shopping patterns regarding floor number was impractical (see Table A6.15 to Table A6.28 in Appendix 6). Thus, our discussion about common sub-patterns in sequential shopping patterns only analyzed the sub-patterns related to type of stores. Since shoppers may not only visit the mall for shopping, but perhaps also for eating the analyses examined stop at anchor stores (including department store and/or the supermarket) and food-and-beverage stores. Appendix 6 presents the data on shoppers’ sequential shopping patterns. As seen in Appendix 6, the higher the sequence length of a particular pattern, the smaller the number of altenative sequences.

The analysis of sub-patterns of anchor stores and food-and-beverage stores in sequential shopping patterns concerns eight patterns:

stop at food-and-beverage

and all anchor stores

and no anchor stores

and at supermarket

and at departmen store(s)

not stop at food-and-beverage

but at all anchor stores

nor at any anchor stores

but at supermarket

but at departmen store(s)


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Table 8.3 presents an overview of sub-patterns of anchor store and food-and-beverage store stops. Regarding food-and-beverage store stops, the majority of shoppers (52.4%) had sub-patterns without stop at a food-and-beverage store. This means that almost one out of 2 shoppers visited a food-and-beverage store. Just somewhat more than one out of 2 shoppers (51.8%) visited at least one anchor store. Apparently, both type of stores are visited as often. However, at the level stores, the individual anchor stores are visited more often than the individual food-and-beverage stores.

As seen on Table 8.3 shows most of shoppers with a sequence length between 3 and 7 did not stop at any anchor stores, except those with sequence length 6. Shoppers who stopped only at the supermarket mostly made between 3 and 9 of sequence length, except those with sequence length 7. Shoppers who tended to stop at department stores mostly with sequence length 6 and above. The results from shoppers with sequence length 9 and above are difficult to interpret, because the data was limited.

Table 8.3 Sub-patterns of Anchor Store and Food-and-Beverage Store Stop

stop at food-and-beverage store(s) (47.6%)

not stop at food-and-beverage store(s) (52.4%)

and no anchor stores

and all anchor stores

and super-market

and dep

store(s)

nor at anchor stores

but at all

anchor stores

but at super-market

but at dep

store(s)

(26.5%) (4.2%) (9.0%) (7.8%) (21.7)% (6.6%) (15.1%) (9.0%) Sequence length 3 (N=27) 4.82% 0% 0% 0% 4.82% 0% 4.82% 1.81% Sequence length 4 (N=33) 6.63% 0% 1.81% 0% 6.02% 0.60% 3.61% 1.20% Sequence length 5 (N=34) 7.23% 0.60% 2.41% 0% 6.02% 1.20% 2.41% 0.60% Sequence length 6 (N=12) 1.81% 0% 0.60% 0% 1.20% 0.60% 1.81% 1.20% Sequence length 7 (N=17) 4.22% 0% 0% 1.81% 1.81% 0.60% 0.60% 1.20% Sequence length 8 (N=16) 0.0% 2.41% 2.41% 1.20% 0.60% 1.20% 0% 1.81% Sequence length 9 (N=10) 1.20% 0% 0.60% 1.81% 0% 0.60% 1.81% 0% Sequence length 10 (N=6) 0% 0% 0.60% 1.81% 0.60% 0.60% 0% 0% Sequence length 11 (N=4) 0% 0.60% 0% 1.20% 0.60% 0% 0% 0% Sequence length 12 (N=3) 0% 0.0% 0.60% 0% 0% 0.60% 0% 0.60% Sequence length 13 (N=1) 0% 0.60% 0% 0% 0% 0% 0% 0% Sequence length 14 (N=1) 0% 0% 0% 0% 0% 0% 0% 0.60% Sequence length 15 (N=1) 0.60% 0% 0% 0% 0% 0% 0% 0% Sequence length 17 (N=1) 0% 0% 0% 0% 0% 0.60% 0% 0%


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However, in general the majority of those shoppers stopped at food and beverage strores with department stores.

As mentioned in chapter 7 the department store in this mall covers four floors. Thus, it is possible that shoppers entered and exited the department store and switched the floor and again entered the department store on the other floors. There were 16.3% of the shoppers who made repetitive stops at the department store, 5.4% who made repetitive stops at food-and-beverage store (but a different one), and only one shopper in the data who made repetitive stops at the supermarket.

It is surprising that findings demonstrate that shoppers did not always visit the supermarket at the end of their sequence length. This may suggest that shoppers visited the supermarket not to buy heavy things, but light things that are still easy to carry with on the stops afterwards.

8.2 Results of Sequential Shopping Patterns

The software DANA developed by Joh et al. (2008) was used to calculate the similarity between the two-dimensional shopping patterns. This calculation implied consideration of the cross-sectional composition of different sequences, the sequential relationships among stops, and the interdependency relationships between the functional attribute and the spatial attribute of each stop. By comparing each shopping pattern against every other pattern, a distance matrix was calculated, which was then used as input to a cluster analysis aimed at identifying homogeneous groups of shopping sequences. A set of 166 shopping patterns implies 13695 pairwise comparisons of two-dimensional shopping patterns with a maximum of 248.00, a minimum of 0, and a mean value of 72.42. K-means cluster analysis was applied to classify the shopping patterns into homogeneous groups. This method aims at segmenting the data in such a way that the within-cluster variation is minimized. We selected K-means because this method is less affected by outliers (Mooi and Sarstedt, 2014). To have some indication of the number of clusters, first a scree diagram was generated for a varying number of clusters. The distinct break (elbow) will indicate the number of clusters at a greatly increased distance (e.g. Punji and Stewart, 1983; Mooi and Sarstedt, 2014). A descriptive analysis was used for profiling the clusters according to shopping characteristics (number of different floors visited and type of stops) and to examine the differences between clusters regarding the shopping pattern such as time duration in the mall and inside stores, ratio of stores-stop-with-purchasing to total of store stops, sub-patterns related to stop behavior, as well as sociodemographics


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(gender and number of accompanying people), day and time when shoppers visited the mall.

8.2.1 K-Means Clustering

Following the procedure described above, Figure 8.1 shows the scree diagram with a distinct break for two clusters. Particularly, in the figure the sharp increase in the distance occurs when switching from a single to a two clusters solution. Thus, in this study, two clusters were identified. As can be seen in Table 8.2, Cluster 1 consists of 35 shoppers (21.08%) and Cluster 2 consists of 131 shoppers (78.92%).

Figure 8.1 The Scree Diagram

8.2.2 Characteristics Clusters

Having decided on two clusters, we compared the clusters in terms of a set of characteristics. Table 8.4 presents the profiles of the clusters in terms of sequence length. It is found that individuals in Cluster 1 tend to make sequence length between 6 and 17. The distribution of sequence length is not uniform. For example, 28.5% of individuals tend to make sequence length 9, while 11.44% of the individuals tend to make sequence length smaller than 6 and larger than 12. The average sequence length for this cluster is 10.03. Compared to Cluster 1, Cluster 2 identifies individuals who tend to make short visits in the sense that their sequence length varies between 3 and 8. There are 71.75% of individuals who tend to make sequence length between 3 to 5, while 28.25% tend to make sequence length between 6 to 8. On average, individuals

0

5000000

10000000

15000000

20000000

25000000

1 2 3 4 5 6 7 8

dist

ance

Number of clusters


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Table 8.4 Profiles of Clusters

Cluster 1 N=35 (21.08%) Cluster 2 N=131 (78.92%)

N % N %

Sequence length sequence length 3 0 0% 27 20.61%

sequence length 4 0 0% 33 25.19%


sequence length 6 1 2.86% 11 8.40%


sequence length 8 7 20.00% 9 6.87%

sequence length 9 10 28.57% 0 0%







sequence length 16 0 0% 0 0%


Average sequence length 10.03 100% 4.89 100%

belonging to Cluster 2 have a sequence length of 4.89. In conclusion, these results indicate that one of the differences between the two clusters is that the first detects complex sequential shopping patterns, whereas the second cluster identifies relatively short sequential shopping patterns.Further analyses compared the clusters in terms of the number of different floors visited and type of stop. It is not a surprise that the characteristics of Cluster 1 which tend to have complex sequential shopping patterns also tend to have variety on visiting floors as well as high average numbers of stops. While the characteristics of Cluster 2 which relatively have short sequential shopping pattern tend to have limited variety on visiting floors and low average number of stops.

Figure 8.2 and 8.3 show the results of sequential movement patterns of each cluster based on shoppers’ movements. As can be seen in both figures the majority of movements occurred on the ground floor between 1st stop and 2nd stop. This is due to the fact that the majority of shoppers entered at the ground floor and only a small number entered at the lower ground floor and the upper ground floor. Moreover, shoppers tended to skip some floors on their movements upwards and downwards. As


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seen on Figure 8.2 shoppers in Cluster 1 (N=35) who made the maximum of 17 sequence lengths did not move around on the 2nd floor and the 3rd floor between the 2nd and 3rd stop. However, shoppers in this cluster made more movements on the 3rd floor compared to shoppers in Cluster 2 (N=131) who made the maximum of 8 seequence lengths (Figure 8.3).

Table 8.5 examines the clusters by number of different floors visited and types of stops. As seen inTable 8.5 shoppers in cluster 1 visited more different floors than cluster 2. In other words, shoppers in cluster 2 tend not to explore around the mall. We have two clusters that difference in their sequence length and there is also difference in the use of floors. But regarding to the stores and facilities visited the differences are small. It seems findings in terms of types of stop support the previous results: Cluster 2 which captures relatively short sequential shopping patterns elaborates on stopping at grocery and daily needs products of stores, while Cluster 1 which captures complex sequences elaborates on stopping at secondary needs (less important) products of stores.

Table 8.5 Clusters by Number of Different Floors Visited and Types of Stops

Cluster 1 N=35 (21.08%) Cluster 2 N=131 (78.92%)

N % N %

Number of different floors visited

1 floor 0 0.00% 15 11.45%

2 floors 3 8.57% 64 48.85%

3 floors 9 25.71% 36 27.48%

4 floors 10 28.57% 15 11.45%

5 floors 9 25.71% 1 0.76% 6 floors 4 11.43% 0 0.00%

Average number of different floors visited 4.06 2.41

Type of stop Supermarket stop 16 5.69% 43 11.38%

Department store stop 50 17.79% 39 10.32%

Retail store stop 101 35.94% 114 30.16%

Food-and-beverage stop 26 9.25% 65 17.20%

Entertainment stop 21 7.47% 12 3.17%

Facilities stop 30 10.68% 63 16.67%

Public spaces stop 37 13.17% 42 11.11%

Average number of stops 8.03 2.89


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Table 8.6 Comparison of Shopping Patterns between Clusters Cluster 1 Cluster 2 N=35 (21.08%) N=131 (78.92%)

Range of duration of mall visit in minutes 31 to 480 minutes 4 to 293 minutes Average duration of mall visit in minutes 135.74 minutes 70.39 minutes

Duration of mall visit Less than 30 minutes 0% 17.60%

30 minutes - 1 hour 22.90% 31.30%

1 hour - 2 hours 31.40% 41.20%

> 2 hours 45.70% 9.90%

Range of time for store stops 21 to 299 minutes 0 to 207 minutes

Average time for store stops 91.16 minutes 51 minutes

Average of store stops 6.97 stores 2.57 stores Ratio of store-stops-with-purchasing to total of store stops

0% 8.57% 12.98%

1%-25% 22.86% 7.63%

26%-50% 37.14% 18.32%

51%-99% 28.57% 11.45% 100% 2.86% 49.62% Sub-patterns related to stop behavior

stop at food-and-beverage and all anchor stores 8.57% 3.05%

stop at food-and-beverage but not at anchor store 8.57% 31.30%

stop at food-and-beverage and supermarket 11.43% 8.40%

stop at food-and-beverage and departmen store(s) 25.71% 3.05%

not stop at food-and-beverage, nor at anchor store 8.57% 25.19%

not stop at food-and-beverage but at all anchor stores 17.14% 3.82%

not stop at food-and-beverage but at supermarket 8.57% 16.79%

not stop at food-and-beverage but at departmen store(s) 11.43% 8.40%

Table 8.6 presents differences between the clusters by time spent in the mall, time spent in the stores, ratio of stops with a purchase, and sub-patterns. As seen in Table 8.6, Cluster 1 which tend to have complex sequences with large number of mixed store type visits consists of individuals who tend to spend on average long time in the mall and in the stores. While Cluster 2 which relatively have short shopping sequences consists of individuals who tend to spend on average short time in the mall and in the stores.

If we compared the store-stops-with-purchasing to the total number of stops, as expected, over 49.6% of individuals who were categorized in Cluster 2 tend to always


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Table 8.7 Comparison of Shoppers’ Profiles between Clusters Cluster 1 Cluster 2 N=35 (21.08%) N=131 (78.92%) Gender Male 22.9% 37.4% Female 77.1% 62.6% Number of accompanying persons 0 14.3% 40.5%

> 1 85.7% 59.5% Day Weekdays 74.3% 87% Weekend days 25.7% 13% Time Morning to afternoon (10:00 – 16:00) 45.7% 46.6% Afternoon to night (16:01 – 22:00) 54.3% 53.4%

purchase merchandise when they stopped, while only 2.9% of individuals who were categorized in Cluster 1 tend to always purchase on every stop they made (Table 8.6). The differences between clusters can be described by sub-patterns related to stop behavior. It can be seen Cluster 1 which tend to have complex sequences consists of individuals who tended to stop at food-and-beverage stores with combination of stop at anchor stores, while cluster 2 which tend to have short shopping sequences consists of individuals who tended not to stop at food-and-beverage stores combined with stops at anchor stores.

Table 8.7 compares the sociodemographic profiles of shoppers who were categorized in the clusters by examining their gender, number of accompanying persons, day and time of shoppers visited the mall during the survey. Shoppers who were categorized in cluster 1 had higher percentages of females than shoppers who were categorized in cluster 2. The majority of shoppers who were categorized in Cluster 2 (40.5%) concern to go alone to the mall, while 85.7% of shoppers who were categorized in Cluster 1 tend to go with accompany. Both clusters had a larger percentage of shoppers who visited the mall on weekdays. As for time and day, differences between the clusters are negligible.

8.3 Cluster Membership

In the previous section, we have classified the sequential shopping patterns into two clusters with regard to the sequential shopping patterns. To understand the effect of sociodemographic characteristics, day and time on the membership of clusters, a logistic regression analysis was applied.


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Table 8.8 Variables Used in Logistic Regression Analysis Variable Categories Gender Male Female Number of accompanying persons 0 > 1

Day Weekdays

Weekend days

Time Morning to afternoon (10:00 – 16:00)

Afternoon to night (16:01 – 22:00)

8.3.1 Logistic Regression Analysis

The dependent variable is a binary variable representing the clusters. The independent variables consist of dummy-coded sociodemographics, namely gender and number of accompanying persons, day of the week (weekdays versus weekends) and time of day (morning to afternoon vs. afternoon to night). Table 8.8 shows the categorization of the independent variables that were used in the analysis.

8.3.2 Results

Table 8.9 shows the estimation results of the logistic regression analysis. Cluster 2 is used as the base category. The value of Cox and Snell R2 is 11.2% and Nagelkerke R2 is 17.5%, showing a modesty strong relationship. The estimated model indicates that the intercept is not significant with p>0.10. However, the sociodemographic characteristics, day, and time of day are statistically significant, and the same applies to the interaction between day and time of the day. Keeping in mind that the first cluster represents the more complex shopping patterns, the results show that being male and alone is associated with lower odds to belonging to cluster 1.

Table 8.9 Parameter Estimates B SE Sig. Intercept 0.91 0.69 0.19 Gender Male -1.06 0.49 0.03 Number of accompany 0 (alone) -1.44 0.53 0.01 Day Weekdays -1.81 0.72 0.01 Time Morning to afternoon -1.76 0.95 0.06 Day x Time Weekdays and morning to afternoon 2.02 1.04 0.05

Reference category is cluster 2 Note: R2 = 0.112 (Cox and Snell), 0.175 (Nagelkerke)


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Regarding day of the week, the odds of shopping patterns observed for weekdays belonging to cluster 1 are smaller. It suggests that the tendency of becoming engaged in complex shopping patterns is higher for weekends. As for time of the day, the odds of belonging to cluster 1 are smaller when the mall visit is between the mornings to afternoon. However, when individuals visited the mall on weekdays during the morning to afternoon time window compensates in part for the combination of the two main effects.


This study contributes to a better understanding of shopping behavior, particularly as it relates to shopping sequences. To obtain insights in sequential shopping patterns we applied a multidimensional sequence alignment method (MDSAM). The method is used to measure the degree of (dis)similarity between pairs of shopping sequences and is followed by a k-means cluster analysis to classify clusters. In addition, we investigated the common sub-patterns in sequential shopping patterns.

The investigation about common sub-patterns in shoppers sequential shopping patterns examined type of stores visited, specifically the anchor stores and food-and-beverage stores. The results suggest that about half of the shoppers visit one of the two anchor stores. This indicates that the anchor stores have a primary role in this mall.

The cluster analysis addresses two distinct clusters according to sequential shopping patterns: types of stores visited and the floor which the store is located. As expected the two clusters suggest different shopping behavior and sociodemographic characteristics of shoppers. The largest cluster has shoppers with the smallest range of sequential length patterns, the shortest time of visiting the mall, and somewhat limited number of floors exploring. It is not surprising that shoppers in this cluster tend to have the highest ratio of store-stops with purchasing to total of store stops. Shoppers in this cluster have a tendency to visit more stores than eating places. The results suggest that the majority of shoppers in this mall are practical shoppers who visit the mall for buying particular products. The smallest cluster has shoppers with a large range of sequential length following with a large number of store and floors visits. Shoppers in this cluster tend to spent long time in the mall as well in the stores. In addition, shoppers in this cluster have tendency to combine shopping and eating. The results suggest that a small number of shoppers in this mall visits the mall not only for shopping but also eating and exploring the mall.


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Overall, the results of these analyses offer insight to better understand shoppers’ behavior regarding their sequential shopping patterns. A logistic regression analysis was conducted to analyze how selected sociodemographics, day of the week and time of day influence cluster membership. The results show significantly the shopping patterns differ by gender, number of accompanying persons, day of the week, and time of day. Specifically, there is a significant tendency for male shoppers and shoppers without accompany to be in the largest cluster. According to the time of the visit, the largest cluster also has a significant tendency for shoppers who come at the weekdays during morning to afternoon.

The findings of this study provide strong empirical support for understanding shoppers behavior with regard to sequential shopping patterns.

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9 CONCLUSIONS AND DISCUSSIONWith the growth of malls in the world, especially in Asia, understanding shopping behavior is an important thing for business practitioners and mall managers to formulate and execute effective development and marketing strategies. Previous research has shown that shopping in the shopping mall requires several decisions to consider. Before going shopping, which is called pre-shopping decisions, and while already at the shopping place, which is called mall-use shopping decisions. The pre-shopping decisions are made to choose which mall to go for shopping and to arrange what to do while shopping. The mall-use shopping decisions relate to shoppers’ decisions on stop and movement behavior in the shopping mall, such as to decide where to stop to visit stores or facilities, to plan which path to stroll and circulate in the mall, and to arrange the order or sequence of stop and movement behavior.

This final chapter draws together the conclusions of shopping behavior study regarding pre-shopping and mall-use shopping decisions in Jakarta shopping mall. At the outset of this study, a number of limitations were noted. Finally, this chapter discusses some suggestions that can be implemented by business practitioners, mall managers and retailers, as well as the future recommendations.

9.1 Conclusions

The purpose of this study was to examine individual shopping behavior regarding pre-shopping and mall-use shopping decisions. To develop an adequate understanding of the shopping behavior in the shopping mall, we proposed a comprehensive framework in explaining the pre-shopping and mall-use shopping decisions in the shopping malls. Our study on the literature review provided the impotant issues regarding pre-shopping and mall-use shopping decisions and some gaps in the research studies that needs to be investigated. In particular, literature shows most of the studies mainly focused on the United States and Europe and very few studies explored multi-story shopping malls. Moreover, studies which discuss shopping behavior in Asia, particularly in Indonesia, are still limited. For those reasons, in this thesis we investigated shopping behavior with regard to the pre-shopping and mall-use shopping decisions in Jakarta multi-story malls.

9 CONCLUSIONS AND DISCUSSION

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Indonesia shopping malls, specifically those located in Jakarta, were selected as the cases of this study since Jakarta has the most rapid malls’ evolution. Jakarta has witnessed a different shopping malls’ evolution compared to the Western world, therefore its shopping malls’ characteristics do not follow the Western malls’ categories. However, there is no literature which discussed these differences, nor recommended a suitable classification. Thus, before exploring the shopping behavior, Part I of this study filled the research gap by contributing a specific classification of Jakarta’s shopping malls. This study proposed five key attributes for classifying data of multi-story malls in Jakarta from 1961 up to 2010, including size of the malls, number of floors, feature of tenants, themes, and type of mall ownership. A Hierarchical Cluster Analysis classifies 106 shopping malls into three types of shopping malls, namely modern, local, and classic shopping mall. In this study three malls which represent three types of malls in South Jakarta were selected as the basis of our data collection in examining pre-shopping decisions.

Part II in this study explored the pre-shopping decisions behavior. The purpose of this part was to investigate shoppers’ considerations before going to a shopping mall. A survey was conducted among shoppers who just finished their shopping at three types of shopping malls to investigate shoppers’ motivations to visit the mall, factors to take into consideration in choosing the mall, shoppers’ evaluation of the mall’s image dimensions, and shoppers’ sociodemographics background. The evaluation of the mall’s image dimensions in this study involved with location and convenience, store variety, merchandise selection and quality, price, advertisement and promotion, atmospherics (mall comfort and visual appearance, space arrangement, quality of facilities), personal service, and social environment. In addition, shoppers also evaluated the overall evaluation of each mall dimensions. The collected data set consists of 218 respondents in the local shopping mall, 225 respondents in the modern shopping mall, and 227 respondents in the classic shopping mall. Chapter 4 focused on the needs of shoppers and the specific needs shoppers wish to fulfill when visiting the malls. Descriptive analyses were applied to examine the sociodemographic backgrounds of shoppers, shoppers’ motivations to visit the malls, and their reasons to choose the malls. While chapter 5 aimed to understand the features of shopping mall which significantly influence shopping malls choice. The investigation on shoppers’ evaluations of mall’s dimensions applied descriptive analyses to describe the shoppers’ evaluations on each mall and t-test to examine significant shopping mall’s dimensions as well as to inspect whether shoppers’ evaluations show systematic differences between the three types of malls.


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The results in chapter 4 indicate that each type of malls had different sociodemographic backgrounds of shoppers. Moreover, shoppers in each mall differ in their motives to visit the mall and their consideration factors behind the mall choice. These results support the literature which showed that different mall classifications led to different target of shoppers. The shoppers’ evaluations of mall’s dimensions were examined separately for each type of malls in chapter 5. The findings show that shoppers’ evaluation of mall’s dimensions were not significantly different in each mall regarding overall evaluation of social environment. In the same way, trip to the mall, quality of food and beverage stores, helpfulness and friendliness of greeters/receptionists, helpfulness and friendliness of security services, and helpfulness of customer services were not significantly different in each mall. However, evaluation of mall’s dimensions was significantly different for each type of malls, particularly in terms of accessibility by motorcycle, variety of leisure facilities, attractiveness of architecture design, signs and decorations in the public spaces, easiness to find a praying room, cleanliness and quality of the praying room, quality, cleanliness, and odor in the toilets, and number of public seats. Differences may be due to the fact that the malls in this study offer a clear contrast on mall’s attributes related to the eight malls’ dimensions.

Part III explored the mall-use decisions behavior. Shoppers’ shopping behavior inside the shopping malls was studied. Chapter 6 aimed to investigate tangible and intangible conditions which attract specific patterns of behavior and to examine shopping behavior patterns inside the mall. The data was collected through a survey by asking shoppers in three types of shopping malls about the names of the stores they visited, as well as the total expenditure (in food and beverages and non-food-beverages) and duration of time spent in the malls. The main focus of the study in this dissertation is on sequential shopping behavior because this is an important part in shopping process which has not been explored in retailing studies. Chapter 7 and 8 attempted to investigate the sequential shopping behavior and its relationship with shoppers’ sociodemographic backgrounds. The data in these chapters was collected through tracking/ observation to get comprehensive and accurate information about shopping behavior inside a multi-story classic shopping mall. The tracking was an unobtrusive observation meaning there was no contact or communication between the observers and the shoppers. Shoppers were tracked randomly starting from when they entered the mall until they exited the mall. The data recorded all shopping activities including stop behavior and movement pattern, time duration, as well as locations or positions where the actions happened.


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Although women and younger people make up the majority of the clientele of the malls, differences were found which can be largely explained by the specific products and stores, activity, and accessibility/location of the mall. The findings in chapter 6 offer important information with regard to differences of shopping behavior in three types of shopping malls. The results demonstrate that shoppers had different shopping behavior in each type of malls related to their time duration inside the shopping malls and expenses. Specifically, in terms of time duration findings in this study show a big difference compared to the studies in the Western malls. The findings suggest that on average shoppers who stayed the longest in the mall, did not spend the highest amount of money. This study also shows that number of store visits did not always increase time spent in the mall. This means, while the mall was successful to make shoppers getting around and visiting the stores, it does not mean that these shoppers were spending money. In all malls, apparel-and-accessories, food, and eating-places were three types of stores which always had the highest visits. To distinguish shoppers’ store visits behavior patterns a Hierarchical Cluster Analysis was applied. Four types of shopping styles were suggested regarding the most likely stores visited: the grocery shoppers who mainly visited the food type of stores, the fashion shoppers who were highly interested in visiting the apparel-and-accessories type of stores, the social shoppers who mainly visited the eating-places type of stores, and the recreational shoppers who most likely visited the media-and-special-interest type of stores and some different combination of other types of stores. All types of shopping malls had every types of shoppers’ shopping styles. However, each type of malls has different shoppers’ shopping styles majority. These findings enhance the understanding that shoppers had preferences regarding specific of stores when visiting a type of malls, accordingly mall managers in each type of shopping malls could improve the store variety related to their shoppers.

Focusing on sequential shopping behavior, specifically on shoppers’ stop behavior and movement pattern inside the mall, the findings in Chapter 7 demonstrate that shoppers’ highest activity inside the mall was visiting stores and facilities. Furthermore, since this study was taken in a multi-story mall, shoppers also switched floors to visit store or facilities. The results indicate the highest activity took place on the ground floor where the gates were located. The higher the floor the smaller the number of the visits. These results support the previous findings that the shoppers most likely stroll close to the floor where the gates are located (see e.g. Dolan et al., 2006; Kerr et al., 2001). As in this study we applied unobtrusive observation to collect the data, thus data about shoppers’ sociodemographics was only based on visuals such as gender and the number of companion that shoppers had, day of week, and time of day when the shoppers


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visited the mall. The analyses on sociodemographics indicate shoppers with accompany spend longer than those without accompany. Regarding the most popular store visits our findings in the sequence patterns demonstrate a similar results like in chapter 6 that shoppers made the highest number of apparel-and-accessories store visits, following with eating-places visits. An interesting finding is that the number of stores on each floor had no relation with the number of visits. This result may be explained by the fact that stores in the mall provide different products and arrangement.

A sequential measurement analysis was employed in Chapter 8 to analyze the data observed by means of tracking. Two different clusters varied regarding shoppers’ sequence lengths patterns behavior by using k-means cluster analysis. The profiles of the clusters demonstrate that shoppers in the clusters were different compared to the ways they changed floors, the total number of store visits, the purchasing behavior, and the duration of stay in the mall. More specifically, the cluster with the largest membership have the smallest range of sequential length patterns, the shortest time of visiting the mall and of spending time in the stores, and somewhat limited number of floors exploring. Moreover, shoppers in this cluster have the highest ratio of store-stops with purchasing to total of store stops. In general, therefore results suggest that the largest cluster presents a type of practical shoppers who visits the mall for a shopping purpose (buying products). On the contrary, the other cluster has the smallest membership. The shoppers in this cluster have a large range of sequential length following with a large number of stores and floors visit. Shoppers in this cluster tend to spent long time in the mall and in the stores. The results of this cluster presents a type of shoppers who visits the mall not only for shopping but also eating and exploring the mall. These results are consistent with those of other studies and suggest that shoppers with short shopping experiences in the mall make more transactions than shoppers with long shopping experiences (Brown, 1992). Furthermore, the findings demonstrate that shoppers who visit anchor stores seem to have a larger number than other stores. This suggests that the anchor stores have a primary role in this mall.

The purpose of this study was to examine the relationship between shoppers’ sociodemographics and shopping behavior patterns particularly on sequential behavior. Results suggest that there was no relation between number of stores on each floors and store visits’ pattern within the same floor. Nevertheless, a high number of passing shoppers on a floor showed high visits on stores, it did not give an assurance that shoppers did purchasing. Using a logistic regression, we analyzed the effect of gender, number of accompany, day and time on the membership of clusters. Our


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findings show that the shopping patterns were significantly differed by gender, number of accompanying persons, day of week, and time of day. Specifically, there was a significant tendency for male shoppers and shoppers without accompany to be in the cluster with the largest membership, the type of practical shoppers. The shoppers in the largest cluster tend to visit the mall in weekdays during the morning to afternoon period.

9.2 Limitations

While investigating pre-shopping decisions and shoppers’ shopping styles, this study examined three types of shopping malls. However, the respondents in each of the malls were not the same. Different respondents were examined for different types of malls. This means the assessments of the mall’s dimensions in the results were not evaluated from the same perspectives. Therefore, the results of evaluations of mall’s dimensions may not be transferable to generalize shopping malls in Indonesia. In future research respondents should be asked to evaluate a number of different types of shopping malls to get more insight into the influence of the malls’ attributes on the evaluations.

Another limitation is with the sequence behavior data. When we recorded the sequence of stop behavior data not all stores in the mall had visitors. Therefore, we made only seven stop categories related to stop at stores and facilities. This means this study has a limitation on answering questions regarding the perfect tenant mix. This is an important issues for future work. In addition, the tracking on sequence of shopping behavior in this study was collected in only one shopping mall due to the permit constrains.

By using unobtrusive tracking our study on sequential shopping patterns had lack of contact with the respondents during the observation. Therefore, analyses on the sequential shopping patterns was unable to examine sociodemographics profiles, except gender and number of company. Consequently, it also set a limit on understanding the effects of sociodemographics on shoppers’ behavior.

9.3 Contributions and Managerial Implications

The contribution of this PhD study is two-fold. First, very few studies have adopted this general, integrated conceptual framework before. Second, compared to the number of


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studies in the United States and Europe, studies about shopping mall choice and behavior inside those malls in Asia are still very limited, especially in Indonesia.

This research suggests Indonesian shopping mall classifications, which could benefit the mall industry and assist the retail real estate professionals working in Indonesia, particularly in setting up marketing and management strategies. For business practitioners the three types of Jakarta malls generate specific market segments in each mall. Findings represent different sociodemographic profiles in each shopping mall, which could sharpen the focus of target market in each type of malls.

This research has proven that shoppers evaluated mall’s dimensions differently in each type of shopping malls. Furthermore, this study can identify the strengths and weaknesses regarding shopping mall’s dimensions in each mall. The findings also help business practitioners and mall managers to formulate strategic plans to improve shopping mall’s dimensions in particular about location and convenience, store variety, merchandise selection and quality, price, advertisement and promotion, atmospherics, personal service, and social environment dimensions. This brings useful implications for business practitioners and mall managers in delivering their service regarding mall’s dimensions and maintaining a high level of shopper evaluation. The mall managers of each type of shopping malls in this study can utilize the results to answer the needs of their business’ target markets.

The cases in this study were collected in Jakarta shopping malls. Therefore, the findings may act as a basis in developing shopping mall’s dimensions in Indonesia. Shoppers’ evaluation of the attributes of Indonesian mall’s dimensions had sharp contrasts between one another regarding accessibility by motorcycle, variety of leisure facilities, attractiveness of architecture design, signs and decorations in public spaces, easiness to find praying room, cleanliness and quality of praying room, quality, cleanliness, and odor in the toilets, and number of public seats. To wider their market business practitioners and mall managers can give first priority to these dimensions.

As shown in this dissertation, the type of store visits can yield shoppers’ preferences in visiting stores and preferences of segments of shoppers. The preferences in visiting stores and segments appear to call for strategic management initiatives. Given the fact that each type of malls has got a specific shopping style, results suggest that marketing practitioners and mall developers could begin searching for the commonalities among the shoppers’ shopping styles. Therefore, the mall development design may address the needs of ideal type of stores and their target market’s sociodemographics.


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The findings in this study demonstrate that the stop behavior which is related with store visits appeared to be favored in accordance to the floors. In a multi-story mall the higher the floor, the fewer shoppers visit. This point could be considered in designing a multi-story shopping mall. Business practitioners and mall managers can use these results to arrange compositions of stores which attract shoppers to the higher floor. Chapter 8 related to the difference between two clusters regarding shoppers’ sequential behavior; shoppers who explore the mall and visit many stores, but without many purchases and shoppers who have short time, not to visit many stores, but to have many purchases. From a managerial point of view, it might be of interest that the majority of shoppers in the case study are from the second cluster - shoppers who tend to have short time, not to visit many stores, but to have many purchases. Thus, mall managers could focus to identify and target this kind of shoppers.

Although the findings in this study are not completely new and most likely are similar to the previous studies, the findings could assure business practitioners, mall managers, and retailers of the better understanding of Asian shopping behavior, particularly Jakarta. This study provides insightful understanding regarding shoppers’ behavior inside a multi-story shopping mall, some challenges for future research remain. A key challenge is to collect the data. It is recommended that further research investigates larger number of respondents with similar characteristics and evaluates data on different types of malls. A sample representative of the general population is suggested to provide more generalizability.

Future studies in a multi-story mall could evaluate the progress towards mall-use shopping decision, specifically about sequence behavior. In this study, the data showed a contrast in which some stores had high number of visits while similar types of stores had none. There are two things to improve in the future study. First, future study should make more observations. Second, future study could expore reasons for the phenomenon why a store had less store visits. For example, investigating the relationship between store visits and the configuration of the mall and between store visits and stores’ brands and characteristics such as the size of stores.

In the future, mall-use shopping decisions could be further investigated by getting the shoppers’ strolling distance and exact routes leading to their purchase. With this intention the further study could search for an appropriate technology advancement that could provide such ways to gather shoppers’ data. It is strongly recommended that further study investigate sociodemographics information which may include age,


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occupation, income, relationship with companion(s), and purchase intentions. This additional data will enhance data in sequence behavior, resulting in better profiles and analyses.

Finally, future research in this field should be conducted throughout Asia to address the issue of limited literature regarding Asian shoppers. Extensive respondent reports will provide better descriptions of the characteristics of Asian shoppers.

REFERENCES

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REFERENCES

Ahmed, Z. U., Ghingold, M., & Dahari, Z. (2007). Malaysian shopping mall behavior: an exploratory study. Asia Pacific Journal of Marketing and Logistics, 19(4), 331-348.

Allard, T., Babin, B. J., & Chebat, J. C. (2009). When income matters: Customers evaluation of shopping malls’ hedonic and utilitarian orientations. Journal of Retailing and Consumer Services, 16(1), 40-49.

Andreu, L., Bigné, E., Chumpitaz, R., & Swaen, V. (2006). How does the perceived retail environment influence consumers' emotional experience? Evidence from two retail settings. International Review of Retail, Distribution and Consumer Research, 16(5), 559-578.

Anselmsson, J. (2006). Sources of customer satisfaction with shopping malls: a comparative study of different customer segments. International Review of Retail, Distribution and Consumer Research, 16(1), 115-138.

Anselmsson, J., & Johansson, U. (2007). Corporate social responsibility and the positioning of grocery brands: An exploratory study of retailer and manufacturer brands at point of purchase. International Journal of Retail and Distribution Management, 35(10), 835-856.

Areni, C. S., & Kim, D. (1994). The influence of in-store lighting on consumers' examination of merchandise in a wine store. International Journal of Research in Marketing, 11(2), 117-125.

Arentze, T. A., Oppewal, H., & Timmermans, H. J. (2005). A multipurpose shopping trip model to assess retail agglomeration effects. Journal of Marketing Research, 42(1), 109-115.

Baker, J. (1986). The role of the environment in marketing services: The consumer perspective. The services challenge: Integrating for competitive advantage, 1(1), 79-84.

Baker, J., Grewal, D., & Parasuraman, A. (1994). The influence of store environment on quality inferences and store image. Journal of the Academy of Marketing Science, 22(4), 328-339.

REFERENCES

183

Baker, J., Parasuraman, A., Grewal, D., & Voss, G. B. (2002). The influence of multiple store environment cues on perceived merchandise value and patronage intentions. Journal of Marketing, 66(2), 120-141.

Baker, S. M. (2006). Consumer normalcy: Understanding the value of shopping through narratives of consumers with visual impairments. Journal of Retailing, 82(1), 37-50.

Bakewell, C., & Mitchell, V. W. (2004). Male consumer decision-making styles. The International Review of Retail, Distribution and Consumer Research, 14(2), 223-240.

Ballantine, P. W., Jack, R., & Parsons, A. G. (2010). Atmospheric cues and their effect on the hedonic retail experience. International Journal of Retail & Distribution Management, 38(8), 641-653.

Ballantine, P. W., Parsons, A., & Comeskey, K. (2015). A conceptual model of the holistic effects of atmospheric cues in fashion retailing. International Journal of Retail & Distribution Management, 43(6), 503-517

Beatty, S. E., & Ferrell, M. E. (1998). Impulse buying: Modeling its precursors. Journal of Retailing, 74(2), 169-191.

Bellizzi, J. A., & Hite, R. E. (1992). Environmental color, consumer feelings, and purchase likelihood. Psychology & Marketing, 9(5), 347-363.

Bitner, M. J. (1992). Servicescapes: the impact of physical surroundings on customers and employees. The Journal of Marketing, 57-71.

Bloch, P. H., Ridgway, N. M., & Dawson, S. A. (1994). The shopping mall as consumer habitat. Journal of Retailing, 70(1), 23-42.

Borges, A., Chebat, J. C., & Babin, B. J. (2010). Does a companion always enhance the shopping experience?. Journal of Retailing and Consumer Services, 17(4), 294-299.

Borgers, A., & Timmermans, H. (1986a). A Model of Pedestrian Route Choice and Demand for Retail Facilities within Inner-City Shopping Areas. Geographical Analysis, 18(2), 115-128.

Borgers, A., & Timmermans, H. J. P. (1986b). City centre entry points, store location patterns and pedestrian route choice behavior: A microlevel simulation model. Socio-economic Planning Sciences, 20(1), 25-31.

Borgers, A. W. J., Kemperman, A. D. A. M., Timmermans, H. J., Zhu, W., Joh, C., Kurose, S., Saarloos, D. & Zhang, J. (2008, May). Alternative ways of measuring activities and

REFERENCES

184

movement patterns of transients in urban areas: International experiences. In Proceedings ICTSC Conference, Annecy, France (pp. 5-25).

Borgers, A., Kemperman, A., & Timmermans, H. (2009). Modeling pedestrian movement in shopping street segments. In Timmermans, H. (Eds.), Pedestrian behavior: models, data collection and applications (pp. 87-111). Bingley, UK: Emerald Group Publishing Limited.

BPS Provinsi DKI Jakarta. (2010). Population Density in Jakarta’s District 2010 [report data]. Retrieved May 20, 2011: http://www.jakarta.go.id/jakv1/

Brengman, M., Willems, K., & Joye, Y. (2012). The impact of in-store greenery on customers. Psychology & Marketing, 29(11), 807-821.

Brown, S. (1991a). Tenant placement in planned shopping centres: implications of an observation survey. Journal of Property Research, 8(2), 179-187.

Brown, S. (1991b). Shopper circulation in a planned shopping centre. International Journal of Retail & Distribution Management, 19(1).

Brown, S. (1992). Tenant mix, tenant placement and shopper behavior in a planned shopping centre. Service Industries Journal, 12(3), 384-403.

Cachon, G. P., & Feldman, P. (2015). Price commitments with strategic consumers: Why it can be optimal to discount more frequently… than optimal. Manufacturing & Service Operations Management, 17(3), 399-410.

Cai, Y., & Shannon, R. (2012). Personal values and mall shopping behavior: The mediating role of attitude and intention among Chinese and Thai consumers. Australasian Marketing Journal (AMJ), 20(1), 37-47.

Cao, X., Handy, S. L., & Mokhtarian, P. L. (2006). The influences of the built environment and residential self-selection on pedestrian behavior: evidence from Austin, TX. Transportation, 33(1), 1-20.

Carter, C. C., & Haloupek, W. J. (2002). Dispersion of stores of the same type in shopping malls: theory and preliminary evidence. Journal of Property Research, 19(4), 291-311.

Chan, O. Y., & 陳靄恩. (2015). An analytical study of the landscaping and greenery in shopping centers of Hong Kong. (Theses Online). Hong Kong University, Hong Kong.

REFERENCES

185

Chang, E., Burns, L. D., & Francis, S. K. (2004). Gender differences in the dimensional structure of apparel shopping satisfaction among Korean consumers: The role of hedonic shopping value. Clothing and Textiles Research Journal, 22(4), 185-199.

Chebat, J. C., & Michon, R. (2003). Impact of ambient odors on mall shoppers' emotions, cognition, and spending: A test of competitive causal theories. Journal of Business Research, 56(7), 529-539.

Chebat, J. C., Gélinas-Chebat, C., & Therrien, K. (2005). Lost in a mall, the effects of gender, familiarity with the shopping mall and the shopping values on shoppers' wayfinding processes. Journal of Business Research, 58(11), 1590-1598.

Chebat, J. C., Sirgy, M. J., & St-James, V. (2006). Upscale image transfer from malls to stores: A self-image congruence explanation. Journal of Business Research, 59(12), 1288-1296.

Chebat, J. C., Morrin, M., & Chebat, D. R. (2009). Does age attenuate the impact of pleasant ambient scent on consumer response? Environment and Behavior, 41(2), 258-267.

Chebat, J. C., Sirgy, M. J., & Grzeskowiak, S. (2010). How can shopping mall management best capture mall image?. Journal of Business Research, 63(7), 735-740.

Chebat, J. C., Haj-Salem, N., & Oliveira, S. (2014). Why shopping pals make malls different?. Journal of Retailing and Consumer Services, 21(2), 77-85.

Christaller, W. (1966). Central Places in Southern Germany. (Baskin, C.W., Translator), Englewood Cliffs, NJ.: Prentice-Hall. (Original work published 1933)

City Map of Jakarta. (n.d.). Retrieved May 20, 2011 from Wikipedia: http://en.wikipedia.org/wiki/Jakarta

Cobb, C. J., & Hoyer, W. D. (1986). Planned versus impulse purchase behavior. Journal of Retailing, 62(4), 384-409

Coleman, P. (2006). Shopping Environments : Evolution, Planning, and Design. Oxford: Architectural Press.

Cox, A. D., Cox, D., & Anderson, R. D. (2005). Reassessing the pleasures of store shopping. Journal of Business Research, 58(3), 250-259.

Craig, C. S., Ghosh, A., & McLafferty, S. (1984). Models of the retail location process-A Review. journal of Retailing, 60(1), 5-36.

http://en.wikipedia.org/wiki/Jakarta

REFERENCES

186

Dabholkar, P. A., Thorpe, D. I., & Rentz, J. O. (1995). A measure of service quality for retail stores: scale development and validation. Journal of the Academy of Marketing Science, 24(1), 3-16.

Dawson, S., Bloch, P. H., & Ridgway, N. (1990). Shopping Motives, Emotional States, and. Journal of Retailing, 66, 4.

Davis, L., & Hodges, N. (2012). Consumer shopping value: An investigation of shopping trip value, in-store shopping value and retail format. Journal of Retailing and Consumer Services, 19(2), 229-239.

Dellaert, B., Borgers, A., & Timmermans, H. (1995). A day in the city: Using conjoint choice experiments to model urban tourists' choice of activity packages. Tourism Management, 16(5), 347-353.

DeLisle, J. R. (2007). Shopping Center Classifications: Challenges and Opportunities. Retrieved February 28, 2011, from http://jrdelisle.com/research/NewSCDef_V23_WP1.pdf

DeLisle, J. R. (2009, February). ICSC Research. Retrieved from Toward the Global Classification of Shopping Centers: http://www.icsc.org/srch/rsrch/wp/GlobalRetailClass_Feb2009.pdf

Dennis, C., Murphy, J., Marsland, D., Cockett, T., & Patel, T. (2002). Measuring image: shopping centre case studies. The International Review of Retail, Distribution and Consumer Research, 12(4), 355-373.

Dennis, C. (2005). Objects of desire: Consumer behavior in shopping centre choices. New York, NY: Palgrave Macmillan.

De Nisco, A., & Warnaby, G. (2014). Urban design and tenant variety influences on consumers' emotions and approach behavior. Journal of Business Research, 67(2), 211-217.

de W. Bruwer, J. (1997). Solving the ideal tenant mix puzzle for a proposed shopping centre: a practical research methodology. Property Management,15(3), 160-172.

Dholakia, R. R. (1999). Going shopping: key determinants of shopping behaviors and motivations. International Journal of Retail & Distribution Management, 27(4), 154-165.

http://www.icsc.org/srch/rsrch/wp/GlobalRetailClass_Feb2009.pdf

REFERENCES

187

Dick H. W. and Rimmer, P.J. (1998). Beyond the Third World City: The New Urban Geography of South-east Asia. Urban Studies, Vol. 35, No. 12, p.2303-2321

Dijkstra, J., Jessurun, J., de Vries, B., & Timmermans, H. J. P. (2006). Agent architecture for simulating pedestrians in the built environment. InInternational Workshop on Agents in Traffic and Transportation (pp. 8-15).

Dolan, M. S., Weiss, L. A., Lewis, R. A., Pietrobelli, A., Heo, M., & Faith, M. S. (2006). ‘Take the stairs instead of the escalator’: effect of environmental prompts on community stair use and implications for a national ‘Small Steps’ campaign. Obesity Reviews, 7(1), 25-32.

Donovan, Robert. J. and Rossiter, John. R. (1982). Store atmosphere: an environmental psychology approach. Journal of Retailing, 58(1), 34-57.

Donovan, R. J., Rossiter, J. R., Marcoolyn, G., & Nesdale, A. (1994). Store atmosphere and purchasing behavior. Journal of Retailing, 70(3), 283-294.

El-Adly, M. I. (2007). Shopping malls attractiveness: a segmentation approach. International Journal of Retail & Distribution Management, 35(11), 936-950.

El Hedhli, K., Chebat, J. C., & Sirgy, M. J. (2013). Shopping well-being at the mall: Construct, antecedents, and consequences. Journal of Business Research, 66(7), 856-863.

Field, A. (2005). Discovering Statistics Using SPSS. London: Sage Publications.

Finn, A. (1989). Consumer acceptance of unobtrusive observation in a shopping centre. Enhancing Knowledge Development in Marketing, American Marketing Association, Chicago, 176-181.

Finn, A., & Louviere, J. J. (1996). Shopping Center Image, Consideration, and Choice : Anchor Store Contribution. Journal of Business Research , 241-251.

Fiore, A. M., & Ogle, J. P. (2000). Facilitating Students' Integration of Textiles and Clothing Subject Matter Part One: Dimensions of a Model and a Taxonomy. Clothing and Textiles Research Journal, 18(1), 31-45.

Frasquet, M., Gil, I., & Molla, A. (2001). Shopping-centre selection modelling: a segmentation approach. The International Review of Retail, Distribution and Consumer Research, 11(1), 23-38.

REFERENCES

188

Gärling, T., Biel, A., & Gustafsson, M. (1998). Different kinds and roles of environmental uncertainty. Journal of Environmental Psychology, 18(1), 75-83.

Gilbert, D. 2003. Retail marketing management. Harlow: Financial Times Prentice Hall.

Gilboa, S. (2009). A segmentation study of Israeli mall customers. Journal of Retailing and Consumer Services, 16(2), 135-144.

Gilboa, S., & Vilnai-Yavetz, I. (2010). Four generations of mall visitors in Israel: A study of mall activities, visiting patterns, and products purchased. Journal of Retailing and Consumer Services, 17(6), 501-511.

Gilboa, S., & Vilnai-Yavetz, I. (2013). Shop until you drop? An exploratory analysis of mall experiences. European Journal of Marketing, 47(1/2), 239-259.

Ghosh, P., Tripathi, V., Saini, S., & Agrawal, S. (2010). Shopping orientation and behavioral patterns of Indian consumers: study of a Tier II city. International Journal of Services, Economics and Management, 2(2), 121-136.

Grace, D., & O’Cass, A. (2005). An examination of the antecedents of repatronage intentions across different retail store formats. Journal of Retailing and Consumer Services, 12(4), 227-243.

Graves, P. (2013). Consumerology, New Edition: The Truth about Consumers and the Psychology of Shopping. London: Nicholas Brealey Publishing.

Grewal, D., Krishnan, R., Baker, J., & Borin, N. (1998). The effect of store name, brand name and price discounts on consumers' evaluations and purchase intentions. Journal of retailing, 74(3), 331-352.

Guy, C. M. (1998). Classification of retail stores and shopping centres: some methodological issues. GeoJournal, 45, 255-264.

Halper, E.B. (1991). Tenant build supermarket leases (Part 2). Real Estate Review, Summer, pp. 72-81.

Hart, C., Stachow, G., & Cadogan, J. W. (2013). Conceptualising town centre image and the customer experience. Journal of Marketing Management, 29(15-16), 1753-1781.

Hartline, M. D., & Ferrell, O. C. (1996). The management of customer-contact service employees: an empirical investigation. The Journal of Marketing, 52-70.

Hartono, R. (2009). Jabodetabek Mall Directory 2009. (Y. W.Utomo, Ed.) Jakarta: APPBI DPD DKI.

REFERENCES

189

Haytko, D. L., & Baker, J. (2004). It’s all at the mall: exploring adolescent girls’ experiences. Journal of Retailing, 80(1), 67-83.

Henderson, J. C., Chee, L., Mun, C. N., & Lee, C. (2011). Shopping, tourism and retailing in Singapore. Managing Leisure, 16(1), 36-48.

Helgesen, Ø., & Nesset, E. (2010). Gender, store satisfaction and antecedents: a case study of a grocery store. Journal of Consumer Marketing, 27(2), 114-126.

Herlambang, S. (2009). Profil Proyek Super Development di Jakarta : studi indikasi maksimalisasi produksi dan penggunaan ruang. Between Sustainable and Super Development. Jakarta.

Hill, M. R. (1982). Spatial structure and decision-making aspects of pedestrian route selection through an urban environment. (Doctoral Dissertation). University of Nebraska, Lincoln.

Hirsch, A. R. (1995). Effects of ambient odors on slot-machine usage in a Las Vegas casino. Psychology & Marketing, 12(7), 585-594.

Hirsch, J., Segerer, M., Klein, K., & Wiegelmann, T. (2016). The analysis of customer density, tenant placement and coupling inside a shopping centre with GIS. Journal of Property Research, 33(1), 37-63.

Howard, E. (1997). The management of shopping centres: conflict or collaboration?. The International Review of Retail, Distribution and Consumer Research, 7(3), 263-285.

Hu, H., & Jasper, C. R. (2004). Men and women: a comparison of shopping mall behavior. Journal of Shopping Center Research, 11(1), 113-131.

Huff, D. L. (1963). A probabilistic analysis of consumer spatial behavior. American Marketing Association.

Ibrahim, M., & Wee, N. (2002). The importance of entertainment in the shopping center experience: evidence from Singapore. Journal of Real Estate Portfolio Management, 8(3), 239-254.

Isaacson, M., & Shoval, N. (2006). Application of tracking technologies to the study of pedestrian spatial behavior. The Professional Geographer, 58(2), 172-183.

REFERENCES

190

Jackson, V., Stoel, L., & Brantley, A. (2011). Mall attributes and shopping value: Differences by gender and generational cohort. Journal of Retailing and Consumer Services, 18(1), 1-9.

Jarboe, G. R., & McDaniel, C. D. (1987). A profile of browsers in regional shopping malls. Journal of the Academy of Marketing Science, 15(1), 46-53.

Jhamb, D., & Kiran, R. (2012). Trendy shopping replacing traditional format preferences. African Journal of Business Management, 6(11), 4196.

Joh, C. H., Arentze, T. A., & Timmermans, H. J. (2001). Multidimensional Sequence Alignment Methods for Activity-Travel Pattern Analysis: A Comparison of Dynamic Programming and Genetic Algorithms. Geographical Analysis, 33(3), 247-270.

Joh, C. H., Arentze, T., Hofman, F., & Timmermans, H. (2002). Activity pattern similarity: a multidimensional sequence alignment method. Transportation Research Part B: Methodological, 36(5), 385-403.

Joh, Chang-Hyeon (2004), Measuring and Predicting Adaptation in Multidimensional Activity-Travel Patterns. (Doctoral dissertation). Technische Universiteit Eindhoven, Eindhoven

Joh, C.H., Arentze, T.A., Timmermans, H.J.P. (2008). DANA: Dissimilarity Analysis of Activity-Travel Patterns. Paper presented at EIRASS, Eindhoven University of Technology. Eindhoven, The Netherlands

Jones, M. A. (1999). Entertaining shopping experiences: an exploratory investigation. Journal of Retailing and Consumer Services, 6(3), 129-139.

Kadin-Indonesia /Indonesian Chambers of Commerce & Industry. (February, 2009). Retrieved October 27, 2010, from PP No.112/2007 Penataan & Pembinaan Pasar Tradisional, Pusat Perbelanjaan & Toko Modern: http://www.kadin-indonesia.or.id/enm/images/dokumen/KADIN-137-3613-11022009.pdf

Kemperman, A. D., Borgers, A. W., & Timmermans, H. J. (2009). Tourist shopping behavior in a historic downtown area. Tourism management, 30(2), 208-218.

Kerr, J., Eves, F. F., & Carroll, D. (2001). The influence of poster prompts on stair use: The effects of setting, poster size and content. British Journal of Health Psychology, 6(4), 397-405.

http://www.kadin-indonesia.or.id/enm/images/dokumen/KADIN-137-3613-11022009.pdf

http://www.kadin-indonesia.or.id/enm/images/dokumen/KADIN-137-3613-11022009.pdf

REFERENCES

191

Khare, A. (2011). Mall shopping behavior of Indian small town consumers. Journal of Retailing and Consumer Services, 18(1), 110-118.

Khare, A., Pandey, S. K., & Bhardwaj, P. (2014). Impact of Culture, Cosmopolitanism, and Price on Local Store Loyalty: An Empirical Study from India. Journal of International Consumer Marketing, 26(3), 185-200.

Kim, Y. K., Kang, J., & Kim, M. (2005). The relationships among family and social interaction, loneliness, mall shopping motivation, and mall spending of older consumers. Psychology & Marketing, 22(12), 995-1015.

Kim, H. Y., & Kim, Y. K. (2008). Shopping enjoyment and store shopping modes: the moderating influence of chronic time pressure. Journal of Retailing and Consumer Services, 15(5), 410-419.

Kingston, B. (1994). Basket, bag, and trolley: a history of shopping in Australia. Melbourne, Australia : Oxford University Press.

Knack, R. E. (2000). Retail vs. e-tail. Planning, 66(7).

Konishi, H., & Sandfort, M. T. (2002). Expanding demand through price advertisement. International Journal of Industrial Organization, 20(7), 965-994.

Koolhaas, R., Chuihua, J. C., Inaba, J., & Tsung Leong, S. (Eds.) (2002). Harvard Design School Guide to Shopping. Köln: Taschen

Kotler, P. (1973). Atmospherics as a marketing tool. Journal of retailing,49(4), 48-64.

Kotler, P., & Keller, K. L. (2006). Marketing management. Analysis, Planning and Control. Prentice Hall.

Kotzé, T., North, E., Stols, M., & Venter, L. (2012). Gender differences in sources of shopping enjoyment. International Journal of Consumer Studies, 36(4), 416-424.

Kurose, S., Borgers, A. W., & Timmermans, H. J. (2001). Classifying pedestrian shopping behavior according to implied heuristic choice rules. Environment and Planning B: Planning and Design, 28(3), 405-418.

Kurose, S., Deguchi, A. and Zhao, S. (2009). Comparative Study of Pedestrian Behavior in Central Shopping Areas of East Asian Cities. In Timmermans, H. (Eds.), Pedestrian Behavior: Models, Data Collection And Applications (pp. 267-282). Bingley, UK: Emerald Group Publishing Limited.

REFERENCES

192

Kuruvilla, S. J., Joshi, N., & Shah, N. (2009). Do men and women really shop differently? An exploration of gender differences in mall shopping in India. International Journal of Consumer Studies, 33(6), 715-723.

Kwon, H., Ha, S., & Im, H. (2016). The impact of perceived similarity to other customers on shopping mall satisfaction. Journal of Retailing and Consumer Services, 28, 304-309.

Lam, S. Y. (2001). The effects of store environment on shopping behaviors: A critical review. Advances in Consumer Research, 28, 190-197.

Laroche, M., Teng, L., Michon, R., & Chebat, J. C. (2005). Incorporating service quality into consumer mall shopping decision making: a comparison between English and French Canadian consumers. Journal of Services Marketing, 19(3), 157-163.

Lee, S., Min, C., Yoo, C., & Song, J. (2013, September). Understanding customer malling behavior in an urban shopping mall using smartphones. In Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication (pp. 901-910). ACM.

LeHew, M. L., & Fairhurst, A. E. (2000). US shopping mall attributes: an exploratory investigation of their relationship to retail productivity. International Journal of Retail & Distribution Management, 28(6), 261-279.

Leszczyc, P. T. P., Sinha, A., & Sahgal, A. (2004). The effect of multi-purpose shopping on pricing and location strategy for grocery stores. Journal of Retailing, 80(2), 85-99.

Levy, M., and Weit, B. (2006). Retailing Management. Boston, MA: McGraw-Hill.

Lloyd, A. E., Yip, L. S., & Luk, S. T. (2011). An examination of the differences in retail service evaluation between domestic and tourist shoppers in Hong Kong. Tourism Management, 32(3), 520-533.

Losch, A. (1954). The Economics of Location: Transl. from the Second Revised Ed. by WH Woglow with the Assistance of WF Stolper. Yale University Press.

Maclnnis, D. J., & Price, L. L. (1987). The role of imagery in information processing: Review and extensions. Journal of Consumer Research, 13(4), 473-491.

Marjanen, H. (1995). Longitudinal study on consumer spatial shopping behaviour with special reference to out-of-town shopping: Experiences from Turku, Finland. Journal of Retailing and consumer Services, 2(3), 163-174.

REFERENCES

193

Marjanen, H. (2000). Retailing in rural Finland and the challenge of nearby cities. International Journal of Retail & Distribution Management, 28(4/5), 194-206.

Martin, C. A. (2009). Consumption motivation and perceptions of malls: a comparison of mothers and daughters. Journal of Marketing Theory and Practice, 17(1), 49-62.

Matthews, H., Taylor, M., Percy-Smith, B., & Limb, M. (2000). The Unacceptable Flaneur The Shopping Mall as a Teenage Hangout. Childhood, 7(3), 279-294.

McGoldrick, P. J., & Thompson, M. G. (1992a). The role of image in the attraction of the out-of-town centre. International Review of Retail, Distribution and Consumer Research, 2(1), 81-98.

McGoldrick, P. J., & Thompson, M. G. (1992b). Regional shopping centers: Out-of-town vs. in-town. Avebury: Aldershot.

McGoldrick, P. (2002). Retail Marketing (2nd ed.). Maidenhead: McGraw-Hill.

Mehrabian, A., & Russell, J. A. (1974). An approach to environmental psychology. MA: The MIT Press.

Mehta, N., & Chugan, P. K. (2013). The impact of visual merchandising on impulse buying behavior of consumer: a case from Central Mall of Ahmedabad India. Universal Journal of Management, 1(2), 76-8.

Michon, R., Chebat, J. C., & Turley, L. W. (2005). Mall atmospherics: the interaction effects of the mall environment on shopping behavior. Journal of Business Research, 58(5), 576-583.

Milliman, R. E. (1982). Using background music to affect the behavior of supermarket shoppers. Journal of Marketing, 86-91.

Millonig, A., & Gartner, G. (2007). Monitoring pedestrian spatio-temporal behaviour. BMI, 296, 29-42.

Millonig, A., & Gartner, G. (2011). Identifying motion and interest patterns of shoppers for developing personalised wayfinding tools. Journal of Location Based Services, 5(1), 3-21.

Mooi, E., & Sarstedt, M. (2014). A concise guide to market research: The Process, Data, and Methods Using IBM SPSS Statistics. Verlag Berlin Heidelberg: Springer.

REFERENCES

194

Morrin, M., & Chebat, J. C. (2005). Person-place congruency the interactive effects of shopper style and atmospherics on consumer expenditures. Journal of Service Research, 8(2), 181-191.

Morin, S., Dubé, L., & Chebat, J. C. (2007). The role of pleasant music in servicescapes: A test of the dual model of environmental perception. Journal of Retailing, 83(1), 115-130.

Mower, J. M., Kim, M., & Childs, M. L. (2012). Exterior atmospherics and consumer behavior: influence of landscaping and window display. Journal of Fashion Marketing and Management: An International Journal, 16(4), 442-453.

Neo, L. W. (2005). The 4Rs of Asian Shopping Center Management. Portland: Marshall Cavendish Academic.

Nicholls, J. A. F., Li, F., Kranendonk, C. J., & Roslow, S. (2002). The seven year itch? Mall shoppers across time. Journal of Consumer Marketing, 19(2), 149-165.

Oakes, S., Patterson, A., & Oakes, H. (2013). Shopping soundtracks: evaluating the musicscape using introspective data. Arts Marketing: An International Journal, 3(1), 41-57.

Okamoto, K., Utsumi, A., Yamazoe, H., Miyashita, T., Abe, S., Takahashi, K., & Hagita, N. (2011, June). Classification of Pedestrian Behavior in a Shopping Mall based on LRF and Camera Observations. In MVA (pp. 1-5).

Ooi, J. T., & Sim, L. L. (2007). The magnetism of suburban shopping centers: do size and Cineplex matter?. Journal of Property Investment & Finance,25(2), 111-135.

Oppewal, H. (1995). Conjoint experiments and retail planning: modelling consumer choice of shopping centre and retailer reactive behavior (Doctoral dissertation). Technische Universiteit Eindhoven, Eindhoven.

Oppewal, H., & Timmermans, H. (1999). Modeling consumer perception of public space in shopping centers. Environment and Behavior, 31(1), 45-65.

Othman, M. N., & Lim, S. H. (1997). Consumer behavior in shopping malls: a study of urban Malaysian consumers. Malaysian Journal of Consumer & Family Economics, 79-90.

Pan, Y., & Zinkhan, G. M. (2006). Determinants of retail patronage: a meta-analytical perspective. Journal of Retailing, 82(3), 229-243.

REFERENCES

195

Parsons, A. G. (2003). Assessing the effectiveness of shopping mall promotions: customer analysis. International Journal of Retail & Distribution Management, 31(2), 74-79.

Petruzzellis, L., Chebat, J. C., & Palumbo, A. (2015). “Hey Dee-Jay Let’s Play that Song and Keep me Shopping All Day Long”. The Effect of Famous Background Music on Consumer Shopping Behavior. In Ideas in Marketing: Finding the New and Polishing the Old (pp. 756-765). Springer International Publishing.

Phillips, L. W., & Sternthal, B. (1977). Age differences in information processing: a perspective on the aged consumer. Journal of Marketing Research, 444-457.

Primo, J.E. (1988). Picking power-centre tenant mix. Marketing News, 5 December, pp. 10-11.

Prastiana, U. et.al. (2010). 3 Decades of Jakarta Property Market. Jakarta: Procon Research & Publication.

Puccinelli, N. M., Goodstein, R. C., Grewal, D., Price, R., Raghubir, P., & Stewart, D. (2009). Customer experience management in retailing: understanding the buying process. Journal of Retailing, 85(1), 15-30.

Punj, G. and Stewart, D. W. (1983). Cluster Analysis in Marketing Research: Review and Suggestions for Application. Journal of Marketing Research, Vol. 20, No. 2, pp. 134-148

Raajpoot, N. A., Sharma, A., & Chebat, J. C. (2008). The role of gender and work status in shopping center patronage. Journal of Business Research, 61(8), 825-833.

Rahman, H.A.A. (2010, December). Jakarta shopping center. (Widiyani, personal communication,)

Rahman, H.A.A. (2011, January). The business of Jakarta shopping center. (Widiyani, personal communication)

Reynolds, K. E., & Beatty, S. E. (1999). Customer benefits and company consequences of customer-salesperson relationships in retailing. Journal of Retailing, 75(1), 11-32.

Santosa, H. (CEO Agung Podomoro Group Indonesia). (2010, September 24). Tenancy Mix in A Shopping Centre [Power Point Slides]. Jakarta, Indonesia: Available from GMT Property Management.

REFERENCES

196

Saarloos, D., Joh, C. H., Zhang, J., & Fujiwara, A. (2010). A segmentation study of pedestrian weekend activity patterns in a central business district. Journal of Retailing and Consumer Services, 17(2), 119-129.

Sankoff, D., & Kruskal, J. B. (1983). Time warps, string edits, and macromolecules: the theory and practice of sequence comparison. Reading: Addison-Wesley Publication, 1983, edited by Sankoff, David; Kruskal, Joseph B., 1.

Severin, V., Louviere, J. J., & Finn, A. (2001). The stability of retail shopping choices over time and across countries. Journal of Retailing, 77(2), 185-202.

Sinha, P. K., & Uniyal, D. P. (2005). Using observational research for behavioral segmentation of shoppers. Journal of Retailing and Consumer Services, 12(1), 35-48.

Simmers, C. S., & Keith, N. K. (2015). Measuring Retail Store Service Quality: The Disparity Between The Retail Service Quality Scale (Rsqs) And Comment Cards. Academy of Marketing Studies Journal, 19(2), 117.

Singh, H., & Prashar, S. (2014). Anatomy of shopping experience for malls in Mumbai: A confirmatory factor analysis approach. Journal of Retailing and Consumer Services, 21(2), 220-228.

Sit, J., Merrilees, B., & Birch, D. (2003). Entertainment-seeking shopping centre patrons: the missing segments. International Journal of Retail & Distribution Management, 31(2), 80-94.

Sjohirin. (2010, September 22). Shopping Centre Management Course. Jakarta, Indonesia: Available from GMT Property Management.

Skogster, P., Uotila, V., & Ojala, L. (2008). From mornings to evenings: is there variation in shopping behavior between different hours of the day?. International Journal of Consumer Studies, 32(1), 65-74.

Spangenberg, E. R., Crowley, A. E., & Henderson, P. W. (1996). Improving the store environment: do olfactory cues affect evaluations and behaviors?. The Journal of Marketing, 67-80.

Spangenberg, E. R., Grohmann, B., & Sprott, D. E. (2005). It's beginning to smell (and sound) a lot like Christmas: the interactive effects of ambient scent and music in a retail setting. Journal of Business Research, 58(11), 1583-1589.

REFERENCES

197

Sprotles, G. B., & Kendall, E. L. (1986). A methodology for profiling consumers' decision-making styles. Journal of Consumer Affairs, 20(2), 267-279.

Stoel, L., Wickliffe, V., & Lee, K. H. (2004). Attribute beliefs and spending as antecedents to shopping value. Journal of Business Research, 57(10), 1067-1073.

Susilo, Y. (2010, September 23). Indonesian Market & Consumer Trends. [Power Point Slides]. Jakarta, Indonesia: Available from GMT Property Management.

Tan, A. A., de Vries, B., & Timmermans, H. J. (2006). Using a stereo panoramic interactive navigation system to measure pedestrian activity scheduling behavior: a test of validity. Environment and Planning B: Planning and Design, 33(4), 541-557.

Tanaka, H. and R. Shibasaki (2005). 3-D Spatial Behaviors Or Urban Lives, A Smart Mobile Mapping And Visualizing System. Proceedings the 10th International Conference on Computers in Urban Planning and Urban Management, London, United Kingdom.

Tauber, E. M. (1972). Why do people shop?. The Journal of Marketing, 46-49.

Taylor, S. A., Nicholson, J. D., Milan, J., & Martinez, R. V. (1997). Assessing the Roles of Service Quality and Customer Satisfaction in the Formation of the Purchase Intentions of Mexican Consumer. Journal of Marketing Theory and Practice, 5(1), 78-90.

Teknomo, K. (2002). Microscopic pedestrian flow characteristics: Development of an image processing data collection and simulation model. (Doctoral dissertation). Tohoku University, Japan.

Teller, C., & Reutterer, T. (2008). The evolving concept of retail attractiveness: What makes retail agglomerations attractive when customers shop at them?. Journal of Retailing and Consumer Services, 15(3), 127-143.

Tendai, M., & Crispen, C. (2009). In-store shopping environment and impulsive buying. African Journal of Marketing Management, 1(4), 102-108.

Timothy, D. J. (2005). Shopping tourism, retailing, and leisure (Vol. 23). Channel View Publications.

Titus, P. A., & Everett, P. B. (1995). The consumer retail search process: a conceptual model and research agenda. Journal of the Academy of Marketing Science, 23(2), 106-119.

Tootelian, D. H., & Gaedeke, R. M. (1992). The Team Market: An Exploratory Analysis of Income, Spending and Shopping Patterns. Journal of Consumer Marketing, 9(4), 35-44.

REFERENCES

198

Turley, L. W., & Milliman, R. E. (2000). Atmospheric effects on shopping behavior: a review of the experimental evidence. Journal of Business Research, 49(2), 193-211.

Underhill, P. (1999). Why we shop: The science of shopping. New York: Touchstone.

Underhill, P. (2004). Call of the mall: How we shop. London: Profile Books.

Urban Land Institute (2004). The Dollars and Cents of Shopping Centers: 2004. Washington, DC: The Urban Land Institute.

Vanderbeck, R. M., & Johnson, Jr, J. H. (2000). " That's the only place where you can hang out": Urban young people and the space of the mall. Urban Geography, 21(1), 5-25.

Van der Hagen, X., Borgers, A., & Timmermans, H. (1991). Spatiotemporal sequencing processes of pedestrians in urban retail environments. Papers in Regional Science, 70(1), 37-52.

Wall, A. (2005). Victor Gruen: from urban shop to new city. Barcelona: Actar.

Wakefield, K. L., & Barnes, J. H. (1997). Retailing hedonic consumption: a model of sales promotion of a leisure service. Journal of Retailing, 72(4), 409-427.

Wakefield, K. L., & Baker, J. (1998). Excitement at the mall: determinants and effects on shopping response. Journal of Retailing, 74(4), 515-539.

Website Asosiasi Pusat Pengelola Belanja Indonesia. (2008). Retrieved October 29, 2010 from Daftar Anggota: http://appbi-dki.org/

Westbrook, R. A., & Black, W. C. (1985). A motivation-based shopper typology. Journal of Retailing, 61(1), 78-103

Wong, G. K. M., Lu, Y., & Yuan, L. L. (2001). SCATTR: an instrument for measuring shopping centre attractiveness. International Journal of Retail & Distribution Management, 29(2), 76-86.

Woodruffe-Burton, H., & Wakenshaw, S. (2011). Revisiting experiential values of shopping: consumers' self and identity. Marketing Intelligence & Planning, 29(1), 69-85.

Yalch, R. F., & Spangenberg, E. R. (2000). The effects of music in a retail setting on real and perceived shopping times. Journal of Business Research,49(2), 139-147.

Yavas, U. (2001). Patronage motives and product purchase patterns: a correspondence analysis. Marketing Intelligence & Planning, 19(2), 97-102.

http://appbi-dki.org/

REFERENCES

199

Yavas, U. (2003). A multi-attribute approach to understanding shopper segments. International Journal of Retail & Distribution Management, 31(11), 541-548.

Yim Yiu, C., & Xu, S. Y. (2012). A tenant-mix model for shopping malls. European Journal of Marketing, 46(3/4), 524-541.

Yip, T. C., Chan, K., & Poon, E. (2012). Attributes of young consumers' favorite retail shops: a qualitative study. Journal of Consumer Marketing, 29(7), 545-552.

Yuo, T. T., Crosby, N., Lizieri, C. M., & McCann, P. (2004). Tenant mix variety in regional shopping centres: some UK empirical analyses. Working Papers in Real Estate & Planning. 02/04. Working Paper. University of Reading, Reading. pp29.

Zacharias, J. (2000). Shopping behavior at Alexis-Nihon plaza in Montreal.Journal of Shopping Center Research, 7(2), 67-79.

Zacharias, J. (2001a). Path choice and visual stimuli: signs of human activity and architecture. Journal of Environmental Psychology, 21(4), 341-352.

Zacharias, J. (2001b). Pedestrian behavior pedestrian behavior and perception in urban walking environments. Journal of Planning Literature, 16(1), 3-18.

Zacharias, J. (2002, November). Choosing a path in the underground: visual information and preference. In ACUUS International Conference Urban underground space: a resource for cities, Torino (pp. 14-16).

Zacharias, J., & Schinazi, V. (2003). The Impact of an Entertainment Retrofit on the Performance of a Shopping Center. Journal of Shopping Center Research, 10(1), 29-44.

Zacharias, J. (2006). Exploratory Spatial Behavior in Real and Virtual Environments. Landscape and Urban Planning, 78(1), 1-13.

Zeisel, J. (2006). Inquiry by Design: Environment/Behavior/Neuroscience in Architecture, Interiors, Landscape, and Planning. New York: WW Norton & Co.

APPENDICES

201

APPENDIX 1 The Questionnaire

The same questionnaire was used in three types of shopping malls in Bahasa Indonesia using the A4 format. This is the translated version in English.

QUESTIONNAIRE

Age years old Gender □Male □ Female

Status □ Single □ Married Etnic 1

2 3 4 5 6 7 8 9 10 11 12 13 14 15

Aceh North Sumatera West Sumatera Riau Jambi South Sumatera Bengkulu Lampung Bangka-Belitung Jakarta West Java Yogyakarta Central Java East Java Bali

16 17 18 19 20 21 22 23 24 25 26 27 28 29

West Nusa Tenggara East Nusa Tenggara West Kalimantan Central Kalimantan South Kalimantan North Sulawesi Central Sulawesi South Sulawesi East South Sulawesi Gorontalo Maluku North Maluku Papua non-Indonesian

Occupation □ □ □

Student Employee Entrepreneur

□ □

Housewife Retired

Education □ □

High school Polytechnic

□ □

Academy University

Expenditure for Food and Beverage: IDR ………….

Expenditure for Non-Food and Beverage: IDR …………. Home Address □

□ □

West Jakarta North Jakarta Central Jakarta

□ □

East Jakarta South Jakarta

Office Address □ □ □

West Jakarta North Jakarta Central Jakarta

□ □

East Jakarta South Jakarta

Overall evaluation What is your overall evaluation of the location and convenience access to this shopping mall?

Terrible □ □ □ □ □ □ □ Excellent What is your overall evaluation of the prices in this shopping mall?

Terrible □ □ □ □ □ □ □ Excellent What is your overall evaluation of the range of store, merchandise selection and quality in this shopping mall?

Terrible □ □ □ □ □ □ □ Excellent What is your overall evaluation of the advertising and promotion in this shopping mall?

Terrible □ □ □ □ □ □ □ Excellent What is your overall evaluation of the comfort and visual appearance of this shopping mall?

Terrible □ □ □ □ □ □ □ Excellent What is you overall evaluation of the space arrangement of this shopping mall?

Terrible □ □ □ □ □ □ □ Excellent

APPENDIX 1 THE QUESTIONNAIRE

202

What is you overall evaluation of the quality of facilites in this shopping mall? Terrible □ □ □ □ □ □ □ Excellent

What is you overall evaluation of personal services (receptionist, security, staff) in this shopping mall? Terrible □ □ □ □ □ □ □ Excellent

What is you overall evaluation of the social environment (other shoppers) in this shopping mall? Terrible □ □ □ □ □ □ □ Excellent

Location & Convenience The accessibility of this shopping mall by public transportation (bus/busway) is:

Terrible □ □ □ □ □ □ □ Excellent □ unknown/never use

The accessibility of this shopping mall by private car is: Terrible □ □ □ □ □ □ □ Excellent □ unknown/never use

The accessibility of this shopping mall by motorcycle is: Terrible □ □ □ □ □ □ □ Excellent □ unknown/never use

The accessibility of this shopping mall by bicycle is: Terrible □ □ □ □ □ □ □ Excellent □ unknown/never use

The ease to get parking in this shopping mall is: Terrible □ □ □ □ □ □ □ Excellent □ unknown/never use

The parking cost in this shopping mall are: Terrible □ □ □ □ □ □ □ Excellent □ unknown/never use

The opening hours of this shopping mall are: Terrible □ □ □ □ □ □ □ Excellent

My satisfaction trip to this shopping mall today is: Terrible □ □ □ □ □ □ □ Excellent

The duration of my trip to this shopping mall today was:

□ < 1 hour □ 1-2hours □ 2-3 hours □ 3-4 hours □ >4 hours

By □ Private Car □ Motorcycle □ Bicycle □ Foot/Walking □ Bus/busway □ Taxi

□ From house □ From office □ From other place Price The price level of food & beverages in this shopping mall is:

Terrible □ □ □ □ □ □ □ Excellent The price level of the fashion stores in this shopping mall is:

Terrible □ □ □ □ □ □ □ Excellent The price level of the supermarket(s) in this shopping mall is:

Terrible □ □ □ □ □ □ □ Excellent The presence of particular high-price-image stores in this shopping mall is:

Terrible □ □ □ □ □ □ □ Excellent The presence of particular low-price-image stores in this shopping mall is:

Terrible □ □ □ □ □ □ □ Excellent Store Variety, Merchandise Selection and Quality The number of stores in this shopping mall is:

Terrible □ □ □ □ □ □ □ Excellent The type and variety of stores in this shopping mall is:

Terrible □ □ □ □ □ □ □ Excellent Product selections in stores in this shopping mall are:


203

Terrible □ □ □ □ □ □ □ Excellent The variety in major stores (department stores and supermarkets) in this shopping mall is:

Terrible □ □ □ □ □ □ □ Excellent The variety of food in the food court in this shopping mall is:

Terrible □ □ □ □ □ □ □ Excellent The variety of leisure facilities in this shopping mall is:

Terrible □ □ □ □ □ □ □ Excellent The quality of banking facilities in this shopping mall is:

Terrible □ □ □ □ □ □ □ Excellent The variety of banking facilities in this shopping mall is:

Terrible □ □ □ □ □ □ □ Excellent The quality of food and beverage stores in this shopping mall is:

Terrible □ □ □ □ □ □ □ Excellent The variety of food and beverage stores in this shopping mall is:

Terrible □ □ □ □ □ □ □ Excellent The quality of children facilities (playground, kids zone, kids club, etc.) in this shopping mall is:

Terrible □ □ □ □ □ □ □ Excellent The variety of children facilities in this shopping mall is:

Terrible □ □ □ □ □ □ □ Excellent The quality of entertainment facilities (cinema, karaoke, game mall, etc.) in this shopping mall is:

Terrible □ □ □ □ □ □ □ Excellent The variety of entertainment facilities in this shopping mall is:

Terrible □ □ □ □ □ □ □ Excellent The quality of health facilities (gym, clinic, dentist, etc.) in this shopping mall is:

Terrible □ □ □ □ □ □ □ Excellent The variety of health facilities in this shopping mall is:

Terrible □ □ □ □ □ □ □ Excellent The quality of beauty facilities (salon, spa, slimming mall, etc.) in this shopping mall is:

Terrible □ □ □ □ □ □ □ Excellent The variety of beauty facilities in this shopping mall is:

Terrible □ □ □ □ □ □ □ Excellent Advertising & Promotion The quality of activities/ events/ exhibitions in this shopping mall is:

Terrible □ □ □ □ □ □ □ Excellent The attractiveness of activities/ events/ exhibitions in this shopping mall is:

Terrible □ □ □ □ □ □ □ Excellent The frequency of activities/ events/ exhibitions in this shopping mall is:

Terrible □ □ □ □ □ □ □ Excellent The special sales promotions in this shopping mall are:

Terrible □ □ □ □ □ □ □ Excellent Mall Comfort and Visual Appearance The cleanliness of stores and public space in this shopping mall is:

Terrible □ □ □ □ □ □ □ Excellent The safety of stores and public space in this shopping mall is:

Terrible □ □ □ □ □ □ □ Excellent The attractiveness of architecture design of this shopping mall is:

Terrible □ □ □ □ □ □ □ Excellent The attractiveness of interior design of this shopping mall is:

Terrible □ □ □ □ □ □ □ Excellent The attractiveness of window displays in this shopping mall are:

Terrible □ □ □ □ □ □ □ Excellent The attractiveness of interior wall and floor color in this shopping mall is:



204

The attractiveness of ceiling and lighting in this shopping mall are: Terrible □ □ □ □ □ □ □ Excellent

The artwork in the public space in this shopping mall is: Terrible □ □ □ □ □ □ □ Excellent

The signs and decorations in the public space in this shopping mall are: Terrible □ □ □ □ □ □ □ Excellent

The visual appearance of public space in this shopping mall is: Terrible □ □ □ □ □ □ □ Excellent

The quality of garden and greenery in this shopping mall is: Terrible □ □ □ □ □ □ □ Excellent

The building style in this shopping mall is: Classic □ □ □ □ □ □ □ Modern

The building style in this shopping mall is: Terrible □ □ □ □ □ □ □ Excellent

The public space atmosphere in this shopping mall is: Terrible □ □ □ □ □ □ □ Excellent

The food court’s atmosphere in this shopping mall is: Terrible □ □ □ □ □ □ □ Excellent

The quality of lighting in this shopping mall is: Terrible □ □ □ □ □ □ □ Excellent

The type of music in this shopping mall is: Terrible □ □ □ □ □ □ □ Excellent

The volume of the music in this shopping mall is Extremely soft

□ □ □ □ □ □ □ Extremely strong

The comfort in music sound in this shopping mall is: Terrible □ □ □ □ □ □ □ Excellent

The odor in the public space in this shopping mall is: Extremely soft


The comfort of odor in the public space in this shopping mall is: Terrible □ □ □ □ □ □ □ Excellent

The odor in elevators in this shopping mall is: Extremely soft


The comfort of odor in elevators in this shopping mall is: Terrible □ □ □ □ □ □ □ Excellent

The comfort in cigarettes’ smoke in the public space in this shopping mall is: Extremely soft


The comfort in cigarettes’ smoke in the public space in this shopping mall is: Terrible □ □ □ □ □ □ □ Excellent

The temperature in this shopping mall is: Extremely cold

□ □ □ □ □ □ □ Extremely hot

The thermal comfort in this shopping mall is: Terrible □ □ □ □ □ □ □ Excellent

Space Arrangement The ease to find the stores in this shopping mall is:


The ease to find escalators in this shopping mall is:


205


The ease to find elevators in this shopping mall is: Terrible □ □ □ □ □ □ □ Excellent □ unknown/never use

The ease to find toilets in this shopping mall is: Terrible □ □ □ □ □ □ □ Excellent □ unknown/never use

The ease to find the praying room in this shopping mall is: Terrible □ □ □ □ □ □ □ Excellent □ unknown/never use

The ease to find the nursing room in this shopping mall is: Terrible □ □ □ □ □ □ □ Excellent □ unknown/never use

The ease to find the ATMs in this shopping mall is: Terrible □ □ □ □ □ □ □ Excellent □ unknown/never use

The ease to find public seats in this shopping mall is: Terrible □ □ □ □ □ □ □ Excellent □ unknown/never use

Quality of Facilities The quality of toilets in this shopping mall is:


The cleanliness of toilets in this shopping mall is: Terrible □ □ □ □ □ □ □ Excellent □ unknown/never use

The odor in toilets in this shopping mall is: Terrible □ □ □ □ □ □ □ Excellent □ unknown/never use

The quality of the praying room in this shopping mall is: Terrible □ □ □ □ □ □ □ Excellent □ unknown/never use

The cleanliness of the praying room in this shopping mall is: Terrible □ □ □ □ □ □ □ Excellent □ unknown/never use

The odor in the praying room in this shopping mall is: Terrible □ □ □ □ □ □ □ Excellent □ unknown/never use

The quality of the nursing room in this shopping mall is: Terrible □ □ □ □ □ □ □ Excellent □ unknown/never use

The cleanliness of the nursing room in this shopping mall is: Terrible □ □ □ □ □ □ □ Excellent □ unknown/never use

The odor in the nursing room in this shopping mall is: Terrible □ □ □ □ □ □ □ Excellent □ unknown/never use

The quality of public seats in this shopping mall is: Terrible □ □ □ □ □ □ □ Excellent □ unknown/never use


206

Number of pubic seats in this shopping mall is:


Social Environment The politeness of other shoppers in this mall is

Terrible □ □ □ □ □ □ □ Excellent The social behavior of other shoppers in this mall is

Terrible □ □ □ □ □ □ □ Excellent Personal Service The helpfulness of greeters/receptionists in this shopping mall is

Terrible □ □ □ □ □ □ □ Excellent The friendliness of greeters/receptionists in this shopping mall is

Terrible □ □ □ □ □ □ □ Excellent The helpfulness of security in this shopping mall is

Terrible □ □ □ □ □ □ □ Excellent The friendliness of security in this shopping mall is

Terrible □ □ □ □ □ □ □ Excellent The helpfulness of customer services in this shopping mall is

Terrible □ □ □ □ □ □ □ Excellent The friendliness of customer services in this shopping mall is


Day □ Weekdays □ Weekends

Weather □ Raining □ Sunny

Shopping mall’s activity

□ Exhibition □ Performance /Demonstration

□ Competition □ Combination

Accompany’s gender □ Male #... □ Female #...

Accompany’s relationship □ Friends □ Family

What factors made you choose this shopping mall

1 2 3 4 5

…………………… …………………… …………………… ………………….. ……………………

Motivation/ purpose to go to the shopping mall: ……………………

Duration of shopping: ……………………minutes From …………… to ………………… o’clock

How many stores did you visit today? ………………….. stores

Mention the name of the stores you visited

1 2 3 4 5

…………………… ………………….. ………………….. ………………….. …………………..

6 7 8 9 10

………………….. ………………….. …………………… ………………….. …………………..

What kind of facilities did you use today? Fill in the blank with the name of facilities.

1 2 3 4 5 6 7 8

Toilet/praying room/nursing room/parking Children facilities ………………….. Entertainment facilities ………………….. Heath facilities ………………….. Financial facilities ………………….. Public seats Beauty facilities ………………….. Food and beverages facilities …………………..


207

Which factors , if any, did satisfy you with your shopping experience today?

a b c d e

…………………… ………………….. ………………….. ………………….. …………………..

Would you consider coming back to this shopping mall? Yes/No

208

APPENDIX 2 Results of Descriptive Analyses of Evaluations of Mall Dimensions across the Selected Shopping Malls

Figure A2.1 a Evaluation of Location and Convenience Dimension

3.33%

4.55%

4.55%

13.78%

9.78%

13.11%

3.62%

4.05%

3.73%

3.70%

2.88%

7.78%

7.78%

6.44%

4.84%

3.05%

2.17%

3.56%

6.67%

6.31%

7.24%

12.61%

6.70%

5.59%

14.20%

7.14%

7.91%

18.84%

4.19%

7.78%

31.11%

10.23%

12.87%

10.75%

10.23%

10.15%

7.10%

5.43%

3.26%

3.15%

10.22%

10.67%

11.17%

23.98%

25.68%

25.84%

27.95%

30.86%

29.37%

23.74%

26.09%

20.96%

30.00%

18.89%

34.09%

21.29%

36.02%

34.09%

31.98%

44.26%

32.07%

26.05%

25.68%

25.24%

16.89%

20.44%

17.96%

24.89%

28.83%

20.57%

30.43%

24.07%

24.60%

25.90%

23.19%

26.35%

27.78%

14.44%

17.05%

27.23%

31.18%

17.05%

27.92%

30.05%

27.17%

25.58%

31.98%

24.76%

26.67%

25.33%

18.93%

23.98%

22.07%

26.32%

21.12%

22.22%

23.02%

25.18%

19.57%

25.75%

12.22%

15.56%

18.18%

21.78%

13.98%

18.18%

19.29%

12.57%

19.02%

31.16%

29.73%

28.10%

14.67%

23.56%

19.90%

14.93%

6.76%

19.62%

9.94%

4.94%

13.49%

12.95%

8.70%

20.36%

14.44%

8.89%

13.64%

8.42%

2.69%

13.64%

6.09%

3.28%

12.50%

11.16%

7.21%

19.05%

14.22%

3.56%

12.62%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

Acce

ssib

ility

by

publ

ictr

anpo

rtat

ion

Acce

ssib

ility

by

priv

ate

car

Acce

ssib

ility

by

priv

ate

mot

orcy

cle

Acce

ssib

ility

by

bicy

cle

Ease

to g

et th

epa

rkin

g sp

ace

Park

ing

cost

Ope

ning

hou

rsof

the

mal

lTr

ip to

the

mal

l

terrible very poor poor fair good very good excellent

APPENDIX 2 RESULTS OF DESCRIPTIVE ANALYSES OF EVALUATIONS OF MALL DIMENSIONS

209

Figure A2.1 b Frequency Distribution of Transport Mode

Figure A2.1 c Frequency Distribution of Duration of Travel Time

Figure A2.1 d Frequency Distribution of the Starting Point of the Trip

38.57%

30.67%

14.88%

21.08%

24.89%

42.79%

5.83%

3.56%

6.51%

22.42%

28.00%

31.16%

11.66%

12.89%

4.19%

0.45%

0.47%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Classic Mall

Modern Mall

Local Mall

Private car Motorcycle By foot Bus Taxi Bicycle

68.16%

44.20%

62.33%

25.11%

46.43%

32.56%

7.14%

4.19%

3.59%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Classic Mall

Modern Mall

Local Mall

<1 hour 1-2 hours 2-3 hours >3 hours

57.78%

56.89%

73.30%

24.00%

26.22%

11.17%

18.22%

16.89%

15.53%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Classic Mall

Modern Mall

Local Mall

Home Office/ School Other place


210

Figure A2.2 Evaluation of Price Dimension

6.17%

4.44%

4.59%

7.93%

9.33%

4.59%

3.96%

2.67%

10.13%

12.50%

7.80%

9.25%

7.59%

4.61%

34.80%

34.67%

29.82%

22.47%

24.89%

21.10%

22.03%

30.67%

22.48%

28.63%

39.29%

27.98%

31.28%

30.36%

23.04%

35.24%

35.11%

29.82%

36.12%

38.67%

28.44%

31.28%

34.67%

27.52%

30.84%

31.25%

30.28%

23.79%

32.59%

29.49%

18.94%

21.78%

20.64%

23.79%

21.78%

31.65%

29.52%

24.44%

31.19%

21.15%

12.05%

22.94%

22.47%

25.00%

28.11%

2.20%

2.22%

13.30%

5.29%

4.89%

11.47%

11.89%

7.11%

16.06%

4.85%

1.79%

6.42%

9.25%

2.68%

13.36%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LMPr

ice

leve

l of

food

&be

vera

gePr

ice

leve

l of

fash

ion

Pric

e le

vel o

fsu

perm

arke

t

Pres

ence

of

part

icul

ar h

igh-

pric

e im

age

stor

es

Pres

ence

of

part

icul

ar lo

w-

pric

e im

age

stor

es

terrible very good good fair poor very poor excellent


211

Figure A2.3a Evaluation of Store Variety, Merchandise Selection and Quality Dimension

7.52%

4.89%

10.13%

5.78%

6.61%

6.22%

4.85%

4.46%

3.23%

13.66%

3.57%

3.21%

11.31%

4.95%

29.65%

25.33%

17.89%

29.52%

27.11%

18.81%

29.52%

24.44%

18.89%

26.43%

24.55%

15.21%

25.55%

20.09%

15.14%

31.22%

24.77%

17.89%

30.53%

39.56%

28.44%

31.72%

36.44%

32.57%

29.96%

41.78%

30.88%

26.87%

37.05%

38.25%

26.87%

38.84%

33.03%

28.96%

37.84%

32.57%

26.11%

22.22%

34.40%

19.82%

24.00%

32.11%

25.55%

22.67%

36.87%

26.43%

24.11%

29.03%

22.91%

25.00%

31.65%

18.55%

23.87%

29.82%

5.31%

6.22%

17.43%

7.49%

5.33%

15.60%

7.05%

4.00%

11.52%

13.22%

7.59%

13.82%

6.61%

12.05%

15.60%

5.86%

15.60%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LMN

umbe

r of

stor

es

Type

and

varie

ty o

fst

ores

Prod

uct

sele

ctio

ns in

stor

esVa

riety

of

maj

or st

ores

Varie

ty o

f foo

din

food

cou

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Varie

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fle

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212

Figure A2.3b Evaluation of Store Variety, Merchandise Selection and Quality Dimension

4.41%

4.89%

5.50%

3.98%

5.33%

4.13%

3.52%

3.96%

3.11%

6.17%

4.44%

2.76%

9.25%

6.67%

3.23%

4.85%

6.67%

3.69%

7.93%

7.11%

3.24%

12.78%

10.22%

9.63%

13.66%

11.56%

8.29%

3.96%

6.22%

4.15%

4.41%

4.91%

5.53%

14.54%

22.67%

16.97%

16.37%

25.33%

18.35%

16.74%

20.00%

21.56%

18.50%

22.22%

18.35%

25.99%

30.22%

21.66%

24.23%

32.44%

20.28%

17.62%

28.89%

20.74%

20.70%

32.89%

20.37%

23.35%

33.78%

21.56%

21.15%

34.22%

21.20%

24.23%

32.44%

22.58%

25.11%

33.04%

23.50%

24.67%

32.44%

26.61%

28.32%

31.56%

27.52%

30.40%

34.22%

23.39%

29.07%

30.22%

23.39%

22.91%

33.78%

22.58%

27.75%

31.11%

25.35%

25.11%

28.44%

20.74%

25.99%

29.33%

22.22%

18.50%

21.78%

25.69%

18.50%

18.22%

26.73%

21.15%

25.78%

23.96%

18.50%

27.68%

22.58%

30.84%

24.00%

22.94%

26.55%

25.33%

25.23%

31.72%

31.11%

33.94%

29.07%

30.22%

34.40%

26.43%

21.78%

31.80%

22.47%

21.33%

29.95%

30.40%

24.89%

31.80%

25.11%

16.89%

34.26%

12.33%

14.22%

21.10%

11.89%

12.89%

17.51%

20.26%

13.33%

24.88%

22.03%

9.38%

21.20%

17.18%

9.78%

18.35%

15.93%

8.00%

16.97%

15.86%

11.11%

18.35%

16.30%

13.33%

20.18%

8.81%

4.00%

14.29%

7.05%

2.67%

14.29%

14.54%

8.00%

18.43%

12.78%

10.22%

15.28%

3.96%

1.78%

5.50%

3.08%

2.67%

8.76%

8.37%

2.22%

7.83%

6.61%

4.02%

11.06%

7.93%

2.67%

8.72%

7.96%

2.67%

7.34%

8.37%

4.89%

5.53%

8.37%

5.33%

5.99%

4.41%

2.67%

4.15%

5.29%

3.11%

4.17%

25.11%

17.33%

14.68%

27.31%

18.22%

15.21%

19.82%

19.11%

15.21%

20.70%

20.09%

14.75%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LMQ

ualit

y of

bank

ing

faci

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Varie

ty o

fba

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cilit

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Qua

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Varie

ty o

ffo

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vera

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Qua

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ofch

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cilit

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Varie

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fch

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Qua

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men

tfa

cilit

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Varie

ty o

fen

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men

tfa

cilit

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Qua

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alth

faci

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Varie

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faci

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Qua

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auty

faci

litie

s

Varie

ty o

fbe

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faci

litie

s

terrible very poor poor fair good very good excellent never use


213

Figure A2.4 Evaluation of Advertising and Promotion Dimension

4.9%

3.6%

1.8%

4.9%

5.3%

2.3%

4.0%

5.3%

0.9%

3.5%

3.1%

1.4%

18.1%

20.4%

9.6%

18.1%

20.0%

10.1%

19.0%

17.8%

9.6%

14.6%

15.6%

10.1%

32.3%

35.1%

32.1%

37.6%

35.1%

33.5%

33.2%

36.9%

32.6%

39.8%

37.3%

31.7%

24.3%

25.3%

28.4%

23.5%

26.7%

23.9%

26.5%

24.4%

25.2%

21.7%

28.4%

30.3%

14.6%

12.4%

19.7%

13.7%

9.8%

22.5%

14.6%

9.3%

25.7%

15.9%

11.1%

21.1%

4.4%

1.8%

8.3%

1.3%

3.1%

7.3%

2.2%

6.2%

5.0%

3.5%

4.4%

5.5%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LMSp

ecia

l sal

espr

omot

ions

Freq

uenc

y of

activ

ities

/ev

ents

/ex

hibi

tions

Attr

activ

enes

sof

act

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even

ts/

exhi

bitio

ns

Qua

ltiy

ofac

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even

ts/

exhi

bitio

ns



214

Figure A2.5a Evaluation of the Mall Comfort and Visual Appearance Dimension

1.3%

2.2%

1.3%

1.3%

7.0%

9.8%

3.7%

5.7%

8.9%

5.6%

7.9%

9.4%

8.7%

7.5%

9.8%

7.3%

7.9%

8.9%

5.0%

4.0%

6.2%

2.8%

4.0%

7.1%

6.0%

25.6%

39.6%

28.0%

26.4%

39.6%

29.6%

26.9%

35.3%

27.5%

26.9%

37.3%

24.3%

25.1%

35.6%

21.6%

15.9%

29.8%

23.0%

21.1%

29.8%

21.6%

30.0%

28.9%

29.4%

34.4%

29.3%

27.3%

32.2%

30.4%

25.7%

31.3%

32.0%

29.4%

26.9%

31.6%

32.1%

31.3%

37.3%

34.1%

29.1%

37.3%

32.6%

27.8%

16.0%

28.9%

25.1%

17.3%

30.1%

25.1%

18.8%

29.4%

24.7%

14.7%

28.9%

29.1%

19.1%

28.4%

33.9%

18.2%

28.1%

29.1%

18.7%

27.1%

7.9%

4.9%

9.2%

7.5%

4.4%

7.4%

7.0%

4.9%

8.7%

8.8%

5.3%

10.1%

8.4%

3.1%

12.4%

14.5%

7.6%

11.5%

16.3%

5.8%

12.4%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LMAt

trac

tiven

ess

of c

eilin

g an

dlig

htin

g

Attr

activ

enes

sof

inte

rior w

all

and

floor

col

or

Attr

activ

enes

sof

win

dow

disp

lay

Attr

activ

enes

sof

inte

rior

desig

n

Attr

activ

enes

sof

arch

itect

ure

desig

n

Safe

ty o

fst

ores

and

publ

ic sp

ace

Clea

nlin

ess o

fst

ores

and

publ

ic sp

ace



215

Figure A2.5b Evaluation of the Mall Comfort and Visual Appearance Dimension

5.3%

4.9%

1.8%

2.7%

2.3%

1.8%

1.8%

4.9%

5.4%

4.6%

1.8%

2.2%

3.1%

2.7%

5.8%

2.2%

1.9%

7.0%

12.4%

4.6%

6.2%

4.9%

2.3%

6.2%

5.3%

3.2%

6.6%

6.2%

3.7%

8.4%

12.1%

7.0%

19.5%

20.5%

12.0%

8.8%

12.9%

7.8%

10.6%

14.2%

6.0%

12.4%

18.3%

10.6%

30.4%

37.8%

31.2%

22.5%

37.1%

26.6%

24.2%

28.4%

27.1%

27.3%

33.3%

28.9%

25.2%

31.7%

23.8%

28.3%

29.9%

27.3%

28.3%

35.1%

26.1%

28.8%

36.0%

30.3%

34.1%

35.3%

26.9%

30.4%

28.9%

31.7%

36.1%

40.6%

28.9%

37.0%

40.9%

32.6%

37.4%

40.4%

31.7%

31.4%

37.1%

31.8%

21.7%

27.7%

25.5%

38.1%

32.0%

35.3%

30.1%

31.6%

27.5%

25.7%

25.9%

25.9%

24.2%

12.0%

21.1%

26.0%

12.9%

33.0%

24.7%

21.8%

28.4%

21.6%

16.0%

28.0%

23.9%

14.7%

30.4%

16.4%

9.4%

22.7%

16.8%

14.7%

24.3%

20.4%

11.6%

27.5%

14.2%

12.5%

23.1%

5.7%

5.8%

8.7%

8.8%

4.5%

8.3%

5.3%

2.7%

7.8%

6.2%

3.1%

7.3%

8.8%

3.1%

6.5%

4.0%

2.2%

6.5%

5.3%

3.1%

6.0%

6.2%

4.0%

8.3%

6.2%

4.9%

11.1%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

Type

of m

usic

Qua

lity

oflig

htin

gFo

od c

ourt

'sat

mos

pher

ePu

blic

spac

es'

atm

osph

ere

Build

ing

styl

e

Qua

lity

ofga

rden

s and

gree

nery

Visu

alat

trac

tiven

ess

of p

ublic

spac

e

Sign

s and

deco

ratio

ns in

the

publ

icsp

ace

Artw

ork

in th

epu

blic

spac

e



216

Figure A2.5c Evaluation of the Mall Comfort and Visual Appearance Dimension

Figure A2.5d Evaluation of Odor and Music

1.8%

3.1%

3.1%

3.7%

2.2%

2.7%

1.8%

5.7%

4.9%

5.5%

3.1%

4.9%

4.1%

4.9%

2.7%

5.0%

4.8%

4.4%

3.7%

5.7%

5.3%

6.0%

34.4%

41.3%

34.1%

22.9%

31.1%

23.0%

23.9%

41.3%

22.5%

24.2%

37.8%

23.0%

34.4%

43.6%

31.2%

29.5%

37.8%

30.9%

28.2%

36.4%

30.0%

36.3%

40.4%

39.9%

30.4%

37.3%

35.0%

37.9%

36.9%

35.3%

19.8%

6.7%

19.8%

22.0%

13.8%

21.7%

22.1%

8.0%

21.6%

26.4%

12.9%

27.6%

13.2%

8.4%

19.7%

8.8%

6.2%

8.3%

23.3%

10.7%

14.7%

11.5%

7.1%

10.1%

13.7%

5.3%

9.2%

7.9%

2.7%

5.0%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

CMMMLMCM

MMLMCM

MMLMCM

MMLMCM

MMLM

Ther

mal

com

fort

Smok

e in

publ

icsp

ace

Odo

r in

elev

ator

Odo

r in

publ

icsp

ace

Com

fort

of

mus

icso

und


3.2%

1.4%

2.2%

3.2%

2.7%

1.4%

1.3%

0.4%

3.1%

0.9%

10.6%

4.4%

17.0%

4.0%

8.4%

6.9%

6.6%

3.1%

4.1%

4.4%

6.2%

6.0%

28.6%

28.4%

35.8%

21.1%

31.1%

25.2%

27.0%

42.7%

28.4%

24.7%

38.2%

27.8%

48.0%

51.1%

34.9%

26.4%

32.9%

29.8%

29.6%

38.7%

34.9%

29.5%

39.6%

37.5%

9.3%

11.6%

6.4%

22.5%

16.4%

18.3%

23.9%

9.3%

22.9%

26.4%

9.8%

22.2%

3.6%

24.7%

8.4%

15.1%

10.6%

5.8%

8.3%

13.7%

3.1%

4.6%

0% 20% 40% 60% 80% 100%

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

Smok

e in

publ

ic sp

ace

Odo

r in

elev

ator

Odo

r in

publ

icsp

ace

Volu

me

ofm

usic

extremely soft very soft farily soft so-so fairly strong very strong extremely strong


217

Figure A2.5e Evaluation of Temperature

Figure A2.5f Evaluation of Building Style

4.0%

4.0%

5.1%

14.1%

6.2%

14.7%

21.6%

26.7%

22.6%

41.4%

44.9%

39.2%

13.7%

15.1%

13.4%

4.4%

2.2%

4.1%

0% 20% 40% 60% 80% 100%

Classic Mall

Modern Mall

Local Mall

extremely cold very cold farily cold so-so fairly hot very hot extremely hot

5.7%

12.5%

7.8%

22.9%

34.4%

23.5%

33.5%

33.9%

33.2%

30.0%

16.5%

26.7%

6.6%

2.2%

6.0%

0% 20% 40% 60% 80% 100%

Classic Mall

Modern Mall

Local Mall

classic much classic somewhat classicneither classic or modern somewhat modern much modern


218

Figure A2.6 Evaluation of Space Arrangement Dimension

4.4%

5.0%

3.7%

7.2%

5.2%

3.1%

3.0%

1.9%

1.8%

2.3%

6.7%

9.5%

4.2%

2.3%

2.5%

3.8%

9.6%

4.2%

2.6%

5.0%

2.2%

5.4%

2.8%

4.9%

7.1%

3.2%

3.1%

2.3%

21.8%

24.4%

15.3%

4.7%

6.8%

7.0%

9.4%

24.1%

8.3%

14.4%

16.3%

7.6%

7.6%

24.6%

10.6%

11.2%

19.2%

11.0%

3.1%

19.6%

3.2%

6.6%

15.2%

6.4%

18.7%

29.9%

25.1%

12.7%

24.2%

19.0%

33.0%

30.1%

26.0%

24.2%

31.2%

19.9%

28.6%

35.3%

29.0%

26.8%

29.0%

17.9%

20.3%

27.2%

21.1%

20.7%

29.9%

25.7%

24.9%

19.5%

19.1%

25.5%

32.0%

24.0%

29.2%

19.3%

31.3%

22.7%

19.8%

24.2%

23.7%

19.2%

27.6%

29.5%

24.6%

20.2%

26.4%

29.5%

29.4%

33.9%

31.3%

33.0%

15.6%

9.0%

22.3%

34.0%

25.6%

27.5%

15.1%

8.4%

20.8%

20.6%

20.3%

27.5%

26.8%

10.3%

21.2%

19.6%

14.7%

31.2%

33.5%

17.4%

31.7%

26.9%

16.1%

23.9%

8.0%

2.7%

10.2%

21.7%

9.1%

20.0%

7.5%

1.2%

4.2%

12.4%

4.5%

18.0%

10.3%

4.5%

8.3%

6.3%

5.4%

14.2%

16.3%

5.8%

13.3%

10.1%

3.1%

8.7%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

Ease

to fi

ndpu

blic

seat

sEa

se to

find

ATM

Ease

to fi

ndnu

rsin

g ro

omEa

se to

find

pray

ing

room

Ease

to fi

ndto

ilets

Ease

to fi

ndel

evat

ors

Ease

to fi

ndes

cala

tors

Ease

to fi

ndst

ores



219

Figure A2.7 Evaluation of Quality of Facilities Dimension

12.4%

10.2%

8.3%

5.7%

7.1%

4.6%

1.8%

3.1%

1.8%

1.8%

2.3%

3.6%

2.2%

2.6%

9.8%

6.9%

3.1%

9.8%

5.0%

7.1%

3.2%

20.4%

28.4%

16.1%

16.3%

24.0%

14.7%

3.5%

4.4%

1.4%

2.6%

5.3%

1.8%

4.0%

4.0%

2.3%

7.5%

8.4%

4.2%

7.5%

8.0%

4.1%

7.5%

7.6%

4.1%

7.5%

28.9%

7.3%

7.9%

28.0%

10.1%

7.0%

27.6%

8.3%

27.1%

32.4%

22.5%

23.8%

34.2%

25.7%

11.9%

12.0%

10.1%

13.7%

11.6%

10.1%

14.5%

13.3%

11.5%

22.9%

32.4%

15.8%

22.6%

31.1%

14.2%

20.4%

26.2%

16.1%

24.7%

31.1%

36.2%

22.5%

32.4%

29.4%

23.8%

33.8%

32.1%

18.7%

13.3%

20.6%

27.3%

18.7%

21.6%

18.9%

9.8%

12.8%

14.5%

7.1%

15.6%

13.2%

5.3%

12.8%

24.2%

20.4%

26.5%

19.9%

20.0%

25.2%

22.1%

28.0%

22.9%

31.3%

14.2%

23.9%

26.9%

13.8%

28.9%

27.8%

17.8%

27.1%

9.3%

4.4%

20.6%

15.4%

5.8%

19.3%

6.6%

4.0%

10.1%

8.4%

4.0%

8.7%

7.0%

4.4%

9.2%

16.7%

19.6%

27.0%

21.2%

19.6%

26.6%

21.2%

16.4%

30.3%

18.9%

7.6%

16.1%

24.7%

7.6%

19.7%

24.7%

5.3%

19.3%

4.9%

1.8%

6.4%

7.5%

3.1%

9.6%

2.6%

0.9%

3.7%

4.4%

2.2%

1.8%

4.4%

0.9%

1.8%

7.9%

3.6%

14.4%

10.2%

4.9%

23.4%

9.7%

7.1%

21.1%

11.5%

2.2%

6.0%

11.9%

3.6%

5.0%

12.3%

4.0%

7.3%

1.3%1.8%1.8%

1.8%1.4%

54.6%66.7%

59.2%54.6%

66.7%59.2%

55.1%67.6%

59.2%17.6%

11.6%7.9%

16.8%10.2%

5.5%16.8%

10.2%5.0%2.6%

1.8%2.6%

2.6%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LMN

umbe

r of

publ

ic se

ats

Qua

lity

ofpu

blic

seat

s

Odo

r in

nurs

ing

room

Clea

nlin

ess

of n

ursin

gro

om

Qua

lity

ofnu

rsin

gro

om

Odo

r in

pray

ing

room

Qua

lity

ofpr

ayin

gro

om

Clea

nlin

ess

of p

rayi

ngro

omO

dor i

nto

ilet

Clea

nlin

ess

of to

ilet

Qua

lity

ofto

ilet

terrible very poor poor fair good very good excellent never use


220

Figure A2.8 Evaluation of Social Environment Dimension

Figure A2.9 Evaluation of Personal Service Dimension

4.9%

1.3%

2.3%

4.0%

2.3%

8.0%

15.1%

8.3%

8.0%

16.0%

11.0%

29.6%

38.7%

33.5%

31.0%

35.6%

30.7%

26.5%

27.1%

27.5%

25.2%

29.8%

26.1%

22.1%

14.7%

20.6%

23.0%

13.8%

22.5%

6.2%

2.2%

6.9%

6.6%

2.2%

5.5%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

CM

MM

LM

CM

MM

LM

Soci

albe

havi

or o

fot

her

shop

pers

Polit

enes

s of

othe

rsh

oppe

rs


3.62%

5.80%

5.53%

3.57%

5.36%

5.99%

6.64%

4.89%

4.61%

5.75%

4.89%

3.69%

4.42%

5.33%

4.61%

3.54%

4.89%

3.69%

30.32%

27.23%

26.73%

32.59%

29.91%

28.57%

27.43%

27.56%

27.65%

30.09%

29.33%

27.65%

26.55%

27.56%

26.27%

27.43%

25.78%

32.26%

28.96%

31.25%

19.82%

29.02%

28.13%

23.04%

29.20%

28.44%

21.66%

28.32%

25.33%

27.65%

30.09%

28.00%

25.81%

32.74%

29.33%

23.96%

21.27%

28.57%

33.64%

22.77%

31.25%

31.34%

24.34%

32.00%

34.56%

24.34%

33.33%

27.19%

26.11%

32.44%

34.10%

23.01%

33.33%

33.64%

13.12%

6.25%

13.82%

9.38%

4.02%

10.60%

11.50%

7.11%

10.60%

10.62%

7.11%

12.90%

11.95%

6.22%

8.29%

11.06%

6.67%

5.53%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

CM

MM

LM

Frie

ndlin

ess

of c

usto

mer

serv

ices

Help

fuln

ess

of c

usto

mer

serv

ices

Frie

ndlin

ess

of se

curit

yse

rvic

es

Help

fuln

ess

of se

curit

yse

rvic

es

Frie

ndlin

ess

of g

reet

ers/

rece

ptio

nist

s

Help

fuln

ess

of g

reet

ers/

rece

ptio

nist

s


221

APPENDIX 3 Results of Independent t-Tests of Evaluations of Malls’ Cognitive Dimension

Table A3.1 Independent t-Test Results for Evaluation of Mall’s Dimensions between Shoppers in LM (N=218) and MM (N=225)

Levene's Test for Equality of Variances

t-test for Equality of Means

F Sig. t df Sig. (2-tailed)

General evaluation of location and convenience

Equal variances assumed .04 .83 2.23 441.00 .03 Equal variances not assumed 2.23 441.00 .03

General evaluation of price Equal variances assumed .39 .53 2.09 440.00 .04 Equal variances not assumed 2.09 436.19 .04

General evaluation of store variety, merchandise selection and quality

Equal variances assumed 4.57 .03 3.85 440.00 .00 Equal variances not assumed 3.85 436.28 .00

General evaluation of advertising and promotion


General evaluations of mall's comfort and visual appearance


General evaluation of space arrangement


General evaluation of personal service


General evaluation of quality of facilities


General evaluation of social environment


Accessibility by public tranportation


Accessibility by private car Equal variances assumed .03 .86 2.48 286.00 .01 Equal variances not assumed 2.46 262.83 .01



Accessibility by bicycle Equal variances assumed .40 .53 2.17 176.00 .03 Equal variances not assumed 2.17 175.97 .03

Ease to get the parking space


Parking cost Equal variances assumed 5.69 .02 3.05 365.00 .00 Equal variances not assumed 3.05 352.87 .00

Opening hours of the mall Equal variances assumed 5.96 .02 2.76 430.00 .01 Equal variances not assumed 2.75 425.71 .01

Trip to the mall Equal variances assumed 6.41 .01 .22 429.00 .83 Equal variances not assumed .22 407.86 .83

Price level of food and beverages


Price level of fashion Equal variances assumed 5.29 .02 2.63 441.00 .01 Equal variances not assumed 2.63 425.92 .01

Price level of supermarket Equal variances assumed 8.98 .00 3.27 441.00 .00 Equal variances not assumed 3.27 433.31 .00

APPENDIX 3 RESULTS OF INDEPENDENT T-TESTS OF EVALUATIONS OF MALLS’ COGNITIVE DIMENSION

222





Number of stores Equal variances assumed 4.11 .04 5.42 441.00 .00 Equal variances not assumed 5.43 440.68 .00

Types and variety of stores Equal variances assumed 1.18 .28 5.24 441.00 .00 Equal variances not assumed 5.25 440.97 .00

Product selection in stores Equal variances assumed 3.42 .06 4.97 440.00 .00 Equal variances not assumed 4.97 439.41 .00

Variety of major stores Equal variances assumed .56 .45 3.43 439.00 .00 Equal variances not assumed 3.43 438.81 .00

Variety of food in food courts


Variety of leisure facilities Equal variances assumed 3.16 .08 3.59 438.00 .00 Equal variances not assumed 3.59 434.25 .00

Quality of banking facilities Equal variances assumed 3.38 .07 2.59 416.00 .01 Equal variances not assumed 2.59 409.04 .01

Variety of banking facilities Equal variances assumed 3.61 .06 2.99 419.00 .00 Equal variances not assumed 2.98 412.39 .00





Quality of children facilities Equal variances assumed 10.98 .00 4.06 417.00 .00 Equal variances not assumed 4.04 399.28 .00

Variety of children facilities Equal variances assumed 6.33 .01 4.98 415.00 .00 Equal variances not assumed 4.96 399.37 .00

Quality of entertainment facilities


Variety of entertainment facilities


Quality of health facilities Equal variances assumed 2.62 .11 2.52 370.00 .01 Equal variances not assumed 2.52 360.41 .01

Variety of health facilities Equal variances assumed .89 .35 3.56 366.00 .00 Equal variances not assumed 3.56 360.96 .00

Quality of beauty facilities Equal variances assumed 1.84 .18 3.86 364.00 .00 Equal variances not assumed 3.87 357.84 .00

Variety of beauty facilities Equal variances assumed 7.07 .01 3.44 362.00 .00 Equal variances not assumed 3.46 350.04 .00

Qualtiy of activities/ events/exhibitions


Attractiveness of activities/ events/exhibitions


Frequency of activities/ events/exhibitions


Special sales promotions Equal variances assumed .32 .57 5.02 441.00 .00 Equal variances not assumed 5.02 440.10 .00

Cleanliness of stores and public space


Safety of stores and public space





223

Attractiveness of interior design


Attractiveness of window display






Artwork in the public space Equal variances assumed .94 .33 4.25 438.00 .00 Equal variances not assumed 4.24 433.12 .00



Visual attractiveness of the public space


Quality of gardens and greenery


Building style Equal variances assumed .02 .89 4.32 436.00 .00 Equal variances not assumed 4.32 432.92 .00

Public spaces'atmosphere Equal variances assumed 1.19 .28 3.35 441.00 .00 Equal variances not assumed 3.34 434.75 .00

Food court's atmosphere Equal variances assumed 2.45 .12 2.39 441.00 .02 Equal variances not assumed 2.39 433.73 .02

Quality of lighting Equal variances assumed 6.45 .01 4.38 440.00 .00 Equal variances not assumed 4.38 428.09 .00

Type of music Equal variances assumed .17 .68 3.52 441.00 .00 Equal variances not assumed 3.51 440.25 .00

Comfort in music sound Equal variances assumed 3.33 .07 2.84 441.00 .00 Equal variances not assumed 2.83 427.27 .00

Odor in the public space Equal variances assumed .39 .53 4.11 440.00 .00 Equal variances not assumed 4.10 434.83 .00

Odor in the elevator Equal variances assumed 1.15 .28 3.32 441.00 .00 Equal variances not assumed 3.32 427.19 .00

Smoke in public space Equal variances assumed 4.39 .04 1.05 440.00 .30 Equal variances not assumed 1.04 417.57 .30

Thermal comfort Equal variances assumed 1.88 .17 2.76 440.00 .01 Equal variances not assumed 2.75 433.40 .01

Ease to find stores Equal variances assumed 2.15 .14 4.37 440.00 .00 Equal variances not assumed 4.37 439.95 .00

Ease to find escalators Equal variances assumed 1.32 .25 6.03 440.00 .00 Equal variances not assumed 6.04 439.70 .00

Ease to find elevators Equal variances assumed 2.10 .15 4.89 440.00 .00 Equal variances not assumed 4.88 429.44 .00

Ease to find toilets Equal variances assumed .59 .44 5.28 439.00 .00 Equal variances not assumed 5.28 438.48 .00

Ease to find the praying room


Ease to find nursing room Equal variances assumed .02 .89 3.77 177.00 .00 Equal variances not assumed 3.77 174.51 .00

Ease to find ATM Equal variances assumed 8.25 .00 2.32 417.00 .02 Equal variances not assumed 2.30 399.84 .02

Ease to find public seats Equal variances assumed 6.51 .01 5.22 434.00 .00 Equal variances not assumed 5.21 426.20 .00

Quality of toilets Equal variances assumed .76 .39 6.56 437.00 .00 Equal variances not assumed 6.56 436.53 .00


224

Cleanliness of toilets Equal variances assumed .01 .91 6.58 437.00 .00 Equal variances not assumed 6.59 436.62 .00

Odor in toilets Equal variances assumed .01 .90 6.08 435.00 .00 Equal variances not assumed 6.08 434.28 .00

Cleanliness of the praying room


Quality of the praying room Equal variances assumed .85 .36 7.44 406.00 .00 Equal variances not assumed 7.44 400.49 .00

Odor in the praying room Equal variances assumed .15 .70 4.65 395.00 .00 Equal variances not assumed 4.65 392.50 .00

Quality of nursing room Equal variances assumed .20 .66 2.61 160.00 .01 Equal variances not assumed 2.61 153.36 .01

Cleanliness of nursing room Equal variances assumed .87 .35 2.14 162.00 .03 Equal variances not assumed 2.13 152.21 .04

Odor in the nursing room Equal variances assumed .41 .52 2.66 162.00 .01 Equal variances not assumed 2.69 161.76 .01

Quality of public seats Equal variances assumed 8.56 .00 5.32 434.00 .00 Equal variances not assumed 5.31 424.41 .00

Number of public seats Equal variances assumed 10.18 .00 6.12 433.00 .00 Equal variances not assumed 6.10 417.17 .00

Politeness of other shoppers


Social behavior of other shoppers



Equal variances assumed .33 .57 -.89 440.00 .37 Equal variances not assumed -.89 437.95 .37


Equal variances assumed .91 .34 .65 440.00 .52 Equal variances not assumed .65 436.57 .52

Helpfulness of security services


Friendliness of security services

Equal variances assumed 4.38 .04 .65 440.00 .52 Equal variances not assumed .65 431.27 .52

Helpfulness of customer services


Friendliness of customer services



225

Table A3.2 Independent t-Test Results for Evaluation of Mall’s Dimensions between Shoppers in LM (N=218) and CM (N=227)

Levene's Test for

Equality of Variances


F Sig. t df Sig. (2-

tailed) General evaluation of location and convenience


General evaluation of price Equal variances assumed .69 .41 2.16 442.00 .03 Equal variances not assumed 2.16 432.89 .03








Equal variances assumed 1.10 .30 -.28 441.00 .78 Equal variances not assumed -.28 431.48 .78









Accessibility by private car Equal variances assumed .07 .80 .84 285.00 .40 Equal variances not assumed .84 268.81 .40



Accessibility by bicycle Equal variances assumed .86 .36 -.29 176.00 .77 Equal variances not assumed -.29 173.99 .77



Parking cost Equal variances assumed .01 .93 1.85 379.00 .07 Equal variances not assumed 1.85 374.80 .07

Opening hours of the mall Equal variances assumed .29 .59 1.97 423.00 .05 Equal variances not assumed 1.97 423.00 .05

Trip to the mall Equal variances assumed .34 .56 -.36 429.00 .72 Equal variances not assumed -.36 424.56 .72



Price level of fashion Equal variances assumed .26 .61 2.88 443.00 .00 Equal variances not assumed 2.88 441.60 .00

Price level of supermarket Equal variances assumed .46 .50 1.38 443.00 .17 Equal variances not assumed 1.38 442.09 .17






226

Number of stores Equal variances assumed .20 .65 5.84 442.00 .00

Equal variances not assumed 5.84 442.00 .00 Types and variety of stores Equal variances assumed 1.18 .28 6.08 443.00 .00

Equal variances not assumed 6.09 439.51 .00 Product selection in stores Equal variances assumed .77 .38 4.49 442.00 .00

Equal variances not assumed 4.50 438.72 .00 Variety of major stores Equal variances assumed 4.74 .03 2.24 442.00 .03

Equal variances not assumed 2.24 435.62 .03 Variety of food in food courts



Quality of banking facilities Equal variances assumed 1.13 .29 -1.10 406.00 .27 Equal variances not assumed -1.10 399.06 .27

Variety of banking facilities Equal variances assumed .11 .74 -.08 408.00 .93 Equal variances not assumed -.08 407.07 .93





Quality of children facilities Equal variances assumed .25 .62 2.49 411.00 .01 Equal variances not assumed 2.49 410.99 .01

Variety of children facilities Equal variances assumed .19 .66 3.59 410.00 .00 Equal variances not assumed 3.59 410.00 .00





Quality of health facilities Equal variances assumed 1.30 .26 2.85 354.00 .00 Equal variances not assumed 2.84 345.55 .00

Variety of health facilities Equal variances assumed 1.20 .27 3.64 347.00 .00 Equal variances not assumed 3.63 339.27 .00

Quality of beauty facilities Equal variances assumed .50 .48 .75 364.00 .45 Equal variances not assumed .75 361.81 .46

Variety of beauty facilities Equal variances assumed .01 .91 1.04 363.00 .30 Equal variances not assumed 1.04 362.93 .30

Qualtiy of activities/events/exhibitions


Attractiveness of activities/events/exhibitions


Frequency of activities/events/exhibitions


Special sales promotions Equal variances assumed 1.29 .26 3.84 442.00 .00 Equal variances not assumed 3.84 441.07 .00


Equal variances assumed .72 .40 -1.30 443.00 .19 Equal variances not assumed -1.30 442.85 .19










227





Artwork in the public space Equal variances assumed .10 .76 3.79 440.00 .00 Equal variances not assumed 3.79 439.92 .00







Building style Equal variances assumed 1.84 .18 1.09 438.00 .28 Equal variances not assumed 1.09 436.60 .28

Public spaces'atmosphere Equal variances assumed .04 .84 1.50 443.00 .14 Equal variances not assumed 1.50 442.89 .14

Food court's atmosphere Equal variances assumed .06 .80 1.75 443.00 .08 Equal variances not assumed 1.76 442.58 .08

Quality of lighting Equal variances assumed .50 .48 .81 443.00 .42 Equal variances not assumed .81 442.79 .42

Type of music Equal variances assumed .01 .94 .49 443.00 .62 Equal variances not assumed .49 441.15 .62

Comfort in music sound Equal variances assumed .81 .37 -.31 443.00 .76 Equal variances not assumed -.31 438.15 .76

Odor in the public space Equal variances assumed 1.93 .17 -.71 442.00 .48 Equal variances not assumed -.71 441.95 .48

Odor in the elevator Equal variances assumed .65 .42 -.15 442.00 .88 Equal variances not assumed -.15 441.98 .88

Smoke in public space Equal variances assumed .10 .76 -3.24 442.00 .00 Equal variances not assumed -3.23 424.37 .00

Thermal comfort Equal variances assumed .12 .73 .03 442.00 .98 Equal variances not assumed .03 441.54 .98

Ease to find stores Equal variances assumed .02 .88 -1.02 443.00 .31 Equal variances not assumed -1.02 442.82 .31

Ease to find escalators Equal variances assumed .10 .75 -1.07 443.00 .29 Equal variances not assumed -1.07 442.25 .29

Ease to find elevators Equal variances assumed 1.39 .24 3.11 441.00 .00 Equal variances not assumed 3.11 435.24 .00

Ease to find toilets Equal variances assumed .04 .84 -1.33 439.00 .18 Equal variances not assumed -1.33 438.98 .18



Ease to find nursing room Equal variances assumed .56 .45 -.33 200.00 .74 Equal variances not assumed -.33 193.13 .74

Ease to find ATM Equal variances assumed 2.83 .09 -1.95 410.00 .05 Equal variances not assumed -1.95 400.11 .05

Ease to find public seats Equal variances assumed .07 .80 1.91 438.00 .06 Equal variances not assumed 1.91 437.59 .06

Quality of toilets Equal variances assumed .76 .38 -3.16 435.00 .00 Equal variances not assumed -3.16 432.84 .00

Cleanliness of toilets Equal variances assumed .05 .83 -3.12 435.00 .00 Equal variances not assumed -3.12 434.98 .00

Odor in toilets Equal variances assumed .95 .33 -3.41 433.00 .00 Equal variances not assumed -3.41 430.83 .00


228



Quality of the praying room Equal variances assumed .01 .92 4.20 392.00 .00 Equal variances not assumed 4.20 384.66 .00

Odor in the praying room Equal variances assumed .09 .77 2.83 383.00 .00 Equal variances not assumed 2.84 382.92 .00

Quality of nursing room Equal variances assumed .07 .79 -.39 189.00 .69 Equal variances not assumed -.39 182.64 .69

Cleanliness of nursing room Equal variances assumed .00 .99 -.58 190.00 .57 Equal variances not assumed -.57 184.11 .57

Odor in the nursing room Equal variances assumed 1.41 .24 .85 190.00 .40 Equal variances not assumed .84 173.17 .40

Quality of public seats Equal variances assumed .37 .54 .88 438.00 .38 Equal variances not assumed .88 435.46 .38

Number of public seats Equal variances assumed 1.32 .25 3.39 434.00 .00 Equal variances not assumed 3.38 432.62 .00








Equal variances assumed .00 .96 .00 441.00 1.00 Equal variances not assumed .00 440.60 1.00










229

Table A3.3 Independent t-Test Results for Evaluation of Mall’s Dimensions between Shoppers in MM (N=225) and CM (N=227)

Levene's Test for Equality of Variances


F Sig. t df Sig. (2-tailed)

General evaluation of location and convenience


General evaluation of price Equal variances assumed .04 .85 .02 450.00 .98 Equal variances not assumed .02 448.78 .98


Equal variances assumed 1.34 .25 -1.35 450.00 .18 Equal variances not assumed -1.35 448.15 .18















Accessibility by private car Equal variances assumed .23 .63 -1.72 321.00 .09 Equal variances not assumed -1.72 320.13 .09



Accessibility by bicycle Equal variances assumed 2.54 .11 -2.55 178.00 .01 Equal variances not assumed -2.55 175.45 .01



Parking cost Equal variances assumed 6.01 .01 -1.14 378.00 .25 Equal variances not assumed -1.15 375.52 .25

Opening hours of the mall Equal variances assumed 3.17 .08 -.70 435.00 .49 Equal variances not assumed -.70 430.41 .49

Trip to the mall Equal variances assumed 3.63 .06 -.62 448.00 .54 Equal variances not assumed -.62 440.92 .54



Price level of fashion Equal variances assumed 3.05 .08 .45 450.00 .66 Equal variances not assumed .45 442.22 .66

Price level of supermarket Equal variances assumed 5.12 .02 -1.85 450.00 .06 Equal variances not assumed -1.85 446.52 .06


Equal variances assumed 7 .01 -2.40 449.00 .02 Equal variances not assumed -2.41 436.91 .02




230

Number of stores Equal variances assumed 2.29 .13 .49 449.00 .63

Equal variances not assumed .49 448.75 .63 Types and variety of stores Equal variances assumed 3.81 .05 1.07 450.00 .29

Equal variances not assumed 1.07 447.04 .28 Product selection in stores Equal variances assumed 5.97 .01 -.14 450.00 .89

Equal variances not assumed -.14 443.30 .89 Variety of major stores Equal variances assumed 7.17 .01 -.98 449.00 .33

Equal variances not assumed -.98 444.48 .33 Variety of food in food courts



Quality of banking facilities Equal variances assumed .92 .34 -3.87 426.00 .00 Equal variances not assumed -3.88 426.00 .00

Variety of banking facilities Equal variances assumed 2.58 .11 -3.13 425.00 .00 Equal variances not assumed -3.12 422.37 .00




Equal variances assumed 1.08 .30 -.39 445.00 .70 Equal variances not assumed -.39 441.59 .70

Quality of children facilities Equal variances assumed 6.92 .01 -1.33 420.00 .18 Equal variances not assumed -1.32 402.94 .19

Variety of children facilities Equal variances assumed 3.53 .06 -1.05 419.00 .30 Equal variances not assumed -1.05 403.16 .30





Quality of health facilities Equal variances assumed 8.10 .00 .69 354.00 .49 Equal variances not assumed .69 322.07 .49

Variety of health facilities Equal variances assumed 4.65 .03 .46 347.00 .65 Equal variances not assumed .45 323.92 .65

Quality of beauty facilities Equal variances assumed 4.23 .04 -2.91 362.00 .00 Equal variances not assumed -2.91 347.13 .00

Variety of beauty facilities Equal variances assumed 8.24 .00 -2.32 357.00 .02 Equal variances not assumed -2.32 343.48 .02

Qualtiy of activities/events/exhibitions


Attractiveness of activities/events/exhibitions


Frequency of activities/events/exhibitions


Special sales promotions Equal variances assumed 2.80 .09 -.96 449.00 .34 Equal variances not assumed -.96 445.30 .34












231





Artwork in the public space Equal variances assumed 1.62 .20 -.27 448.00 .79 Equal variances not assumed -.27 444.21 .79







Building style Equal variances assumed 1.74 .19 -3.00 448.00 .00 Equal variances not assumed -3.00 439.36 .00

Public spaces'atmosphere Equal variances assumed .68 .41 -1.76 450.00 .08 Equal variances not assumed -1.76 445.20 .08

Food court's atmosphere Equal variances assumed 2.93 .09 -.47 450.00 .64 Equal variances not assumed -.47 438.87 .64

Quality of lighting Equal variances assumed 2.63 .11 -3.50 449.00 .00 Equal variances not assumed -3.51 439.76 .00

Type of music Equal variances assumed .13 .72 -3.10 450.00 .00 Equal variances not assumed -3.10 449.75 .00

Comfort in music sound Equal variances assumed .85 .36 -3.30 450.00 .00 Equal variances not assumed -3.30 447.47 .00

Odor in the public space Equal variances assumed 4.60 .03 -4.81 450.00 .00 Equal variances not assumed -4.82 445.66 .00

Odor in the elevator Equal variances assumed 3.99 .05 -3.45 449.00 .00 Equal variances not assumed -3.45 435.90 .00

Smoke in public space Equal variances assumed 5.14 .02 -4.78 450.00 .00 Equal variances not assumed -4.78 449.62 .00

Thermal comfort Equal variances assumed 3.08 .08 -2.74 450.00 .01 Equal variances not assumed -2.74 446.29 .01

Ease to find stores Equal variances assumed 1.68 .20 -5.38 449.00 .00 Equal variances not assumed -5.38 448.56 .00

Ease to find escalators Equal variances assumed .75 .39 -7.14 449.00 .00 Equal variances not assumed -7.14 446.97 .00

Ease to find elevators Equal variances assumed .05 .83 -1.80 447.00 .07 Equal variances not assumed -1.80 446.20 .07

Ease to find toilets Equal variances assumed .90 .34 -6.60 446.00 .00 Equal variances not assumed -6.60 445.67 .00



Ease to find nursing room Equal variances assumed .36 .55 -4.37 187.00 .00 Equal variances not assumed -4.34 170.76 .00

Ease to find ATM Equal variances assumed 1.81 .18 -4.59 429.00 .00 Equal variances not assumed -4.59 427.91 .00

Ease to find public seats Equal variances assumed 7.97 .00 -3.20 444.00 .00 Equal variances not assumed -3.20 439.19 .00

Quality of toilets Equal variances assumed .00 .96 -9.94 442.00 .00 Equal variances not assumed -9.94 441.37 .00

Cleanliness of toilets Equal variances assumed .10 .75 -9.58 442.00 .00 Equal variances not assumed -9.59 441.76 .00

Odor in toilets Equal variances assumed .73 .39 -9.69 442.00 .00 Equal variances not assumed -9.69 441.59 .00


232



Quality of the praying room Equal variances assumed .57 .45 -3.13 388.00 .00 Equal variances not assumed -3.14 387.84 .00

Odor in the praying room Equal variances assumed .01 .94 -1.74 384.00 .08 Equal variances not assumed -1.74 380.59 .08

Quality of nursing room Equal variances assumed .06 .81 -3.14 173.00 .00 Equal variances not assumed -3.11 149.67 .00

Cleanliness of nursing room Equal variances assumed .99 .32 -2.78 176.00 .01 Equal variances not assumed -2.73 148.44 .01

Odor in the nursing room Equal variances assumed .27 .61 -2.16 176.00 .03 Equal variances not assumed -2.15 156.33 .03

Quality of public seats Equal variances assumed 5.62 .02 -4.52 444.00 .00 Equal variances not assumed -4.52 441.54 .00

Number of public seats

















Equal variances assumed 3.09 .08 .00 444.00 1.00 Equal variances not assumed .00 432.67 1.00

233

APPENDIX 4 The Observation Sheets

For each respondent, observers prepared a set of observation sheets using a color-pen, a correction-fluid-pen, and sticky-notes. The observation sheets consisted of seven pages, including a page for the profile of the respondent and the instructional guidelines, and six pages of layout plans. Time information was recorded by observers’ mobile phone.

Figu

re A

4.1

The

Obs

erva

tion

Shee

t: Th

e In

stru

ctio

n

APPENDIX 4 THE OBSERVATION SHEETS

234

Fig

ure

A4.2

The

Obs

erva

tion

Shee

t: A

Sam

ple

of L

ayou

t Pla

n an

d Tr

acki

ng D

ata

235

APPENDIX 5 Classic Mall: Type of Stores, Store Classification, and Number of Visits

Due to the confidentiality, all stores are identified by unit number with a description of major items purchased, tenant space category, type of store, and size of store. Additionally, N visits shows the number of respondents who visited a store during the survey.

Table A5.1 Type of Stores, Store Classification, and Number of Visits: Lower Ground Floor

no unit number description type of stores/

facility store classification size (m2)

N

visits

1 1B01 office stationery specialty store media-and-special interest

293.1 5

2 1B02 pet shop specialty store media-and-special

interest 86.8 2

3 1B03 magazine kiosk specialty store media-and-special interest

40 1

4 1B04 souvenirs specialty store media-and-special interest

8 0

5 1C01 beauty center specialty store health-beauty 86.4 0

6 1C02 pharmacy specialty store health-beauty 85.23 3

7 1C03 pharmacy specialty store health-beauty 80 3

8 1F01 gadget shop specialty store furnishing 45 0

9 1F02 home appliances shop

specialty store furnishing 95.1 0

10 1G01 american fast food

specialty store eating-places 178.64 5

11 1G02 oriental fast food specialty store eating-places 106.27 2

12 1G03 Indonesian food specialty store eating-places 99.7 0

13 1G04 Indonesian food specialty store eating-places 90.2 7

14 1G05 bread & cakes specialty store eating-places 47.28 0

15 1G06 bread & cakes specialty store eating-places 6.9 1

16 1G07 bubble drinks specialty store eating-places 9 1

17 1G08 fast food specialty store eating-places 7.3 1

18 1G09 bread & cakes specialty store eating-places 4 1




22 1H01 money changer facility product service 26.45 1

APPENDIX 5 CLASSIC MALL: TYPE OF STORES, STORE CLASSIFICATION, AND NUMBER OF VISITS

236

23 1J01 children's playground

specialty store entertainment-and-education

8 2

24 1J02 temporary event specialty store entertainment-and-education

120 1

25 1K01 supermarket anchor store food 7032.74 59

26 1K02 diary-food specialty store food 53.11 2

27 1L01 praying room & toilet

facility public-service n/a 13

total 8621.22 110

Table A5.2 Type of Stores, Store Classification, and Number of Visits: Ground floor



N visits

1 2A01 fashion

universal specialty store apparel-and-

accessories 122.4 6

2 2A02 watch specialty store apparel-and-accessories

18.86 0

3 2A03 jewelry specialty store apparel-and-accessories

27.5 0

4 2A04 bags and shoes specialty store apparel-and-accessories

61.33 5

5 2A05 women fashion specialty store apparel-and-accessories

49.92 0

6 2A06 international brand of watch

specialty store apparel-and-accessories

23.28 0

7 2A07 women accessories


18 3

8 2A08 lady's shoes specialty store apparel-and-accessories

47 6


65.3 1

10 2A10 international brand of bags


110.2 0

11 2A11 international brand of shoes


80.7 2

12 2A12 international brand of sport fashion


91.2 4

13 2A13 fashion universal


75.42 1



130.5 2

15 2B01 magazine kiosk specialty store media-and-special interest

2 1

16 2B02 international brand of book store

specialty store media-and-special interest

78.3 5

17 2C01 cosmetics and perfumes

specialty store health-beauty 29.2 1


237

18 2C02 optical stores specialty store health-beauty 44.84 0

19 2C03 optical stores specialty store health-beauty 66 1

20 2C04 international brand of cosmetics and perfumes


72.37 11

21 2D01 department store: cosmetics & women collections

anchor store general merchandise 2868.11 37

22 2G01 ice cream café specialty store eating-places 73.4 0

23 2G02 cakes & coffee café


24 2G03 oriental fastfood specialty store eating-places 132.1 5

25 2G04 western restaurant


26 2G05 steak restaurant specialty store eating-places 313.6 0

27 2G06 pancake restaurant


28 2G07 pizza restaurant specialty store eating-places 121.1 4

29 2G08 international cafe


30 2H01 atm facility product service 61.8 3

31 2H02 Bank facility product service 97.2 1


33 2J01 temporary event


205 1

34 2J02 temporary event


5 1

35 2L01 toilet facility public service n/a 13


total 5631.24 153

Table A5.3 Type of Stores, Store Classification, and Number of Visits: Upper Ground floor

no unit number Description type of stores/

facility stores classification size (m2) N

visits



890.2 2

2 3A02 bags and shoes


286.2 8

3 3A03 international brand of fashion universal


188.3 1


238

4 3A04 women accessories


13.5 1



160.2 8



183.6 3

7 3A07 sport shoes specialty store apparel-and-accessories

171.2 4

8 3A08 women’s lingerie


40.99 0

9 3A09 moslem's clothing


90.91 2

10 3A10 women fashion


40.88 2



38.48 0



63.68 3



69.59 4



75.59 1



39.35 0

16 3B01 toys shop specialty store media-and-special interest

160.2 0

17 3B02 toys & souvenirs


2 2



2 0



2 1



2 1

21 3C01 beauty salon specialty store health-beauty 86.17 2

22 3D01 department store

specialty store general merchandise

541.6 0

23 3D02 department store women collections

anchor store general merchandise

2818.16 31

24 3J01 learning center


297 4

25 3J02 karaoke specialty store entertainment-and-education

701.9 2

26 3L01 toilet faciltiy public service n/a 1

total 6965.7 83


239

Table A5.4 Type of Stores, Store Classification, and Number of Visits: 1st floor



N visits

1 4A01 men’s underwear


12.26 0


38.72 1

3 4A03 bags specialty store apparel-and-accessories

64.2 0

4 4A04 ladies shoes specialty store apparel-and-accessories

38.02 0

5 4A05 jewelry specialty store apparel-and-accessories

43.41 0

6 4A06 women fashion specialty store apparel-and-accessories

39.5 2

7 4B01 books store specialty store media-and-special interest

1447.7 36



2 0



2 3



2 0



2 0

12 4C01 beauty salon specialty store health-beauty 84.4 3

13 4C02 personal/health cares


14 4D01 department store men collections

anchor store general merchandise 2821.66 16

15 4F01 gadget shop specialty store furnishing 42.34 2

16 4G01 café specialty store eating-places 85.54 3

17 4G02 oriental restaurant




19 4G04 bread & restaurant


20 4G05 Japanese restaurant


21 4G06 international ice cream kiosk

specialty store eating-places 11 0


23 4G08 steak restaurant






26 4H01 bank & atm facility product service 82.31 7


240





186.3 2



123 0



20 1

32 4K01 snacks & chips specialty store food 35.9 1


total 6189.32 120

Table A5.5 Type of Store, Store Classification, and Number of Visits: 2nd floor

no unit number description type of store/

facility store classification size (m2) N visits



31 1

2 5A03 sport shoes specialty store apparel-and-accessories

37 1

3 5A04 baby fashion


25.45 0

4 5A05 sleeping underware


14 0

5 5B01 toys shop specialty store media-and-special interest

553.46 3

6 5B02 music & instruments shop


217.2 1

7 5B03 records/ tapes shops


37.37 2

8 5C01 beauty salon


9 5C02 beauty salon


10 5C03 beauty salon


11 5C04 reflexiology specialty store health-beauty 33.5 1

12 5C05 personal health care


13 5C06 personal health care


14 5C07 hair & nail accessories


15 5D01 department store - children collection

anchor store general merchandise

2816.42 5


241

16 5F01 electronic appliances


17 5F02 camera shop


18 5F03 gadget shop


19 5F04 gadget shop


20 5F05 gadget shop


21 5F06 gadget shop




23 5G02 restaurant specialty store eating-places 223.2 11

24 5G03 Indonesian cafe & restaurant


25 5H01 tour travel facility product service 33.08 0

26 5L01 toilet & nursing room

facility public service n/a 6

27 5H02 library facility public service 117.54 1

total 6487.46 50

Table A5.6 Type of Stores, Store Classification, and Number of Visits: 3rd floor



N visits

1 6A01 universal fashion


353.72 3

2 6A02 children fashion


42.97 1



42.56 1



40.2 1



32.2 1

6 6A06 accessories specialty store apparel-and-accessories

33.6 10



37.95 2

8 6A08 accessories specialty store apparel-and-accessories

41.08 10

9 6B01 christian's books & records


32.31 0

10 6B02 military toys


35.62 3


242

11 6B03 game parlor


32.31 0

12 6G01 Indonesian & western restaurant




15 6G04 Japanese restaurant


16 6G05 Indonesian restaurant


17 6G06 Indonesian restaurant


18 6G07 hotdog kiosk


19 6G08 ice cream kiosk


20 6G09 cakes kiosk specialty store eating-places 3 0



58.8 2



442.28 3

23 6J03 cinema specialty store entertainment-and-education

2039.53 10

24 6J04 promotion booth


2 2

25 6J05 event (books)


100 2

26 6H01 daycare specialty store entertainment-and-education

175.95 0

27 6H02 service church

facility public service 277.42 0


total 5650.47 64

243

APPENDIX 6 Sub-patterns in Sequential Shopping Patterns

Each bracket represents one stop: [dep store] denotes “stop at department store”, [supermarket] denotes “stop at supermarket”, [*] denotes stop at retail stores or public space or facility.

Table A6.1 Stop sub-patterns: Sequence Length 3 (N=27) Sequence length 3 Frequency [enter] [*] [exit] 16 [enter] [dep store] [exit] 3 [enter] [supermarket] [exit] 8

Table A6.2 Stop sub-patterns: Sequence Length 4 (N=33) Sequence length 4 Frequency [enter] [dep store] [supermarket] [exit] 1 [enter] [*] [dep store] [exit] 2 [enter] [supermarket] [*] [exit] 4 [enter] [*] [supermarket] [exit] 5 [enter] [*] [*] [exit] 21

Table A6.3 Stop sub-patterns: Sequence Length 5 (N=34) Sequence length 5 Frequency [enter] [*] [*] [supermarket] [exit] 3 [enter] [*] [*] [*] [exit] 22 [enter] [*] [supermarket] [*] [exit] 3 [enter] [dep store] [*] [supermarket] [exit] 1 [enter] [dep store] [dep store] [*] [exit] 1 [enter] [dep store] [supermarket] [*] [exit] 1 [enter] [supermarket] [*] [dep store] [exit] 1 [enter] [supermarket] [*] [*] [exit] 2

Table A6.4 Stop sub-patterns: Sequenc Length 6 (N=12) Sequence length 6 Frequency [enter] [*] [supermarket] [*] [*] [exit] 2 [enter] [*] [*] [*] [*] [exit] 5 [enter] [*] [*] [*] [supermarket] [exit] 1 [enter] [dep store] [dep store] [*] [*] [exit] 2 [enter] [supermarket] [*] [*] [*] [exit] 1 [enter] [dep store] [supermarket] [*] [dep store] [exit] 1

Table A6.5 Stop sub-patterns: Sequence Length 7 (N=17) Sequence length 7 Frequency [enter] [*] [*] [*] [*] [*] [exit] 10 [enter] [*] [*] [dep store] [dep store] [*] [exit] 1 [enter] [*] [*] [dep store] [dep store] [dep store] [exit] 1 [enter] [*] [dep store] [dep store] [*] [*] [exit] 1 [enter] [dep store] [*] [*] [*] [dep store] [exit] 1 [enter] [dep store] [dep store] [*] [supermarket] [dep store] [exit] 1 [enter] [dep store] [dep store] [dep store] [*] [dep store] [exit] 1 [enter] [supermarket] [*] [supermarket] [*] [*] [exit] 1

APPENDIX 6 SUB-PATTERNS IN SEQUENTIAL SHOPPING PATTERNS

244

Tabl

e A6

.6 S

top

sub-

patt

erns

: Seq

uenc

e Le

ngth

8 (N

=16)

Sequ

ence

leng

th 8

Freq

uenc

y [e

nter

] [*

] [*

] [*

] [d

ep st

ore]

[*

] [*

] [e

xit]

2

[ent

er]

[*]

[*]

[dep

stor

e]

[*]

[sup

erm

arke

t]

[*]

[exi

t]

2 [e

nter

] [*

] [*

] [s

uper

mar

ket]

[*

] [*

] [d

ep st

ore]

[e

xit]

1

[ent

er]

[*]

[dep

stor

e]

[dep

stor

e]

[*]

[*]

[*]

[exi

t]

1 [e

nter

] [*

] [*

] [*

] [s

uper

mar

ket]

[*

] [*

] [e

xit]

1

[ent

er]

[*]

[*]

[*]

[*]

[*]

[sup

erm

arke

t]

[exi

t]

1 [e

nter

] [*

] [*

] [*

] [*

] [s

uper

mar

ket]

[*

] [e

xit]

1

[ent

er]

[*]

[*]

[dep

stor

e]

[dep

stor

e]

[*]

[sup

erm

arke

t]

[exi

t]

1 [e

nter

] [*

] [d

ep st

ore]

[d

ep st

ore]

[d

ep st

ore]

[d

ep st

ore]

*

[exi

t]

1 [e

nter

] [*

] [*

] [*

] [*

] [*

] [*

] [e

xit]

1

[ent

er]

[dep

stor

e]

[dep

stor

e]

[*]

[*]

[sup

erm

arke

t]

[*]

[exi

t]

1 [e

nter

] [d

ep st

ore]

[d

ep st

ore]

[d

ep st

ore]

[d

ep st

ore]

[s

uper

mar

ket]

[*

] [e

xit]

1

[ent

er]

[dep

stor

e]

[dep

stor

e]

[dep

stor

e]

[*]

[*]

[dep

stor

e]

[exi

t]

1 [e

nter

] [s

uper

mar

ket]

[*

] [*

] [*

] [*

] [*

] [e

xit]

1

Ta

ble

A6.7

Sto

p su

b-pa

tter

ns: S

eque

nce

Leng

th 9

(N=1

0)

Sequ

ence

leng

th 9

Fr

eque

ncy

[ent

er]

[*]

[*]

[*]

[*]

[*]

[*]

[*]

[exi

t]

2 [e

nter

] [d

ep st

ore]

[*

] [*

] [*

] [*

] [*

] [d

ep st

ore]

[e

xit]

2

[ent

er]

[*]

[*]

[*]

[*]

[*]

[*]

[sup

erm

arke

t]

[exi

t]

3 [e

nter

] [*

] [*

] [d

ep st

ore]

[*

] [*

] [*

] [s

uper

mar

ket]

[e

xit]

1

[ent

er]

[*]

[*]

[*]

[*]

[*]

[sup

erm

arke

t]

[*]

[exi

t]

1 [e

nter

] [*

] [*

] [*

] [*

] [*

] [*

] [d

ep st

ore]

[e

xit]

1

Ta

ble

A6.8

Sto

p su

b-pa

tter

ns: S

eque

nce

Leng

th 1

0 (N

=6)

Sequ

ence

leng

th 1

0

Freq

uenc

y [e

nter

] [s

uper

mar

ket]

[*

] [*

] [*

] [*

] [*

] [d

ep st

ore]

[*

] [e

xit]

1

[ent

er]

[dep

stor

e]

[*]

[*]

[*]

[*]

[*]

[*]

[dep

stor

e]

[exi

t]

1 [e

nter

] [d

ep st

ore]

[*

] [*

] [*

] [*

] [*

] [*

] [*

] [e

xit]

1

[ent

er]

[*]

[*]

[*]

[*]

[*]

[*]

[*]

[*]

[exi

t]

1 [e

nter

] [*

] [*

] [*

] [s

uper

mar

ket]

[*

] [*

] [*

] [*

] [e

xit]

1

[ent

er]

[*]

[*]

[*]

[*]

[dep

stor

e]

[*]

[*]

[dep

stor

e]

[exi

t]

1


245

Tabl

e A6

.9 S

top

sub-

patt

erns

: Seq

uenc

e Le

ngth

11

(N=4

)

Se

quen

ce le

ngth

11

Freq

uenc

y [e

nter

] [*

] [*

] [*

] [d

ep st

ore]

[d

ep st

ore]

[*

] [*

] [*

] [*

] [e

xit]

1

[ent

er]

[*]

[*]

[*]

[*]

[*]

[*]

[*]

[*]

[*]

[exi

t]

1 [e

nter

] [*

] [*

] [*

] [d

ep st

ore]

[d

ep st

ore]

[s

uper

mar

ket]

[*

] [*

] [*

] [e

xit]

1

[ent

er]

[*]

[*]

[dep

stor

e]

[*]

[*]

[*]

[*]

[dep

stor

e]

[*]

[exi

t]

1 Ta

ble

A6.1

0 St

op su

b-pa

tter

ns: S

eque

nce

Leng

th 1

2 (N

=3)

Se

quen

ce le

ngth

12

Fr

eque

ncy

[ent

er]

[*]

[*]

[dep

stor

e]

[dep

stor

e]

[dep

stor

e]

[dep

stor

e]

[dep

stor

e]

[*]

[sup

erm

arke

t]

[*]

[exi

t]

1 [e

nter

] [d

ep st

ore]

[d

ep st

ore]

[*

] [*

] [*

] [*

] [*

] [*

] [*

] [*

] [e

xit]

1

[ent

er]

[*]

[*]

[*]

[*]

[*]

[*]

[*]

[*]

[*]

[sup

erm

arke

t]

[exi

t]

1 Ta

ble

A6.1

1 St

op su

b-pa

tter

ns: S

eque

nce

Leng

th 1

3 (N

=1)

Sequ

ence

leng

th 1

3

Fr

eque

ncy

[ent

er]

[sup

erm

arke

t]

[*]

[*]

[*]

[dep

stor

e]

[dep

stor

e]

[dep

stor

e]

[*]

[*]

[*]

[*]

[exi

t]

1 Ta

ble

A6.1

2 St

op su

b-pa

tter

ns: S

eque

nce

Leng

th 1

4 (N

=1)

Sequ

ence

leng

th 1

4

Freq

uenc

y [e

nter

] [*

] [d

ep st

ore]

[*

] [*

] [*

] [*

] [*

] [*

] [*

] [d

ep st

ore]

[*

] [*

] [e

xit]

1

Ta

ble

A6.1

3 St

op su

b-pa

tter

ns: S

eque

nce

Leng

th 1

5 (N

=1)

Sequ

ence

leng

th 1

5

Freq

uenc

y [e

nter

] [*

] [*

] [*

] [*

] [*

] [*

] [*

] [*

] [*

] [*

] [*

] [*

] [*

] [e

xit]

1

Ta

ble

A6.1

4 St

op su

b-pa

tter

ns: S

eque

nce

Leng

th 1

7 (N

=1)

Sequ

ence

leng

th 1

7

Freq

uenc

y [e

nter

] [*

] [d

ep st

ore]

[d

ep st

ore]

[*

] [*

] [*

] [*

] [*

] [*

] [*

] [*

] [*

] [*

] [d

ep st

ore]

[s

uper

mar

ket]

[e

xit]

1


246

In the following, each row represents a floor; ‘---‘ denotes that shoppers stay on the same floor, while ‘*’ denotes shoppers switch to a floor other than the ground floor, the lower ground floor, and the upper ground floor. Table A6.15 Floor sub-patterns: Sequence length 3 (N=27) Sequence length 3 frequency ground floor lower GF ground floor 11 ground floor --- ground floor 6 ground floor * ground floor 10

Table A6.16 Floor sub-patterns: Sequence length 4 (N=33) Sequence length 4 Frequency ground floor lower GF ground floor --- 2 ground floor lower GF --- ground floor 2 ground floor lower GF * ground floor 1 ground floor upper GF lower GF --- 1 ground floor upper GF lower GF ground floor 1 ground floor --- lower GF --- 1 ground floor --- lower GF ground floor 2 ground floor --- upper GF ground floor 1 ground floor --- --- ground floor 4 ground floor --- * ground floor 3 ground floor * lower GF ground floor 2 ground floor * ground floor --- 5 ground floor * upper GF ground floor 2 ground floor * --- ground floor 3 ground floor * * ground floor 3

Table A6.17 Floor sub-patterns: Sequence length 5 (N=34) Sequence length 5 frequency

lower GF --- ground floor * lower GF 1 lower GF ground floor --- lower GF --- 1 lower GF ground floor --- * ground floor 1 lower GF ground floor * lower GF --- 1 lower GF --- --- --- lower GF 1

ground floor lower GF ground floor --- ground floor 1 ground floor lower GF --- ground floor --- 1 ground floor lower GF --- --- ground floor 2 ground floor upper GF lower GF * ground floor 1 ground floor upper GF ground floor --- ground floor 2 ground floor upper GF --- * ground floor 1 ground floor upper GF * * ground floor 1 ground floor --- lower GF ground floor --- 2 ground floor --- lower GF --- ground floor 1 ground floor --- lower GF * ground floor 1 ground floor --- --- lower GF ground floor 1 ground floor --- --- ground floor --- 3 ground floor --- --- * ground floor 1 ground floor --- * ground floor --- 2 ground floor --- * upper GF ground floor 2 ground floor --- * --- ground floor 3 ground floor * --- upper GF ground floor 1


247

ground floor * --- --- ground floor 1 ground floor * * lower GF ground floor 1 ground floor * * * ground floor 1

Table A6.18 Floor sub-patterns: Sequence length 6 (N=12) Sequence length 6 frequency ground floor lower GF --- --- ground floor --- 1 ground floor lower GF --- * --- ground floor 1 ground floor lower GF * --- * ground floor 1 ground floor --- lower GF --- ground floor --- 1 ground floor --- upper GF --- * ground floor 1 ground floor --- --- --- lower GF ground floor 1 ground floor --- * upper GF ground floor --- 1 ground floor upper GF --- ground floor --- ground floor 1 ground floor * lower GF * upper GF ground floor 1 ground floor * upper GF ground floor --- ground floor 1 ground floor * --- upper GF --- ground floor 1

upper GF --- lower GF upper GF --- upper GF 1


248

Tabl

e A6

.19

Floo

r sub

-pat

tern

s: S

eque

nce

leng

th 7

(N=1

7)

Sequ

ence

leng

th 7

fr

eque

ncy

grou

nd fl

oor

uppe

r GF

grou

nd fl

oor

---

low

er G

F gr

ound

floo

r ---

1

grou

nd fl

oor

low

er G

F ---

---

gr

ound

floo

r *

grou

nd fl

oor

1 gr

ound

floo

r lo

wer

GF

* *

---

grou

nd fl

oor

grou

nd fl

oor

1 gr

ound

floo

r lo

wer

GF

---

---

* gr

ound

floo

r ---

1

grou

nd fl

oor

low

er G

F *

---

low

er G

F ---

gr

ound

floo

r 1

grou

nd fl

oor

---

uppe

r GF

---

grou

nd fl

oor

---

grou

nd fl

oor

1 gr

ound

floo

r ---

up

per G

F ---

*

---

grou

nd fl

oor

1 gr

ound

floo

r ---

up

per G

F *

---

---

grou

nd fl

oor

1 gr

ound

floo

r ---

up

per G

F *

---

---

grou

nd fl

oor

1 gr

ound

floo

r ---

*

* *

grou

nd fl

oor

---

1 gr

ound

floo

r ---

*

* ---

up

per G

F gr

ound

floo

r 1

grou

nd fl

oor

---

---

uppe

r GF

* gr

ound

floo

r ---

1

grou

nd fl

oor

---

---

---

---

---

grou

nd fl

oor

1 gr

ound

floo

r *

grou

nd fl

oor

low

er g

roun

d fl.

---

gr

ound

floo

r ---

1

grou

nd fl

oor

* ---

---

up

per G

F gr

ound

floo

r ---

1

grou

nd fl

oor

* ---

---

*

---

grou

nd fl

oor

1 gr

ound

floo

r *

---

* lo

wer

GF

* gr

ound

floo

r 1

Tabl

e A.

20 F

loor

sub-

patt

erns

: Seq

uenc

e le

ngth

8 (N

=16)

Se

quen

ce le

ngth

8

fr

eque

ncy

low

er G

F gr

ound

floo

r *

---

---

low

er G

F ---

gr

ound

floo

r 1

grou

nd fl

oor

low

er G

F up

per G

F *

* *

grou

nd fl

oor

---

1 gr

ound

floo

r up

per G

F *

* gr

ound

floo

r lo

wer

GF

grou

nd fl

oor

---

1 gr

ound

floo

r up

per G

F *

* ---

*

grou

nd fl

oor

---

1 gr

ound

floo

r up

per G

F *

* up

per G

F ---

lo

wer

GF

grou

nd fl

oor

1 gr

ound

floo

r up

per G

F ---

*

grou

nd fl

oor

* gr

ound

floo

r ---

1

grou

nd fl

oor

* lo

wer

GF

---

---

grou

nd fl

oor

---

grou

nd fl

oor

1 gr

ound

floo

r *

---

* ---

gr

ound

floo

r lo

wer

GF

grou

nd fl

oor

1 gr

ound

floo

r ---

up

per G

F *

uppe

r GF

grou

nd fl

oor

---

grou

nd fl

oor

1 gr

ound

floo

r ---

up

per G

F *

* up

per G

F gr

ound

floo

r ---

1

grou

nd fl

oor

---

uppe

r GF

---

* lo

wer

GF

grou

nd fl

oor

---

1 gr

ound

floo

r ---

*

* ---

lo

wer

GF

grou

nd fl

oor

---

1 gr

ound

floo

r ---

*

---

uppe

r GF

low

er G

F ---

gr

ound

floo

r 1


249

Tabl

e A6

.20

(con

tinue

d)

Sequ

ence

leng

th 8

freq

uenc

y gr

ound

floo

r ---

*

---

---

---

---

grou

nd fl

oor

1 gr

ound

floo

r ---

---

lo

wer

GF

---

---

---

grou

nd fl

oor

1 gr

ound

floo

r ---

---

---

up

per G

F ---

lo

wer

GF

grou

nd fl

oor

1 Ta

ble

A6.2

1 Fl

oor s

ub-p

atte

rns:

Seq

uenc

e le

ngth

9 (N

=10)

Se

quen

ce le

ngth

9

freq

uenc

y gr

ound

floo

r up

per G

F gr

ound

floo

r up

per G

F *

* ---

lo

wer

GF

grou

nd fl

oor

1 gr

ound

floo

r *

low

er G

F ---

---

---

---

---

gr

ound

floo

r 1

grou

nd fl

oor

* *

* ---

up

per G

F ---

lo

wer

GF

grou

nd fl

oor

1 gr

ound

floo

r *

* ---

*

* up

per G

F gr

ound

floo

r ---

1

grou

nd fl

oor

---

low

er G

F *

* ---

---

*

grou

nd fl

oor

1 gr

ound

floo

r ---

gr

ound

floo

r up

per G

F ---

*

---

---

grou

nd fl

oor

1 gr

ound

floo

r ---

gr

ound

floo

r ---

up

per G

F *

* lo

wer

GF

grou

nd fl

oor

1 gr

ound

floo

r ---

*

* *

uppe

r gro

und

fl.

grou

nd fl

oor

low

er G

F gr

ound

floo

r 1

grou

nd fl

oor

---

* ---

up

per G

F *

* gr

ound

floo

r ---

1

uppe

r GF

---

* ---

---

---

---

up

per G

F ---

1

Tabl

e A6

.22

Floo

r sub

-pat

tern

s: S

eque

nce

leng

th 1

0 (N

=6)

Sequ

ence

leng

th 1

0

freq

uenc

y gr

ound

floo

r lo

wer

GF

grou

nd fl

oor

uppe

r GF

* *

---

* *

grou

nd fl

oor

1 gr

ound

floo

r ---

*

---

---

grou

nd fl

oor

---

uppe

r GF

grou

nd fl

oor

---

1 gr

ound

floo

r up

per G

F ---

gr

ound

floo

r ---

*

* ---

up

per G

F gr

ound

floo

r 1

grou

nd fl

oor

uppe

r GF

* ---

---

---

---

up

per G

F gr

ound

floo

r ---

1

grou

nd fl

oor

* ---

gr

ound

floo

r lo

wer

GF

---

---

grou

nd fl

oor

---

grou

nd fl

oor

1 gr

ound

floo

r *

* ---

lo

wer

GF

grou

nd fl

oor

* up

per G

F gr

ound

floo

r ---

1


250

Tabl

e A6

.24

Floo

r sub

-pat

tern

s: S

eque

nce

leng

th 1

2 (N

=3)

Sequ

ence

leng

th 1

2

freq

uenc

y gr

ound

floo

r ---

---

---

up

per G

F *

uppe

r GF

grou

nd fl

oor

---

low

er G

F gr

ound

floo

r ---

1

grou

nd fl

oor

---

uppe

r GF

---

* ---

---

---

---

---

*

grou

nd fl

oor

1 gr

ound

floo

r ---

*

---

---

---

* gr

ound

floo

r ---

*

low

er G

F gr

ound

floo

r 1

Tabl

e A6

.25

Floo

r sub

-pat

tern

s: S

eque

nce

leng

th 1

3 (N

=1)

Sequ

ence

leng

th 1

3

fr

eque

ncy

grou

nd fl

oor

low

er G

F gr

ound

floo

r up

per G

F *

---

uppe

r GF

grou

nd fl

oor

---

---

---

* gr

ound

floo

r 1

Tabl

e A6

.26

Floo

r sub

-pat

tern

s: S

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251

AUTHOR INDEX A Abe, S. 55, 134 Ahmed, Z. U. xvii, 1, 3, 37, 40, 46 Agrawal, S. 2 Allard, T. 34 Anderson, R. D. 51 Andreu, L. 62 Anselmsson, J. 34, 52, 67 Areni, C. S. 45 Arentze, T. A. 33, 37, 159, 160, 165 B Babin, B. J. 34, 54 Baker, J. 34, 40, 42, 45, 46, 48, 49, 50, 52, 58, 62 , 72 Baker, S. M. 37 Bakewell, C. 66 Ballantine, P. W. 2, 44 Barnes, J. H. 2 Beatty, S. E. 51, 52, 54 Bellizzi, J. A. 45 Bhardwaj, P. 44 Biel, A. 135 Bigné, E. 62 Birch, D. 2, 34, 36, 42, 51, 52, 63 Bitner, M. J. 2, 50 Black, W. C. 49 Bloch, P. H. 38, 40, 112, 136, 143 Borges A. 54 Borgers, A. 52, 55, 56, 57, 136, 137, 138, 140 Borin, N. 52 BPS Provinsi DKI Jakarta 12 Brantley, A. 35, 44, 48 Brengman, M. 45

Brown, S. 2, 40, 57,58, 110, 114, 136, 140, 181 Burns, L. D. 111 C Cachon, G. P. 43 Cadogan, J. W. 35 Cai, Y. 67 Cao, X. 55 Carroll, D. 57, 58, 180 Carter, C. C. 35, 53 Chan, K. 40, 41, 114 Chan, O. Y. 47 Chang, E. 111 Chebat, D. R. 2, 67 Chebat, J. C. 2, 34, 36, 41, 43, 44, 45, 46, 47, 49, 51, 52, 54, 57, 67, 115, 135 Chee, L. xvii, 1 Childs, M. L. 44 Christaller, W. 35 Chugan, P. K. 52 Chumpitaz, R. 62 Chuihua, J. C. 9 Cobb, C. J. 51 Cockett, T. 62 Coleman, P. 9, 19, 20 34, 37 Comeskey, K. 44 Cox, A. D. 51 Cox, D. 51 Craig, C. S. 35 Crispen, C. 43, 52 Crosby, N. 111 Crowley, A. E. 44 D

AUTHOR INDEX

252

Dabholkar, P. A. 49 Dahari, Z. xvii, 1, 3, 37, 40, 46 Dawson, S. 38, 40, 112, 136, 143 Davis, L. 42, 47 de Vries, B. 51, 133 de W. Bruwer 40, 110, 113 Deguchi, A. 55 Dellaert, B. 56 DeLisle, J. R. 19, 20 Dennis, C. 33, 62 De Nisco, A. 40 Dholakia, R. R. 33 Dick H. W. xvii, 1 Dijkstra, J. 51 Dolan, M. S. 56, 177 Donovan R. J. 44 Dubé, L. 45 E El-Adly, M. I. 38, 39, 40 El Hedhli, K. 36 Everett, P. B. 52 Eves, F. F. 57, 58, 180 F Fairhurst, A. E. xix, 42 Faith, M. S. 56, 177 Feldman, P. 43 Ferrell, M. E. 52, 54 Ferrell, O. C. 50 Field, A. 133 Finn, A. 37, 42, 138 Fiore, A. M. 2 Francis, S. K. 111 Frasquet, M. 40 Fujiwara, A. 134, 135, 156

G Gaedeke, R. M. 71 Gärling, T. 135 Gartner, G. 134, 135 Gélinas-Chebat, C. 57 Gil, I. 40 Gilbert, D. 37 Gilboa, S. 52, 117 Ghingold, M. xvii, 1, 3, 37, 40, 46 Ghosh, A. 35 Ghosh, P. 2 Goodstein, R. C. 34 Grace, D. 41 Graves, P. 133 Grewal, D. 34, 45, 50, 52 Grohmann, B. 44 Grzeskowiak, S. 2, 41, 43, 44 Gustafsson, M. 135 Guy, C. M. 14, 19, 20 H Ha, S. 48 Haj-Salem, N. 34 Hagita, N. 55, 134 Haloupek, W. J. 35, 53 Halper, E.B 37. Handy, S. L. 55 Hart, C. 35 Hartline, M. D. 50 Hartono, R. 12 Haytko, D. L. 34, 40, 42, 48, 49, 72 Henderson, J. C. xvii, 1 Henderson, P. W. 44 Helgesen, Ø. 33 Heo, M . 56, 177 Herlambang, S. xvii, 1 Hill, M. R. 133

AUTHOR INDEX

253

Hirsch, A. R. 44 Hirsch, J. 56, 154 Hite, R. E. 45 Hodges, N. 42, 47 Hofman, F. 159, 160 Hoyer, W. D. 51 Hu, H. 47 Huff, D. L. 35 I Ibrahim, M. 33 Im, H. 48 Inaba, J. 9 Isaacson, M. 135 J Jack, R. 2 Jackson, V. 35, 44, 48 Jarboe, G. R. 52 Jasper, C. R. 47 Jessurun, J. 51 Jhamb, D. 41 Joh, C. H. 134, 135, 136, 137, 156, 159, 160, 165 Johansson, U. 34, 52 Johnson, Jr, J. H. 32 Jones, M. A. Joshi, N. 64 Joye, Y. 45 K Kadin-Indonesia /Indonesian Chambers of Commerce & Industry 12 Kang, J. 49, 50 Keith, N. K. 49 Keller, K. L. 37

Kemperman, A. D. 56, 57, 136, 137, 138, 140 Kendall, E. L. 112 Kerr, J. 57, 58, 180 Khare, A. 44, 49, 54 Kim, D. 45 Kim, H. Y. 52 Kim, M. 44, 49, 50 Kim, Y. K. 49, 50, 52 Kingston, B. 9 Kiran, R. 41 Klein, K. 56, 154 Knack, R. E. 47 Konishi, H. 37 Koolhaas, R. 9 Kotler, P. 37, 43 Kotzé, T. 48, 69 Kranendonk, C. J. 2, 35, 36, 38, 52, 53 Krishnan, R. 52 Kruskal, J. B. 157 Kurose, S. 55, 57, 136, 137 Kuruvilla, S. J. 64 Kwon, H. 48 L Lam, S. Y. 2 Laroche, M. 51 Lee, C. xvii, 1 Lee, K. H. 33 Lee, S. 135 LeHew, M. L. xix, 42 Leszczyc, P. T. P. 32, 33, 36, 37 Lewis, R. A. 56, 177 Levy, M. 37 Li, F. 2, 35, 36, 38, 52, 53 Lim, S. H. 143 Limb, M. 32

AUTHOR INDEX

254

Lizieri, C. M. 111 Llyod, A.E. 41 Losch, A. 35 Louviere, J. J. 37, 42 Lu, Y. 40 Luk, S. T. 41 M Maclnnis, D. J. 34 Marcoolyn, G. 44, 51 Marjanen 36, 40, 66 Martin, C. A. 71 Marsland, D. 62 Martinez, R. V. 49 Matthews, H. 32 McCann, P. 111 McDaniel, C. D. 52 McGoldrick, P. J. 2, 42, 62, 112 McLafferty, S. 35 Mehrabian, A. 43 Mehta, N. 52 Merrilees, B. 2, 34, 36, 42, 51, 52, 63 Michon, R. 2, 45, 46, 51 Milan, J. 49 Milliman, R. E. ; 2, 44 Millonig, A. 134, 135 Min, C. 135 Mitchell, V. W. 66 Miyashita, T. 55, 134 Mokhtarian, P. L. 55 Molla, A. 40 Mooi, E. 163 Morrin, M. 2, 67, 115, 135 Morin, S. 45 Mower, J. M. 44 Mun, C. N. xvii, 1 Murphy, J. 62

N Neo, L. W. 14, 19, 20 Nesdale, A. 44, 51 Nesset, E. 33 Nicholls, J. A. F. 2, 35, 36, 38, 52, 53 Nicholson, J. D. 49 North, E. 48, 69 O O’Cass, A. 41 Oakes, H. 45 Oakes, S. 45 Ogle, J. P. 2 Ojala, L. 65 Okamoto, K. 55, 134 Oliveira, S. 34 Ooi, J. T. 105 Oppewal, H. 33, 37, 41 Othman, M. N. 143 P Palumbo, A. 47 Pan, Y. 32, 40 Pandey, S. K. 44 Parasuraman, A. 45, 50 Parsons, A. G. 2, 44 Patel, T. 62 Patterson, A. 45 Percy-Smith, B. 32 Petruzzellis, L. 47 Phillips, L. W. 43 Pietrobelli, A. 56, 177 Poon, E. 40, 41, 114 Primo, J.E. 37 Prastiana, U. 12 Prashar, S. 35

AUTHOR INDEX

255

Price, L.L. 34 Price, R. 34 Puccinelli, N. M. 34 Punj, G. 163 R Raajpoot, N. A. 52 Raghubir, P. 34 Rahman, H.A.A. 14 Rentz, J. O. 49 Reutterer, T. 2, 40 Reynolds, K. E. 51 Ridgway, N. M. 38, 40, 112, 136, 143 Rimmer, P.J. xvii, 1 Russell, J. A. 43 Roslow, S. 2, 35, 36, 38, 52, 53 Rossiter, J. R. 44, 51 S Sahgal, A. 32, 33, 36, 37 Saini, S. 2 Sandfort, M. T. 37 Santosa, H. 14 Saarloos, D. 134, 135, 136, 137, 156 Sankoff, D. 157 Sarstedt, M. 163 Schinazi, V. 133, 138 Segerer, M. 56, 154 Severin, V. 42 Shah, N. 64 Shannon, R. 67 Sharma, A. 52 Shibasaki, R. 135 Sinha, A. 32, 33, 36, 37 Sinha, P. K. 2, 13 Sim, L. L. 105 Simmers, C. S. 49

Singh, H. 35 Sirgy, M. J. 2, 36, 41, 43, 44, 49 Sit, J. 2, 34, 36, 42, 51, 52, 63 Sjohirin 14 Shibasaki, R. Shoval, N. 135 Skogster, P. 64 Song, J. 135 Spangenberg, E. R. 44 Sprotles, G. B. 112 Sprott, D. E. 44 Stachow, G. 35 Sternthal, B. 43 Stewart, D. 34 Stewart, D. W. 163 St-James, V. 49 Stoel, L. 33, 35, 44, 48 Stols, M. 48, 69 Susilo, Y. 14 Swaen, V. 62 T Takahashi, K. 55, 134 Tan, A. A. 133 Tanaka, H. 135 Tauber, E. M. Taylor, M. 32 Taylor, S. A. 49 Teknomo, K. 135 Teller, C. 2, 40 Tendai, M. 43, 52 Teng, L. 51 Therrien, K. 57 Thompson, M. G. 2, 62 Thorpe, D. I. 49 Timothy, D. J. 34

AUTHOR INDEX

256

Timmermans, H. J. 33, 37, 51, 52, 55, 56, 57, 133, 136, 137, 138, 140, 159, 160, 165 Titus, P. A. 52 Tootelian, D. H. 71 Tripathi, V. 2 Tsung Leong, S. 9 Turley, L. W. 2, 44, 45 U Uotila, V. 64 Underhill, P. 49 Uniyal, D. P. 2, 113 Urban Land Institute 107 Utsumi, A. 55, 134 V van der Hagen, X. 52 Vanderbeck, R. M. 32 Venter, L. 48, 69 Vilnai-Yavetz, I. 52, 117 Voss, G. B. 45, 50 W Wall, A. 9 Wakefield, K. L. 45, 46, 58, 62 Wakenshaw, S. 47 Warnaby, G. 40 Website Asosiasi Pusat Pengelola Belanja Indonesia 14 Wee, N. 33 Weiss, L. A. 56, 177 Weit, B. 37 Westbrook, R. A. 49 Wickliffe, V. 33 Wiegelmann, T. 56, 154 Willems, K. 45

Wong, G. K. M. 40 Woodruffe-Burton, H. 47 X Xu, S. Y. 2 Y Yalch, R. F. 44 Yamazoe, H. 55, 134 Yavas, U. 39, 40, 42 Yim Yiu, C. 2 Yip, L. S. 41 Yip, T. C. 40, 41, 114 Yoo, C. 135 Yuan, L. L. 38, 40 Yuo, T. T. 111 Z Zacharias, J. 2, 55, 56, 133, 138 Zeisel, J. 135 Zhang, J. 134, 135, 136, 137, 156 Zhao, S. 55 Zhu, W. 136, 137 Zinkhan, G. M. 32, 40 陳靄恩 47

257

SUBJECT INDEX A anchor tenant 12, 33, 37-39, 65, 229-241

atmospherics 1, 31, 32, 34, 43-47, 62, 69, 73, 75, 76, 174, 179

attributes of shopping mall 5, 29, 35

behavior characteristics 104, 117, 119, 121, 122, 125, 128

of fashion shoppers 119

of grocery shoppers 122

of social shoppers 125

of recreational shoppers 128

C

Central Place Theory 35

choice of shopping mall 29, 30, 33, 36, 47, 60, 61, 72

influence factors 30, 174

cluster analysis 117, 155, 158, 162, 170

Classic Shopping Mall 22, 24, 25, 64, 65 69, 70-75, 77-91, 93-101, 109-113, 116-132, 138, 153, 174, 175

CM

see Classic Shopping Mall

complete linkage

see hierarchical cluster analysis

E

evolution of shopping malls 4, 8, 9, 19

H

hierarchical cluster analysis

complete linkage 21, 23

of classifying Jakarta shopping malls 21, 23, 117

SUBJECT INDEX

258

hierarchical cluster analysis

of deriving shopping style 104, 113, 130, 176

ward’s method 113

I

independent t-test 85-90

International Council of Shopping Centers 8, 14, 19, 22, 28

International Trade Center (ITC)

see Trade Center

ISCS

see International Council of Shopping Centers

K

k-means cluster analysis 170, 177

L

LM

see Local Shopping Mall

Local Shopping Mall 22, 24, 26, 65, 69-75, 77-91, 93, 97-101, 108-114, 117-131, 221-228

logistic regression analysis 5, 168, 169, 171

M

MDSAM

see multidimensional sequence alignment methods

MM

see Modern Shopping Mall

mall choice

see choice of shopping mall

mall’s dimensions

see mall image dimensions

SUBJECT INDEX

259

mall image dimensions 31, 34, 52, 62, 75, 76, 77, 101, 174, 175, 178, 179

definition 34

mall-use shopping decision 2-5, 104, 173, 174, 180

market segments 50, 179

Modern Shopping Mall 23, 24, 27, 64, 65, 69, 73, 75, 77-91, 93-101, 109-113, 115, 117-120, 122-131, 174

motivation for shopping 28, 32 ,48, 57, 61, 97, 174

movement behavior 4, 5, 12, 29, 51, 53, 56, 132-137, 155, 173

definition 136, 137

measurement 132

movement pattern 4, 29-31, 51, 53, 134, 135, 143, 146,-148, 153, 154, 157, 164, 175, 176

multidimensional sequence alignment methods 5, 156, 157, 170

multi-purpose shopping 32, 43, 36, 40

multi-story shopping mall 4, 32, 556, 131, 135, 154, 157, 173-176, 180

definition 4

O

Observation 55, 132-139, 145, 151, 153, 178, 180,

unobtrusive observation 134, 175, 176

P

personal service 49, 50, 62, 64, 68, 73, 75-77, 80, 82, 85, 86, 89, 91, 92, 100, 107, 174, 179, 202, 206, 220, 221, 225, 229

pre-shopping decisions 2-5, 30, 60, 61, 73, 74, 104, 173, 174, 178

price 20, 34, 35, 37, 39, 41-43, 50, 51, 62, 63, 68, 79, 75-77, 79, 80, 86, 87, 89, 92-95, 174, 179

price level 20, 22, 24-28, 63, 65, 89

public space 3, 63, 81-83, 87, 88, 90-92, 95, 96, 136, 139, 144-147, 154, 158-160, 165, 175, 179

definition 3

purchasing 2, 30,31,41,44,52,135,136,138,143,153,162,167,170,177

SUBJECT INDEX

260

R

REIT

see Real estate investment trust

Real estate investment trust (REIT) 13, 21

S

Sequence Alignment Methods 156

sequential shopping behavior 3, 5, 154, 175, 176

sequential shopping patterns 32, 154-156, 158, 160, 162, 164, 165, 168, 170, 171, 178

shoppers

fashion shoppers 117, 121-123, 130, 131, 176

grocery shoppers 117-120, 130, 176

social shoppers 117, 124-126, 130, 176

recreational shoppers 117, 127-131, 176

shopping mall

definition 8

history 9-14

Europe 9, 10

Indonesia 10-14

US 9, 10

shopping purpose 2, 29, 30, 41, 177

shopping style 2, 5, 104, 112-117, 120, 122, 125, 128, 130, 131, 176, 178, 179

characteristics 117

definition 112

labeling 117

social environment 31, 34, 47-49, 56, 62, 64, 75-77, 80, 85, 86, 91, 92, 1001, 174, 175, 179, 202, 221, 225, 229

specialty store 20, 37, 39, 41, 138, 140, 141, 142, 148, 150, 158, 235-242

stepwise regression 92, 93

store choice 2, 31, 32, 36, 43

SUBJECT INDEX

261

store classification 107

store tenant 37, 65, 180

definition 37

store visits 2, 5, 35, 38, 51, 52, 54, 55, 104, 105, 108, 109, 111-114, 116-119, 122, 124, 125, 127, 128, 130, 131, 143-145, 149, 151, 152, 154, 176, 177, 179, 180

behavior 104, 118, 130, 176

type of 114, 151, 156, 179

stop behavior 31, 51, 52, 106, 133, 136, 143-145, 162, 167, 168, 175, 176, 178, 180

definition 136

measurement 132

strolling 3, 31, 136, 137, 180

survey approach 60

questionnaire 4, 5, 45, 56, 60, 61, 62, 64, 65, 73, 75, 104, 105, 113, 130, 133, 134, 201

T

tenant mix 2, 4, 13, 14, 19, 37, 40, 55, 178

tracking

data 105, 132-136, 138, 153, 175-178

method 4, 5, 104

unobtrusive 132, 134, 136, 154, 155, 178

trade center 14, 20, 24, 26, 27, 65

262

CURRICULUM VITAE

After finishing her bachelor in architecture at the Department of Architecture, Institut Teknologi Bandung (ITB), Indonesia, Widiyani started her first career as an interior-architect in an architecture firm. She came back to the Department of Architecture, ITB and began her academic career in 1998. Widiyani earned her master degree in architecture at the Department of Architecture ITB, Indonesia in 1999. Her master thesis focused on behavior patterns in a gallery-café. The thesis has inspired her to pursue PhD about shopping behavior with a larger scale of case studies.

Widiyani conducted a PhD project on the role of shopping behavior in the shopping mall of which the results are presented in this dissertation. She recognized there are some differences between Western and Asian shopping malls, as well as the shoppers’ shopping behavior, particularly in Indonesia which have not been explored much by scholars. Therefore, this study specifically discussing the malls’ shopping behavior in Jakarta.

Her current research interests include consumer behavior, retailing, and lifestyle.

Bouwstenen is een publikatiereeksvan de Faculteit Bouwkunde,Technische Universiteit Eindhoven.Zij presenteert resultaten vanonderzoek en andere aktiviteiten ophet vakgebied der Bouwkunde,uitgevoerd in het kader van dezeFaculteit.

Bouwstenen zijn telefonisch tebestellen op nummer040 - 2472383

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nr 86 Zelfredzaam WonenGuus van Vliet

nr 87Een Ensemble met Grootstedelijke AllureJos BosmanHans Schippers

nr 88On the Computation of Well-Structured Graphic Representations in Architectural Design Henri Achten

nr 89De Evolutie van een West-Afrikaanse Vernaculaire ArchitectuurWolf Schijns

nr 90ROMBO TactiekChristoph Maria Ravesloot

nr 91External Coupling between Building Energy Simulation and Computational Fluid DynamicsEry Djunaedy

nr 92Design Research in the Netherlands 2005editors: Henri Achten Kees Dorst Pieter Jan Stappers Bauke de Vries

nr 93Ein Modell zur Baulichen TransformationJalil H. Saber Zaimian

nr 94Human Lighting Demands: Healthy Lighting in an Office EnvironmentMyriam Aries

nr 95A Spatial Decision Support System for the Provision and Monitoring of Urban GreenspaceClaudia Pelizaro

nr 96Leren CreërenAdri Proveniers

nr 97SimlandscapeRob de Waard

nr 98Design Team CommunicationAd den Otter

nr 99Humaan-Ecologisch Georiënteerde WoningbouwJuri Czabanowski

nr 100HambaseMartin de Wit

nr 101Sound Transmission through Pipe Systems and into Building StructuresSusanne Bron-van der Jagt

nr 102Het Bouwkundig ContrapuntJan Francis Boelen

nr 103A Framework for a Multi-Agent Planning Support SystemDick Saarloos

nr 104Bracing Steel Frames with Calcium Silicate Element WallsBright Mweene Ng’andu

nr 105Naar een Nieuwe HoutskeletbouwF.N.G. De Medts

nr 106 and 107Niet gepubliceerd

nr 108GeborgenheidT.E.L. van Pinxteren

nr 109Modelling Strategic Behaviour in Anticipation of CongestionQi Han

nr 110Reflecties op het WoondomeinFred Sanders

nr 111On Assessment of Wind Comfort by Sand ErosionGábor Dezsö

nr 112Bench Heating in Monumental Churches Dionne Limpens-Neilen

nr 113RE. ArchitectureAna Pereira Roders

nr 114Toward Applicable Green ArchitectureUsama El Fiky

nr 115Knowledge Representation under Inherent Uncertainty in a Multi-Agent System for Land Use PlanningLiying Ma

nr 116Integrated Heat Air and Moisture Modeling and SimulationJos van Schijndel

nr 117Concrete Behaviour in Multiaxial CompressionJ.P.W. Bongers

nr 118The Image of the Urban LandscapeAna Moya Pellitero

nr 119The Self-Organizing City in VietnamStephanie Geertman

nr 120A Multi-Agent Planning Support System for Assessing Externalities of Urban Form ScenariosRachel Katoshevski-Cavari

nr 121Den Schulbau Neu Denken, Fühlen und WollenUrs Christian Maurer-Dietrich

nr 122Peter Eisenman Theories and PracticesBernhard Kormoss

nr 123User Simulation of Space UtilisationVincent Tabak

nr 125In Search of a Complex System ModelOswald Devisch

nr 126Lighting at Work:Environmental Study of Direct Effects of Lighting Level and Spectrum onPsycho-Physiological VariablesGrazyna Górnicka

nr 127Flanking Sound Transmission through Lightweight Framed Double Leaf WallsStefan Schoenwald

nr 128Bounded Rationality and Spatio-Temporal Pedestrian Shopping BehaviorWei Zhu

nr 129Travel Information:Impact on Activity Travel PatternZhongwei Sun

nr 130Co-Simulation for Performance Prediction of Innovative Integrated Mechanical Energy Systems in BuildingsMarija Trcka

nr 131Niet gepubliceerd

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nr 132Architectural Cue Model in Evacuation Simulation for Underground Space DesignChengyu Sun

nr 133Uncertainty and Sensitivity Analysis in Building Performance Simulation for Decision Support and Design OptimizationChristina Hopfe

nr 134Facilitating Distributed Collaboration in the AEC/FM Sector Using Semantic Web TechnologiesJacob Beetz

nr 135Circumferentially Adhesive Bonded Glass Panes for Bracing Steel Frame in FaçadesEdwin Huveners

nr 136Influence of Temperature on Concrete Beams Strengthened in Flexure with CFRPErnst-Lucas Klamer

nr 137Sturen op KlantwaardeJos Smeets

nr 139Lateral Behavior of Steel Frames with Discretely Connected Precast Concrete Infill PanelsPaul Teewen

nr 140Integral Design Method in the Context of Sustainable Building DesignPerica Savanovic

nr 141Household Activity-Travel Behavior: Implementation of Within-Household InteractionsRenni Anggraini

nr 142Design Research in the Netherlands 2010Henri Achten

nr 143Modelling Life Trajectories and Transport Mode Choice Using Bayesian Belief NetworksMarloes Verhoeven

nr 144Assessing Construction Project Performance in GhanaWilliam Gyadu-Asiedu

nr 145Empowering Seniors through Domotic HomesMasi Mohammadi

nr 146An Integral Design Concept forEcological Self-Compacting ConcreteMartin Hunger

nr 147Governing Multi-Actor Decision Processes in Dutch Industrial Area RedevelopmentErik Blokhuis

nr 148A Multifunctional Design Approach for Sustainable ConcreteGötz Hüsken

nr 149Quality Monitoring in Infrastructural Design-Build ProjectsRuben Favié

nr 150Assessment Matrix for Conservation of Valuable Timber StructuresMichael Abels

nr 151Co-simulation of Building Energy Simulation and Computational Fluid Dynamics for Whole-Building Heat, Air and Moisture EngineeringMohammad Mirsadeghi

nr 152External Coupling of Building Energy Simulation and Building Element Heat, Air and Moisture SimulationDaniel Cóstola

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nr 153Adaptive Decision Making In Multi-Stakeholder Retail Planning Ingrid Janssen

nr 154Landscape GeneratorKymo Slager

nr 155Constraint Specification in ArchitectureRemco Niemeijer

nr 156A Need-Based Approach to Dynamic Activity GenerationLinda Nijland

nr 157Modeling Office Firm Dynamics in an Agent-Based Micro Simulation FrameworkGustavo Garcia Manzato

nr 158Lightweight Floor System for Vibration ComfortSander Zegers

nr 159Aanpasbaarheid van de DraagstructuurRoel Gijsbers

nr 160'Village in the City' in Guangzhou, ChinaYanliu Lin

nr 161Climate Risk Assessment in MuseumsMarco Martens

nr 162Social Activity-Travel PatternsPauline van den Berg

nr 163Sound Concentration Caused by Curved SurfacesMartijn Vercammen

nr 164Design of Environmentally Friendly Calcium Sulfate-Based Building Materials: Towards an Improved Indoor Air QualityQingliang Yu

nr 165Beyond Uniform Thermal Comfort on the Effects of Non-Uniformity and Individual PhysiologyLisje Schellen

nr 166Sustainable Residential DistrictsGaby Abdalla

nr 167Towards a Performance Assessment Methodology using Computational Simulation for Air Distribution System Designs in Operating RoomsMônica do Amaral Melhado

nr 168Strategic Decision Modeling in Brownfield RedevelopmentBrano Glumac

nr 169Pamela: A Parking Analysis Model for Predicting Effects in Local AreasPeter van der Waerden

nr 170A Vision Driven Wayfinding Simulation-System Based on the Architectural Features Perceived in the Office EnvironmentQunli Chen

nr 171Measuring Mental Representations Underlying Activity-Travel ChoicesOliver Horeni

nr 172Modelling the Effects of Social Networks on Activity and Travel BehaviourNicole Ronald

nr 173Uncertainty Propagation and Sensitivity Analysis Techniques in Building Performance Simulation to Support Conceptual Building and System DesignChristian Struck

nr 174Numerical Modeling of Micro-Scale Wind-Induced Pollutant Dispersion in the Built EnvironmentPierre Gousseau

nr 175Modeling Recreation Choices over the Family LifecycleAnna Beatriz Grigolon

nr 176Experimental and Numerical Analysis of Mixing Ventilation at Laminar, Transitional and Turbulent Slot Reynolds NumbersTwan van Hooff

nr 177Collaborative Design Support:Workshops to Stimulate Interaction and Knowledge Exchange Between PractitionersEmile M.C.J. Quanjel

nr 178Future-Proof Platforms for Aging-in-PlaceMichiel Brink

nr 179Motivate: A Context-Aware Mobile Application forPhysical Activity PromotionYuzhong Lin

nr 180Experience the City:Analysis of Space-Time Behaviour and Spatial Learning Anastasia Moiseeva

nr 181Unbonded Post-Tensioned Shear Walls of Calcium Silicate Element MasonryLex van der Meer

nr 182Construction and Demolition Waste Recycling into Innovative Building Materials for Sustainable Construction in TanzaniaMwita M. Sabai

nr 183Durability of Concretewith Emphasis on Chloride MigrationPrzemys�aw Spiesz

nr 184Computational Modeling of Urban Wind Flow and Natural Ventilation Potential of Buildings Rubina Ramponi

nr 185A Distributed Dynamic Simulation Mechanism for Buildings Automation and Control SystemsAzzedine Yahiaoui

nr 186Modeling Cognitive Learning of UrbanNetworks in Daily Activity-Travel BehaviorSehnaz Cenani Durmazoglu

nr 187Functionality and Adaptability of Design Solutions for Public Apartment Buildingsin GhanaStephen Agyefi-Mensah

nr 188A Construction Waste Generation Model for Developing CountriesLilliana Abarca-Guerrero

nr 189Synchronizing Networks:The Modeling of Supernetworks for Activity-Travel BehaviorFeixiong Liao

nr 190Time and Money Allocation Decisions in Out-of-Home Leisure Activity Choices Gamze Zeynep Dane

nr 191How to Measure Added Value of CRE and Building Design Rianne Appel-Meulenbroek

nr 192Secondary Materials in Cement-Based Products:Treatment, Modeling and Environmental InteractionMiruna Florea

nr 193Concepts for the Robustness Improvement of Self-Compacting Concrete: Effects of Admixtures and Mixture Components on the Rheology and Early Hydration at Varying TemperaturesWolfram Schmidt

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nr 194Modelling and Simulation of Virtual Natural Lighting Solutions in BuildingsRizki A. Mangkuto

nr 195Nano-Silica Production at Low Temperatures from the Dissolution of Olivine - Synthesis, Tailoring and ModellingAlberto Lazaro Garcia

nr 196Building Energy Simulation Based Assessment of Industrial Halls for Design SupportBruno Lee

nr 197Computational Performance Prediction of the Potential of Hybrid Adaptable Thermal Storage Concepts for Lightweight Low-Energy Houses Pieter-Jan Hoes

nr 198Application of Nano-Silica in Concrete George Quercia Bianchi

nr 199Dynamics of Social Networks and Activity Travel BehaviourFariya Sharmeen

nr 200Building Structural Design Generation and Optimisation including Spatial ModificationJuan Manuel Davila Delgado

nr 201Hydration and Thermal Decomposition of Cement/Calcium-Sulphate Based MaterialsAriën de Korte

nr 202Republiek van Beelden:De Politieke Werkingen van het Ontwerp in Regionale PlanvormingBart de Zwart

nr 203Effects of Energy Price Increases on Individual Activity-Travel Repertoires and Energy ConsumptionDujuan Yang

nr 204Geometry and Ventilation:Evaluation of the Leeward Sawtooth Roof Potential in the Natural Ventilation of BuildingsJorge Isaac Perén Montero

nr 205Computational Modelling of Evaporative Cooling as a Climate Change Adaptation Measure at the Spatial Scale of Buildings and StreetsHamid Montazeri

nr 206Local Buckling of Aluminium Beams in Fire ConditionsRonald van der Meulen

nr 207Historic Urban Landscapes:Framing the Integration of Urban and Heritage Planning in Multilevel GovernanceLoes Veldpaus

nr 208Sustainable Transformation of the Cities:Urban Design Pragmatics to Achieve a Sustainable CityErnesto Antonio Zumelzu Scheel

nr 209Development of Sustainable Protective Ultra-High Performance Fibre Reinforced Concrete (UHPFRC):Design, Assessment and ModelingRui Yu

nr 210Uncertainty in Modeling Activity-Travel Demand in Complex Uban SystemsSoora Rasouli

nr 211Simulation-based Performance Assessment of Climate Adaptive Greenhouse ShellsChul-sung Lee

nr 212Green Cities:Modelling the Spatial Transformation of the Urban Environment using Renewable Energy TechnologiesSaleh Mohammadi

nr 213A Bounded Rationality Model of Short and Long-Term Dynamics of Activity-Travel BehaviorIfigeneia Psarra

nr 214Effects of Pricing Strategies on Dynamic Repertoires of Activity-Travel BehaviourElaheh Khademi

nr 215Handstorm Principles for Creative and Collaborative WorkingFrans van Gassel

nr 216Light Conditions in Nursing Homes:Visual Comfort and Visual Functioning of Residents Marianne M. Sinoo

nr 217Woonsporen:De Sociale en Ruimtelijke Biografie van een Stedelijk Bouwblok in de Amsterdamse Transvaalbuurt Hüseyin Hüsnü Yegenoglu

nr 218Studies on User Control in Ambient Intelligent SystemsBerent Willem Meerbeek

nr 219Daily Livings in a Smart Home:Users’ Living Preference Modeling of Smart HomesErfaneh Allameh

nr 220Smart Home Design:Spatial Preference Modeling of Smart HomesMohammadali Heidari Jozam

nr 221Wonen:Discoursen, Praktijken, PerspectievenJos Smeets

nr 222Personal Control over Indoor Climate in Offices:Impact on Comfort, Health and ProductivityAtze Christiaan Boerstra

nr 223Personalized Route Finding in Multimodal Transportation NetworksJianwe Zhang

nr 224The Design of an Adaptive Healing Room for Stroke PatientsElke Daemen

nr 225Experimental and Numerical Analysis of Climate Change Induced Risks to Historic Buildings and CollectionsZara Huijbregts

nr 226Wind Flow Modeling in Urban Areas Through Experimental and Numerical TechniquesAlessio Ricci

nr 227Clever Climate Control for Culture:Energy Efficient Indoor Climate Control Strategies for Museums Respecting Collection Preservation and Thermal Comfort of VisitorsRick Kramer

nr 228nog niet bekend / gepubliceerd


nr 230Environmental assessment of Building Integrated Photovoltaics:Numerical and Experimental Carrying Capacity Based ApproachMichiel Ritzen

nr 231Design and Performance of Plasticizing Admixture and Secondary Minerals in Alkali Activated Concrete:Sustaining a Concrete FutureArno Keulen


nr 233 Stage Acoustics and Sound Exposure in Performance and Rehearsal Spaces for Orchestras: Methods for Physical MeasurementsRemy Wenmaekers

nr 234Municipal Solid Waste Incineration (MSWI) Bottom Ash:From Waste to Value Characterization, Treatments and ApplicationPei Tang

nr 235Large Eddy Simulations Applied to Wind Loading and Pollutant DispersionMattia Ricci

nr 236Alkali Activated Slag-Fly Ash Binders: Design, Modeling and ApplicationXu Gao

nr 237Sodium Carbonate Activated Slag: Reaction Analysis, Microstructural Modification & Engineering ApplicationBo Yuan

nr 238Shopping Behavior in Malls Widiyani

nr 239Smart Grid-Building Energy Interactions;Demand Side Power Flexibility in Office BuildingsKennedy Otieno Aduda

Documents

Shopping behavior in malls - Pure - Aanmelden · Shopping Behavior in Malls . PROEFSCHRIFT . ter verkrijging van de graad van doctor aan de Technische Universiteit Eindhoven, op gezag