A SERVICE PROFIT CHAIN PERSPECTIVE DISSERTATION

DRIVING RETAIL STORE PERFORMANCE:A SERVICE PROFIT CHAIN PERSPECTIVE

DISSERTATION

Presented in Partial Fulfillment of the Requirements for

the Degree Doctor of Philosophy in the Graduate School

of The Ohio State University

By

Todd Michael Stodnick, M.A.

*****

The Ohio State University

2005

Dissertation Committee:

Professor David A. Collier, Ph.D., Adviser Approved by

Professor W.C. Benton, Ph.D.

Len Schlesinger, Ph.D., Limited Brands Adviser

Graduate Program in Business Administration

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ABSTRACT

One service management model that has been gaining momentum in academic

and practitioner circles alike is the service profit chain. First introduced in the early

1990’s, the service profit chain offers a structural framework to service management

(Heskett et al, 1994). The theory basically asserts that providing employees with a

superior internal working environment will lead to satisfied employees who are both

loyal to the organization and able to provide the customer with an excellent service

experience. Customers will recognize and value the outstanding service afforded them.

Over time they will exhibit loyalty behaviors such as continued purchasing and increased

referrals. These loyalty behaviors will generate both market share and profitability

increases for the service firm.

Despite its widespread adoption by many service industry leaders (e.g. Southwest

Airlines, Progressive Insurance, etc) and a growing amount of academic literary attention

to the topic, very little empirical research has attempted to validate the basic tenets within

the service profit chain. As such, the primary objective of this research is to test the

structural framework presented in the service profit chain. Two structural models,

incorporating nine distinct hypotheses, are the means by which this objective is carried

out. To support this primary objective, several secondary objectives must be met.

Because this research will use several constructs that have yet to be rigorously validated,

much time and attention must be devoted to scale development. The population frame

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used in this study will be one large retail chain within the women’s specialty apparel

industry.

Seven of the nine hypotheses are supported, two are not. The overall fit statistics

of the two models employed suggest that the models do fit the data well, indicating

support for the underlying theory behind the service profit chain. A summary of the

hypotheses includes: 1.) internal service quality drives both employee satisfaction and

loyalty, 2.) employee satisfaction drives employee loyalty 3.) total retail experience

drives a customer’s perception of retail value and their satisfaction, 4.) customer

satisfaction drives customer loyalty.

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Dedicated to Our Jacqueline, There is A Light That Never Goes Out

I dreamt about you last night,and I fell out of bed twice….

VVHo will believe my verse in time to come,If it were filled with your most high deserts?

Though yet heaven knows it is but as a tombWhich hides your life , and shows not half your parts:

If I could write the beauty of your eyes,And in fresh numbers number all your graces,

The age to come would say this Poet lies,Such heavenly touches nere toucht earthly faces.So should my papers (yellowed with their age)

Be scorn'd,like old men of less truth then tongue,And your true rights be termed a Poets rage,

And stretched meter of an Antique song. But were some child of yours alive that time, You should live twice in it,and in my rhyme.

(Shakespeare, Sonnet XVII)

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ACKNOWLEDGMENTS

I would like to thank first my advisor David Collier for his invaluable support

throughout this entire dissertation process. His willingness to share his wealth of

knowledge and expertise has made my work all the better. I would also like to extend my

gratitude to my committee members, W.C. Benton and Len Schlesinger, for their

assistance and guidance. Both have positively shaped this work and contributed to my

growth as a researcher.

For their financial contributions to this research, I am grateful to the

LimitedBrands, Inc. I owe thanks to many people throughout the organization who made

the data collection process not only possible but also trouble free. Special thanks are

extended to Dave Klein and Julie Beckman for their tireless efforts.

I would like to thank the Ohio State University, and specifically the Fisher

College of Business and the Management Science Department, for their support over the

last four and half years. I am grateful for all the educational possibilities offered to me.

Finally, I would be remiss not to mention the endless support of my family and

friends throughout this process. They provided inspiration and encouragement in times

of need without which this work would not have been possible.

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VITA

June 28, 1972………………………..Born, Cleveland, Ohio

1994…………………………………B.A., Business Administration (Honors), MountUnion College

1996…………………………………M.A., English, University of Manchester, England

1996 – 2000…………………………Materials Manager, Dexter Axle, Elkhart, Indiana

2002…………………………………M.A., Business Administration, The Ohio StateUniversity

2000 – 2004…………………………Graduate Research and Teaching Associate, TheOhio State University

2004 – current .................................. Visiting Assistant Professor, University of NorthTexas

PUBLICATIONS

REFEREED PROCEEDINGS:

Stodnick, M. and Collier, D. “What contributes to total retail experience?” Proceedingsof the 35th National Decision Sciences Institute Annual Meeting, Boston, 2004.

vii

Stodnick, M. and Collier. D. “Defining internal service quality – Results of a pilot studyin specialty retailing.” Proceedings of the Midwest Regional Decision SciencesInstitute Annual Meeting, Toledo, Ohio 2004.

FIELDS OF STUDY

Major Field: Business Administration

Concentration: Operations Management

Minor Field: Industrial Engineering

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TABLE OF CONTENTS

PageAbstract............................................................................................................... ii

Dedication.......................................................................................................... iv

Acknowledgements ............................................................................................. v

Vita .................................................................................................................... vi

List of Tables ..................................................................................................... xi

List of Figures .................................................................................................. xiii

Chapters:

1. Introduction.................................................................................................... 11.1. Research motivation....................................................................... 101.2. Research objectives........................................................................ 121.3. Research theory and hypotheses ..................................................... 161.4. Research methods .......................................................................... 171.5. Layout of dissertation..................................................................... 19

2. Literature Review – Operating Strategy and Service Delivery System .......... 222.1. Service profit chain ........................................................................ 24

2.1.1. Empirical support ............................................................ 262.1.2. Service profit chain parallels............................................ 31

2.2. Internal service quality ................................................................... 382.2.1. Internal service quality parallels....................................... 412.2.2. Linking internal service quality to employee indicators.... 522.2.3. Individual dimensions of internal service quality ............. 58

2.3. Employee satisfaction, loyalty and productivity.............................. 70

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2.4. Summary........................................................................................ 75

3. Literature Review – Service Concept and Target Market .............................. 773.1. Total retail experience.................................................................... 77

3.1.1. Parallels to total retail experience..................................... 813.1.2. Individual dimensions of total retail experience ............... 83

3.2. Value ............................................................................................. 953.3. Customer satisfaction and loyalty................................................... 983.4. Summary.......................................................................................103

4. Measurement Model Development ..............................................................1054.1. Methodology.................................................................................106

4.1.1. Pilot study ......................................................................1064.1.2. Main study......................................................................110

4.2. Population frame...........................................................................1134.2.1. Sampling plan – pilot study ............................................1154.2.2. Sampling plan – main study............................................116

4.3. Survey development......................................................................1174.4. Measurement model ......................................................................118

4.4.1. Pilot study factor development........................................1184.4.2. Main study factor development.......................................129

4.5. Summary.......................................................................................140

5. Structural Models and Analysis ...................................................................1435.1. Structural equation modeling.........................................................1455.2. Employee model ...........................................................................146

5.2.1. Composition of internal service quality...........................1475.2.2. Linking internal service quality to satisfaction, loyalty and productivity..............................................................153

5.3. Customer model ............................................................................1665.3.1. Composition of total retail experience.............................1675.3.2. Linearity between customer satisfaction and customer loyalty ............................................................................1735.3.3. Linking total retail experience, value, satisfaction and loyalty......................................................................175

5.4. Summary.......................................................................................185

6. Summary and future research ......................................................................1886.1. Research objective ........................................................................1886.2. Overview of the study ...................................................................1896.3. Summary of research findings .......................................................192

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6.4. Contributions of research ..............................................................1976.4.1. Managerial contributions ................................................1986.4.2. Academic contributions ..................................................200

6.5. Limitations and future research .....................................................204

Bibliography ....................................................................................................211

Appendices.......................................................................................................234

Appendix A Composition of internal service quality......................................234Appendix B Summary of empirical evidence relating internal service quality to other service profit chain variables ........................................236Appendix C Summary of empirical evidence relating employee satisfaction to other service profit chain variables ........................................237Appendix D Summary of empirical evidence relating employee loyalty to other service profit chain variables ........................................238Appendix E Summary of empirical evidence relating employee productivity to other service profit chain variables ........................................239Appendix F Summary of empirical evidence relating external service quality to other service profit chain variables ........................................240Appendix G Summary of empirical evidence relating value to other service profit chain variables .....................................................242Appendix H Summary of empirical evidence relating customer satisfaction to other service profit chain variables ........................................243Appendix I Summary of empirical evidence relating customer loyalty to other service profit chain variables ........................................245Appendix J Final survey instruments ...........................................................246Appendix K List of stores using in main data collection ...............................253

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LIST OF TABLES

Table Page

1.1. Anecdotal evidence supporting the service profit chain .............................. 9

2.1. Wright and Boswell (2002) HRM typology................................................ 46

3.1. Service quality paradigms .......................................................................... 85

3.2. Effects generated by customer satisfaction and loyalty............................... 101

4.1. Summary of structural equation modeling fit indices.................................. 112

4.2. Results of pilot study construct development -- Empowerment .................. 119

4.3. Results of pilot study construct development – Work Design ..................... 120

4.4. Results of pilot study construct development -- Rewards and Recognition 121

4.5. Results of pilot study construct development -- Employee Satisfaction ..... 122

4.6. Results of pilot study construct development -- Employee Productivity .... 123

4.7. Results of pilot study construct development -- Servicescape .................... 124

4.8. Summary of pilot study construct development – Employee Portion .......... 125

4.9 Summary of pilot study construct development – Customer Portion........... 126

4.10. Factor correlation analysis between internal service quality dimensions andemployee outcome measures...................................................................... 128

4.11. Factor correlation analysis between total retail experience dimensions andcustomer outcome measures....................................................................... 129

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4.12. Results of main study construct development -- Training and Coaching, GoalManagement, Teamwork ........................................................................... 132

4.13. Results of main study construct development -- Work Design, Support –Management, Support – Tools ................................................................... 133

4.14. Results of main study construct development -- Empowerment, Rewards andRecognition, Employee Loyalty, Employee Productivity ........................... 134

4.15. Results of main study construct development -- Product Quality, ProductAvailability and Selection, Service Quality ................................................ 135

4.16. Results of main study construct development -- Store Layout, Servicescape,Value ......................................................................................................... 136

4.17. Results of main study construct development -- Customer Satisfaction,Customer Loyalty ...................................................................................... 137

4.18. Summary of main study construct development – Employee Portion ......... 139

4.19. Summary of main study construct development – Customer Portion .......... 140

5.1. Notational abbreviations used in this research ............................................ 148

5.2. Structural equation results for employee model.......................................... 155

5.3. Structural equation results for employee model, revised............................. 162

5.4. Fit indices for employee model .................................................................. 164

5.5. Hierarchical regression results – Total Retail Experience ........................... 172

5.6. Hierarchical regression results – Test for linearity...................................... 175

5.7. Structural equation results for customer model........................................... 178

5.8. Fit indices for customer model ................................................................... 182

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LIST OF FIGURES

Figure Page

1.1. Heskett et al’s (1994, 1997, 2001) service profit chain model .................... 3

1.2. Interdisciplinary nature of service profit chain ........................................... 15

1.3. Research methodology............................................................................... 17

2.1. Service profit chain and strategic service vision ......................................... 26

2.2. Kamakura et al’s (2002) structural equation model .................................... 30

2.3. Generalized Kamakura et al (2002) model ................................................. 30

2.4. Cycle of failure (Schlesinger and Heskett, 1991)........................................ 32

2.5. Cycle of success (Schlesinger and Heskett, 1991) ...................................... 34

2.6 Eisenberger’s (1986) perceived organizational support model.................... 53

2.7. Hackman and Oldham’s (1976, 1980) work design model ......................... 54

2.8. Roger et al’s (1994) work design model..................................................... 67

2.9. Hom and Griffeth’s (1991) turnover model ................................................ 71

3.1. Terblanche and Boshoff’s (2001) total retail experience schema ................ 79

3.2. Five dimensional representation of total retail experience .......................... 81

3.3. Zeithhaml et al’s (1996) means-end model................................................. 88

3.4. Shemwell et al’s (1998) variant of the means-end model............................ 89

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3.5. McDougall and Levesque’s (2000) means-end variant ............................... 90

3.6. Bitner’s (1992) servicescape model............................................................ 94

3.7. Common value paradigm (Patterson and Spreng, 1997) ............................. 97

5.1. Generic representation of employee model................................................. 147

5.2. Internal service quality composition, part I................................................. 149

5.3. Internal service quality composition, part II ............................................... 150

5.4. Structural equation results for employee model.......................................... 154

5.5. Structural equation results for employee model, revised............................. 161

5.6. Generic representation of customer model ................................................. 167

5.7. Total retail experience composition............................................................ 168

5.8. A potential non-linear effect ...................................................................... 174

5.9. Structural equation results for customer model........................................... 177

1

CHAPTER 1

INTRODUCTION

The principles contained within the service profit chain seem so simple, yet the

end results of their applications can be extremely powerful. Providing an excellent

working environment will boost employee satisfaction. Happy employees become loyal

employees. Over time, loyal employees learn the service processes more thoroughly and

become more productive. Productive employees enhance the quality of the customer’s

shopping experience. Customers value a high-quality shopping experience and become

very satisfied. Satisfied customers become loyal customers. Loyal customers shop more

frequently and purchase both larger volumes of their regular products and more ancillary

products. Overall, this dynamic eventually leads to increased sales and profitability.

The logic of this reasoning seems patently obvious yet the first time it was put

together into one comprehensive framework was only a decade ago. It was at that time

that a group of researchers fashioned the service management model called “the service

profit chain” (Schlesinger and Heskett, 1991; Schlesinger and Zornitsky, 1991; Heskett et

al, 1994; Heskett et al, 1997). The service profit chain was developed from an analysis of

service organizations with the aim of linking operational resource investments to

marketing, operating and financial outcomes. Specifically, the framework, depicted in

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Figure 1.1, lays the foundation for determining the causal relationship among four

distinct groups of variables – management practices (such as training), employee

outcomes (such as satisfaction), customer outcomes (such as perceived quality and value)

and market outcomes (such as revenue growth and profitability). For example, what

effect does increasing employee satisfaction have on the customer’s perception of value?

Furthermore, by what path does this effect occur?

3

4

In a time of increased service industry competition, coupled with more demanding

customers, the service profit chain “helps managers target new investments to develop

service and satisfaction levels for maximum competitive impact, widening the gap

between service leaders and their merely good competitors” (Heskett et al, 1994). As

will be discussed in chapter 2, the service profit chain focuses on creating both employee

and customer loyalty – recognizing that each is mirrored by the other. Both groups,

employees and customers, create value for the organization; as such, they need to be the

“center of management concern.”

Each link within the service profit chain will be briefly introduced here.

Following the organizational strategy of Heskett et al (1994), the review will start at the

end of the service profit chain and will move backwards towards its origin.

Customer Loyalty Drivers Profitability and Growth

Until recently the driving priority of most service firms has been to target most of

their investments and resources towards gaining new customers in hopes of expanding

market share. However, over the past fifteen years, a new theory is emerging – defensive

marketing (Fornell and Wernerfelt, 1987). Defensive marketing theorists note that one

significant drawback to a strategy of focusing solely on winning new customers is that

current customers feel neglected and have a much higher potential to defect to a rival

service provider. Reichheld and Sasser (1990) echo this thought in their work on

customer defections. They basically conclude that quality of market share, measured in

terms of customer loyalty, is a far better predictor of future business success than quantity

of market share. Their analysis shows that a 5% increase in customer loyalty can lead to

5

profitability increases of between 25% and 85%. Numerous reasons have been given in

justifying the link between customer loyalty and profitability, see chapter 3 for a full

review. The most salient reasons predict that increased customer loyalty leads to:

decreased advertising costs (Nowack and Washburn, 1998; Remler and Brown, 1999;

Anderson and Fornell, 2000), increased referrals (Anderson et al, 1997; Remler and

Brown, 1999; McDougal and Levesque, 2000), more frequent purchases (Anderson et al,

1994; Sirohi et al, 1998) and reduced transaction costs (Potts, 1988; Anderson et al, 1994;

Anderson et al, 1997, Mittal and Lasser, 1998).

Customer Satisfaction Drives Customer Loyalty

The best way to increase customer loyalty is to create what Heskett et al (1994)

call “apostles” – customers that are so satisfied that they not only continue buying from

an organization but they also “convert the uninitiated”. Customer satisfaction has been

anecdotally and empirically shown to be the best predictor of customer loyalty (e.g.

Soderlund, 1998; see section __ for a thorough discussion of this claim). A study carried

out at Xerox corporation showed that when surveyed, customer who answered a perfect

score of “5” on a satisfaction question where six times more likely to repurchase Xerox

equipment than those who answered “4” (Heskett et al, 1994).

Value and External Service Quality Drive Customer Satisfaction

Customers today are more value driven than they ever have been in the past

(Patterson and Spreng, 1997). They are looking to derive more benefit from a product or

service while seeking to minimize their investment. The most traditional definitions of

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value bear this out. The benefits received can come in the form of product quality,

friendly service, rapid service, attractive storefronts, wide selection of merchandise

and/or a convenient location. Customer investment in a product or service includes not

only direct costs, such as price, but also indirect costs, such as time. Companies such as

Progressive Insurance have become leaders known for having extraordinarily high levels

of customer satisfaction based on the value they deliver to their customers – in their case,

Progressive’s CAT (catastrophe) team provides rapid handling of all accident claims

(Heskett et al, 1994).

Employee Productivity Drives External Service Quality and Value

What can be more frustrating for a business person who has one hour for lunch

than to sit at a restaurant waiting thirty minutes to even be welcomed by a

waiter/waitress? Or after an extensive wait, the order to be incorrect? As discussed

above, customers value good service quality. And who is more likely to be capable of

delivering exceptional service encounters than employees who are highly productive? By

definition, productive employees will be able to provide a better benefits to cost ratio

than average or non-productive employees. Whether “productive” means working more

quickly or being able to accomplish many different tasks, productive employees will

enhance the customer’s service experience.

Employee Loyalty Drives Employee Productivity

All else being equal, e.g. motivation, desire, career goals, etc., employees who

have worked for a service firm for extended periods of time will be more familiar with

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not only the service execution task but also with customers’ unique needs than their

newly hired counterparts. As such, employee loyalty, manifested in the form of tenure,

has been shown to be one of the strongest predictors of employee productivity (Sheridan,

1992; Wayne et al, 1997; Eisenberger et al, 2001). Heskett et al (1994) point to a study

that has determined that the cost of replacing an automobile sales agent with five to eight

years of experience with a new employee could be as much as $36,000 in lost sales, not

withstanding the additional hiring and training costs. Other studies have shown that

customer satisfaction, a possible surrogate for employee productivity, is heavily

influenced by employee loyalty – customers feel more confident that a long standing

employee will be able to meet their unique needs than a newly hired employee (Schneider

and Bowen, 1985; Silvestro and Cross, 2000).

Employee Satisfaction Drives Employee Loyalty

In their meta-analysis, Petty et al (1984) showed that the number one predictor of

employee loyalty is employee satisfaction. Employees who are happy and satisfied with

their work, co-workers, pay and overall surroundings are much more likely to remain

with an organization. News exposes are filled with stories about the best places in

America to work, and what do all these places have in common? – extremely low

employee turnover. Southwest Airlines, recently named one of the top ten places to

work, has annual turnover rates of less than 5%, and this is within an industry known for

its high turnover (Heskett et al, 1994).

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Internal Service Quality Drives Employee Satisfaction?

Internal service quality can be defined as “feelings that employees have towards

their jobs, colleagues, and companies” (Heskett et al, 1994). It results from high quality

support services and organizational policies that enable employees to deliver results to

customers. As will be discussed in chapter 2, employees who feel that their organization

cares about their well being and invests in their development will become more satisfied

with their jobs. Some of the basic practices that lead to a supportive internal working

environment include: high quality training programs, superior support from management

in terms of tools and resources to serve customers, reward and recognition programs

based on merit and employee involvement and empowerment programs.

Service profit chain theory has gained tremendous velocity in both practitioner

and academic literature alike. Much of the research supporting the theory is case based

and anecdotal. Below is a brief listing of some of the supporting evidence used in service

profit chain research. This list begins with evidence of individual links within the chain

and moves toward an overall application of service profit chain theory itself.

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Path Evidence Source

Customer Loyalty _Revenue Growth

Lifetime value of a Fed Excustomer is $360,000, Dominoscustomer is $4,000, Cadillaccustomer is $320,000. Indirecteffects through word of mouth, maybe 5* as much.

Gremler and Brown,1999

Customer Loyalty _Profitability

An increase in customer loyalty by5% can boost profitability by 100% Reicheld, 1996

Customer Satisfaction _Customer Loyalty

A customer who answers with asatisfaction score of 5/5 is six timesas likely to repurchase than acustomer who answers 4/5.

Heskett et al, 1994

Customer Satisfaction _Customer Loyalty

Dissatisfied customers registered a30% higher intent to leave thensatisfied customers.

Heskett et al, 1994

Employee Satisfaction _Customer Satisfaction

When a customer’s primary servicecontact leaves an organization,customer satisfaction drops onaverage by 20%

Heskett et al, 1994

Employee Loyalty _Increased BusinessPerformance

Taco Bell stores with the lowestemployee turnover rates have 55%higher sales and 20% higher profitsthan those with highest turnover

Heskett et al, 1994

Employee Loyalty _Increased Profitability

Replacement and training costs of anew associate are roughly 7.5*annual salary

Rust et al, 1996

Internal Service Quality _Revenue Growth

Nordstrom’s investments inemployee satisfaction anddevelopment programs has led tothe company to enjoy 2* thenational retail average sales persquare foot ratio.

Schlesinger andHeskett, 1991

Entire service profit chain

Sears implemented the serviceprofit chain theory company wide.In 2 years company went from aloss of $3.9 billion to a gain of$752 million

Rucci et al, 1998

Table 1.1. Anecdotal evidence supporting service profit chain

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While these anecdotes begin to pave a path into the validation of service profit

chain theory, the path they clear is quite rough. Detailed empirical evidence is now

needed at this crucial stage in the development of service profit chain theory (Loveman,

1998). Utilizing structural equation modeling, this research provides the most fine

grained analysis to date of the linkages found in the service profit chain within one

service setting, thus providing a more rigorous test of the theory’s validity.

1.1. Research Motivation

The main motivation for this research is to help a women’s specialty fashion retail

chain determine the most important drivers of retail store performance. Using a service

profit chain framework, in essence, this study will try to establish what operational

practices lead to improved store performance – with performance being measured on

several different dimensions. The problem is a common one faced by many large retail

chains – if all of the retail outlets carry the same lines of merchandise and have primarily

the same operating procedures, why does store performance differ so significantly from

one store unit to the next? Obtaining an answer to this question can lead to significant

improvements in competitive capability. One of the deliverables of this study will be a

list of the most salient predictors of store performance. A service firm can use this list as

a resource allocation tool when faced with limited budgets. Furthermore, a service firm

can get the highest marginal benefit of its operational investments if it can somehow tie

those investments to operating and market based performance measures. A second

deliverable of this study will be the illumination of the paths through which performance

improvements occur.

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In illuminating such paths, the study will show retail organizations what aspects

of the shopping experience the customers weigh most heavily in determining satisfaction,

e.g. product quality, service quality, store layout, etc. It will also show the importance of

the role of perceived value in driving customer satisfaction and loyalty. The same types

of questions will be investigated as pertaining to the employees. What are the main

drivers of employee satisfaction? How do those factors affect not only employee

satisfaction but also employee loyalty – is the path direct, indirect or a combination? As

will be shown, solving these issues can lead to higher retail store competitiveness.

The second motivation stems from a desire to fill a notable gap in academic

literature on service management. Despite its widespread use in management practice, as

evidenced by firms such as Taco Bell, Southwest Airlines, Sears, Progressive Insurance,

MCI, SAS, and Fairfield Inn (Heskett et al, 2001) and the acceptance of its theory in

academic literature, no study to date has comprehensively verified the causal linkages of

the service profit chain in one setting (Loveman, 1998; Silvestro and Cross, 2000;

Kamurka et al, 2002). There are two underlying causes behind this paucity of empirical

studies. The first reason stems from the interdisciplinary nature of the framework itself –

service/operations management, marketing, human resource theory, organizational

behavior, personnel psychology, etc. There is a wealth of information in each of these

fields that must be studied before any modeling attempts are taken. The second reason is

the sample size required to validate the service profit chain model. Because of the

multitude of constructs used in the service profit chain, a very large data set is needed.

This study has overcome these obstacles to provide the most comprehensive look at the

service profit chain to date.

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1.2. Research Objectives

The primary objective of this research stems directly from the motivation

described in the previous section – namely, to determine the drivers of specialty retail

store performance and illuminate the paths by which those drivers affect performance.

This study uses service profit chain theory as the organizing framework to research the

drivers and the paths. Will the driver prescribed by the service profit chain, internal

service quality, drive employee outcome measures, which in turn drive operational

performance metrics, which in turn drive market related performance metrics? This

research will use two structural equation models to test these questions, in essence,

testing the validity of the service profit chain theory.

In order to achieve the primary objective of validating service profit chain theory,

several supporting objectives must be obtained. First, two new second order factors must

be created. As Heskett et al (1994, 1997, 2001) posit, internal service quality is the

primary driver of the entire service profit chain model. Yet to date there has not been a

comprehensive empirical construction of this factor, see section 2.2 for a complete

elucidation of this argument. As such, a literature survey is needed to identify the main

dimensions of internal service quality so that a rigorous empirical definition can be

derived. This research will devise and validate an eight dimensional, second order

internal service quality construct.

The second higher level construct that needs to be developed is what Heskett et al

(1994) refer to as external service quality -- the “results” that are delivered to the

customer. The results include concepts that are captured in traditional service quality

definitions, e.g. process quality and product quality, but furthermore, include concepts

13

that fall outside of the traditional service quality definitions, e.g. servicescape, product

selection and availability, and store layout. One emerging stream of research that closely

resembles the anecdotes Heskett et al (1994, 1997, 2001) provide in their description of

external service quality is total retail experience (Terblance and Boshoff, 2001). Total

retail experience can be defined as “all the elements that encourage or inhibit consumers

during their contact with the retailer” (Berman and Evans, 1998). This research will

couple Terblance and Boshooff’s (2001) preliminary work into total retail experience

with other empirically tested theory from service management and marketing fields to

create a valid second order construct that can be used in structural models testing service

profit chain theory.

As well as creating these new second order constructs, first order constructs that

have been developed outside of the service management literature will have to be applied

in the retail setting. Some of the constructs that have been developed outside of the

services management area that will be used in this study include: value, perceived

organizational support, work design, employee satisfaction, employee loyalty, customer

satisfaction and customer loyalty. This research will provide a ground to the validation

of these constructs in a service management setting.

In creating these constructs, a secondary objective will be to provide an extensive

literature review of several different disciplines: operations/service management,

marketing, human resource management, organizational behavior and personnel

psychology. As shown in Figure 1.2 all of these disciplines play an important role in

shaping the service profit chain framework. The literature review must blend all of these

disciplines together, drawing parallels when possible. Extent theory from these various

14

disciplines, as well as their constructs, will be integrated into a retail business context.

This inter-disciplinary approach will yield a more comprehensive understanding of

traditional service management theories and constructs as well as lighting the path for

future researchers to expand upon.

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1.3 Research Hypotheses

Each link embedded within the two service profit chain structural equation

models will be treated as an individual hypothesis. Furthermore, the overall fit of the

models representing the two halves of the service profit chain will be assessed. Each

hypothesis is listed below; the literature reviews in chapters 2 and 3 will present

theoretical and empirical justification for each of the links.

H1a: Internal service quality is positively associated with employee satisfaction.

H1b: Internal service quality is positively associated with employee loyalty.

H1c: Employee satisfaction is positively associated with employee loyalty.

H1d: Employee satisfaction is positively associated with employee productivity.

H1e: Employee loyalty is positively associated with employee productivity.

H2a: Total retail experience is positively associated with value.

H2b: Total retail experience is positively associated with customer satisfaction.

H2c: Value is positively associated with customer satisfaction.

H2d: Customer satisfaction is positively associated with customer loyalty.

17

1.4. Research Methods

The major steps involved in this research study are summarized in Figure 1.3.

The items to be included in the survey instrument were identified from a comprehensive

review of literature from a variety of disciplines: operations management, marketing,

human resource management, organizational behavior and psychology. When possible,

existing scales that have been shown to be reliable and valid were adapted for use in this

study. When such scales did not exist, items were generated from anecdotes given in

theoretical writings. After the original development of the survey instrument, iterative

revisions were made based on input from operations management academicians and

practicing managers from the retail outlet targeted in this study.

Instrument development

Pilot study data collection at 5 retail locations

Factor assessment – unidimensionality, reliability, convergent validity

Instrument modifications

Main study data collection at 90 retail locations

Factor assessment – unidimensionality, reliability, convergent and divergent validity

Structural equation modeling: customer and employee models

Instrument development

Pilot study data collection at 5 retail locations

Factor assessment – unidimensionality, reliability, convergent validity

Instrument modifications

Main study data collection at 90 retail locations

Factor assessment – unidimensionality, reliability, convergent and divergent validity

Structural equation modeling: customer and employee models

Figure 1.3. Research methodology

18

Pilot data was collected from five retail locations in the Dayton, OH and

Cincinnati, OH areas. Sixty-two customer surveys were collected, representing a

response rate of 25%, and fifty employee surveys were collected, a response rate of 77%.

Before subjecting the data to factor development, reverse coded items were fixed,

descriptive statistics were computed and checks for normality were conducted. Factor

development began with reliability assessment via Cronbach alpha calculations.

Unidimensionality and convergent validity were assessed through confirmatory factor

analysis using a maximum likelihood approach.

Based on the results of the pilot study factor development modifications to the

survey instrument needed to be made, section 4.4 details these changes. After the

necessary modifications, data was collected from ninety locations of the women’s

specialty retailer. In total, 872 employee responses were gathered, resulting in a response

rate of 65%, 1,076 customer responses were gathered, resulting in a response rate of 24%.

Factor development followed the same methodology that was used in the pilot study

development with the exception that the larger sample sizes gathered through the main

data collection allowed for the assessment of divergent validity as well.

Following factor development, the two portions of the service profit chain are

tested using structural equation modeling. Individual path coefficients are analyzed and

the results are used to test the hypotheses laid out in section 1.3. A more macroscopic

approach is taken by assessing overall model fit to see if the overall framework of the

service profit chain is supported.

19

1.5. Layout of dissertation.

Chapter 1 of this dissertation provides an introduction to the motivations,

objectives, hypotheses and methodologies used in this study. The service profit chain

theory, and its implied causal chain, is introduced and anecdotal evidence is given as to

its plausibility. The motivation of the research is laid out both from an academic and

practitioner standpoint. Nine different hypotheses are explicitly stated. A summary of

the methodologies used to develop the factors and to test the hypotheses is then given.

Chapter 2 is the first of two literature review chapters. The first portion of this

chapter is dedicated to reviewing the literature on the service profit chain itself. This

section is further divided into theoretical works that contribute to the service profit chain

concept, empirical evidence for the service profit chain and a review of parallel theories

to the service profit chain. The next major section within this chapter, section 2.2., looks

exclusively at the driver of the service profit chain – internal service quality. Internal

service quality is defined and parallels to similar concepts are drawn. Empirical evidence

of the existence of internal service quality is given. The eight dimensions of internal

service quality are elucidated and each is briefly treated. The final section surveys

literature that investiages the employee outcome variables in the service profit chain:

satisfaction, loyalty and productivity. The variables are looked at singly, and then their

inter-relationships are explored. Throughout this chapter, when factors are being

discussed, special mention is made as to where the items that are used in this research’s

survey instrument are generated from. When possible, earlier studies that used the scales

are explicitly referenced. The chapter ends with a summary of the first five research

hypotheses.

20

Chapter 3 is the second literature review chapter. It looks exclusively at the

customer response to the service offering of the retailer. The chapter begins by

introducing the driver of the customer portion of the service profit chain model – total

retail experience. This factor is analyzed from a holistic perspective and is hypothesized

to be five dimensional. Parallels are drawn between total retail experience and other

customer oriented valuation frameworks. Each of the five total retail experience

dimensions is then analyzed individually. The second half of chapter 3 introduces three

other constructs: value, customer satisfaction and customer loyalty. Each of these

constructs is again developed individually followed by a discussion detailing their inter-

relationships. In a method similar to that used in chapter 2, explicit references are given

for survey item generation. The chapter concludes with a summary of four research

hypotheses.

The first portion of chapter 4 details the sampling plans used in both the pilot and

main studies. The pilot study and main study methodologies are both detailed. The next

section discusses the population frame of this research. One large retailer of women’s

specialty apparel is chosen. Advantages and disadvantages of the population frame are

discussed. The next section within this chapter, section 4.3, describes the steps involved

in the survey instrument development. This section is followed by an analysis of the

measurement model. Factor development is done on the pilot data. As the results

indicate, minor changes needed to be made to the survey instrument. The results of the

measurement model of the main survey data show that the changes were successful –the

factors are shown to be reliable, uni-dimensional and valid. The chapter ends with a

discussion of the contributions made by the measurement model.

21

The penultimate chapter, chapter 5, begins with a discussion of why structural

equation modeling was chosen as the data analytic tool to be used in this research.

Advantages and disadvantages of the methodology are given. Structural equation

modeling is then applied to the two models embedded within the service profit chain: the

employee model and the customer model. The structural equation results are used to test

each of the individual hypotheses described in chapters 2 and 3 as well as testing the

overall fit of the service profit chain theory. Discussion of the results of each model is

focused on the contributions made to both academic and practitioner literature.

Chapter 6 concludes this research with a summary of the research objectives and

methodologies used to support those objectives. The research findings are again

presented with special emphasis on how our results compliment and develop previous

research. The contributions of this research are reviewed from both a managerial and

academic perspective. The dissertation ends with a discussion of the limitations of this

work and future research ideas.

22

CHAPTER 2

LITERATURE REVIEW: OPERATING STRATEGY AND SERVICE

DELIVERY SYSTEM

A substantial literature review of the service profit chain requires the survey of

many different disciplines: operations management, human resource management,

personnel psychology, organizational behavior and marketing, just to name a few.

Figure 1.2 illustrates where each of the aforementioned disciplines contributes most

saliently. Clearly there is substantial overlap between the disciplines. The complexity of

surveying the literature does not stop there. Many times researchers in two different

disciplines study the same concept and/or theory but attach different labels and

terminology, hence time will be dedicated to elucidating these parallels when they occur.

To make matters even more convoluted, the constructs within the service profit chain

itself also overlap to a remarkable extent. For this literature review to be of worth, it

must look at constructs singly as well as their interaction. Significant time will thus be

dedicated to both.

In order to simplify the complexity of the literature review task we take pains to

organize the material as clearly and formally as possible. The first part of this chapter

23

lays out the general framework of the literature review; each section within the review

itself details its organization more precisely. Chapter 2 deals exclusively with the first

portion of the service profit chain: “the operating strategy and service delivery system”,

herein simplified to “the employee model” . The chapter begins with a discussion of the

theory of the service profit chain and how the concept came into being. This section is

followed by a treatment of empirical studies of the comprehensive service profit chain

model. A section detailing enterprise models similar to the service profit chain follows.

The notion of internal service quality is then introduced from a theoretical standpoint,

followed by a review of empirical research into the holistic concept. Concepts similar to

internal service quality are then discussed. This section is followed by a presentation of

the operationalization of internal service quality as an eight dimensional construct. Each

dimension is analyzed independently. The chapter ends with discussions of employee

satisfaction, loyalty and productivity.

Chapter 3 is dedicated to explicating the latter portion of the service profit chain,

focusing on the notions of external service quality (which we will call total retail

experience), value, customer satisfaction and customer loyalty. The chapter begins with

an analysis of the different dimensions of total retail experience in the extant literature.

Within this discussion we show how our rendering is a merging of several of the most

widely accepted definitions. The relationship between total retail experience and external

measures such as customer satisfaction, customer loyalty and business performance is

then explored. Following this section is an in-depth review of the relationships among

the external measures themselves.

24

2.1. Service profit chain

The service profit chain was developed from an analysis of service organizations

with the aim of linking operational resource investments to marketing, operational and

financial outcomes. Specifically, the framework lays the foundation for putting hard

numbers to traditionally soft measures. For example, what effect does increasing

employee satisfaction have on operational and financial outcomes? This question begs

the follow up: what operational investments can an organization make to improve

employee satisfaction? Previous literature has suggested that increasing employee

satisfaction would indeed boost sales and profitability, but no work has yet shown the

route by which those increases would occur. The service profit chain allows managers to

visualize the exact causal linkages between operational investment programs aimed at

increasing employee satisfaction and business performance (among other relationships).

An application of the service profit chain at Sears illustrates that a five unit increase in

employee attitude (as measured through Sears’ TPI survey) leads to a 1.3 unit increase in

customer satisfaction, which would in turn leads to a 0.5% increase in revenue growth

(Rucci et al, 1998).

Loveman and Heskett (1999) describe the idea trail that led to the development of

the causal structure of the chain itself. Basically, the service profit chain combines three

distinct, but closely related, streams of research: the cycle of employee capability, the

customer quality/value equation and the cycle of customer loyalty. The cycle of

employee capability takes a human resource perspective on the relationships between

employee satisfaction, turnover and productivity; this portion is described in more detail

in the remaining sections in this chapter. The quality/value equation describes how

25

customers perceive and assess service offerings. The cycle of customer loyalty examines

the effect that customer satisfaction has on customer loyalty and how they both play a

vital role in determining financial outcomes; these latter two sections are described more

fully in Chapter 3. Much had previously been written on these three streams individually

but it was not until the mid 1990’s that a group of Harvard researchers consolidated them

to form one comprehensive service management model.

As indicated in the introduction, the service profit chain, in essence, is an

enterprise model that theorizes that the investment in and development of employees

leads to satisfied service personnel. These satisfied employees remain with the

organization for extended periods of time and become very good at what they do,

enhancing their own productivity and the customer’s service experience. Customers not

only recognize but also become extremely satisfied with the increased quality and value

of the service offering. Satisfied customers become loyal customers and continue their

relationship with the service providing organization long into the future, increasing both

the organization’s market share and profitability.

To understand best the service profit chain beyond the simple anecdotes it is

important that one is able to place it within the context of the overall operating strategy of

a service organization. Heskett et al (1997) assert that the service profit chain provides

the means for implementing a firm’s strategic service vision. The strategic service vision

includes developing an operating strategy and service concept and segmenting the

customer market. This vision determines the goals and performance expectations of the

organization. Once these goals have been established, the service profit chain is a tool

26

that can be used to execute the goals. Figure 2.1 is a simplified illustration of how the

service profit chain model can be used within an organization.

Operating Strategy

Service Concept

Target Market

Strategic Service Vision

Cycle of employee capability

Quality and Value Equation

Cycle of customer loyalty

Service Profit Chain

Profit Model

Operating Strategy

Service Concept

Target Market

Strategic Service Vision

Cycle of employee capability

Quality and Value Equation

Cycle of customer loyalty

Service Profit Chain

Profit Model

Figure 2.1. Service profit chain and strategic service vision

This diagram indicates that the service profit chain, in essence, outlines the operational

execution of a firm’s strategy.

2.1.1. Empirical support

To date, only three attempts have been made to validate empirically large portions

of the service profit chain framework: Loveman (1998), Silvestro and Cross (2001) and

Kamakura et al (2002). While the three studies have collectively laid the foundation of

testing the causal linkages, their findings are tempered by limitations. As a group they

suffer from limited construct development and methodological weaknesses. Because of

their importance, in relation to this study, each paper is examined on an individual basis.

27

The setting for Loveman’s (1998) study of the service profit chain is the

commercial bank industry. For the main study, Loveman analyzes 450 bank branches,

gathering data from employees, customers and the banks themselves. Because the

authors are unconvinced of the exact causal nature between the variables in the service

profit chain they use regression analysis and avoid methods that imply causality. Our

contention with their choice is twofold. First, performing one at a time correlation

analysis (e.g. regression) does not control for family wise error. Although the 95%

confidence level is used for each test independently, there are 25 total correlation tests

conducted. The family wise error for the twenty-five tests is much larger than their stated

.05; in fact, it is nearly .70, calculated as [1 – (1 – _)n], where n = the number of

correlation tests. Moreover, any significant correlations found between two variables

may be a result of mediating variables that are not included in the individual regressions.

For example, suppose a significant correlation is found between variables A and B; it is

entirely possible that A has no direct effect on B, but rather A influences C which in turn

influences B. One at a time correlation analysis will incorrectly attribute a direct effect of

A onto B. Our second contention with their choice is that while there may certainly be

some reciprocation in the service profit chain model, these effects are minimal; there is

enough theoretical and empirical evidence of the causal nature of the relationships to

begin testing causal models of the framework (the following sections will detail all of this

evidence).

With two minor exceptions, the hypotheses tested by Loveman (1998) are

generally supported. The two exceptions are: 1.) customer satisfaction is not positively

correlated with employee satisfaction and 2.) revenue growth and profitability are not

28

correlated with customer loyalty. All the other relationships theorized in the service

profit chain are supported. However, it is important to note that Loveman (1998) does not

specifically test all the causal linkages in the service profit chain framework. For

example, the study does not create an external service quality construct. Instead,

employee satisfaction and loyalty are used as surrogates. This is a major weakness

because no indicator of a “deliverable” to the customer is used – a central tenet in Heskett

et al’s (1997, 2001) work. Furthermore, although several questions are asked for each

construct, no scale development is done: no tests are performed for reliability, validity,

uni-dimensionality, etc.

Silvestro and Cross’s (2000) research into UK supermarkets suffers from many of

the same limitations as Loveman’s (1998) earlier work. Due to their limited sample size

of six stores, Silvestro and Cross (2000) are restricted to using one at a time correlations.

With a sample size of six, a correlation of 0.81 is needed to achieve significance using an

alpha level of 0.05. There are dire consequences to this methodology. First, power is

extremely low. With only six observations it is hard to detect significant correlations.

This weakness may shed some light on their results – 25% of the hypotheses that they test

are insignificant. Second, if a correlation between two variables is greater than 0.81,

serious consideration must be given as to whether the two variables are independent. For

those pairs of variables where the correlation is above 0.81, no confidence interval is

given around the parameter estimate. The confidence interval could contain 1.0,

indicating that the two variables are actually the same. A correlation coefficient of 0.98

between service value and customer loyalty is only one of the many examples where

parameter estimates are well over 0.95. Tests of divergent validity would resolve this

29

second limitation and show whether two independent constructs are being measured or

whether the two constructs really merge into a single one. Like the Loveman (1998)

study, no construct development is done.

Similar to Loveman (1998), the correlation analysis does not map directly onto

the service profit chain framework. Silvestro and Cross (2000) test correlations between

every set of variables, even those that are not theorized to influence each other directly.

Again, this could lead to misguided findings. The effect of mediating variables should be

included when testing whether there is a significant correlation between items on

opposite ends of the service profit chain framework. In total, twenty-one correlation tests

are carried out. Only nine of the twenty-one hypotheses are supported. Five of the

twenty-one hypotheses actually have negative relationships where positive ones are

predicted. Lack of construct development, choice of methodology and limited sample

size all call into question the validity and generalizability of the findings.

The most recent attempt to validate empirically a portion of the service profit

chain is found in Kamakura et al (2002). This study uses structural equation modeling to

test a derivative model of the service profit chain in the Brazilian banking industry.

Figure 2.2 is an illustration of the structural model the authors test.

30

# of PersonnelPerceptions

of Personnel

Perceptions of

Equipment

Customer Behavior

# of Equipment

Profit

.22.62

.19.08

.13

-.04

-.12

# of PersonnelPerceptions

of Personnel

Perceptions of

Equipment

Customer Behavior

# of Equipment

Profit

.22.62

.19.08

.13

-.04

-.12

Figure 2.2. Kamakura et al’s (2002) structural equation model

Essentially this study focuses only on portions of the latter half of the service profit chain.

The variables # of personnel, # of equipment, perceptions of personnel and perceptions of

equipment more closely resemble a customer contact construct than anything else.

Basically the model degenerates into the following:

Amount of Customer Contact

Customer Satisfaction

Business Performance

Amount of Customer Contact



Figure 2.3. Generalized Kamakura et al’s (2002) model

The results simply state that there are two implications of increasing the number of

service personnel and equipment: a direct negative effect on profits through increased

expenses and an indirect positive effect on profits through increased customer

31

satisfaction. The interpretation of the findings, as well as the model itself, is a far cry

from that originally proposed by Heskett et al (1994). In fact, the study does not even

survey employees, arguably the most important group within the service profit chain.

Taken collectively, these three research attempts still leave much to be desired in

service profit chain research. All three attempts acknowledge the difficulty of pursuing

rigorous service profit chain research: large sample sizes, new construct development,

three different sampling populations (employees, customers, business unit) and

sophisticated data analysis tools. These three articles have beaten a trail into service

profit chain research but much of the terrain still needs to be explored.

2.1.2. Service profit chain parallels

Relatively speaking, service profit chain research is still in its infancy; it has had

only a decade to take root. Because of this, a thorough review of the principles contained

within its framework requires one to look to other models whose theory parallels that

proposed by Heskett et al (1994, 1997). The models that will be discussed in this section

include: the cycles of failure/success, relationship value management, the Malcolm

Baldrige quality model, the European Excellence quality model and the attachment

framework. Although each of these models has its own nuances, they are all

fundamentally grounded in the same concept as the service profit chain: developing

supportive human resource policies, policies that view employees as valuable resources,

will have a positive impact on both intermediate customer satisfaction measures and long

term financial performance measures. Because this tenet is also the cornerstone of the

32

service profit chain theory; all these parallels serve to justify Heskett et al’s (1994, 1997)

framework and increase its face validity.

The clearest parallel to the service profit chain is a variation proposed only three

years earlier by the same authors – the cycle of failure in services (Schlesinger and

Heskett, 1991). This model laid the foundation upon which the service profit chain

would grow. The model, illustrated in Figure 2.4, demonstrates the linkages between

employee indicators, customer perceptions and business performance.

High customer turnover

Repeated emphasis on attracting new customers

Emphasis on rules rather than service

Use of technology to control quality

Payment of low wages

Minimization of training

Development of employee boredom

Inability of employee to respond to customer problems

Employee dissatisfaction, poor service attitude

Customer dissatisfaction

Lack of continuity in relationship with customer

High employee turnover, poor service quality

Failure to develop customer loyalty

Low profit margins

Narrow design of jobs to accommodate low skill level

Minimization of selection effort

Employee

Custom

er














Low profit margins
















Low profit margins



Employee

Custom

er

Figure 2.4. Cycle of failure (Schlesinger and Heskett, 1991)

33

The cycle tells the story of the “typical” large American retail business. The organization

designs its customer contact positions to be filled by people who are willing to work for

wages marginally above statutory minimums. Because the labor market for this wage

rate is primarily made up of unskilled laborers, the organization must simplify jobs as

much as possible by reducing them to repetitive, boring tasks that require minimal

training. Little effort is given to develop talent and/or employee satisfaction and the

results are predictable: high employee turnover, low productivity and dissatisfied

customers. Traditional management responses to this state of affairs only aggravate the

problem: minimize commitment to employees through reduction in selection, training

and development activities. These reductions lead to even less satisfied and more

unmotivated employees who are incapable of meeting customer needs. Business

performance suffers proportionately, repeated cuts are made and the vicious cycle repeats

itself. The article ends with case study examples of service firms who have used the

theory behind the cycle of failure to create their own cycle of success (see Figure 2.5):

Au Bon Pain, Dayton Hudson, Fidelity Bank, ServiceMaster and Wells Fargo. Ever

since they began investing in and developing employees these firms have reaped

substantial long term benefits and have become leaders in their respective industries.

Heskett et al (1994, 1997, 2001) use these same examples when building their service

profit chain framework.

34

Low customer turnover

Repeated emphasis on customer loyalty and retention

Training, human resource practices and empowerment of frontline personnel to control quality

Above average wages

Extensive training

Employee satisfaction, positive service attitude

High customer satisfaction

Continuity in relationship with customer

Lowered turnover, high service quality

High customer loyalty

Higher profit margins

Broadened job designs

Intensified selection effort

Employee

Custom

er

Low customer turnover

Repeated emphasis on customer loyalty and retention

Training, human resource practices and empowerment of frontline personnel to control quality

Above average wages

Extensive training

Employee satisfaction, positive service attitude

High customer satisfaction

Continuity in relationship with customer

Lowered turnover, high service quality

High customer loyalty

Higher profit margins

Broadened job designs

Intensified selection effort

Employee

Custom

er

Figure 2.5. Cycle of success (Schlesinger and Heskett, 1991)

A similar model proposed from the customer’s viewpoint is given in relationship

value management literature (Gronroos; 1997; Payne et al, 2000; Payne et al, 2001). This

value based marketing paradigm posits that the value creation process for an organization

is driven by employees. The underlying resultant dimensions of employee value include

employee satisfaction, retention and productivity. Drawing from Heskett et al’s work

(1994, 1997) Payne et al (2000, 2001) hypothesize that in order for employees to realize

their potential in creating value they must enjoy a positive internal service quality. The

focus of these employee enhancing programs - programs such as training, empowerment,

feedback and reward systems - should move from short-sighted transition specific

35

perspectives to a holistic long term view of relationship building. As such, investing in

employees will, in the long term, build resources that can provide value and competitive

advantage.

Relationship marketing goes beyond focus on the employee; it also

simultaneously looks to the customer, another key component in the marketing mix. Just

as employees can create value, customers can too. Customers assess the value that

employees create in service delivery process and, if satisfied, will continue to purchase

from the organization. Their continued purchase patterns in themselves create value

through increased frequency of purchases and referrals – eventually leading to higher

revenue growth and increased profitability. Repeat customers are the key to

organizational success and as such organizations should be striving to make this group as

satisfied as possible. But as Payne et al (2001) point out, more often than not,

organizations direct their marketing and service efforts simply towards attaining new

customers at the expense of keeping the older, more profitable customers happy. So, just

as the service profit chain posits, a satisfaction mirror exists between employees and

customers and both images in the mirror are positively correlated with business

performance.

The service quality literature contains two enterprise models that have much in

common with the service profit chain model: the Malcolm Baldrige National Quality

Award (MBNQA) and the European Excellence Quality model (EFQM) models. Both of

these models theorize that the focus on and the development of employees will lead to

improved customer satisfaction measures, which in turn will result in improved business

performance. The Malcolm Baldrige National Quality Award was developed in 1987 by

36

the National Institute of Standards and Technology as a means of promoting quality

awareness and practices among U.S. firms. Among other things, the model asserts that

human resource development/management is part of the ‘system’ that drives business

performance. Included in its classification of human resource development practices are

many of the same practices put forth in Heskett et al’s service profit chain: training,

development, communication, etc. The MBNQA framework has been validated in both

manufacturing and service settings (Wilson and Collier, 2000; Meyer and Collier, 2001;

Goldstein and Schweikhart, 2002).

The European Excellence Quality Model is the European brother of the MBNQA

award and based on many of the same underlying theories. In this model, ‘people

management’ is seen as an enabler of three different performance dimensions: employee,

customer and business. In short, the model asserts that creating supportive human

resource policies aimed at developing employees will have direct effects on employee

satisfaction, employee loyalty and employee productivity. Like the service profit chain,

it also declares that such policies will have positive indirect effects on customer

satisfaction and loyalty, which eventually impacts business performance. The links in the

EFQM model have also been validated using structural equation modeling (Eskildsen and

Dahlgaard, 2000).

An early precursor of the service profit chain can be found in what is known as

the attachment framework (Ulrich et al, 1991). The basic premise of the attachment

framework is that employee attachment leads to customer attachment which leads to

competitive advantage. Attachment usually manifests itself through commitment

behavior. Customers who see employees who are committed to their organization will

37

notice their dedication and use it to form a positive opinion about the organization itself.

The logic is simple: only high quality organizations could produce truly dedicated

employees. Over time, a shared mind-set develops between customer and employee,

resulting first in attached customers who become satisfied and loyal and, second, in

increased revenues and margins.

Similar to the service profit chain’s linkages between internal service quality and

employee satisfaction, employee attachment is a function of an organization’s investment

in their employees. Human resource practices that influence attachment include many of

the same practices found in Heskett et al’s (1994, 1997) internal service quality: work

design, reward systems, training programs, empowerment, goal management and

management support. So, in essence, the attachment theory suggests a framework nearly

identical to the service profit chain: supportive human resource practices influence

employee indicators which influence customer indicators which in turn lead to improved

business performance.

In summary, these five frameworks (cycle of failure/success, relationship value

marketing, MBNQA, EFQM and attachment) closely parallel the model set forth in

service profit chain research. Collectively, they lend support and validation for the

service profit chain theory. It is also interesting to note that the models discussed are

from the three different research streams that Heskett et al (1997) claim to be combining;

employee management, customer management and service quality.

38

2.2. Internal service quality

Roughly defined, internal service quality results from high quality support

services and organizational policies that enable employees to deliver results, in terms of

service quality and value, to customers (Hallowell et al, 1996). In another light, internal

service quality encapsulates all of those factors that contribute to employee satisfaction

while also fostering the creation of customer value (Heskett et al, 1997). Researchers

have tended to focus on providing anecdotal evidence of what they call internal service

quality rather than cataloguing a precise set of practices that compose the construct itself.

Schlesinger and Heskett (1991) describe Nordstrom’s “obsession” with internal service

quality dimensions, such as communicating store goals to employees, empowering

employees to handle all sorts of customer needs and complaints and rewarding

salespeople for a job well done. Heskett et al (1997) give dozens of examples of

organizations building support for their front line service workers – Fairfield’s dedication

to hiring and training team players, Au Bon Pain’s commitment to providing

advancement and growth opportunities to its employees and Sears’ overall commitment

to making itself a compelling place to work are just a few examples. Although each of

these organizations use different sets of practices to express it, they all understand the

criticality of investing in employees as resources to be valued, rather than costs to be

minimized.

To date, there are only two studies that seek to develop an internal service quality

construct. Neither of the studies does so systematically or rigorously. Hallowell et al

(1991) are the first to embark on empirical research using an internal service quality

framework. They theorize that there are eight dimensions to internal service quality:

39

communication, teamwork, training, management support, tools, policies and procedures,

rewards/recognition and goal alignment. The reference list used to justify their selection

is extremely light. Using the insurance industry as their sampling frame, the researchers

ask two to three questions for each of the eight dimensions. Principal components

analysis is used to check the uni-dimensionality of each of the eight dimensions, although

the results are not given. Furthermore, no mention is made of reliability assessment,

convergent, divergent or nomological (or predictive) validity. Even though the authors

have a very large sample size, approximately 7,500 respondents, they do not use factor

analysis to test whether the eight individual dimensions do indeed load onto a single

second order internal service quality factor; for all the reader can tell, the eight

dimensions could be completely independent. Single summated variables representing

each of the eight dimensions are then entered independently into regression equations and

shown to be positive predictors of job satisfaction. It is important to note that their

regression equations show significant signs of multi-collinearity between the eight

dimensions. This problem could easily be overcome by using a second order factor

model. In testing a second hypothesis, six of the eight dimensions, all but

communication and rewards, are shown to be positive predictors of employees’

perceptions of their capability to meet customer needs (a surrogate measure for employee

productivity and/or service quality).

The other attempt to explore the composition of internal service quality is work

by Edvardsson et al (1997). In their study of the psychosocial work environment

surrounding Swedish employees of a computer service company, they propose a fifteen

dimensional representation of internal service quality. Borrowing from research into

40

stress profiles and quality profiles, Edvardsson et al (1997) use factor analysis to develop

the fifteen following factors: responsiveness, ergonomical conditions, workload,

relationship to immediate supervisor, physical work environment, relationship to co-

workers, worry about employment, relationship to management, decision latitude, salary,

stimulation from work, personal and material resources, goals and information,

interpersonal consideration and reliability. All fifteen factors are shown to be

unidimensional, but again reliability and validity tests are not performed. An exploratory

second order factor analysis is done on the fifteen factors and, as the authors point out,

the resultant four factor solution is neither simple nor meaningful. After seeing the

results, the authors basically give up trying to form a higher order internal service quality

construct.

Two later studies take very cursory looks at internal service quality: Silvestro

and Cross (2000) and Kamakura et al (2002). In their superficial one-at-a-time

correlation analysis of the service profit chain, Silvestro and Cross (2000) find that

internal service quality is positively correlated with financial performance, measured as

profitability. However, the multi-faceted concept of internal service quality is measured

through a single objective question – ratio of planned working hours to actual working

hours. The researchers make the argument that as the ratio of planned hours to actual

hours increases (meaning less time is given to carry out the actual work), the workplace is

perceived to become more stressful; hence, internal service quality will diminish.

Therefore, there will be a negative association between the measured ratio and

profitability. Kamakura et al (2002) use a similar strategy when measuring internal

service quality. Within the banking industry, they employ the number of service

41

personnel and number of ATM’s as a surrogate for representing internal service quality.

Again, they claim that fewer employees leads to more stressful workplaces. There is no

theoretical support or justification for use of these measures. Their representation of

internal service quality is tenuous at best.

2.2.1. Internal service quality parallels

The idea of creating a supportive internal environment to boost employee

satisfaction, loyalty and/or productivity is certainly not unique to Heskett et al’s notion of

internal service quality. For several decades researchers have explored how to create

supportive work environments. This section discusses some ideas similar to the concept

of internal service quality. Included in this section is a discussion of: organizational

culture, organizational climate, human resource management, high commitment human

resource practices, supportive human resources, innovative human resources, quality of

work life, perceived organizational support and the work design model. Although none

of these systems map one to one onto internal service quality, they all do lend support for

the notion of long term investment in employees as resources to be valued rather than as

constraints to be minimized. Each of the corollaries is treated only briefly; further

reading is suggested for those wishing to pursue additional research. For each parallel,

discussion includes a conceptual definition of the research stream followed by a summary

of construct development and empirical evidence of its relationship with various other

employee, customer and business performance indicators.

Organizational culture has been heavily studied since Schneider, in

Organizational Climate and Culture (1990), notes a lack of scholarly criticism. Most

42

organizational researchers agree that culture can be thought of as a set of cognitions

shared by members of a social unit. It is comprised of the assumptions, values, norms

and tangible artifacts of the leading organizational members. These assumptions, values,

norms and tangible artifacts are shaped by an organization’s human resource policies

regarding internal service quality elements. The values of the organization have been

shown to influence the behavioral norms and attitudes of its employees, which in turn,

influence the behavior of the organization’s customers (cf. Chatman and Jehn, 1991;

O’Reilly et al, 1991; Sheridan, 1992;). Specifically, Kerr and Slocum (1987) show that

organizational culture influences human resource policies, such as reward systems and

development programs, which in turn influence employees’ organizational commitment,

tenure and productivity. In a similar vein, Sheridan (1992) shows that positive

organizational culture impacts employee voluntary survival rates and employee

productivity. O’Reilly et al (1991) demonstrate that the effect of organizational culture

on turnover rates may be mediated by employee job satisfaction, a concept identical to

that found within the service profit chain.

Much like internal service quality, organizational culture has been shown to be a

multidimensional construct; in fact, the two upper level constructs are comprised of

similar dimensions. A brief summary of the seven traditional culture dimensions is given

below. To a great extent, these seven dimensions overlap the eight dimensions of internal

service quality.

• Detail – A ‘work task’ dimension stressing the values of analytical aptitude andorientations toward precision and accuracy.

• Stability – A ‘work task’ dimension stressing the values of predictability, qualitywork and rule orientation.

43

• Innovation – A ‘work task’ dimension emphasizing an organization’s focus onrisk taking, responsiveness and learning.

• Team orientation – An ‘interpersonal dimension’ focusing on the norms ofcollaboration, helpfulness and teamwork.

• Respect for people – An ‘interpersonal dimension’ stressing norms of fairness,equitability and tolerance.

• Outcome – An ‘individual actions’ dimension regarding norms of highexpectations, personal achievement and reward structure.

• Aggressiveness – An ‘individual actions’ dimension focusing on the norms ofcompetition in an organization.

Unlike internal service quality, this construct has been well developed and empirically

validated. O’Reilly, et al (1991) developed the Organizational Culture Profile (OCP) in

1991. The scale consists of 54 items which represent the seven distinct factors. The

scale has been validated in dozens of different settings and industries (O’Reilly et al,

1991; Sheridan, 1992; Cooper-Thomas et al, 2004).

The clearest parallel to organizational culture is organizational climate. Rogg et

al (2001) define organizational climate as a “set of shared attitudes, values and beliefs

about how an organization operates.” Relative to organizational culture, climate

perceptions are temporary and changeable. Climate is shaped by an organization’s

history, expectations, unwritten rules and social mores. Taken jointly this collection

affects the behavior of everyone within, and to a certain degree outside of, the

organization. Schneider et al (1980) propose two distinct types of climatic orientations

for service providing firms– ‘bureaucratic orientation’ and ‘enthusiastic orientation’. A

bureaucratic orientation is a climate wherein rules and procedures are stressed, often

diverting energy away from the actual servicing of customers. Enthusiastic orientations

44

incorporate philosophies of highly flexible, interpersonal communities. Researchers have

primarily agreed on the dimensions that create organizational climate (cf. Schneider et al,

1980; Schneider and Bowen, 1985; Rogg et al, 2001). The four most common

dimensions are listed below; again, these dimensions map quite nicely onto the internal

service quality construct.

• Managerial behavior – Planning, organizing and managing standards ofservice for customer satisfaction and quality.

• Systems support – Personnel, operations, systems, technical and otherancillary support to employees.

• Cooperation/Coordination – Teamwork, helpfulness and communication insupport of satisfying customers.

• Work Design – Job enrichment, rotation and flexibility programs.

Schneider and Bowen (1980) demonstrate that enthusiastic climate orientations

are positively correlated with customers’ perceptions of service quality and customer

satisfaction. Schneider and Bowen (1985) and Rogg et al (2001) both expand upon this

study to show that climate in fact mediates the relationship between human resource

policies and customer satisfaction. It is important to note that the human resource

practices used in these studies are also a subset of Heskett et al’s (1994) definition of

internal service quality. The practices include quality training programs, selective hiring

practices, job enrichment programs, work design methods and performance management.

One stream of research actually investigates the interaction of organizational

climate, organizational culture and human resource practices – the social context model

(Rogg et al, 2001). The social context model avers that climate and culture serve to

mediate the linkages between human resource systems and organizational effectiveness.

45

Specifically, the theoretical model asserts that an organization’s cultural values influence

the type of human resource systems that are developed, which in turn shape the

organization’s climate. And it is the dynamic synergies between the three constructs that

affect employee satisfaction, loyalty, productivity and ultimately organizational

effectiveness. The following sections discuss some of the human resource systems that

can be incorporated into social context model or can stand on their own as effectors of

organizational performance.

Several different human resource management (HRM) frameworks closely

resemble the notions found in the service profit chain, specifically those within internal

service quality. This research briefly touches upon four of the most common HRM

frameworks: high performance human resources, high commitment human resources,

supportive human resources and innovative human resources. Before describing these

frameworks it is important to put them in context. Wright and Boswell (2002) propose a

typology of human resource management research. They suggest a two dimensional

categorization using number of human resource practices and level of analysis as the two

axes. Table 2.1 is an illustration of their typology.

46

Traditional HRM

Industrial Psychology

Psychological Contract

Employment Relationship

Individual

Isolated Functions Strategic HRM

Industrial Relations

High Performance Work

Systems

OrganizationLevel of Analysis

SingleMultiple

Number of HRM Practices

Traditional HRM

Industrial Psychology

Psychological Contract

Employment Relationship

Individual

Isolated Functions Strategic HRM

Industrial Relations

High Performance Work

Systems

OrganizationLevel of Analysis

SingleMultiple

Number of HRM Practices

Table 2.1. Wright and Boswell (2002) HRM typology

According to this classification, research into internal service quality and the service

profit chain would simultaneously span two dimensions of this typology. Establishing a

relationship between internal service quality elements and employee satisfaction and

loyalty would clearly fall into the individual level of analysis and multiple number of

human resource practices box. This introductory part of the analysis draws primarily on

psychology literature. Establishing a relationship between internal service quality and

organizational outcomes (e.g. service quality, revenue growth, etc) would fall into the

organizational level of analysis and multiple number of human resource practices box.

This secondary part of the analysis chiefly uses service operations literature. This kind of

boundary spanning research is fairly unique, due to its reliance on very large sampling

plans, is extremely important, because it allows for the investigation of the interaction

between the individual and the organization, and is desperately needed as indicated by

Wright and Boswell (2002) in their call for multi-level human resource research. As an

interesting aside, Wright and Boswell (2002) also direct future researchers to investigate

47

variance in human resource practices within organizations, noting that there is a

recognizable difference between corporate policy and unit level implementation. This

research takes just such a perspective.

The term “high performance” work system is relatively new in academic

literature. Huselid (1995) introduces the term when he applies Barney’s (1991) resource-

based theory of the firm to human resource management. Huselid argues that high

performance human resource practices can lead to sustainable competitive advantage

because they 1.) add value, 2.) are rare, 3.) can not easily be imitated and 4.) are not

subject to replacement. This theory is even more salient given the setting, ie. specialty

retailing, of the current study – a service based organization where customer contact is

high.

Huselid breaks down his system of high performance human resource practices

into two major categories: those that improve employee skills and organizational

structures, and those that improve employee motivation. Within the former group he

includes specific practices such as formal information sharing programs, comprehensive

training programs, selective recruitment and hiring and employee participation in quality

of work life programs. The latter group consists of formal performance appraisal systems

and performance based promotion and wage raise increase programs. These two

categories, which together include many internal service quality dimensions, are shown

to impact both intermediate employee outcomes, such as turnover and productivity, as

well as short and long term measures of corporate financial performance, both market

based, Tobin’s q, and accounting based, return on capital. In fact, Huselid’s (1991)

results are very similar to the theory proposed by the service profit chain; namely, that

48

employee turnover, satisfaction and productivity mediate the relationship between human

resource practices and organizational performance. In addition, employees under these

high performance conditions are thought to be more likely to engage in organizational

citizenship behaviors and unrewarded behaviors that are believed to be critical to

organizational success.

A research stream that borrows from both high performance human resources and

organizational climate is that of high commitment human resource management, a field

that has been gaining velocity in academic literature. The origin of this research stream

can be traced to Arthur’s (1992, 1994) empirical work classifying different types of

human resource systems. His two categories of ‘control’ and ‘commitment’ practices

closely resemble Schneider’s (1980) ‘enthusiastic’ and ‘bureaucratic’ climate groups.

Control approaches aim to increase efficiency and reduce labor costs by imposing

regulations, strict work rules and standardized procedures. In contrast, commitment

systems shape preferred employee behaviors by forging psychological bonds between

organizational and individual needs and goals. In general, commitment systems are

characterized by high levels of employee involvement and empowerment, comprehensive

training programs, equitable internal and external rewards and emphasis on teamwork

and participation.

To date the best operationalized model of high commitment work systems is

given by Whitener (2001). High commitment human resource management is shown to

be a five dimensional construct made up of developmental appraisal, selective staffing,

comprehensive training, internally equitable rewards and externally competitive awards.

Arthur’s (1994) view is a little broader, including decentralization and participation.

49

Taken together, these two operationalizations closely resemble the internal service

quality construct used in this study. In terms of performance implications, Arthur (1994)

finds that high commitment systems lead to higher employee productivity, lower costs

and lower employee turnover. Whitener’s (2001) findings are similar; she demonstrates

that high commitment systems lead to increased employee loyalty and productivity, as

measured through organizational commitment.

Building on commitment research, Allen et al (2003) focus on human resource

practices that suggest investment in employees and show recognition for employee

contributions, signaling that a company is generally supportive of its employees. These

‘supportive’ human resource practices include participation in decision making, fairness

of rewards and growth opportunities; again, all three are elements of internal service

quality. Using a social exchange theory framework (Blau, 1964), Allen et al’s (2003)

analysis reveals a positive relationship between supportive human resource practices and

employee satisfaction and loyalty.

MacDuffie (1995) takes one of the most holistic approaches to human resource

management in his research into ‘innovative’ human resource practices. MacDuffie

(1995) argues that human resource practices affect performance, not individually, but as

interrelated elements in an internally consistent ‘bundle’. The bundles mutually reinforce

conditions that support employee motivation and skill acquisition. Employee knowledge

about products, processes, organizational goals and customers can create organizational

capabilities more difficult to imitate than readily purchased technological advancements

in the processes or products themselves. MacDuffie distinguishes between practices that

affect the organization of work and those that affect the employees as individuals. Work

50

system practices are geared towards increasing organizational knowledge and include use

of work teams and problem-solving groups, job rotation and work design. Whilst

innovative human resource policies aimed at individuals, e.g. training programs,

empowerment, selective hiring, etc, primarily drive employee motivation and

commitment. Collectively, these innovative human resource bundles, made up almost

exclusive of internal service quality dimensions, improve employee productivity and

service quality.

A practitioner oriented research stream that closely parallels internal service

quality is found in the quality of work life literature (QWL). The term quality of work

life was first introduced in 1972 during an international labor relations conference. Lau

(2001) recently defines quality of work life as “the favorable conditions and

environments of a workplace that support and promote employee satisfaction.” Quality

of work life programs generally focus on reward management, job security, promotion

and advancement opportunities and employee involvement, teamwork and job

enrichment programs – all central tenets of Heskett et al’s (1994, 1997) internal service

quality construct. Using longitudinal data, Havlovic (1991) demonstrates that quality of

work life programs can dramatically reduce employee turnover, absentee rate, accident

rate and the number of grievances filed. Lau (2001) takes a broader perspective and

shows that firms who actively pursue quality of work life programs outperform those that

do not on several key business performance metrics: sales growth, asset growth, return

on asset growth and average profit margin. Lau (2001) tests these relationships directly

but references the service profit chain framework as the causal structure that captures all

51

of the indirect links between quality of work life, which he notes is a surrogate for

measuring internal service quality, and business performance.

Psychology literature abounds with employee satisfaction research; specifically,

research into the precursors of employee satisfaction. One of the most popular research

streams within psychology and organizational behavior is the idea of ‘perceived

organizational support’ (POS). Rhoades et al (1986) are credited with developing the

term perceived organizational support. In their seminal work they attach the following

description: “employees form general beliefs concerning how much the organization

values their contributions and cares about their well-being.” Such perceived support

depends on the same attributional processes that people generally use to infer the

commitment by others to social relationships. Perceived organizational support is

influenced by the frequency, extremity and judged sincerity of statements of praise and

approval (Blau, 1964). Other rewards and signals of commitment include pay,

recognition, job enrichment, promotion opportunities and training programs. Basically,

any program or practice that contributes to employees’ impressions of how dedicated

their company is to their development and growth as individuals can be classified as a

dimension of perceived organizational support. Heskett et al (1994, 1997) use this same

argument as a basis of their conjectured link between internal service quality and

employee satisfaction.

Perceived organizational support is generally measured using the survey of

perceived organizational support (SPOS) scale which was originally developed by

Eisenberger et al (1986). The original scale contains 36 items, but most research uses

only a subset; anywhere from five to twenty questions (cf. Rhoades and Eisenberger,

52

2002). Perceived organizational support has been linked to many different outcomes.

The earliest work shows that perceived organizational support can increase employee

satisfaction and productivity while also decreasing employee turnover and absentee rates

(Eisenberger and Huntington, 1986; Eisenberger et al 1986). However, over the last

eighteen years, researchers have shown that perceived organizational support can

influence many other wide reaching outcomes. These outcomes include, but are not

limited to: affective commitment, extra-role performance, in-role performance, withdraw

cognitions, organizational citizenship behavior and employee tenure (see Rhoades and

Eisenberger (2002) for a summary). Figure 2.6 provides an illustration of the most

widely accepted causal model of perceived organizational support (Eisenberger et al,

1986). Please note that perceived organizational support is measured using elements very

similar to internal service quality elements: organizational support – management,

organizational support – tools, rewards and recognition, training, empowerment and work

design.

53

POS

Felt Obligation

Positive Mood

Affective Commitment

Org. Spontaneity

In-role Performance

WithdrawlBehavior

POS

Felt Obligation

Positive Mood


Org. Spontaneity

In-role Performance

WithdrawlBehavior

Figure 2.6. Eisenberger’s (1986) perceived organizational support model

The relationships in the POS model closely resemble those in the service profit chain.

POS, a quasi-surrogate for internal service quality, influences positive mood, a surrogate

for employee satisfaction, which in turn influences in-role performance, a surrogate for

productivity, and withdrawal behaviors, a surrogate for employee loyalty.

Hackman and Oldham’s work design model (WDM), developed in the mid

1970’s, encapsulates much of the same ideology as the concept of internal service

quality. Hackman and Oldham (1976, 1980) stress that processes within an organization

must be designed to meet not only the technical demands of the customer but also the

emotional and mental needs of the employees who work within it. In the WDM model,

core job characteristics influence critical psychological stages which in turn influence

work outcomes; see Figure 2.7 for an illustration.

54

Core Job Characteristics

Critical psychological stages

Outcomes

Skill Variety

Task Identity

Skill Significance

Autonomy

Feedback

Experienced meaningfulness of the work

Experience responsibility for outcomes

Knowledge of the results of work activities

High internal work morale

High “growth” satisfaction

High general job satisfaction

High work effectiveness

Core Job Characteristics

Critical psychological stages

Outcomes

Skill Variety

Task Identity

Skill Significance

Autonomy

Feedback

Experienced meaningfulness of the work

Experience responsibility for outcomes

Knowledge of the results of work activities

High internal work morale

High “growth” satisfaction

High general job satisfaction

High work effectiveness

Figure 2.7. Hackman and Oldham’s (1976, 1980) work design model

The driver of the model, core job characteristics, incorporates several of the dimensions

of internal service quality – work design, empowerment, training and rewards and

recognition. These dimensions drive employee satisfaction and productivity, as

hypothesized in the service profit chain. The Hackman and Oldham model has been

validated across a wide range of industries, both manufacturing and service related (cf.

Evans and Lindsay, 1996; Eskildsen and Dahlgaard, 2000).

The previous section describes empirical linkages that have been found between

internal service quality elements, as measured through various other theoretical

frameworks, and numerous employee outcome measures. Collectively they serve to

justify not only the link between internal service quality and employee satisfaction but

55

also our specific rendering of the internal service quality construct (as will be discussed

in detail in Section 2.2.3). The eight dimensions we are using for internal service quality

are by far the most common practices found throughout the literature we surveyed,

regardless of discipline.

The following section will provide theoretical justification on two different yet

similar theoretical grounds for these relationships – specifically, the relationship between

internal service quality and employee satisfaction and loyalty. The two theories that will

be explored, namely social exchange theory and the inducements/contributions

framework, both originate in social psychology where they were used to explain

relationships among individuals; over the last forty years, they have found their way into

economic and business management literature as methods of describing the relationships

between individuals and business organizations.

2.2.2. Theory linking internal service quality to employee indicators

The most commonly cited theory used as justification for a positive link between

human resource management and employee related outcomes is social exchange theory

(Homans, 1961; Blau, 1964; Schneider et al, 1980; Wayne et al 1997; Rhoades and

Eisenberger, 2002). Social exchange theory originally explained the motivation behind

the attitudes and behaviors exchanged between individuals (Homans, 1961; Blau, 1964).

The key premise of social exchange theory is that human behavior in essence is an

exchange of social and material resources; to put it in economic terms, social interaction

is an exchange of costs and rewards. In deciding what is fair, humans develop a

perception of the outcome level of their relationship with another individual. There are

56

primarily two ways to evaluate outcome levels: a comparison level (CL) and a

comparison level of alternatives (CLalt). CL is an absolute judgment as to whether the

social exchange (ie. the relationship) provides benefits that outweigh costs. CLalt

considers the best payoffs available outside of the current relationship. Regardless of

which outcome level is used to base judgments, humans will strive to minimize costs and

maximize rewards resulting in a positive ‘balance of trade’. An individual will seek to

maintain a relationship whose outcome is positive; in order to maintain the relationship

the individual will try to give beneficial results back to the other party. The exchange

between two individuals will continue indefinitely as long as both individuals have

positive outcome levels. If an individual has a negative balance of trade, they will

quickly look to terminate the exchange. Clearly there are direct parallels to modern

economic theory – power aside, trade between two parties will occur when both parties

benefit from the trade itself.

A group of researchers, most notably Eisenberger et al (1986), later expand upon

this theory and apply it to relationships between individuals and the organizations they

work for. Drawing on psychology literature, they note that employees form general

perceptions about the intentions and attitudes of the organization that employs them from

the policies and procedures that directly affect the employees themselves. In effect,

employees attribute human-like characteristics to their employing organization on the

basis of the treatment they receive (Levinson, 1965). Eisenberger et al (1986) predict that

beneficial actions directed at employees by the organization and/or its representatives

will increase the likelihood of reciprocal benefit flow from individual back to

organization. Moreover, the benefit to employees need not be strictly monetary in the

57

form of high wage rates and ancillary benefits. Benefits could include practices that lead

to personal growth, including training and empowerment, to personal well-being and to

personal recognition. When employees perceive that their organizations are valuing them

and investing in them, they will feel obliged to return the value they obtained by

becoming more productive and loyal. Wayne et al (1997) suggest that a pattern of

reciprocity develops over time between the employee and the organization; sometimes

this pattern is referred to as the norm of reciprocity (Gouldner, 1960). Putting it back into

the service profit chain perspective, organizations that invest in internal service quality

(positive benefit to employees) will in return see increases in employee loyalty, employee

productivity and service levels (positive benefit to the organization).

The argument behind social exchange theory is consistent with the inducements-

contributions framework of voluntary turnover, proposed by March and Simon in 1958.

The inducements-contributions framework serves as the foundation of much of

contemporary turnover theory (Hom and Griffeth, 1995; Allen et al, 2003). March and

Simon argue that an employee’s decision to continue participation in an organization is

based on the balance between the inducements offered by the organization and the

contributions expected of the employee. An employee who perceives greater

inducements is less likely to terminate the working relationship. Again, an organization

that offers support in the form of greater internal service quality may be seen as offering

an inducement to the employee. In return employees will be willing to repay the

organization by increasing their contributions.

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2.2.3. Individual dimensions of internal service quality

In order to detail a specific measurement of internal service quality we blend the

theoretical, descriptive work of the service profit chain with the more rigorously

grounded, academically validated work of those parallel fields described in section 2.2.1.

The service profit chain anecdotes are used to generate a list of potential internal service

quality elements and then that list is validated with empirical support derived from other

disciplines (e.g. human resource management, personnel psychology, organizational

behavior, etc). Specifically, we look for empirical justification that each of the potential

internal service quality practices influences employee satisfaction as theorized in the

service profit chain model. Appendix A details empirical support for the eight most

commonly cited effectors of employee satisfaction. Appendix B details support of

empirical research that validates the relationship between internal service quality and the

other variables in the service profit chain model. From this analysis we have decided to

use an eight dimensional representation of internal service quality. Each dimension is

listed below and briefly described; each will be discussed in more detail in the following

sections.

1. Training and Coaching – the amount and quality of training programs as wellas on the job performance feedback

2. Goal Management – store goals in line with customer needs andcommunicated to employees

3. Teamwork – employees work in teams to serve customers

4. Empowerment – employees have latitude and authority to meet uniquecustomer needs

5. Work Design – amount of workload, stress and ambiguity involved inemployees’ job responsibilities

59

6. Support – Management – managements’ valuation of employees opinions andwell being

7. Support – Tools – technology, information and operating policies supportfront line workers ability to serve customers

8. Rewards and Recognition – monetary awards and recognition programs areadequate and linked to performance

Sections 2.2. and 2.2.1. offer a literature review of internal service quality, and its

parallels, from a holistic point of view; namely, as a set of interrelated practices. The

following sections provide a cursory review of our eight internal service quality

dimensions independently. The sections serve to enhance the validity not only of our

definition and operationalization of the internal quality construct itself but also for the

link between internal service quality and the other variables in the service profit chain

model. The discussion of each dimension begins with a summary of the evidence linking

it to employee measures such as satisfaction and loyalty. A summary and justification of

the survey instrument apropos of each dimension is then given.

TRAINING:

One of the most widely studied human resource practices in the service segment

is employee training. Vast amounts of research have demonstrated that the training of

frontline employees in both job-related and behavior-related skill sets can have wide

ranging personal and organizational effects. Some of these effects include those

predicted by the service profit chain model: increased job satisfaction (Ulrich et al, 1991;

Hallowell et al, 1996; Wright and Boswell, 2002), increased loyalty (Havlovic, 1991;

60

Wayne et al, 1997), increased ability to meet customer needs (Wayne et al, 1997;

Silvestro and Cross, 2000), improved service quality (Silvestro and Cross, 2000;

Kamakura et al, 2002), increased customer satisfaction (Tornow and Wiley, 1991; Rogg

et al, 2001) and improved business performance (Tornow and Wiley, 1991; Huselid,

1995). Employees who are well trained and are capable of meeting customer needs feel

better and more satisfied with their jobs and are more likely to remain with their

employer. Furthermore, employee knowledge about products, processes and customer

needs helps to create organizational capabilities that are difficult to imitate and cannot be

readily substituted by technological advancements.

Two recent trends are emerging in training literature. The first is a shift towards

research exploring the interaction of training and service failures (Hart et al 1990). A

study by Bitner et al (1990) shows that more than 40% of unsatisfactory service

encounters result from employees’ inability to respond to service failures – this inability

stems from lack of proper training. Boshoff and Allen (2000) demonstrate that

employees who do not possess the requisite job skills, skills developed through training,

fail to provide satisfactory service when dealing with customer complaints. As service

recovery and failure analysis gains more attention in both academic circles and business

practice, research into the effect of training on employees’ service capability will

continue to grow exponentially. A more recent development in training literature is the

impact training programs have on team performance (Wright and Boswell, 2002).

Environments where employees work in teams necessitate training not only in product

and process attributes but also in interpersonal skills. Mathieu et al (2000) find that

61

training and job rotation positively influence team performance. Frayne and Geringer

(2000) show similar results in an insurance setting.

This research relies heavily on previous empirical research in regards to

measuring employee training. In order to be as comprehensive as possible, several

different aspects of training are surveyed. The most obvious and common question used

in training research is whether an employee receives enough initial training when first

taking a job (Huselid, 1995; MacDuffie, 1995). However, the amount of initial training

certainly does not guarantee an employee’s success. In a retail environment, new

products and processes are continually being introduced; therefore, it is crucial that

employees receive adequate additional training throughout the term of their employment

(MacDuffie, 1995; Babakus et al, 2003). In addition to measuring the amount of training

given, it is critical to measure the quality of the training programs (Wayne et al, 1997).

For training to be effective it is essential that management gives feedback regarding an

employee’s job performance and specific recommendations on how to improve (Huselid,

1995).

GOALS:

Borrowing from research into manufacturing strategy and goal setting, Heskett et

al (1994, 1997) describe the importance a service organization should place on goal

setting. Organizational goals should be clearly communicated to employees on a regular

basis so that the entire employment team is working towards the same objectives.

Employees who understand their organizational values are more likely to go out of their

way to fulfill these goals. Employees who do not understand their organization’s goals

62

are more likely to feel conflicted, limiting their ability to satisfy customer needs.

Organizational goal setting and management have been positively linked to immediate

employee measures as well as long term business performance. Schlesinger and Bowen

(1985) show that goal management can have a positive effect on employee satisfaction.

Havlovic (1991) and Huselid (1995) show that goal management can increase employee

loyalty. Tornow and Wiley (1991) and Arthur (1994) demonstrate a positive association

with employee productivity. Zeithaml et al (1988) and Schneider and Bowen (1993)

illustrate the positive link between goal management and customers’ perceptions of

service quality. Meyer et al (1999) and Rogg et al (2001) both demonstrate the

importance of goal management and leadership in positively influencing customer

satisfaction measures. Taking the analysis a step further, Silvestro and Cross (2000) and

Kamakura et al (2002) show how goal management can affect business performance.

There are no comprehensive scales in extant literature to measure goal

management in service organizations. When developing the survey instrument we rely,

when possible, on items that other researchers have used but have had to supplement

these questions with additional ones prescribed by the anecdotes found in Heskett et al

(1994, 1997). The instrument measures goal management with five questions based on

eliciting employee feelings regarding management’s communication of goals, procedures

and policy changes (Hallowell et al, 1997). However, communicating goals is not

enough; the goals of the company must also be aligned with customer needs (Hallowell et

al, 1997). An obvious link exists between organizational goals being in line with

customer needs and customer satisfaction. Research has also shown a secondary effect of

goal management: employees who work in an environment of conflicting organizational

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and customer needs are less satisfied and productive and more likely to leave the

company (Rogers et al, 1994; Varca, 1997).

TEAMWORK:

Work teams have been extolled as one of the most significant business

innovations of the 1990’s, helping organizations achieve productivity and service

breakthroughs. The past fifteen years have seen an explosion not only in academic

research of work teams but also in practical implementations of the teaming model. One

reason for this increase is that work team structures play a vital supporting role in

emerging and advanced business practices such as total quality management, just-in-time

production, lean manufacturing, business process reengineering, etc. (Wisner and Feist,

2001). Teaming, often combined with cross-training, creates flexibility for an

organization, especially in retail environments where demand and the need for customer

service can fluctuate drastically within very small time intervals. The use of work teams

has been positively linked to employee measures such as satisfaction (Rucci et al, 1998;

Rhoades and Eisenberger, 2002), productivity (Schlesinger and Zornitsky, 1991;

Sheridan, 1992) and loyalty (Havlovic, 1991; Arthur, 1994). Teaming has also been

linked to intermediate performance measures such as external quality (Zeithaml et al,

1988; MacDuffie, 1995) and customer satisfaction (Silvestro and Cross, 2000; Rogg et al,

2001). Finally, overall business performance has also been shown to be positively related

to the use of work teams (Lau, 2000; Kamakura et al, 2002).

The survey instrument used in this study is designed to capture not only the

emphasis that management places on teamwork but also the actual amount of teamwork

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that takes place on a regular basis. While this particular retailer assigns sales associates

to distinct zones within a store, customer demand is in no way evenly spread out across

the zones at all times. Therefore, it is necessary for idle sales associates to help in work

zones that are overburdened. Work by Hallowell et al (1991) and MacDuffie (1995)

provides a basis for the specific items used in this survey.

EMPOWERMENT:

Empowerment has been defined in many different ways, but one common thread

running through all the definitions is that it involves giving employees latitude and

discretion over certain task-related activities (Schlesinger and Heskett, 1991; Bowen and

Lawler, 1992; Rafiq and Ahmed, 1998; Babakus et al, 2003). Customers play active

roles in many service processes, and with this increased involvement comes increased

variability. Organizations cannot often control or predict how customers will behave

during the service process. Due to this lack of control over the external environment,

organizations often find it helpful to empower their employees. Front-line employees are

often given discretion during the service delivery process. Many customers will have

unique needs than cannot be fulfilled by scripted responses from employees. Employees

must be capable of handling these situations when they arise. Moreover, customers may

find it tiresome if every request they have of an employee is met with a standard “let me

check with my manager” response. Empowerment is especially crucial when service

failures occur (Bitner, 1990; Bowen and Lawler, 1992; Spreng et al, 1995; Boshoff and

Allen, 2000). Because of their boundary-spanning roles, front line employees can

65

provide quick, appropriate and equitable responses to dissatisfied customers (Babakus et

al, 2003).

Empowerment has been linked to a wealth of outcomes, behavioral and business

alike. There is substantial evidence that empowerment leads to increased employee

satisfaction as employees tend to feel more important in their jobs (Zeithaml et al, 1988;

Niehoff et al, 1990; Spreitzer et al, 1997). Lau (2000) demonstrates that higher levels of

empowerment increase employee productivity and loyalty. Using more of a customer-

oriented framework, other researchers also show that empowerment can lead to increased

service quality (Zeithaml et al, 1988; Silvestro and Cross, 2000), customer satisfaction

(Rafiq and Ahmed, 1998) and business performance (Kamakura et al, 2002). These latter

findings result from empowered employees being able to respond quickly to unique

customization needs often required in service settings. Empowerment, much like

teamwork and training, is also a fundamental cornerstone of modern business practices

such as just-in-time production, total quality management and lean manufacturing. As

such, it has been indirectly tied to operational outcomes over the past twenty years in

these respective streams of research.

Although empowerment has been shown to be a multi-dimensional construct

(Spreitzer et al, 1997; Kraimer et al, 1999) most research that investigates its short and

long term implications uses only general type questions to assess degree of employee

empowerment (Babakus et al, 2003). Because of the extensive length of the survey

instrument used in this study, this simpler approach to measure empowerment is taken.

Five survey items based on those used by Edvardsson et al (1997), Rafiq and Ahmed

66

(1998) and Meyer et al (1999) are used. Specifically, the items ask questions regarding

authority, latitude and independence.

WORK DESIGN

One aspect of internal service quality often overlooked in business literature but

very prevalent in psychology literature as an influencer of employee satisfaction, loyalty

and productivity is work design. By work design, we do not mean the technical aspects

of balancing job activities to form equivalent job tasks, but rather the behavioral facets of

work design related to stress, ambiguity, tension and conflict. Rogers et al (1994)

provides definitions of these terms. Role conflict occurs when an individual is expected

to engage in inconsistent behavior as a result of receiving contradictory demands – most

often between their manager’s instruction and their own value system. Role clarity has

been defined as the degree to which individuals understand the exact requirements of

their job. Ambiguity is simply the opposite of clarity; it occurs when individuals are

confused as to their specific responsibilities. Stress, which can be caused by any number

of working conditions, is a general feeling of emotional or physical tension. All four of

these dimensions have been linked to employee satisfaction, loyalty and productivity

(Rogers et al, 1994; Spreitzer et al, 1997; Varca, 1999). One common work design

model is proposed, and validated empirically, by Rogers et al (1994), see Figure 2.8.

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Empathy

Role Conflict

Job tension

Job satisfaction

Role Clarity

Empathy

Role Conflict

Job tension

Job satisfaction

Role Clarity

Figure 2.8. Roger et al’s (1994) work design model

When employees feel less conflict, tension and ambiguity, they become more confident

in their job competence, resulting in gains in satisfaction, loyalty, productivity and even

customer satisfaction (Kamakura et al, 2002). The survey instrument used in this study is

heavily influenced by Rogers et al’s (1994) framework, especially the inclusion of

specific questions regarding conflict, ambiguity, stress and workload.

SUPPORT – MANAGEMENT

The support that employees feel they receive from management and the

organization is crucial to developing employee satisfaction. Two streams of research

combine to form this internal service quality construct – perceived organizational support

and leader-member exchange theory. Perceived organizational support was introduced in

section 2.2.1 and for sake of brevity is not detailed again here. In summary, employees

form beliefs about how well-supported and valued they are from an organizational

perspective. Like perceived organizational support, leader-member exchange uses social

exchange theory to describe how individuals interact. Unlike perceived organizational

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support, which focuses on interactions between the organization and the employee,

leader-member exchange focuses on interactions between the employee and his/her

immediate supervisor. Leader-member exchange proponents claim that an employee’s

immediate supervisor plays a larger role in influencing the employee’s behavior and

satisfaction than the more tacit role played by the organization as an entity (Sparrowe and

Linden, 1997; Kacmar et al, 2003).

The survey instrument used in this research asks two questions from each frame

of reference: perceived organization support and leader-member exchange. The

perceived organizational support questions elicit employee responses concerning their

perception of the organization in general and how it values and supports them. We draw

on research from Eisenberger et al (1986) for the development of these items. The two

leader-member exchange questions educe an employee’s perception of the relationship

they have with their immediate supervisor. The questions are based on those proposed by

Wayne et al (1997).

SUPPORT – TOOLS

Heskett et al (1994, 1997) write about how discouraging it can be for well-

intended employees, who want to serve the customer to the best of their ability, to feel

that they do not have the given resources or tools needed to carry out their job. They give

numerous examples of instances where organizations actually hand-cuff themselves by

not giving employees the capability to serve the customer. In these instances, not only is

productivity immediately reduced, but in addition, over time, employees become

discouraged with their jobs and decreases in employee satisfaction, and hence customer

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satisfaction, soon follow. Schneider et al (1980) validate this theory in their study of

employee support services in bank branches. Employees who felt that the organization

did not give them the appropriate tools required to carry out their job experienced less job

satisfaction and thus the organizational itself suffered a higher degree of turnover.

The survey instrument is designed to elicit employee perceptions of the support

they are given in the form of resources and information they need to service the customer.

In addition, employee perceptions of whether store policies and procedures inhibit their

ability to satisfy unique customer needs are also explore. The work of Hallowell et al

(1991) and Edvardsson et al (1997) provides a basis for constructing the specific survey

items.

REWARDS and RECOGNITION

Compensation management is one of the most widely studied topics within

human resource management literature; it is especially prevalent within service

frameworks (Wright and Boswell, 2002). Researchers have explored both the monetary

and non-monetary reward systems used by organizations. Within the monetary

categories, most studies focus on wage rates, pay for performance, bonus pay, incentive

pay and/or merit raises. Non-monetary reward systems generally focus on recognizing

outstanding employees with non-financial awards. The awards can be either formal

programs, such as employee of the month, or informal, such as congratulatory remarks

from a supervisor for a job well done. Both of these dimensions have been positively

linked to job satisfaction and loyalty (Brown and Peterson, 1993; Bowen et al, 1999;

Lawler, 2000). They have also been linked to external measures such as service quality

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(Bowen and Johnston, 1999) and customer satisfaction (Tornow and Wiley, 1991;

Kamakura et al, 2002).

This survey blends both monetary and non-monetary award items. Monetary

items include wage rate, benefits and promotion opportunities (Babakus et al, 2003; Allen

et al, 2003). We draw on previous work from Rhoades et al (2001) and Zemke (2002) to

craft the non-monetary rewards and recognition items.

2.3. Employee satisfaction, loyalty and productivity

One of the most heavily researched areas in human resource management and

personnel psychology is the study of the relationships among employee satisfaction,

employee loyalty and employee productivity. Because of the interaction between the

three variables it is nearly impossible to look at each independently through a

microscopic lens. As such, this section details the linkages among the set of employee

indicators. The section begins with a discussion of the relationship between employee

satisfaction and employee loyalty, then moves to a review of the connection between

employee satisfaction and employee productivity and ends with an examination of the

association between employee loyalty and employee productivity. The section concludes

by detailing the scales used to measure each of the three constructs.

The link between employee satisfaction and employee loyalty is as intuitive as it

is validated, see Appendix C for a listing of empirical support. From a logical perspective

the reasoning is simple: as an employee becomes more satisfied with their job, they are

more likely to continue working at that job. Numerous meta-analyses have empirically

validated this link from a ‘bird’s eye’ perspective: Carsten and Spector (1987), Hom and

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Griffeth (1995) and Griffeth et al (2000). These studies look at the direct link between

satisfaction and loyalty. Other researchers have explored in more detailed analyses all of

the mediating variables between satisfaction and loyalty. Hom and Griffeth’s (1991)

model, illustrated in Figure 2.9, provides the most in-depth analysis of these mediating

variables.

Job Satisfaction

Withdrawal Cognitions

Expected Utility of

Withdrawal

Job Search

Comparison of

Alternatives

Retention

Job Satisfaction

Withdrawal Cognitions

Expected Utility of

Withdrawal

Job Search

Comparison of

Alternatives

Retention

Figure 2.9. Hom and Griffeth’s (1991) turnover model

Basically the model asserts that job satisfaction simultaneously decreases an employee’s

thoughts about quitting and their expected utility of quitting. These consequences

combine to decrease an employee’s job search efforts and their expected utility of taking

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one of the available alternatives. The end result of this entire model is that, as employee

loyalty increases, the organization will enjoy higher retention rates. Allen et al (2003)

propose a simplified version of this model: job satisfaction decreases turnover intentions

which in turn decrease turnover. Both of these studies argue that an organization can

build employee job satisfaction through the use of supportive human resource practices.

Just as satisfied employees tend to work for the same organization longer, they

also tend to put forth more effort and work harder, thus becoming more productive. A

meta-analysis by Petty et al (1984) surveyed dozens of studies that show correlations

ranging from 0.04 to 0.70. The mean correlation across all studies, studies that totaled

over 3,000 employees, is shown to be 0.31. Recently, several researchers have revealed

similar findings by using organizational citizenship behavior as a surrogate for employee

productivity (Moorman et al, 1998). These studies have demonstrated that employee

satisfaction drives factors such as individual initiative, personal industry and

interpersonal helping, all of which are different forms of productivity.

The link between employee loyalty and productivity is even stronger and more

direct than that between satisfaction and productivity. As employees continue working

with an organization, they become more familiar with the service processes and the needs

of the customer. Both of these results lead to increased efficiency and productivity.

Heskett et al (1994) give many anecdotes as evidence of this relationship. For one firm

they surveyed, the average monthly difference between a sales representative with five to

eight years of experience and one with less than one year of experience was

approximately $36,000 in sales. Similar evidence abounds in Heskett et al (1997, 2001)

and Schlesinger and Zornitsky (1991). The support for this linkage extends beyond case

73

studies. McEvoy and Cascio (1987) use a meta-analytic approach of twenty four studies

to demonstrate a significant positive correlation between productivity and loyalty. More

recent work by Sheridan (1992) and Kamakura et al (2002) support these claims.

Appendix D provides a more comprehensive listing of empirical support linking

employee loyalty to the other service profit chain variables; Appendix E does likewise for

employee productivity.

The survey instrument used in this research includes two different measures of

employee satisfaction. Both scales have been well validated and are generally considered

to be the best measures. The first measure is simply one overall question about an

employee’s general satisfaction. The second measure is the job facets scale. This scale

measures five different facets of an employee’s satisfaction: pay and benefits,

opportunities for promotion, relationship with supervisor, relationship with co-workers

and amount of job responsibilities. Both scales have been used extensively in literature.

The former has been used in studies by Schneider et al (1980), Schlesinger and Zornitsky

(1991), Rust et al (1996), Silvestro and Cross (2000) and Eisenberger et al (2001). The

latter has been used by Petty et al (1984), Schneider and Bowen (1985), Hom and

Griffeth (1991), Shore and Tetrick (1991) and Judge et al (2002). A recent study by

Nagy (2002) demonstrates that the two scales are highly correlated and can be used

interchangeably, although he does note that problems could arise with the facets scale if

an employee does not weigh the five satisfaction dimensions equally when forming an

overall satisfaction score.

There are also two different ways in which to measure employee loyalty, and

again, both are quite popular. The first is simply to quantify the length of time an

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employee has worked for a company. Huselid and Day (1991), Schlesinger and

Zornitsky (1991) and Rhodes et al (2001), among many others, prefer this method. Other

researchers argue that too many contextual factors, which cannot be accounted for in a

study, influence actual duration of employment; these researchers thus prefer to measure

an employee’s intent to remain loyal (Hom and Griffeth, 1991; Wayne et al, 1997; Allen

et al, 2003). Using the theory of reasoned action, Allen et al (2003) argue for the validity

of both methods and demonstrate a very high correlation between intent measures and

objective measures of loyalty. We will use intent measures in our modeling but will also

include length of employment to validate our perceptual scale.

The ideal method to measure employee productivity in the retail sector is to use

an objective measure such as sales per shift or sales per hour (Huselid, 1995; Silvestro

and Cross, 2000). However, because of the work design of this particular retailer - that

is, employees are not assigned to customers, but rather zones - using an objective

measure is impossible since employee sales are not individually tracked. A secondary

measure would be a manager’s assessment of an employee’s productivity (Eisenberger et

al, 2001). However, since the surveys are confidential there is no way to match

employees to their manager’s perceptions. Instead, employees’ perceptions of their own

productivity and capability is used. The individual survey items are drawn from Denison

et al (1995) and Spreitzer et al (1997). We do acknowledge a drawback to this approach

in that it suffers from common method bias, since employees are effectively grading

themselves. This approach has also had questionable results in terms of correlating with

objective measures, as well as managerial perception measures.

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2.4. Summary

This chapter surveys the literature from several different disciplines in reviewing

theoretical and empirical work into the service profit chain; specifically, the first half of

the chain, the service delivery system. The chapter draws parallels from Heskett et al’s

(1994, 1997) service profit chain framework to other commonly accepted theoretical

management frameworks – relationship value management, relational marketing, the

cycle of success, perceived organizational support, the Malcolm Baldrige National

Quality Award and the employee attachment framework. Empirical research into the

service profit chain is reviewed and a substantial research gap is identified.

Another result of this review is an eight dimensional representation of internal

service quality, a latent variable representing the quality of work life and the amount of

support front line workers receive from their organization. The eight dimensions used

include: training and coaching, goal management, teamwork, empowerment, work

design, support – management, support – tools and rewards and recognition. Section

2.2.1. draws parallels between internal service quality and other supportive human

resource theories, some of which include: quality of work life, perceived organizational

support, innovative human resource practices, high performance human resource

practices, organizational climate and organizational culture. Using social exchange

theory and the inducements/contributions framework, sections 2.2.2 and 2.2.3. give

theoretical and empirical justification for the expected linkages between internal service

quality (and all of its eight dimensions) and employee satisfaction and employee loyalty.

As such the following two hypotheses, embedded within the service profit chain, will be

tested:

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H1a: Internal service quality is positively associated with employeesatisfaction.

H1b: Internal service quality is positively associated with employeeloyalty.

Section 2.3. draws on a strong stream of research to suggest that satisfied, loyal

employees become more familiar with the service process, and as such, become more

productive. These links have been well validated in both operations and human resource

literature. As such, they lay the foundation for the following three hypotheses (again,

both of which are embedded within the service profit chain):

H1c: Employee satisfaction is positively associated with employeeloyalty.

H1d: Employee satisfaction is positively associated with employeeproductivity.

H1e: Employee loyalty is positively associated with employeeproductivity.

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CHAPTER 3

LITERATURE REVIEW: SERVICE CONCEPT AND TARGET MARKET

Chapter 3 will be dedicated to the customer portion of the service profit chain and

will focus on how customers assess and value an organization’s service offering and the

business implications of these assessments. It will be organized in a similar manner to

chapter 2. Specifically, it will begin by introducing the driver of the customer portion of

the service profit chain – total retail experience. This multidimensional construct will

first be looked at from a collective standpoint, and then each of its five dimensions will

be individually analyzed. Section 3.1.1 will draw parallels between total retail

experience and other customer valuation frameworks. Section 3.2 will detail research

into customers’ perceptions of value. Following this section, section 3.3 will be

dedicated to reviews of customer satisfaction and customer loyalty literature. The

chapter ends with a summary provided in section 3.4.

3.1. Total retail experience

In their work on the service profit chain Heskett et al (1994, 1997, 2001) use the

terms “external service quality” and “external service value” as ways to describe how

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customers assess service operations. Regardless of what term they use to designate the

concept, they give many far-reaching examples to illustrate the notion. For instance, at

Southwest Airlines customers appreciate frequent departures, on-time service and

friendly employees in addition to the low prices they receive (1997). Progressive

insurance customers value quick-response damage assessment and claims processing

(1994). With each example, while the precise criteria of what makes good “service

quality” or “service value” may change, some common categories continually re-occur.

Some of the typical customer demands include: rapid service, knowledgeable and

friendly employees, high quality products, convenient service and aesthetically pleasing

surroundings. These demands are quite comprehensive and extend far beyond traditional

customer assessment scales found within service management literature (e.g. “service”

quality). As such a new assessment tool will be needed.

One emerging research construct that closely resembles all of the concepts found

within Heskett et al’s (1994) notion of external service quality is total retail experience.

Berman and Evans (1998) define total retail experience as “all the elements that

encourage or inhibit consumers during their contact with the retailer.” The examples they

include in their theoretical work closely resemble those used by Heskett et al in their

work on the service profit chain: superior customer service, knowledgeable and friendly

employees, etc. Terblanche and Boshoff (2001a, b) provide further structure for

assessing the dimensions of total retail experience. Their framework breaks total retail

experience into controllable and non-controllable elements. Figure 3.1 illustrates their

structural schema.

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Total Retail Experience

Non-controllable elements

Controllable elements

Service Quality

Product Quality

Product Variety &

Assortment

Internal Store Environment

Store policies


Non-controllable elements

Controllable elements

Service Quality

Product Quality

Product Variety &

Assortment

Internal Store Environment

Store policies

Figure 3.1. Terblanche and Boshoff’s (2001) total retail experience schema

Non-controllable items (non-controllable within a short to medium time frame) include

adequacy of street parking, mall environments, demographics of community, etc.

In their first of two factor development papers, Terblanche and Boshoff (2001b)

survey customers in four different industries: fast food, clothing, supermarkets and

hardware. They subject 24 survey items to exploratory factor analysis. The 24 items

load onto three factors: personal interaction, physical cues, and product variety and

assortment. Note that these three factors do not theoretically correspond to the five

theoretical constructs they proposed at the beginning of their research. Furthermore, they

do not test whether the three first order factors load onto a single higher order dimension.

Thus the reader does not know if the three constructs are actually three related

dimensions of total retail experience or are merely three independent factors. Two of the

three factors, personal interaction and physical cues, are shown to have a positive effect

on customer satisfaction. Although their theoretical development predicts one, no

significant relationship is found between variety and assortment and customer

satisfaction.

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In their second factor development paper, Terblanche and Boshoff (2001a) submit

twenty six items to exploratory factor analysis. The twenty six items now converge to

form five factors: merchandise value, internal store environment, personal interaction,

merchandise variety and complaint handling. Again, these five factors do not resemble

the five controllable total retail experience dimensions that the authors theorize should

exist. Just as they failed to do in their first study, Terblanche and Boshoff (2001a) fail to

test whether or not these five dimensions converge to form a single higher order total

retail experience factor. Furthermore, the authors fail to test the nomological validity of

their constructs by exploring how they relate to other variables they should be positively

correlated with, e.g. customer satisfaction and/or value.

This research will combine Terblanche and Boshoff’s (2001a) revised total retail

experience with Berman and Evans’ (1998) seminal work to create a five dimensional

representation of total retail experience. Figure 3.2 is an illustration of the five

dimensions.

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Servicescape

Product Quality

Product Availability & Selection

Store Layout

Process Quality


Servicescape

Product Quality

Product Availability & Selection

Store Layout

Process Quality

Figure 3.2. Five dimensional representation of total retail experience

Because of the lack of construct development of the total retail experience scale much

care and devotion will be spent in chapter 4 in developing the factor. Furthermore, this

will be the first research that explicitly explores whether a second order factor exists.

The next section of this chapter will lend content and face validity to our rendering of

total retail experience by describing parallels to similar customer assessment frameworks.

Following that section, each of the five dimensions will be analyzed individually.

3.1.1. Parallels to total retail experience.

Although research into total retail experience is still in its infancy, much past

research has been dedicated to developing a comprehensive customer assessment

framework in the service industry. Two of the most closely related concepts are store

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shopping experience and store image. As they add to the face validity of total retail

experience, each will be briefly treated here.

The first parallel, store shopping experience, was introduced by Kerin et al (1992)

in their study of consumer perceptions of retail stores. Kerin et al (1992) posit that “store

shopping experience emerges from a consumer’s interaction with a store’s physical

surroundings, personnel and customer-related policies and practices.” Using theory from

environmental psychology, they theorize that store shopping experience can influence

patronage decisions, satisfaction and purchase intentions – a proposition that is nearly

identical to the theory proposed within the service profit chain. The authors suggest that

store shopping experience is five dimensional in nature: 1.) store cleanliness, 2.) overall

product variety and selection, 3.) check cashing policy, 4.) friendliness of employees,

and 5.) waiting time. These five dimensions closely resemble those put forward within

this study’s five dimensional rendering of total retail experience.

In their study, Kerin et al (1992) use only one question to assess customer

impressions of each of the five dimensions. They do show, however, that these five

questions form a reliable, valid store shopping experience factor. Furthermore, their

structural equation results indicate that store shopping experience is positively related to a

consumer’s perceptions of store value, path estimate of 0.41 (p < .01).

The second customer assessment framework that parallels total retail experience

is one of the most heavily studied research topics in marketing literature – the concept of

store image. Because of the depth of the store image research stream, only a few major

highlights will be discussed here; the reader should consult Zimmer and Golden (1988)

for a more complete review. As far back as Martineau in 1958, retail stores have been

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thought to have “images”. Martineau defines image as “the way in which the store is

defined in the shopper’s mind, partly by its functional qualities and partly by an aura of

psychological activities” (Martineau, 1958). Linquist (1974) added precision to the

definition by stating that store image is comprised of the following seven components:

merchandise (quality and selection), service process, clientele (helpfulness, courtesy, etc),

convenience, promotion and store atmosphere. Since Linquist’s (1974) work, one

common store image scale has developed – INDSCAL (Fenwick, 1974; Stanely and

Sewell, 1976). Zimmer and Golden (1988) let customers openly define store image.

They found that customer responses could be grouped into the following categories:

product quality, process quality, product selection, price, advertising and physical

appearance of the store. Again, these categories closely resemble the five dimensions of

total retail experience that this research will use.

3.1.2. Individual dimensions of total retail experience.

To further understand how total retail experience is related to the other variables

in the service profit chain, each of its components will be analyzed individually, with one

notable exception. Because product quality and service quality continuously appear

together in service management literature, the two dimensions will be reviewed together.

The organization of this section will closely resemble that used in section 2.2.3 which

looked at the individual dimensions of internal service quality. Each of the four

subsections will begin with a theoretical description of the total retail experience

dimension. This will be followed by a review of empirical support that shows how each

dimension is related to the other variables in the service profit chain. When possible,

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specific references will indicate where the survey items used in this research are

generated from.

PRODUCT QUALTITY AND SERVICE QUALITY

One of the most widely researched topics in service management is the concept of

service quality. Several service quality frameworks have been proposed over the past

two decades. The three most common frameworks are the technical quality / functional

quality schema proposed by Gronroos (1984), the SERVQUAL framework proposed by

Parasuraman et al (1985) and the SERVPERF framework proposed by Cronin and Taylor

(1992). Since their inceptions, each of these three frameworks, or slight variations of

them, have been used extensively. Table 3.1 references studies that have used each of the

frameworks. Each of the frameworks will be briefly reviewed. We will then discuss why

we chose to employ a mix of the three quality frameworks.

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Technical Quality /Functional Quality

SERVQUAL SERVPERF

Gronroos (1984) Parasuramen et al (1985) Lee et al (2000)Gronroos (1987) Parasuramen et al (1988) Carman (1990)Gronroos (1990) Parasuramen et al (1991) Babakus and Boller (1992)Gronroos (1993) Parasuramen et al (1994) Cronin and Taylor (1992)Gronroos (1994) Bolten and Drew (1991) Boulding et al (1993)Howcraft (1993) Zeithaml et al (1991) Teas (1993)Ennew and Binks (1999) Sprend and McKoy (1996) Teas (1994)Aldaigan and Buttle (2002) Brown and Swartz (1994) Brown et al (1993)Lassar et al (2002) Asubonteng et al (1996) Cronin and Taylor (1994)Odekerken and Schroeder(2001)

Battle (1996) Babakus and Mangold(1992)

Sharma and Patterson(1999)

Kay et al (2002) Oliver (1993)

Van der Wiele et al (2002) Zeithaml et al (1996) De Ruyter et al (1998)Wiley (1991) Zeithaml et al (1988) Shemwell et al (1998)Nowak and Washburn(1998)Terblanche and Boshoff(2001 a, b)McDougal and Levesque(2000)

Table 3.1. Service quality paradigms

SERVQUAL is quite possibly the most popular service quality framework. As a

whole, it is based on Oliver’s (1980) disconfirmation model. Parasuramen asserts that

customers form global impressions of the quality of a service provider based on

disconfirmation assessments of ten distinct dimensions: reliability, responsiveness,

competence, access, courtesy, communication, credibility, security, understanding and

tangibles. Each customer will “expect” a certain service level on each of the ten

categories listed above. If the service provider can meet or exceed expectations they will

be considered to provide good service quality. The more an organization exceeds the

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customer’s expectations, the higher the customer will rate it. Parasuramen et al (1988)

quickly reduce the ten dimensional list to five dimensions. It is this five dimensional

representation of service quality that is most prevalently used. Each of the dimensions is

briefly described below.

• Reliability – the ability to perform the promised service dependently andaccurately.

• Assurance – The knowledge and courtesy of employees and their abilityto convey trust and confidence.

• Tangibles – The appearance of the physical facilities, equipment, personneland communication materials.

• Empathy – The provision of caring, individualized attention to customers.

• Responsiveness – The willingness to help customers and to provide promptservice.

Four of these five dimensions (reliability, assurance, empathy and responsiveness) focus

on the process of delivering a service; the remaining dimension focuses on the

environment in which the service is carried out.

To operationalize this framework, several items are generated for each of the

dimensions. Each item is then divided into two separate questions by first assessing what

service level is expected, then assessing what service level is delivered. For example,

within the empathy dimension, survey item 1A might read: “I expect employees at ABC

to provide superior individualized attention.” Survey item 1B would then read:

“Employees at ABC provide superior individualized attention.” Each response could be

signaled using a 1 to 7 likert scale, with 7 indicating total agreement with the statement.

A disconfirmation score would be calculated for item 1 by subtracting the score on item

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1A from the score on 1B. A positive number indicates that the organization exceeded

service quality expectations.

The SERVPERF framework is a derivative of Parasuramen et al’s SERVQUAL

framework. The same five dimensions of service quality are used; however, the survey

items employed to measure the dimensions use a different anchoring system. Instead of

asking two questions for each item within a dimension, only one question would be used

and it would simply ask the customer to rate the performance of the service provider on a

specific dimension. Continuing with the example offered in the SERVQUAL section

above, one survey item within the empathy dimension that could be used is “Employees

at ABC provide superior individualized attention.” Again, the customer could use a 7

point likert scale to indicate their response.

The third service quality framework that will be reviewed is the technical quality

/ functional quality framework proposed by Gronroos (1984). Technical quality refers to

the technical outcome of the service, that is, “what the consumer receives as a result of

his interactions with a service firm.” The hotel guest will get a room; the restaurant

patron will get a meal, etc. Functional quality corresponds to the expressive performance

of a service, that is, how the service is performed. The hotel guest would not only like a

nice, clean room but would also like the desk clerks to be friendly and courteous. The

restaurant patron not only would like high quality food, but would also like rapid service.

The consumer will bundle his impression of the technical and functional quality to form

one global impression of the overall quality level of the service provider. Note that,

since its introduction, many researchers have re-labeled technical quality as product

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quality and functional quality as service quality. This research will use the updated

verbiage.

All three different service frameworks have been linked to many different

performance measures, see Appendix F for a detailed listing. The most common

measures include both customer oriented measures, such as perceived value, satisfaction,

and loyalty, and business oriented measures, such as revenue, sales growth and

profitability. Because of its importance in relation to this study’s objectives, one research

stream will be detailed here: the means-end service quality model.

The means-end model was first proposed in a theoretical paper by Zeithaml in

1988. The model relates perceived quality, value and customer behavior. In its original

form, the model asserts that perceived quality, made up of both service quality and

product quality, drives perceived value, which in turn drives future purchase behavior.

Eight years after its inception, Zeithaml et al (1996) test the validity of a variant of their

original model. The model they test is depicted below in Figure 3.3.

Figure 3.3. Zeithaml et al’s (1996) means-end model

Behavioral intentions include willingness to pay more for the product, propensity to

switch to another service provider and willingness to recommend the organization to

family and friends. While their regression analysis provides partial support for the

PerceivedQuality

BehavioralIntention

Actual purchasebehavior

Financialconsequences

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hypothesized model, a few of the behavioral intention variables do not correlate with

perceived quality measures.

Two more recent articles test similar derivates of the means-end model using

more sophisticated data analytic tools. Figure 3.4 illustrates Shemwell et al’s (1998)

proposed variant of the means-end model.

Service Quality


Complaint Behavior


Continuous Commitment

++

++

-

Service Quality


Complaint Behavior



Service Quality


Complaint Behavior



++

++

-

Figure 3.4. Shemwell et al’s (1998) variant of the means-end model

Their structural equation results suggest that service quality, operationalized through the

Parasuramen et al’s (1985) SERVQUAL instrument, has a direct positive effect on

customer satisfaction. Service quality has a positive indirect effect, through customer

satisfaction, on continuous commitment (likelihood to continue patronage) and affective

commitment (likelihood to refer service provider). In addition, the results suggest that

service quality has a negative indirect effect on complaint behavior. These findings

closely resemble the theory found within the service profit chain: total retail experience

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(a portion of which is service quality) leads to customer satisfaction which leads to

customer loyalty.

McDougall and Levesque (2000) provide a means-end model which also closely

resembles the theory found within the service profit chain. They use structural equation

modeling to test the following means-end variant depicted in Figure 3.5.

Core Quality

Relational Quality

Perceived Value


Switching intentions

Loyalty intentions

+

+

++

-

Core Quality

Relational Quality

Perceived Value


Switching intentions

Loyalty intentions

+

+

++

-

Figure 3.5. McDougall and Levesque’s (2000) means-end variant

McDougall and Leveseque’s (2000) operationalizations of core quality and relational

quality are very similar to what this research calls product quality and service quality

respectively. As such, their structural equation results suggest that product and service

quality along, with perceived value, drive customer satisfaction, which in turn drives

customer loyalty. Again, this framework is nearly identical to that found within the

service profit chain.

So far in this section, three different service quality frameworks have been

presented. Empirical evidence relating these frameworks, and service quality in general,

to other variables within the service profit chain, most notably value, customer

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satisfaction and customer loyalty has been reviewed. The remainder of this section will

be dedicated to describing which framework this research will use and how its constructs

will be operationalized.

Deciding which of the three frameworks to use is fairly straightforward in this

study. The industry we have chosen to survey is women’s specialty fashion – an industry

where the product itself, in addition to the way in which it is provided, plays an important

role. Of the three frameworks, Gronroos’ (1984) is the only one explicitly to include

product quality; as such, this research will use his technical / functional quality

framework. As discussed earlier, what Gronroos calls technical quality, this study will

label product quality; what Gronroos calls functional quality, this study will label service

quality. The specific survey items used to capture product quality are derived from

Gronroos’ items (1985, 1987, 1990, 1993). They are tailored to fit into a women’s

apparel setting. The specific survey items used to capture service quality draw on

Parasuramen et al’s (1985, 1988, 1991) work. Specifically we ask questions pertaining to

four of their five dimensions: reliability, assurance, empathy and responsiveness. As

will be discussed later, their fifth service quality dimension, tangibles, is capture in

another total retail experience construct, servicescape. Because of the extended length of

the survey, our survey items will use the simpler performance approach to assessing

quality, as opposed to the lengthier method of calculating disconfirmation scores. So, in

essence, this study actually blends pieces of all three of the most common service quality

frameworks: technical / functional quality, SERVQUAL and SERVPERF.

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PRODUCT AVAILABILITY AND SELECTION

Product selection (variety) and availability are components of the conventional

retail marketing mix (Bitner, 1990). Shoppers would like to be able to choose from an

assortment of different products; both in terms of different styles and different

colors/sizes within one style. This type of assortment has also been hypothesized to build

retail store image (Lindquist, 1974; Zimmer and Golden, 1988). Since one of the most

widely cited customer turnoffs is out-of-stock merchandise, it is also important that stores

keep their merchandise adequately stocked. Customer search costs increase if the

product they want is out of stock; as search costs increase, total costs increase, therefore

perceived value decreases. Both availability and selection have been linked to customer

satisfaction and loyalty behaviors (Wu and Petroshuis, 1987; Sweenty and Stampfl, 1988;

Zimmer and Golden, 1988; Kerin et al, 1992). Some service management researchers

have even included product variety and selection as specific components of product

quality (Yoo et al, 1998; Oderkerken-Schroder et al, 2001). This research will use one

general question to measure each product availability and merchandise selection.

STORE LAYOUT

While it is essential that specialty retailers carry ample selection and stock of

merchandise, it is also important that the store does not become over-crowded, leaving

the customer little room to browse. Aisles should be wide and open to facilitate traffic

flow and allow for comfortable browsing. The layout should make “sense” to the

customer, making it easy for customers to find what they want. Similarly, like items

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should be grouped near each other. Stor layout has been included in marketing mix

research (Bitner, 1990) and store image research (Lindquist, 1974; Zimmer and Golden,

1988). And, as will be shown in the next section, store layout has often been included in

servicescape literature (Bitner, 1990; Bitner, 1992). Past research has linked store layout

to both increased financial performance and customer satisfaction (Bitner, 1990; Bitner,

1992; Wakefield and Blodgett, 1994; Wakefield and Blodgett, 1996; Sirohi et al, 1998).

SERVICESCAPE

The effect of store atmosphere is widely recognized as one of the strongest

influences on consumers’ impressions of their shopping experience. One particular

research stream that is gaining widespread attention in service management literature is

the idea of store servicescape. Bitner (1992) popularized the term servicescape as a way

of describing the man-made physical surroundings of a store. The servicescape has two

distinct influences on the customer. First, relying on Bloom and Reve’s signal theory

(1990) Bitner asserts that consumers use the physical environment as a rich source for

obtaining cues, cues that can predict the service firm’s capability and quality. Second,

using environmental psychology theory, Bitner theorizes that the servicescape will have a

direct, immediate effect on consumers’ moods. For example, vibrant lighting and up

tempo music will create positive, energetic sensations within customers.

As shown in Figure 3.6, servicescape can be broken into three distinct categories:

ambient conditions (temperature, air quality, noise, music, odor), space/function (layout,

equipment, furnishings) and signs, symbols and artifacts (signage, personal artifacts, style

of décor). As discussed in the previous section, store layout will be an independent

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construct in this research. The other two categories, ambient conditions and signs,

symbols and artifacts, will be used to represent servicescape in this study.

Ambient Conditions

--temperature

--air quality

--noise

--music

--odor

Space/Function

--layout

--equipment

--furnishings

Signs, Symbols, Artifacts

--signage

--personal artifacts

--style of decor

Perceived Servicescape

Customer Moderators & Response

Employee Moderators & Response

Approach Behavior

--affiliation

--exploration

--stay longer

--commitment

--spend money

--return

Avoid Behavior

Ambient Conditions

--temperature

--air quality

--noise

--music

--odor

Space/Function

--layout

--equipment

--furnishings

Signs, Symbols, Artifacts

--signage

--personal artifacts

--style of decor

Perceived Servicescape

Customer Moderators & Response

Employee Moderators & Response

Approach Behavior

--affiliation

--exploration

--stay longer

--commitment

--spend money

--return

Avoid Behavior

Figure 3.6. Bitner’s (1992) servicescape model

Empirical research has shown that servicescape is positively related to both customer

satisfaction (Ward et al, 1992; Dabholkar et al, 1996; Sirohi et al, 1998) and customer

loyalty (Wakefield and Blodgett, 1994; Wakefield and Blodgett, 1996). In fact,

Wakefield and Blodgett (1994, 1996) find evidence that servicescape affects customer

satisfaction, which in turn affects customer loyalty – a tenet found within the service

profit chain. The items used to measure servicescape in this research are generated from

Bitner’s (1992) original theoretical work.

Section 3.1 detailed evidence that total retail experience, as a whole, has a

positive direct effect on customer satisfaction and perception of value. Section 3.1.1

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added support to these claims by detailing evidence of how constructs that parallel total

retail experience are positively associated with customer satisfaction and value

perceptions. Section 3.1.2 summarizes evidence of how the five individual dimensions of

total retail experience all positively affect customer satisfaction and value perceptions.

As such, the following two hypotheses are proposed:

H2a: Total retail experience is positively associated with value


3.2. Value

The original service profit chain model did not include a value variable, but in

response to emerging “return on quality” research, Heskett et al (1997) added it in. The

reason for its inclusion is quite straight-forward. The financial benefits of heightened

quality improvement efforts (in this study referred to as total retail experience), which

had been assumed as a matter of faith, came under serious attack in the early 1990’s.

During that time, there were many highly publicized financial failures of companies who

had been at the leading edge of the quality revolution. For example, the Wallace

Company won the Malcolm Baldrige National Quality Award in 1990. However, the

high levels of spending on quality that enabled them to win this award soon produced

unsustainable losses. Two short years later, the Wallace Company filed for bankruptcy

(Hill, 1993). This is just one of the many examples where quality improvement efforts

did not result in increased competitive advantage.

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In response to these highly publicized failures of quality initiatives, Rust et al

(1995) proposed a return on quality framework. The authors basically assert that the

reason for failed quality initiatives is that many initiatives lack justified economic

grounding – the “quality for quality’s sake” view is just too simplistic and naïve. Rust et

al’s (1995) model is based on four assumptions: 1.) quality is an investment, 2.) quality

efforts must be financially accountable, 3.) it is possible to spend too much on quality,

and 4.) not all quality expenditures are equally valid. Basically, the authors claim that all

quality improvement projects should undergo the same financial scrutiny that all the other

expenditure proposals with an organization go through. Just as a firm would calculate a

return on investment for the purchase of new equipment/machinery, it should do the same

for quality projects – hence the term “return on quality”. Heskett et al (1997) use this

reasoning as justification for the inclusion of the value variable. If the variable is left out,

the model would suggest that all quality improvement efforts, regardless of whether or

not they are financially justified or in line with customer needs, will lead to increased

customer satisfaction, and ultimately increased business performance.

The above discussion illustrates that quality is indeed one component of value,

but a more precise definition is needed. Zeithaml (1988) suggests that “perceived value

is the customer’s overall assessment of the utility of a product based on perceptions of

what is received and what is given.” Researchers have quantified this definition into the

following equation (Liljander and Strandvik, 1992; Sweeney, 1994; Rust et al, 1995;

Patterson and Spreng, 1997)

Perceived Value = Perceived Benefits / Perceived Sacrifice.

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Perceived benefits often include, but are not limited to, product quality, service quality,

the emotional state of the customer during/after the purchase, etc (perceived benefits

could also be construed as total retail experience). Perceived sacrifice not only includes

purchase price but also acquisition costs, such as transportation costs and travel and

shopping time, and the risk of failure or poor performance. From the customer’s

viewpoint, obtaining value is a fundamental purchase goal and essential to a successful

exchange. Clearly, customers would like to increase benefits received while minimizing

costs incurred.

Research over the past fifteen years has consistently identified value as one of the

most important measures for gaining competitive advantage (Parasuramen et al, 1997;

Payne and Holt, 1999; Payne et al, 2001). It has been positively linked to customer

satisfaction measures, customer loyalty measures, such as intent to continue purchasing

from the same organization and willingness to recommend the organization to family and

friends, as well as overall business performance measures such as sales growth and

margin increases, see Appendix G for a summary of empirical evidence. Patterson and

Spreng (1997) test the validity of one of the most common value paradigms, illustrated

below in Figure 3.7.

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Perceived Value

Customer Loyalty

Individual Service Quality Dimensions

Customer Satisfaction+

+

+

+

Perceived Value

Customer Loyalty



Perceived Value

Customer Loyalty


Customer Satisfaction+

+

+

+

Figure 3.7. Common value paradigm, Patterson and Spreng (1997)

In its generic form, the model asserts that individual service quality elements, elements

consistent with Parasurmen et al’s (1985) SERVQUAL instrument (reliability,

dependability, responsiveness, etc), have a positive direct affect on both customer

satisfaction and perceived value. In turn value has a direct positive effect on customer

satisfaction, and an indirect effect on customer loyalty. The linkages in this model are

identical to those proposed by the service profit chain. As such, the following hypothesis

is proposed:



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In terms of survey item generation for the value construct, this work relies on items from

Patterson and Spreng (1997) and Sirohi et al (1998). More theoretical justification is

presented for hypothesis “H2d” in the next section, section 3.3. Section 3.3 will also

detail how both variables are operationalized.

3.3. Customer satisfaction and loyalty

Customer satisfaction literature has undergone a dramatic change over the last

fifteen to twenty years. Ittner and Larcker (1998) point toward 1.) a shift “from the cost

of satisfying customers to the value of doing so”, and 2.) how an organization’s focus

should be on satisfying current customers rather than solely on attaining new ones. The

service profit chain uses these same two philosophies. The second aspect will be covered

through a discussion of defensive marketing; the first aspect will be covered through a

discussion of customer lifetime value.

Defensive marketing theorists, such as Fornell and Wernerfelt (1987), have long

argued that companies who focus simply on creating new customers will do so at the

expense of current customers. When an organization’s current customer base feels that

they are exploited and/or neglected, they will soon become disgruntled and look to switch

to other service providers. Hart et al (1990) hypothesize the cost of attracting a new

customer can be over five times higher than the costs of satisfying and keeping a current

customer. Defection analysts such as Reicheld (1990) and Reicheld and Sasser (1996)

support this assertion.

In responding to Ittner and Larker’s (1998) first claim, one must answer the

question of what benefits loyal customers provide that a firm should focus so much

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attention onto them? Heskett et al’s (1994, 1997) service profit chain research posits that

just as satisfied, loyal employees create value for customers, satisfied and loyal customers

create value for the organization. Customers who are extremely satisfied with a service

provider have been labeled “loyalists” (Jones and Sasser, 1995), “champions” (Wilson,

1991) and “apostles” (Heskett et al, 1994). And customers with this high degree of

attachment to a firm exert what Gremler and Brown (1999) the loyalty ripple effect – “the

influence, both direct and indirect, customers have on a firm through (1) generating

interest in the firm by encouraging new customer patronage and (2) other actions or

behavior that create value for an organization.” A thorough literature review of customer

satisfaction research has identified a list, Table 3.2, of some of the potential “other

actions or behaviors” that Gremler and Brown (1999) elude to.

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Effect ReferencesDecrease in # of complaints Reicheld and Sasser (1990), Anderson et al

(1997), Anderson and Fornell (2000)Decrease in # of returns Anderson et al (1997)Reduced transaction cost Potts (1998)Reicheld and Sasser (1990),

Heskett et al (1994), Anderson et al (1994),Anderson et al (1997), Nowack and Washburn(1998), Mittal and Lasser (1998)

Decreased price sensitivity / Abilityto charge price premium

Reicheld and Sasser (1990), Anderson andSullivan (1993), Anderson et al (1994),Anderson et al (1997)

Increased references / word-of-mouth

Reicheld and Sasser (1990), Heskett et al(1994), Reicheld (1996), Anderson et al(1997), Nowack and Washburn (1998), Sirohiet al (1998), Gremler and Brown (1999),McDougal and Levesque (2000)

Decreased cost of attracting newcustomers

Heskett et al (1994), Anderson et al (1994),Anderson et al (1997), McDougal andLevesque (2000)

More frequent purchases of sameproduct/service

Reicheld and Sasser (1990), Clark and Payne(1994), Anderson et al (1997), Sirohi et al(1998), Gremler and Brown (1999)

More ancillary purchases Reicheld and Sasser (1990), Clark and Payne(1994), Anderson et al (1997), Sirohi et al(1998), Gremler and Brown (1999)

Decrease in failure costs /warranties / rework

Anderson et al (1994), Heskett et al (1994),Anderson et al (1997)

Decreased advertising cost Reicheld and Sasser (1990), Heskett et al(1994), Nowack and Washburn (1998),Gremler and Brown (1999), McDougal andLevesque (2000), Anderson and Fornell(2000)

Increased voluntary performance Zeithaml and Bitner (1996), Bettencourt(1997), Gremler and Brown (1999)

Increased co-production Longreick-Hall (1992), Gremler and Brown(1999), Anderson and Fornell (2000)

Increased reputation Anderson et al (1994), Anderson et al (1997)

Table 3.2. Effects generated by customer satisfaction and loyalty

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In terms of the important role that customer satisfaction and loyalty play in

overall business performance, Anderson and Fornell (2000) argue that customer

satisfaction “measures future capacity to provide wealth.” The balanced scorecard

research of Kaplan and Norton (1992, 2001) echo this idea as they include customer

satisfaction among the non-financial leading indicators that can best predict future

organizational performance. Kaplan and Norton are not alone, since their seminal work

in 1992, many researchers have advocated that customer satisfaction and loyalty

indicators should be a component of the next generation accounting principles (Reicheld,

1996; Ittner and Larcker, 1998; Anderson and Fornell, 2000; Rust et al, 2001). These

researchers point to a growing wealth of empirical evidence to support their claims.

Researchers have shown that customer satisfaction and/or customer loyalty is positively

associated with accounting returns (Fornell, 1995), market value of common equity

(Ittner and Larcker, 1998), price to earning rations (Ittner and Larcker, 1998), stock

market performance (Martin, 1998), Tobin’s Q (Mazvancheryl, et al, 1998), sales growth

(Nowak and Washburn, 1998), profitability (Soderlund, 1998) and risk measures (Ittner

and Larcker, 1998). These effects are all predicted in various other customer satisfaction

research streams: customer equity framework (Blattberg and Deighton, 1996), customer

asset management (Rust, 2000; Bolton, 2001), and customer lifetime value (Keane and

Way, 1995; Beyer and Nasr, 1998; Rust et al, 2001).

A great deal of research has investigated the best way to operationalize the

customer satisfaction and customer loyalty constructs. Our customer satisfaction survey

items are developed from similar items used by Wakefield and Blodgett (1994), Voss et

al (1998), Mittal and Kamakura (2001), and Taylor and Hunter (2002). They are general

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questions aimed at elucidating long-term, higher level abstractions than the more

transaction specific items used to measure the total retail experience and value constructs

(Soderlund, 1998). The questions used to measure loyalty include repurchase intent

(Wakefield and Blodgett, 1999; Taylor and Hunter 2002), share of wallet (Hallowell,

1996; Odekerken-Schroder et al, 2001) and likelihood to recommend (Lee et al, 2000;

McDougal and Levesque 2000).

3.4. Summary

This chapter provides a literature review of the principles contained within the

latter portion of the service profit chain, the service concept and target market portions.

The review begins in section 3.1 of customer assessment frameworks of service

providers. Specifically one comprehensive valuation model is proposed: total retail

experience. Because the concept is still in its infancy and lacks a major research stream,

parallel assessment frameworks are analyzed in section 3.1.1 in order to enhance the face

and content validity of total retail experience. Section 3.1.2 then reviews the five

dimensions of total retail experience independently with specific references given to

survey item generation: product quality, service quality, servicescape, product

availability and selection and store layout. The following two hypotheses, embedded

within the service profit chain, are then proposed:



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Section 3.2 reviews literature into how customers value a service offering. A

precise definition of perceived value is given and one of the most widely accepted

frameworks relating quality, value, customer satisfaction and customer loyalty is

presented (Patterson and Spreng, 1997). Section 3.3 blends concepts from defensive

marketing, the balanced scorecard, lifetime value of a customer, defection analysis and

the customer equity framework to describe how customer satisfaction and customer

loyalty measures affect both operational and financial indicators. Specific references are

given as to how this study operationalizes these constructs as the final two hypotheses are

presented:

H2c: Value is positively associated with customer satisfaction


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CHAPTER 4

MEASUREMENT MODEL DEVELOPMENT

This chapter will begin with a discussion of the methodologies employed in this

research. Section 4.1 reviews the theory behind, as well as the steps involved, in the

methodology of this study. Section 4.1.1 deals exclusively with the methodology used

for the pilot study, section 4.1.2 looks at the methodology for the main study. This

chapter will also lay the framework for the sampling plans and population frame to be

used in this research. In particular, section 4.2 will begin by detailing the population to

be used for this study of the service profit chain. Advantages and disadvantages of the

population frame will be discussed. Section 4.2.1 will review the sampling plan used for

the pilot study. Section 4.2.2 will perform a similar task this time referring to the main

study. Section 4.3 details the iterative steps taken to generate the survey instruments

used in the pilot study as well as discussing how revisions are incorporated to produce

instruments for the main study.

Section 4.4.1 presents the results of the pilot study measurement model. Every

factor used in this research is subjected to rigorous tests of reliability and uni-

dimensionality. Because of limited sample sizes, tests of discriminant validity and

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nomological validity are delayed until the main data collection. Several changes need to

be made to the survey instrument before finalizing it for the main study. All of these

proposed changes are given in detail throughout the section. In order to avoid excessive

repetition only those constructs that required revision are detailed in section 4.4.1. All

constructs are detailed in section 4.4.2, a section that looks at the measurement models

used within the main data analysis. The larger sample of the main survey allows for

testing of the discriminant validity of each construct. The results of the data analysis

conducted in section 4.4.2 suggest that the constructs used in the structural equation

models in chapter 5 are reliable, uni-dimensional and valid. The chapter ends with

section 4.5 providing a summary of the measurement model findings.

4.1. Methodology

4.1.1. Pilot study

The main purpose of the pilot study is to gather valuable information about the

survey instrument. It gives a chance to undertake preliminary analysis of the reliability

and validity of the constructs used in the research. As such, the pilot study allows for

purification of the survey instrument and the scales contained therein.

The first step in the pilot study is to develop the survey instrument and to generate

specific items for each construct. Section 4.3 details the steps involved in the survey

construction. The surveys are then distributed according to the process described in

Section 4.2.1. The data from completed surveys is entered into SYSTAT 10.2.

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Prior to conducting any sophisticated factor development, exploratory data

analysis is conducted to understand the basic properties of the data. This analysis

includes, but is not limited to, the following steps:

• Fixing the direction of reverse coded items• Missing data analysis• Analyzing descriptive statistics such as mean, median, mode, range, standard

deviation and variance• Measuring departures from normality based on skewness and kurtosis.

Missing value analysis indicates that items are not systematically omitted. Because of the

very low percentage of missing data, .16% for the employee survey, .11% for the

customer survey, missing observations are deleted list-wise. As a reliability check, two

imputation methods are used for missing data, regression scores and mean substitution.

Both of these methods resulted in similar findings across all statistical tests carried out, as

such, only the results of using the list-wise deletion method are given. Furthermore,

before conducting factor analysis, bivariate correlations are analyzed for all items within

a hypothesized construct. Items within one scale should all be highly correlated.

In terms of factor development the first step is to assess the reliability of each

construct. According to Droge (1996) reliability “is concerned with the extent to which

the measurement process yields consistent results when the process is repeated in some

way.” In other words, it is the degree of consistency or stability of a scale (Ahire and

Devaraj, 2001). If the items of a scale account for a significant part of the variation in the

construct vis-à-vis measurement error, the scale is said to be reliable. The most popular

method of evaluating reliability is calculating a scale’s Cronbach alpha (_) coefficient

(Cronbach, 1951). Although there is no strict threshold of statistical significance using

this method, two popular thresholds have emerged during the last thirty years: 0.60 for

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emerging constructs and 0.70 for maturing constructs (Nunnally, 1978; Hair et al, 2001).

Beyond merely looking at the absolute level of the _ value to assess a scale’s reliability,

we also look at the effect that removing each item individually has on the _ value – this is

sometimes called the “_ if item deleted” method. We specifically look for items the

deletion of which will significantly increase the scale’s reliability.

A common second step in assessing reliability is to calculate an item’s corrected

item to total correlation (CITC) value. Each item within a construct should be highly

correlated with the construct itself. Various different standards are used to assess CITC

values. Kerlinger’s (1978) suggestion of 0.40 is used in this study. We also check to see

if one individual item, or a group of items, within a scale has a relatively weak CITC

score compared to the rest of the group. In such cases, removing the item may be

warranted.

After reliability is established, validity can be assessed. Within this study, three

different types of validity will be investigated: unidimensionality, discriminant and

nomological. Due to sample size limitations of the pilot study, the assessments of

discriminant and nomological validity are reserved until the main data is collected.

However, each type of validity is briefly defined here. Unidimensionality is the extent to

which indicators are associated with each other and represent a single concept (Hattie,

1985). Ideally, all the indicators should measure a single underlying latent concept.

Divergent validity is the degree to which a construct and its indicators differ from other

constructs and their indicators (Campbell and Fiske, 1959). In other words, each

construct should be significantly different from other constructs. Nomological validity,

sometimes referred to as predictive validity, is the extent to which constructs within one

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framework relate to each other in a manner consistent with theory and/or prior research

(Ahire and Devaraj, 2001).

The most common method of assessing unidimensionality is to perform factor

analysis. Factor analysis can aid in determining if a single factor emerges from many

indicators. Several different guidelines will be used in determining the unidimensionality

of a construct.

• Scree plot – A scree plot will be performed for each construct; specifically, welook for the rugged elbow in the plot.

• Eigenvalues – If the factor is indeed unidimensional only a single eigenvalue willbe greater than one.

• Percent of variance explained (%VE) – The items should account for at least 40%of the variance in the factor.

• Factor loadings – All items should have a factor loading above 0.40.

• Communality – All items should exhibit high communality. There is no standardcut-off; researcher judgment is used.

Taken together, this set of five guidelines is a comprehensive test of the

unidimensionality of each factor.

Divergent validity is assessed using several different methods. First, average

inter-scale correlation (AVISC) of items not included in the factor is compared to the _ of

the factor itself. Divergent validity is established if this difference is substantially higher

than zero (Ahire and Devaraj, 2001). Second, the square root of the %VE of the scale is

compared to the AVISC. Again the difference should be substantially greater than zero

(Spreng and MacKoy, 1996; McDougal and Levesque, 2000; Petrick, 2002). Although

there is no statistical cut-off for either of these methods, values of 0.2 and 0.3 have been

used in the past (Ahire and Devaraj, 2001). Finally, using structural equation modeling,

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we calculate a correlation coefficient between each pair of constructs within the study. If

the 95% confidence interval around the estimate does not contain 1.0, there is sufficient

justification that the two scales are distinct (McDougal and Levesque, 2000; Anderson

and Fornell, 2000; Bergozzi and Heatherton, 1994).

4.1.2. Main study

The methodology of the main study closely resembles that of the pilot study. In

fact, the exploratory data analysis and factor development procedures are identical to

those described in Section 4.1.1. However, the larger sample size of the main data

collection allows for the testing of the discriminant and nomological validities of the

constructs used in the research. Nomological validity is established if the two

hypothesized structural equation models are supported.

Our structural equation models are run using the RAMONA procedure embedded

within the SYSTAT 10.2 software. The structural equation analysis begins with an

investigation of all the standardized path coefficients within the two models. Each

individual path should not only be significant but also be in the direction theorized by the

service profit chain. A standard t-test procedure is conducted for each path.

Overall model fit is analyzed comprehensively using several different categories

of fit indices as suggested by Hu and Bentler (1998). This research uses three different

types of incremental fit measures as well as absolute measures. Absolute fit measures

directly assess how well an a priori model reproduces the sample data. Although no

reference model is used to assess the amount of increment in model fit, an implicit or

explicit comparison may be made to a saturated model that exactly reproduces the

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observed covariance matrix, ie. a model that gives “perfect” fit. Incremental fit indices,

sometimes called comparative fit indices, measure the proportionate improvement in fit

by comparing a target model with a more restricted, nested baseline model. The most

commonly used baseline model is a null model in which all the observed variables are

allowed to have variances but are uncorrelated with each other (Bentler and Bonnet,

1980). Table 4.1 gives a summary of the fit indices employed - see Hu and Bentler

(1998) for a more detailed description of each measure. The table lists the fit index itself

along with the range of possible values, the first research to propose the index and the

suggested threshold of acceptable model fit.

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.05 close fit

.08 acceptable fit

.10 fair fit

Hair et al (1998)Unbounded, positive

Root mean square error of approximation (RMSEA)

> 0.90 Steiger (1980)Value between 0 and 1

Gamma hat

< 0.10Chau (1997)Value between 0 and 1

Root mean square residual (RMR)

Absolute Fit Indices

> 0.90 Bentler (1989)Value between 0 and 1

Comparative fix index (CFI)

> 0.90 McDonald and Marsh (1990)

Value between 0 and 1

Relative non -centrality index (RNI)

Type III

> 0.90 Bollen (1989)Value between 0 and 1

BL89

> 0.90 Tucker and Lewis (1973)


Non-normed fit index (NNFI)

Type II


BL86

> 0.90 Bentler and Bonett (1980)


Normed Fit Index (NFI)

Type I

Incremental Fit Indices

.05 close fit

.08 acceptable fit

.10 fair fit

Hair et al (1998)Unbounded, positive

Root mean square error of approximation (RMSEA)

> 0.90 Steiger (1980)Value between 0 and 1

Gamma hat

< 0.10Chau (1997)Value between 0 and 1

Root mean square residual (RMR)

Absolute Fit Indices

> 0.90 Bentler (1989)Value between 0 and 1

Comparative fix index (CFI)

> 0.90 McDonald and Marsh (1990)


Relative non -centrality index (RNI)

Type III


BL89

> 0.90 Tucker and Lewis (1973)


Non-normed fit index (NNFI)

Type II


BL86

> 0.90 Bentler and Bonett (1980)


Normed Fit Index (NFI)

Type I

Incremental Fit Indices

Table 4.1. Summary of structural equation modeling fit indices

Hu and Bentler (1998) suggest calculating several different measures to enhance

reliability when performing structural equation modeling; as such, at least two measures

are calculated for each type of index.

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4.2. Population frame

This study works exclusively with one large retailer of women’s specialty apparel.

The retailer has over 1,000 stores located throughout the U.S. and is considered the

industry leader in its market. Selecting just one organization as the population for this

dissertation has several advantages. First, many of the links within the service profit

chain have been shown to be influenced heavily by contextual factors that this sampling

frame controls. For example, customer satisfaction and customer loyalty are significantly

influenced by industry (Lynch and Schuler, 1990; Fornell, 1992; Cronin and Taylor,

1992; de Ruyter et al, 1998), competition (Jones and Sasser, 1995; Fornell et al, 1996)

and switching costs (Fornell, 1992; Anderson and Fornell, 1994; Gremler and Brown,

1999; Sharma and Patterson, 2000). The link between external service quality and

customer satisfaction is also influenced by industry (Parasuraman et al, 1994; Zeithaml et

al, 1996; Mittal and Lasser, 1998) and switching costs (de Ruyter et al, 1998). Similar

findings occur on the employee portion of the service profit chain. The link between

employee satisfaction and employee loyalty is significantly impacted by industry (Van

Looy et al, 1998; Wong and Kanji, 2001), company size (Huselid, 1995) and unionization

(Huselid, 1995). All of these effects are partialled out with this work’s particular choice

of population frame.

A second advantage of working with one retail organization is the enhanced

ability to collect vast amounts of data. For the purposes of this study, it is necessary to

survey both employees and customers at each participating location. This step would

have been extremely difficult if a blind survey method was employed. Instead, we

obtained commitment from the management team of one retail chain. This division level

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commitment was then filtered down to individual retail locations. The high response

rates and resulting large database sets are a direct product of the selection of population

frame. A final advantage is the extra control we had over the entire survey process. The

retail chain went to great lengths to ensure the integrity of the data collection process.

Corporate managers met with all the district managers involved to make sure everyone

completely understood the data collection requirements. The district managers then met

with individual store managers to ensure that proper guidelines were in place for

implementing the data collection process.

The selection of the population frame does not come without its drawbacks. First,

because of measurement issues within the retail corporation and numerous uncontrolled

for contextual effects (e.g. location, age of store, local competition), it will be impossible

to capture store performance with traditional “hard” measures such as profitability and

sales, thus, this research will not be able to include business performance variables. This

limitation is not devastating because of the high correlation between customer

satisfaction/loyalty measures and traditional accounting measures. This link is one of the

most validated in marketing literature, and there is no reason to believe it would behave

differently in this study than in previous studies (see section 3.3 for a review of this link).

Second, because the influence of brand image is so strong, it is impossible to tie together

the two halves of the service profit chain. In the specialty retail industry, customers’

brand loyalty far outweighs their store loyalty (Bloemer and Lemmick, 1992; Korte,

1995; Huber and Herrman, 2001). This factor makes it nearly impossible to link a

customer to a specific store; hence, impossible to tie them to employees of the same

store. In fact, over the last six months, over 60% of customers shopped at more than one

115

retail location. Complicating the matter even further, the retail chain also generates

extensive sales on-line as well as through a print catalogue – in fact, over the last six

months, 23% of the organization’s customers made a purchase through the catalogue and

19% made a purchase on-line. A customer’s image of the retail organization will be

formed by all three outlets, not just one particular retail location. The only way around

these effects would be to survey employees and customers immediately after an in-depth

service encounter, asking only specific questions about that particular encounter (Cronin

and Taylor, 1992; Storbacka et al, 1998). This method is not a consideration in this

research study because many of the variables in the study are based on perceptions that

have been built up over time (e.g. satisfaction, loyalty) and are not the direct result of a

single service encounter (Oliver, 1989; Yi, 1990; Woodruff et al, 1993; Spreng and

MacKoy, 1996).

4.2.1. Sampling plan – pilot study

Five retail locations in the Cincinnati (OH) and Dayton (OH) areas were selected

for the pilot study. The research team administered the study on site at each of the

locations. Approximately three to four hours were spent at each of the five sites. The

four page employee surveys were given to the store managers who then distributed them

to all employees who had been employed by the organization for at least three months.

To facilitate accurate responses, anonymity was guaranteed to all employees. Employees

were instructed to fill out the surveys during working hours over the next seven days.

Responses were sealed in confidential envelopes and returned to managers. In total, 50

of the 65 eligible employees returned usable surveys – a response rate of 77%.

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The research team approached shoppers as they exited the store in the traditional

exit interview manner. Every shopper was approached. Due to the lengthy nature of the

survey (three pages) it was impossible to elicit verbal responses on site. Instead, the

survey was designed to be taken home. The research team told each shopper the purpose

of the study and asked if they would be willing to participate. Completed surveys were

sent back to the research team in self-addressed pre-stamped envelopes. In order to

achieve an adequate response rate, shoppers who filled out and returned surveys were

entered into a drawing to win a $50 gift certificate. Fifty surveys were given at each of

the five locations. Sixty-two of the 250 surveys were returned, yielding a response rate

of 25%.

4.1.2. Sampling plan – main study

Ten districts were chosen at random to participate in the main study. The ten

districts, including ninety stores in total, spanned the entire United States. A listing of the

ninety stores is given in Appendix K. Each store was sent a packet of materials. The

following items were included in each packet: twenty employee surveys, fifty customer

surveys and an instruction sheet for the manager. The packets were distributed in early

April. The process of surveying employees was identical to the process used in the pilot

study. In total, 872 of the 1,350 eligible employees returned usable surveys – a response

rate of 65%. It was impossible for the research team to go to all ninety stores to

distribute surveys to customers. Instead, store management was responsible for handing

out the surveys, fifty at each store. With this single exception, the main study process

117

was again identical to the pilot study process. In total, 1,076 of the 4,500 surveys were

returned usable, yielding a response rate of 24%.

4.3. Survey development

This study uses two research instruments. The first is a survey designed to obtain

employee responses about the internal working environment of their retail location. The

second is a survey intended to elicit customer responses to their shopping experiences.

The development of each of the two surveys follows the same iterative approach.

When possible, items for each construct are generated from past empirical research (see

Chapters 2 and 3 for specifics for each construct). When items could not be identified,

questions are generated from the multitude of anecdotes and case-studies for each of the

constructs. After this stage, the proposed questions are reviewed by an academic

committee as well as by the corporate and division level managers of the retail chain.

Items are then reworded, added and deleted as necessary.

The two surveys are then used as part of the pilot study. Using the methodology

described in Section 4.1.1 the surveys are refined where needed. Section 4.4.1 details the

results of the pilot data analysis. To avoid excessive replication, only constructs that need

to be modified are discussed in Section 4.4.1. However, all constructs will be discussed

in detail when reviewing with the main survey results in Section 4.4.2. Appendix J

provides a copy of the final survey instrument.

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4.4. Measurement model

This section details the results of the measurement model (that is, factor

development), for both the pilot survey data and the main survey data. It deals

exclusively with all the first order factors. Second order factors are tested through the

structural equation models.

4.4.1. Pilot study factor development

As indicated earlier, this section will treat in detail only the factors that need to be

modified. All other factors will only be summarized in this chapter; they will be explored

more thoroughly in section 4.4.2. The first factor that needed modification is the

employee empowerment factor. The pilot study instrument asked five questions in order

to assess the degree of empowerment of the retail store employees. Table 4.2 illustrates

the results of the factor development. The initial reliability analysis results in a Cronbach

alpha value of 0.672, which is only slightly less than the suggested 0.70 cut-off.

However, when individual CITC values were analyzed, item E4 clearly does not correlate

highly with the other four empowerment questions. The CITC value of .115 and the

squared multiple correlation value of .029 seem to indicate that this question is indeed

substantially different from the other four. The “alpha if deleted” value, shows that the

overall factor reliability can be increased to 0.746 if item E4 is dropped. Furthermore,

when subjected to maximum likelihood factor analysis, question E4 clearly loads onto a

separate secondary factor. It is interesting to note that question E4 is a reverse coded

item. A “good score” on this item is indicated by a response of “1”, all other items are

worded so a “good score” is indicated by responding with “7”. In their studies of service

119

management, Parasuraman et al (1991) and Babakus and Boller (1992) found similar

results, dropped reverse coded items, and suggested the same methodology be applied in

future service management studies. The second half of Table 4.2 shows that the

empowerment construct, when item E4 is dropped, exhibits much higher reliability, uni-

dimensionality and convergent validity. The reliability is increased to 0.746, CITC

values and factor loadings are all above 0.4 and only one factor emerges.

E5

E3

E2

E1

Revised Construct (

?

= .746)

E5

E4

E3

E2

E1

Original Construct (

?

= 0.672)

Item Label

.717--.719.363.480I have enough latitude to follow up on client sales leads as required

.641--.802.476.611I have enough independence to meet customers’ unique needs

.622--.823.488.618I have enough altitude in my job to serve customers to the best of my ability

.722--.667.348.441I have been given enough authority to serve customers to the best of my ability

.483- .346.705.368.342I have enough latitude to follow up on client sales leads as required

.746.795.214.029.115I have to check with my manager before making any decision to help serve a customer

.434- .261.799.580.519I have enough independence to meet customers’ unique needs

.402.163.824.489.531I have enough altitude in my job to serve customers to the best of my ability

.472.326.669.350.396I have been given enough authority to serve customers to the best of my ability

Alpha, if deleted

Factor Loading

#2

Factor Loading

#1

Squared Multiple

Correlation

CITCItem Description

E5

E3

E2

E1

Revised Construct (

?

= .746)

E5

E4

E3

E2

E1


?

= 0.672)

Item Label

.717--.719.363.480I have enough latitude to follow up on client sales leads as required

.641--.802.476.611I have enough independence to meet customers’ unique needs

.622--.823.488.618I have enough altitude in my job to serve customers to the best of my ability

.722--.667.348.441I have been given enough authority to serve customers to the best of my ability

.483- .346.705.368.342I have enough latitude to follow up on client sales leads as required

.746.795.214.029.115I have to check with my manager before making any decision to help serve a customer

.434- .261.799.580.519I have enough independence to meet customers’ unique needs

.402.163.824.489.531I have enough altitude in my job to serve customers to the best of my ability

.472.326.669.350.396I have been given enough authority to serve customers to the best of my ability

Alpha, if deleted

Factor Loading

#2

Factor Loading

#1

Squared Multiple

Correlation


Table 4.2. Results of pilot study construct development -- Empowerment

A similar finding is made when analyzing the work design factor – only this time

a non-reverse coded item needs to be dropped. As shown in Table 4.3, items JD1, JD2,

JD3 are all worded so a “good” score is 1, a “poor” score is 7. Item JD4 uses the more

traditional wording where 7 indicates a “good” score and 1 a “poor” score. JD4 has a

120

CITC value of 0.206 well below the suggested 0.4 cut-off. The item’s factor loading is

also significantly lower than the other three items. Finally, dropping the item increases

the Cronbach alpha value of the scale a substantial amount. With this item deleted, the

remaining factor is enhanced in terms of both reliability and validity. However, because

only three items are left to measure the construct, the main survey will change the

wording of JD4 to make it a reverse coded item, similar to the first three items, in hopes

of improving the overall reliability of the work design factor.

JD3

JD2

JD1

Revised Construct (

?

= 0.623)

JD4

JD3

JD2

JD1


?

= 0.575)

Item Label

.396--.819.261.507My workload is too heavy

.564--.720.171.402I find my job stressful

.573--.725.177.397My job requirements often conflict with customer needs

.623--.419.074.206My job requirements are clear to me




Alpha, if deleted

Factor Loading

#2

Factor Loading

#1

Squared Multiple

Correlation


JD3

JD2

JD1

Revised Construct (

?

= 0.623)

JD4

JD3

JD2

JD1


?

= 0.575)

Item Label




.623--.419.074.206My job requirements are clear to me




Alpha, if deleted

Factor Loading

#2

Factor Loading

#1

Squared Multiple

Correlation


Table 4.3. Results of pilot study construct development – Work Design

The third construct that needs revision is the rewards and recognition construct.

The pilot questionnaire uses six questions to measure this construct. The results of the

factor development are listed in Table 4.4. As can be seen, the construct does exhibit a

high degree of reliability, _ = 0.838, and all CITC values are well above 0.40. However,

when tested for unidimensionality using maximum likelihood factor analysis, two factors

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clearly emerge. Items RR1, RR2, RR3 and RR5 form one factor; items RR4 and RR6

form a second factor. When looking more deeply at the questions, the factors are actually

splitting into one factor for financial awards and a secondary factor for recognition. The

first factor, monetary rewards, will be kept for the main study; in order to maintain

parsimony in the main study, the recognition factor will be dropped. The bottom half of

Table 4.4 demonstrates that the modified rewards and recognition factor, including only

the monetary reward items, is reliable and uni-dimensional.

.830- .039.873.478.494Victoria’s Secret provides good associate benefitsRR2

RR5

RR3

RR2

RR1

Revised Construct (

?

= 0.825)

RR6

RR5

RR4

RR3

RR1


?

= 0.838)

Item Label

.768--.796.455.673Victoria’s Secret provides good opportunities for advancement

.775--.802.419.645Over time, my compensation is linked to my sales performance

.782--.688.404.627Victoria’s Secret provides good associate benefits

.773--.797.422.645Victoria’s Secret provides competitive wages

.803.918.225.735.630When I do a good job, Victoria’s Secret management acknowledges it and thanks me

.780.510.688.611.756Victoria’s Secret provides good opportunities for advancement

.822.945.090.706.522I get personal recognition when I do a great job

.789.412.697.502.689Over time, my compensation is linked to my sales performance

.812.165.801.460.584Victoria’s Secret provides competitive wages

Alpha, if deleted

Factor Loading

#2

Factor Loading

#1

Squared Multiple

Correlation


.830- .039.873.478.494Victoria’s Secret provides good associate benefitsRR2

RR5

RR3

RR2

RR1

Revised Construct (

?

= 0.825)

RR6

RR5

RR4

RR3

RR1


?

= 0.838)

Item Label

.768--.796.455.673Victoria’s Secret provides good opportunities for advancement

.775--.802.419.645Over time, my compensation is linked to my sales performance

.782--.688.404.627Victoria’s Secret provides good associate benefits

.773--.797.422.645Victoria’s Secret provides competitive wages

.803.918.225.735.630When I do a good job, Victoria’s Secret management acknowledges it and thanks me

.780.510.688.611.756Victoria’s Secret provides good opportunities for advancement

.822.945.090.706.522I get personal recognition when I do a great job

.789.412.697.502.689Over time, my compensation is linked to my sales performance

.812.165.801.460.584Victoria’s Secret provides competitive wages

Alpha, if deleted

Factor Loading

#2

Factor Loading

#1

Squared Multiple

Correlation


Table 4.4. Results of pilot study construct development – Rewards and Recognition

As shown in Table 4.5, the five dimensional job facets scale did not prove to be a

reliable, valid measure of employee satisfaction. Two factors clearly emerge in this

122

analysis. Items ES2 and ES3 load onto one factor while the remaining three items load

onto a second factor. The potential for this to happen is discussed by Nagy (2002) – it

occurs when employees give vastly different weights to the five dimensions of

satisfaction. In the retail environment, when asked, the majority of the employees

attached far more weighting to their wage and opportunity for advancement (hence, the

potential for even more wage increases) as their primary drivers of satisfaction. Our

main study will use a single overall and general question to measure employee

satisfaction – “I am very satisfied with my {firm name}shopping experience”. This

approach, one commonly used (Nagy, 2002; Moshavi and Terborg, 2002; Rhoades et al,

2001; Huselid and Day, 1991; Schneider et al, 1980), allows employees to attach their

own tacit ratings to each job facet dimension when forming one general impression of

their satisfaction.

.607.630I am satisfied with the amount and type of job responsibilities I have

ES6

.097.887I am satisfied with the relationship I have with my co-workers

ES5

.190.828I am satisfied with the relationship I have with my supervisor

ES4

.855.337I am satisfied with my opportunities for promotion

ES3

.945.065I am satisfied with my compensationES2

Factor Loading #2

Factor Loading #1

Item DescriptionItem Label

.607.630I am satisfied with the amount and type of job responsibilities I have

ES6

.097.887I am satisfied with the relationship I have with my co-workers

ES5

.190.828I am satisfied with the relationship I have with my supervisor

ES4

.855.337I am satisfied with my opportunities for promotion

ES3

.945.065I am satisfied with my compensationES2

Factor Loading #2

Factor Loading #1


Table 4.5. Results of pilot study construct development – Employee Satisfaction

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The final factor from the employee survey that needs to be modified is the

employee productivity factor. Table 4.6 demonstrates that a clear factor does not emerge

in our study. The overall reliability of the four item scale is 0.519, far below the

suggested 0.70 cut-off. Since deleting no single item makes for dramatic improvement in

the scale, only one question, the most general question, EP1, will be retained for the main

study. Three new questions will be devised in order to obtain a more objective measure

of an employee’s productivity. Questions geared towards eliciting actual employee sales

will be used. These new questions will eliminate the common method bias of employees

subjectively grading their own performance.

.376.308.515I am capable of meeting customer needsEP4

.511.175.286In general, I think VS associates are very productive

EP3

.417.169.397I am more productive than sales associates in other retail stores

EP2

.534.195.242I feel that I am a productive associateEP1

CronbachAlpha if deleted

Squared Multiple Correlation

CITCItem DescriptionItem Label

.376.308.515I am capable of meeting customer needsEP4

.511.175.286In general, I think VS associates are very productive

EP3

.417.169.397I am more productive than sales associates in other retail stores

EP2

.534.195.242I feel that I am a productive associateEP1

CronbachAlpha if deleted

Squared Multiple Correlation

CITCItem DescriptionItem Label

Table 4.6. Results of pilot study construct development – Employee Productivity

Only one factor from the customer survey needed to be modified – the

servicescape factor. As indicated in Chapter 3, to date, no research has built a

comprehensive servicescape factor. The questions used in building our factor were

developed primarily from Bitner’s (1990) theoretical and anecdotal work. As shown in

Table 4.7 the survey used eight questions to elicit responses to the store’s general

124

environment. Although the eight questions demonstrate high reliability, _ = .818, three

factors emerged when the items were subjected to maximum likelihood factor analysis.

Furthermore, the three factors appear to be inexplicable – the items show no clear reason

for loading onto their respective factors (e.g. a factor for each of the five senses, etc). As

such, a few questions will be maintained for the main study, but for the large part, new

questions will be created. The questions will be based mostly on interviews with

customers and employees.

.163.389.222I enjoy the background music that Victoria’s Secret plays

SS9

.145.153.760Victoria’s Secret stores have attractive displays and signs

SS8

.088.139.967The posters and models in Victoria’s Secret stores enhance my shopping experience

SS7

.009.455.602The aromas and scents in Victoria’s Secret stores are soothing and pleasant

SS6

.180.854.195The lighting at Victoria’s Secret is just right allowing me to enjoy my shopping experience

SS5

.270.738.112I find the décor at Victoria’s Secret attractiveSS4

.511.203.067Victoria’s Secret facilities are always kept clean and attractive

SS3

.977.182.109Victoria’s Secret associates have a neat and professional appearance

SS2

Factor Loading

#3

Factor Loading

#2

Factor Loading

#1


.163.389.222I enjoy the background music that Victoria’s Secret plays

SS9

.145.153.760Victoria’s Secret stores have attractive displays and signs

SS8

.088.139.967The posters and models in Victoria’s Secret stores enhance my shopping experience

SS7

.009.455.602The aromas and scents in Victoria’s Secret stores are soothing and pleasant

SS6

.180.854.195The lighting at Victoria’s Secret is just right allowing me to enjoy my shopping experience

SS5


.511.203.067Victoria’s Secret facilities are always kept clean and attractive

SS3

.977.182.109Victoria’s Secret associates have a neat and professional appearance

SS2

Factor Loading

#3

Factor Loading

#2

Factor Loading

#1


Table 4.7. Results of pilot study construct development -- Servicescape

Table 4.8 summarizes the pilot study factor development of all the items included

in the employee survey. It details the number of items used for each factor in the pilot

125

survey, the number of items to be used on the main survey, the reliability of each

modified factor as measured through the cronbach alpha value, the first and second

eigenvalues (to show that each factor is uni-dimensional) and the variance explained by

the items used in the scale. Table 4.9 provides the same summary for the items used in

the customer survey. The details of each factor will be presented in the next section.

No clear factor emerged4Employee Productivity

65.0%2.6, .72.81744Employee Loyalty

11Employee Satisfaction

65.5%2.6, .52.82146Rewards & Recognition

76.4%3.8, .66.92255Support -- Tools

79.7%1.6, .40.75622Support --Management

57.1%1.7, .74.62344Job Design

57.0%2.3, .95.74645Empowerment

54.5%2.2, .77.69533Teamwork

59.1%2.4, .94.73644Goal Management

64.4%4.5, .84.90466Training & Coaching

Variance Explained

1st and 2 nd

EigenvalueCronbachAlpha

# Items Kept

Original # Survey Items

Construct

No clear factor emerged4Employee Productivity

65.0%2.6, .72.81744Employee Loyalty

11Employee Satisfaction

65.5%2.6, .52.82146Rewards & Recognition

76.4%3.8, .66.92255Support -- Tools

79.7%1.6, .40.75622Support --Management

57.1%1.7, .74.62344Job Design

57.0%2.3, .95.74645Empowerment

54.5%2.2, .77.69533Teamwork

59.1%2.4, .94.73644Goal Management

64.4%4.5, .84.90466Training & Coaching

Variance Explained

1st and 2 nd


# Items Kept


Construct

Table 4.8. Summary of pilot study construct development – Employee Portion

126

82.1%2.4, .34.89133Customer Loyalty

70.2%2.8, .68.85844Customer Satisfaction

84.8%2.5, .28.91033Value

No clear factor emerged8Servicescape

73.9%2.2, .52.82233Store Layout

70.2%3.5, .60.89155Service Quality

71.6%1.4, .57.60422Product Availability & Selection

65.4%3.9, .80.89066Product Quality

Variance Explained

1st and 2 nd


# Items Kept


Construct

82.1%2.4, .34.89133Customer Loyalty

70.2%2.8, .68.85844Customer Satisfaction

84.8%2.5, .28.91033Value

No clear factor emerged8Servicescape

73.9%2.2, .52.82233Store Layout

70.2%3.5, .60.89155Service Quality

71.6%1.4, .57.60422Product Availability & Selection

65.4%3.9, .80.89066Product Quality

Variance Explained

1st and 2 nd


# Items Kept


Construct

Table 4.9. Summary of pilot study construct development – Customer Portion

Because of the small sample sizes in the pilot study it is impossible to test the

nomological validity of the proposed service profit chain model with any sophisticated

data analytic tool. However, it would be presumptuous not to try using simplistic tools at

this point before moving to the main study. It is important to know that the reliable, valid

factors described above behave in accordance with the service profit chain theory. With

sample sizes of 49 and 60, the only tool that can be used with any confidence is

correlation analysis. As described in Chapter 2, the service profit chain theorizes that

internal service quality drives employee satisfaction, loyalty and productivity. These

linkages are briefly reviewed by calculating individual item level correlations between all

the items used in the survey. For example, the correlation between the first measure of

coaching and training, CT1, and employee satisfaction, ES1, is 0.301. The correlation,

significant at the p < .05 level, is in the hypothesized direction. This gives us further

127

confidence not only in the service profit chain theory but also in the survey instrument.

Similar calculations are made for each pair of variables in the study; they are presented in

Table 4.10. To save space, correlations are averaged between factors. All individual

correlations are significant at the p<.05 level and in the expected directions, except for

the correlations between employee satisfaction and employee productivity, and employee

loyalty and employee productivity, where none of the correlations are significant. The

insignificant findings between these constructs may be the result of the items used to

measure employee productivity – as indicated earlier, the employee productivity factor

does not appear to be a strong one. This finding gives all the more justification to modify

the employee productivity items for the main study.

128

----.468Employee Loyalty

.468---Employee Satisfaction

.294.358Rewards & Recognition

-.016.015Employee Productivity

.406.339Support – Tools

.603.538Support – Management

.517.294Job Design

.279.318Empowerment

.327.437Teamwork

.393.265Goal Management

.301.273Training & Coaching

Average Correlation with Employee Loyalty

Average Correlation with Employee Satisfaction

Construct

----.468Employee Loyalty

.468---Employee Satisfaction

.294.358Rewards & Recognition

-.016.015Employee Productivity

.406.339Support – Tools

.603.538Support – Management

.517.294Job Design

.279.318Empowerment

.327.437Teamwork

.393.265Goal Management

.301.273Training & Coaching

Average Correlation with Employee Loyalty

Average Correlation with Employee Satisfaction

Construct

Table 4.10. Factor correlation analysis between internal service quality dimensions andemployee outcome measures

The same correlation analysis is done on the customer portion of the service profit

chain. The results, summarized in Table 4.11, confirm the associations implied by the

service profit chain. The five dimensions of total retail experience are all positively

associated with customer satisfaction and customer loyalty. All individual correlations,

173 in total, are significant at the p < .05 level.

129

---.779Customer Loyalty

.779---Customer Satisfaction

.528.689Servicescape

.499.586Store Layout

.601.712Service Quality

.425.655Product Availability & Selection

.560.701Product Quality

Average Correlation with Customer Loyalty

Average Correlation with Customer Satisfaction

Construct

---.779Customer Loyalty

.779---Customer Satisfaction

.528.689Servicescape

.499.586Store Layout

.601.712Service Quality

.425.655Product Availability & Selection

.560.701Product Quality

Average Correlation with Customer Loyalty

Average Correlation with Customer Satisfaction

Construct

Table 4.11. Factor correlation analysis between total retail experience dimensions andcustomer outcome measures.

These correlation tests are by no means meant to serve as total justification of the service

profit chain theory, rather, they are used as a secondary step to ensure that the constructs

used in the study do indeed behave as expected, that is, they begin to exhibit nomological

validity. Comprehensive tests of nomological validity will be carried out in Chapter 5.

4.4.2. Main study factor development

The first part of this section, Tables 4.12 to 4.17 will analyze the reliability of

each factor, using the cronbach alpha value as well as the alpha if deleted value. The

table also contains CITC values and factor loadings – both of these values begin to

examine the unidimensionality of the factors. The second half of this section, Tables

4.18 and 4.19 will further analyze the unidimensionality of the factors by exploring the

130

first and second eigenvalues of each construct and the percent of variance explained

(%VE) by the items within a factor. The last three columns of Tables 4.18 and 4.19 will

explore the discriminant validity of the factors. As indicated earlier, the average inter-

scale correlation (AVISC) between items within one factor and the remaining items in the

study should be significantly different from 1.0. Another measure of divergent validity is

whether the cronbach alpha value minus the AVISC (_ - AVISC) is sufficiently greater

than 0.0. A final measure of discriminant validity is whether the percent of variance

explained by the items within a factor is sufficiently greater than the squared average

inter-scale correlation (%VE – AVISC2). There is no statistical test for the last two

methods, but values of 0.2 and 0.3 have been used in the past (Ahire and Devaraj, 2001).

As illustrated in Tables 4.12 through 4.14, the ten employee factors to be used in

the main study all appear to be highly reliable. In fact, the minimum cronbach alpha

value is 0.7979. Furthermore, each item within each scale has substantial CITC values

and factor loadings, all well above the suggested 0.40 cut-off. A special mention will be

made for those factors that needed to be revised after the pilot study. As indicated in the

pilot study, item JD4 had to be reverse-worded in order for it to be anchored at the same

end point as the other three work design questions. With this adjustment, the work

design scale achieved a reliability of 0.8426. Moreover, item JD4 itself had a CITC value

of 0.6405 and a factor loading of 0.796. Taken together these findings indicate that the

revision resulted in a much stronger work design factor than that presented in the pilot

study.

The pilot study suggested dropping item E4 from the employee survey. As the

results in Table 4.14 indicate, this revision results in an empowerment factor whose

131

reliability is 0.8685. Furthermore, the remaining four items all have the necessary CITC

values and factor loadings. Items RR4 and RR6, two tacit recognition questions, were

dropped from the rewards and recognition scale as suggested by the pilot study. The

resulting factor, made up of four reward oriented items, RR1, RR2, RR3 and RR5, is uni-

dimensional and highly reliable, _ = 0.8079.

Only one of the four items used to measure employee productivity in the pilot

survey was retained for the main survey, item EP1. Three new items were added, EP2,

EP3 and EP4. All three items were designed explicitly to capture objective productivity

measures, in this case measured by sales dollars generated. As demonstrated in Table

4.14, these four items combined to form a highly reliable and uni-dimensional scale.

Tables 4.15 through 4.17 show the results of the main factor development of the

customer survey items. Of the eight factors developed, only one, product availability and

selection, does not have a reliability that exceeds 0.70 – however its cronbach alpha value

is very close, _ = 0.6815. The construct will be used in this study; future researchers

could improve the reliability of the construct by adding additional questions to it – this

study used only two items, MS1 and MS2, to measure the factor. The CITC values and

the factor loadings of the two items both indicate that the factor is uni-dimensional.

A new servicescape factor is introduced in the main survey; the factor is measured

with seven items: SS2, SS3, SS5, SS6, SS7, SS8, SS10. These seven items form a

reliable scale, _ = 0.8770, and all individually meet the CITC and factor loading cut-offs.

In fact, all 33 items used in the customer portion of the main survey meet those respective

thresholds.

132

.6929.875.6712Victoria’s Secret associates communicate well with each otherT3

.6409.902.7382Victoria’s Secret associates often give each other helpT2

.8403.769.5409Victoria’s Secret associates are urged to work in teamsT1

Teamwork (

?

=.7979)

.7465.883.7649Victoria’s Secret communicates clear priorities and relevant inf ormation in a timely manner

G5

.7706.856.7184I get early notification about future changes that will affect m y job and / or store performance

G4

.8062.806.6489Victoria’s Secret management is good at sharing and explaining i ts goalsG3

.8346.741.5688Store goals are in line with customer needsG2

Goal Management (

?

= .8358)

.8511.844.7489Victoria’s Secret does an excellent job of hiring the best peopl eTC7

.8542.828.7338Victoria’s Secret management provides good on -the-job coachingTC5

.8648.772.6685Victoria’s Secret training programs are of high qualityTC4

.8548.830.7298Victoria’s Secret gives me a lot of feedback on how to improve j ob performanceTC3

.8623.790.6931Since originally being hired and trained, I have received additi onal training when necessary

TC2

.8789.718.6058Within the first two months of being hired, I received the train ing necessary to fulfill my job requirements

TC1

Training and Coaching (

?

= .8814)

Alpha if deleted

Factor Loading

CITCDescriptionItem Label

.6929.875.6712Victoria’s Secret associates communicate well with each otherT3

.6409.902.7382Victoria’s Secret associates often give each other helpT2

.8403.769.5409Victoria’s Secret associates are urged to work in teamsT1

Teamwork (

?

=.7979)

.7465.883.7649Victoria’s Secret communicates clear priorities and relevant inf ormation in a timely manner

G5

.7706.856.7184I get early notification about future changes that will affect m y job and / or store performance

G4

.8062.806.6489Victoria’s Secret management is good at sharing and explaining i ts goalsG3

.8346.741.5688Store goals are in line with customer needsG2

Goal Management (

?

= .8358)

.8511.844.7489Victoria’s Secret does an excellent job of hiring the best peopl eTC7

.8542.828.7338Victoria’s Secret management provides good on -the-job coachingTC5

.8648.772.6685Victoria’s Secret training programs are of high qualityTC4

.8548.830.7298Victoria’s Secret gives me a lot of feedback on how to improve j ob performanceTC3

.8623.790.6931Since originally being hired and trained, I have received additi onal training when necessary

TC2

.8789.718.6058Within the first two months of being hired, I received the train ing necessary to fulfill my job requirements

TC1

Training and Coaching (

?

= .8814)

Alpha if deleted

Factor Loading


Tab

le 4

.12.

Res

ults

of

mai

n st

udy

cons

truc

t dev

elop

men

t – T

rain

ing

and

Coa

chin

g, G

oal M

anag

emen

t ,te

amw

ork

133

.8783.828.7206Store policies and procedures support my ability to meet custome r needsST4

.8623.883.7996In general, I have the resources I need to help customers to the best of my abilityST5

.8751.840.7375I have access to information I need in order to better serve cus tomersST3

.8660.859.7751I am given the necessary tools to satisfy customer requirementsST2

.8838.804.7003Store technology supports my ability to meet customer needsST1

Support – Tools (

?

= .8959)

--.919.6898Help from management is widely available if neededSM4

--.919.6898My immediate supervisor values me as an associateSM3

Support – Management (

?

= .8163)

.8183.796.6405My job requirements are not clear to meJD4

.7611.883.7641My workload is too heavyJD3

.7730.868.7383I find my job stressfulJD2

.8409.751.5841My job requirements often conflict with customer needsJD1

Job Design (

?

= .8426)

Alpha if deleted

Factor Loading


.8783.828.7206Store policies and procedures support my ability to meet custome r needsST4

.8623.883.7996In general, I have the resources I need to help customers to the best of my abilityST5

.8751.840.7375I have access to information I need in order to better serve cus tomersST3

.8660.859.7751I am given the necessary tools to satisfy customer requirementsST2

.8838.804.7003Store technology supports my ability to meet customer needsST1

Support – Tools (

?

= .8959)

--.919.6898Help from management is widely available if neededSM4

--.919.6898My immediate supervisor values me as an associateSM3

Support – Management (

?

= .8163)

.8183.796.6405My job requirements are not clear to meJD4

.7611.883.7641My workload is too heavyJD3

.7730.868.7383I find my job stressfulJD2

.8409.751.5841My job requirements often conflict with customer needsJD1

Job Design (

?

= .8426)

Alpha if deleted

Factor Loading


Tab

le 4

.13.

Res

ults

of

mai

n st

udy

cons

truc

t dev

elop

men

t – W

ork

Des

ign,

Sup

port

– M

anag

emen

t, Su

ppor

t --

Too

ls

134

Employee Productivity (

?

= .8040)

.8112.705.5139I feel that I am a productive employeeEP1

.6701.885.7754Within my store, I am a top sellerEP2

.6826.868.7554My average sales per hour is among the best in the storeEP3

.8728.907.8268As soon as I can find another job I am going to leave Victoria’s SecretEL4

.7981.720.5410My productivity has increased the longer I have worked in the st oreEP4

.8745.904.8212I am actively looking for another jobEL3

.9029.849.7390I intend to keep working at Victoria’s Secret long into the futu reEL1

.8827.889.7986I often think about quitting my jobEL2

Employee Loyalty (

?

= .9101)

.7697.780.6020VS provides good opportunities for advancementRR5

.7544.804.6341Over time, my compensation is linked to my sales performanceRR3

.7587.797.6252Victoria’s Secret provides good associate benefitsRR2

.7541.805.6348Victoria’s Secret pays as well or better than other retailersRR1

Rewards & Recognition (

?

= .8079)

.8832.752.5987I have enough latitude to follow up on client sales leads as req uiredE5

.8230.862.7429I have enough independence to meet each customer’s unique needsE3

.7974.909.8064I have enough latitude in my job to serve customers to the best of my abilityE2

.8216.872.7470I have been given enough authority to serve customers to the bes t of my abilityE1

Empowerment (

?

= .8685)

Alpha if deleted

Factor Loading


Employee Productivity (

?

= .8040)

.8112.705.5139I feel that I am a productive employeeEP1

.6701.885.7754Within my store, I am a top sellerEP2

.6826.868.7554My average sales per hour is among the best in the storeEP3

.8728.907.8268As soon as I can find another job I am going to leave Victoria’s SecretEL4

.7981.720.5410My productivity has increased the longer I have worked in the st oreEP4

.8745.904.8212I am actively looking for another jobEL3

.9029.849.7390I intend to keep working at Victoria’s Secret long into the futu reEL1

.8827.889.7986I often think about quitting my jobEL2

Employee Loyalty (

?

= .9101)

.7697.780.6020VS provides good opportunities for advancementRR5

.7544.804.6341Over time, my compensation is linked to my sales performanceRR3

.7587.797.6252Victoria’s Secret provides good associate benefitsRR2

.7541.805.6348Victoria’s Secret pays as well or better than other retailersRR1

Rewards & Recognition (

?

= .8079)

.8832.752.5987I have enough latitude to follow up on client sales leads as req uiredE5

.8230.862.7429I have enough independence to meet each customer’s unique needsE3

.7974.909.8064I have enough latitude in my job to serve customers to the best of my abilityE2

.8216.872.7470I have been given enough authority to serve customers to the bes t of my abilityE1

Empowerment (

?

= .8685)

Alpha if deleted

Factor Loading


Tab

le 4

.14.

Res

ults

of

mai

n st

udy

cons

truc

t dev

elop

men

t – E

mpo

wer

men

t, R

ewar

ds a

nd R

ecog

nitio

n,E

mpl

oyee

Loy

alty

, Em

ploy

ee P

rodu

ctiv

ity

135

.8722.922.8722Victoria’s Secret associates are willing to out of their way to help meSQ4

.9136.721.6187The quality of merchandise at Victoria’s Secret is higher than s imilar merchandise at other stores

PQ2

.8837.892.8276Victoria’s Secret merchandise always meets my quality standardsPQ5

.9011.940.9011Victoria’s Secret associates give caring and individual attentio nSQ3

.7625.844.7625Victoria’s Secret associates have the skills necessary to help m eSQ1

.8136.881.8136I receive prompt service when I shop at Victoria’s SecretSQ2

.8195.885.8195Victoria’s Secret associates are consistently courteous and frie ndlySQ5

Service Quality (

?

= .9376)

--.878.5411Victoria’s Secret has a wide selection of merchandiseMS2

--.878.5411Victoria’s Secret always has the product I want in stockMS1

Product Availability & Selection (

?

= .6815)

.8903.860.7834The quality of merchandise at Victoria’s Secret consistently mee ts my expectationsPQ6

.8878.873.8066The merchandise I buy from Victoria’s Secret is of consistent qu alityPQ4

.8972.830.7473Victoria’s Secret merchandise holds up well after repeated washi ngsPQ3

.8960.831.7530Victoria’s Secret offers merchandise of very high qualityPQ1

Product Quality (

?

= .9109)

Alpha if deleted

Factor Loading


.8722.922.8722Victoria’s Secret associates are willing to out of their way to help meSQ4

.9136.721.6187The quality of merchandise at Victoria’s Secret is higher than s imilar merchandise at other stores

PQ2

.8837.892.8276Victoria’s Secret merchandise always meets my quality standardsPQ5

.9011.940.9011Victoria’s Secret associates give caring and individual attentio nSQ3

.7625.844.7625Victoria’s Secret associates have the skills necessary to help m eSQ1

.8136.881.8136I receive prompt service when I shop at Victoria’s SecretSQ2

.8195.885.8195Victoria’s Secret associates are consistently courteous and frie ndlySQ5

Service Quality (

?

= .9376)

--.878.5411Victoria’s Secret has a wide selection of merchandiseMS2

--.878.5411Victoria’s Secret always has the product I want in stockMS1

Product Availability & Selection (

?

= .6815)

.8903.860.7834The quality of merchandise at Victoria’s Secret consistently mee ts my expectationsPQ6

.8878.873.8066The merchandise I buy from Victoria’s Secret is of consistent qu alityPQ4

.8972.830.7473Victoria’s Secret merchandise holds up well after repeated washi ngsPQ3

.8960.831.7530Victoria’s Secret offers merchandise of very high qualityPQ1

Product Quality (

?

= .9109)

Alpha if deleted

Factor Loading


Tab

le 4

.15.

Res

ults

of

mai

n st

udy

cons

truc

t dev

elop

men

t – P

rodu

ct Q

ualit

y, P

rodu

ct A

vaila

bilit

y an

dSe

lect

ion,

Ser

vice

Qua

lity

136

Value (

?

= .9085)

.8607.926.8301Victoria’s Secret offers merchandise at good valueV1

.8708.919.8152Given the quality of merchandise, Victoria’s Secret offers good pricesV2

.8695.693.5781The Victoria’s Secret store does not seem old and datedSS10

.8794.659.5587I enjoy the background music that Victoria’s Secret playsSS8

.7808.897.7581There is ample space between displays to browse comfortablySL2

.8529.793.7082The aromas and scents used in Victoria’s Secret stores are sooth ing and pleasantSS7

.8572.775.6758Victoria’s Secret stores have attractive posters and modelsSS5

.8504.820.7324The lighting at Victoria’s Secret is set at a good levelSS6

.8755.919.8164Victoria’s Secret offers better value than other stores that sel l similar merchandiseV3


.8596.782.6751Victoria’s Secret facilities are always kept neat and attractiveSS2

Servicescape (

?

= .8770)

.8248.871.7139All merchandise at Victoria’s Secret stores is easily accessibleSL3

.8043.884.7346The layout of Victoria’s Secret stores allows me to take any pat h I like when browsingSL1

Store Layout (

?

= .8596)

Alpha if deleted

Factor Loading


Value (

?

= .9085)

.8607.926.8301Victoria’s Secret offers merchandise at good valueV1

.8708.919.8152Given the quality of merchandise, Victoria’s Secret offers good pricesV2

.8695.693.5781The Victoria’s Secret store does not seem old and datedSS10

.8794.659.5587I enjoy the background music that Victoria’s Secret playsSS8

.7808.897.7581There is ample space between displays to browse comfortablySL2

.8529.793.7082The aromas and scents used in Victoria’s Secret stores are sooth ing and pleasantSS7

.8572.775.6758Victoria’s Secret stores have attractive posters and modelsSS5

.8504.820.7324The lighting at Victoria’s Secret is set at a good levelSS6

.8755.919.8164Victoria’s Secret offers better value than other stores that sel l similar merchandiseV3


.8596.782.6751Victoria’s Secret facilities are always kept neat and attractiveSS2

Servicescape (

?

= .8770)

.8248.871.7139All merchandise at Victoria’s Secret stores is easily accessibleSL3

.8043.884.7346The layout of Victoria’s Secret stores allows me to take any pat h I like when browsingSL1

Store Layout (

?

= .8596)

Alpha if deleted

Factor Loading


Tab

le 4

.16.

Res

ults

of

mai

n st

udy

cons

truc

t dev

elop

men

t – S

tore

Lay

out,

Serv

ices

cape

, Val

ue

137

.9201.825.7076Of all the stores that sell similar types of merchandise, Victor ia’s Secret is my first choiceCS3

.8715.917.8352I am delighted with the shopping experience that Victoria’s Secr et offersCS2

.9111.928.8361I recommend Victoria’s Secret to my friends and familyCL3

.8670.956.8970I intend to remain a Victoria’s Secret customer long into the fu tureCL2

.9142.928.8379I consider myself a loyal customer to Victoria’s SecretCL1

Customer Loyalty (

?

= .9289)

.8778.902.8191I have good feelings when shopping at Victoria’s SecretCS4

.8694.920.8435I am very satisfied with shopping at Victoria’s SecretCS1

Customer Satisfaction (

?

= .9110)

Alpha if deleted

Factor Loading


.9201.825.7076Of all the stores that sell similar types of merchandise, Victor ia’s Secret is my first choiceCS3

.8715.917.8352I am delighted with the shopping experience that Victoria’s Secr et offersCS2

.9111.928.8361I recommend Victoria’s Secret to my friends and familyCL3

.8670.956.8970I intend to remain a Victoria’s Secret customer long into the fu tureCL2

.9142.928.8379I consider myself a loyal customer to Victoria’s SecretCL1

Customer Loyalty (

?

= .9289)

.8778.902.8191I have good feelings when shopping at Victoria’s SecretCS4

.8694.920.8435I am very satisfied with shopping at Victoria’s SecretCS1

Customer Satisfaction (

?

= .9110)

Alpha if deleted

Factor Loading


Tab

le 4

.17.

Res

ults

of

mai

n st

udy

cons

truc

t dev

elop

men

t – C

usto

mer

Sat

isfa

ctio

n, C

usto

mer

Loy

alty

138

The previous tables demonstrate the reliability of each factor as well as the

individual CITC values and factor loadings for each item contained in the main study

surveys. This is just the preliminary step in construct development. Uni-dimensionality

and discriminant validity must also be established. Tables 4.18 and 4.19 were developed

with this aim in mind. In addition to reviewing the reliability of each factor, the tables

also include the first and second eigenvalues for each factor when it is subjected to

maximum likelihood factor analysis. A factor is uni-dimensional if only a single

eigenvalue is greater than 1.0. All the factors used in both the customer and employee

portions of our model do indeed exhibit this property.

The tables also present the percent of variance that the factor can explain in the

individual items. The first eigenvalue of a factor should be able to explain 40% of the

total variance within the factor (Carmines and Zeller, 1979). Again, all the factors used

in this study meet this criteria, in fact, the minimum variance explained value, the

servicescape factor, is 59.4%.

Tables 4.18 and 4.19 also give several measures of discriminant validity for each

of the factors, because employee satisfaction is variable with only one indictor, it is

omitted from the summary. AVISC measures the average correlation between the items

within one factor and all the items outside of the factor. If a factor is truly unique, the

average correlation of its items with items outside of it will be sufficiently different from

1.0. Nearly all of the AVISC values are below 0.5, the exceptions are for the value,

customer satisfaction and customer loyalty factors. These AVISC findings suggest that

the eighteen multiple item factors used in this research are distinctly different from each

other. The AVISC findings are further supported by the calculation of the following two

139

numbers: cronbach alpha minus AVISC, and percent of variance explained minus the

square of AVISC. These values, listed in the last three columns of the tables, are all

sufficiently higher than 0.0. As noted earlier, there is no statistical test of significance for

these methodologies, but past researchers have found that values of 0.20 and 0.30 provide

adequate evidence of discriminant validity (Ahire and Devaraj, 2001). Finally, a last test

for discriminant validity was carried out. Structural equation modeling was employed to

calculate the confidence interval of correlation coefficient between every pair of

constructs used in this study. None of 83 confidence intervals that were generated

contained 1.0, indicating that all the constructs are indeed distinct.

.604.481.42978.8.3683.2.9101Employee Loyalty

.507.452.35663.4.5792.5.8079Rewards & Recognition

.631.723.08263.8.7462.5.8040Employee Productivity

.508.445.45171.1.5913.6.8959Support –Tools

.653.378.43884.5.3101.7.8163Support –Management

.540.465.37868.3.5712.7.8426Job Design

.574.483.38672.3.5692.9.8685Empower –ment

.587.429.36972.3.5682.2.7979Teamwork

.486.397.43867.8.5722.7.8353Goal Management

.470.472.40963.7.6563.8.8814Training & Coaching

% VE –AVISC 2

?

-AVISC

AVISC%VE2nd

eig.1st

eig .

?

Construct

.604.481.42978.8.3683.2.9101Employee Loyalty

.507.452.35663.4.5792.5.8079Rewards & Recognition

.631.723.08263.8.7462.5.8040Employee Productivity

.508.445.45171.1.5913.6.8959Support –Tools

.653.378.43884.5.3101.7.8163Support –Management

.540.465.37868.3.5712.7.8426Job Design

.574.483.38672.3.5692.9.8685Empower –ment

.587.429.36972.3.5682.2.7979Teamwork

.486.397.43867.8.5722.7.8353Goal Management

.470.472.40963.7.6563.8.8814Training & Coaching

% VE –AVISC 2

?

-AVISC

AVISC%VE2nd

eig.1st

eig .

?

Construct

Table 4.18. Summary of main study construct development – Employee Portion

140

.627.427.50287.9.2332.6.9289Customer Loyalty

.453.326.58579.5.4253.2.9110Customer Satisfaction

.571.382.52784.9.2392.5.9085Value

.402.439.43859.4.7174.2.8770Servicescape

.557.386.47478.2.3612.4.8596Store Layout

.573.461.47780.1.3524.0.9376Service Quality

.590.257.42577.1.4591.5.6815Product Avail & Selection

.498.463.44869.9.5964.2.9109Product Quality

% VE –AVISC 2

?

-AVISC

AVISC%VE2nd

eig.1st

eig.

?

Construct

.627.427.50287.9.2332.6.9289Customer Loyalty

.453.326.58579.5.4253.2.9110Customer Satisfaction

.571.382.52784.9.2392.5.9085Value

.402.439.43859.4.7174.2.8770Servicescape

.557.386.47478.2.3612.4.8596Store Layout

.573.461.47780.1.3524.0.9376Service Quality

.590.257.42577.1.4591.5.6815Product Avail & Selection

.498.463.44869.9.5964.2.9109Product Quality

% VE –AVISC 2

?

-AVISC

AVISC%VE2nd

eig.1st

eig.

?

Construct

Table 4.19. Summary of main study construct development – Customer Portion

4.5. Summary

The first part of this chapter, sections 4.1 to 4.3., describes the population frame,

sampling plan and methodologies employed in this research. Advantages and

disadvantages are discussed in each of these sections. In summary, one retail chain in

women’s specialty apparel was selected as the population for this study. Five of the

chain’s locations were selected to participate in the pilot study. The purpose of the pilot

study was to refine the survey instruments to be used in the main study. Ninety of the

approximately 450 retail locations were chosen at random to participate in the main data

collection stage of this research. Two surveys were used at both stages of the data

collection: a four page employee survey and a three page customer survey (which was

later reduced for mailing purposes to two pages for the main study). The employee

surveys were filled out at on-site at the retail locations. The customer surveys were taken

141

home by shoppers and sent back in self-addressed stamped envelopes. Fifty employee

surveys and 62 customer surveys were returned during the pilot stage resulting in

response rates of 77% and 24% respectively. Eight hundred and seventy two employee

surveys were returned and 1,076 customer surveys were returned during the main data

collection stage, yielding response rates of 65% and 24% respectively.

The majority of chapter 4 is dedicated to analyzing the measurement models to be

used in this research. A priori factors are subjected to tests of reliability, uni-

dimensionality and discriminant validity. The pilot study analysis indicated that several

survey items needed to be altered, added, and/or dropped before moving on to the main

data collection stage. These changes are detailed in section 4.4.1.

Section 4.4.2 details the results of the measurement model used in the main study.

The results indicate that the constructs used in this study are highly reliable, measure only

a single theme (uni-dimensional) and are sufficiently different from other constructs

within the study (divergent validity). These results are very important because they allow

for the testing of the linkages within the service profit chain. They are all necessary

requirements for using structural equation modeling.

It is also important to note that while our measurement models confirmed factors

that have been used extensively in service management literature, e.g. service quality,

customer satisfaction, etc., the models have also applied scales that have primarily only

been used in other business disciplines: organizational support – human resource

management, value – marketing, work design – personnel psychology, etc. Furthermore,

the study has incorporated fairly new first order factors that have not yet been well

developed: servicescape (Wakefield and Blodgett, 1996) and product availability and

142

selection. Chapter 5 will continue this tradition in building two new second order

constructs – internal service quality and total retail experience. These two factors will be

the foundation upon which the structural equation models that test the service profit chain

will be built.

143

CHAPTER 5

STRUCTURAL MODELS AND ANALYSIS

Two structural equation models will be used to test the theory underlying the

service profit chain. As discussed in section 4.2, two models are used instead of one due

to the sample frame of this study. The first model, the employee model, will test the

plausibility of the first half of the service profit chain; specifically, it will investigate the

relationship between internal service quality, employee satisfaction, employee loyalty

and employee productivity. The second model, the customer model, will explore the

linkages between total retail experience, value, customer satisfaction and customer

loyalty.

This chapter is organized in the following manner. Section 5.1 will detail why

structural equation modeling is the methodology of choice for this research. Included in

this section is a discussion of the power of the two models. Section 5.2, the employee

model, will be broken into two sections. The first section, section 5.2.1 focuses on the

composition of internal service quality as a second order factor. Once this higher level

construct is established, section 5.2.2 explores how it relates to employee outcome

factors. The individual hypotheses proposed in chapter 2, and summarized below, are

tested, along with a discussion of overall model fit.



144




Both sections, 5.2.1 and 5.2.2, end with a discussion of the contributions this research

makes in regards to the two respective areas.

The organization of Section 5.3 follows the same basic logic of Section 5.2 only it

looks at the customer model. Section 5.3.1 details the measurement of total retail

experience, a second order factor similar to internal service quality. Section 5.3.3

investigates the relationship between total retail experience, value, customer satisfaction

and customer loyalty. The hypotheses laid out in Chapter 3 are treated independently,

followed by an analysis of overall model fit.


H2b: Total retail experience is positively associated with customersatisfaction.


H2d: Customer satisfaction is positively associated with customerloyalty.

Both sections end with a discussion of the contributions of this research.

145

5.1. Structural equation modeling

Structural equation modeling is a multivariate technique that allows for very

powerful statistical analysis. As a technique, it provides many advantages over simpler

statistical methodologies like analysis of variance (ANOVA) or regression. It is because

of these advantages, detailed below, that structural equation modeling is selected as the

data analytic tool of choice.

First, structural equation modeling incorporates the use of latent variables. A

latent variable cannot be measured directly but rather must be represented or measured by

two or more variables (Hair et al, 1998). This definition becomes clearer with an

example from this research. In order to measure a multi-faceted concept such as training

and coaching it is necessary to ask questions regarding initial training, on-going training,

length of training, quality of training, etc. It is impossible to measure this concept using

only a single question; that is, a single indicator. As such, several questions are used to

build a representation of the training and coaching an employee receives. In our study we

use six questions. Using six questions has many advantages over using a single question.

The six question method allows for a much more comprehensive rendering of employees’

perceptions of the training they have received. Furthermore, because reliability is a

function of the number of indicators used, using six questions increases the reliability of

the survey instrument. By definition, a single item measure has zero reliability.

A second major advantage to using structural equation modeling is that it allows

for a variable to act both in a dependent and independent role simultaneously. In the

service profit chain model, many variables fit this description. For example, customer

satisfaction acts as a dependent variable in the equation containing the total retail

146

experience construct, equation (1), while also acting simultaneously as an independent

variable in the equation containing the customer loyalty construct, equation (2).

Customer Satisfaction = _1 * Total Retail Experience + _2 * Value + Error (1)

Customer Loyalty = _3 * Customer Satisfaction + Error (2)

This advantage also makes it possible to determine both the direct and indirect effects of

variables within the service profit chain model. Even more importantly, this advantage

allows for the determination of the causal nature of the relationships within the model;

findings which neither regression nor ANOVA can provide.

The primary concern of using structural equation modeling is obtaining large

enough samples to achieve reasonable power (MacCallum et al, 1996; Fan et al, 1999;

Jackson, 2001). Various researchers have recommended that between five and ten

observations are needed for each path estimate within the structural equation model (Hair

et al, 1998). As discussed in section 4.2, our population frame allows us to select a

sampling plan that results in very large sample yielding substantial power for our

statistical analyses. Using the framework proposed by MacCallum et al (1996), the

power for both structural equation models used in this research approaches 1.0. Degrees

of freedom for the employee and customer models are 619 and 455 respectively. Sample

size is 872 for the employee model and 1,076 for the customer model.

5.2. Employee Model

The employee model concentrates on both the direct and indirect relationships

between internal service quality and employee related outcome variables – satisfaction,

loyalty and productivity. Specifically, our rendering combines both a second order

147

measurement model of internal service quality as well as a structural model linking it to

employee satisfaction and employee loyalty. Employee satisfaction is also directly linked

to employee loyalty and productivity. Finally, employee loyalty is linked to employee

productivity. A generic representation is given in Figure 5.1.

As discussed in Chapter 2, all the links in the chain are hypothesized to be positive.

SYSTAT 10.2’s structural equation modeling software, RAMONA, is used to perform

the analysis.

5.2.1. Composition of internal service quality

The measurement portion of the employee model consists of constructing a

second order internal service quality factor. As discussed in section 2.2, an eight

dimensional representation of internal service quality is used. The eight first order

factors include: training and coaching, goal management, teamwork, empowerment,

work design, organizational support – management, organizational support – tools and

rewards and recognition. The results of the second order construct development are

InternalService Quality

EmployeeSatisfaction

EmployeeLoyalty

EmployeeProductivity

+

+

+

+

+

InternalService Quality


EmployeeLoyalty


+

+

+

+

+

Figure 5.1. Generic representation of employee model

148

illustrated in Figures 5.1 and 5.2. Two figures are used because of spatial limitations. In

reality, only one model is employed and all eight dimensions feed to a single internal

service quality construct. Ovals represent latent variables, rectangles represent manifest

variables. First and second order factor loadings are given in the illustration. The

variances of all the latent variables are set to 1.0 for identification purposes. This

specification results in standardized path coefficients that can be compared in terms of

magnitude. Finally, all path coefficients, first and second order alike, are significant at

the p<.001 level. The following notational schema, shown in Table 5.1, will be used

throughout the remainder of the data analysis.

Notation Explanation Example ofNotation

Description of example

XX# Capital letters followed bynumber

TC1 The first question used to measure the TC(training and coaching) construct

exx# Lowercase “e” followedby two lowercase lettersfollowed by number

etc1 The error term associated with the firstquestion used to measure the TC construct(sometimes called unique variance ofquestion TC1)

exxf Lowercase “e” followedby two lowercase lettersfollowed by lowercase “f”

etcf The error term associated with the latent TCfactor (training and coaching)

XXF Capital letters followed bya capital “F”

TCF The training and coaching factor

Table 5.1. Notational abbreviations used in this research

149

TC2etc2

TC3etc3

TC4etc4

TC5etc5

TC7etc7

TC1etc1 .615

.709

.805

.703

.807

.845

Training&

Coaching

1.0

etcf

G3eg3

G4eg4

G5eg5

G2eg2 .640

.728

.810

.865

1.0

egf

T2et2

T3et3

T1et1 .617

.865

.836

Teamwork

1.0

etf

E2ee2

E3ee3

E5ee5

E1ee1 .864

.916

.784

.626

Empowerment

1.0

eef

InternalServiceQuality

1.0

.837

.843

.626

.679

TC2etc2

TC3etc3

TC4etc4

TC5etc5

TC7etc7

TC1etc1 .615

.709

.805

.703

.807

.845

Training&

Coaching

1.0

etcf

G3eg3

G4eg4

G5eg5

G2eg2 .640

.728

.810

.865

GoalManagement

1.0

egf

T2et2

T3et3

T1et1 .617

.865

.836

Teamwork

1.0

etf

E2ee2

E3ee3

E5ee5

E1ee1 .864

.916

.784

.626

Empowerment

1.0

eef


1.0

.837

.843

.626

.679

Figure 5.2. Internal Service Quality composition, part I

150

Figure 5.3. Internal Service Quality Composition, part II

151

Several important considerations can be taken away from the measurement

portion of the employee model. First, the eight dimensional representation of internal

service quality appears to be a very strong rendering. All eight dimensions exhibit large

second order factor loadings, ranging from 0.626 (teamwork) to 0.843 (goal

management). These findings suggest that employees do indeed develop a broad

conceptualization of their work surroundings, very similar to what previous researchers

have called organization culture, organizational climate and/or human resource

management.

It is also interesting to note that the magnitudes of the factor loadings of the

internal service quality dimensions fall into two groups. The first group consists of

training and coaching (0.837 factor loading), goal management (0.843), organizational

support – management (0.807) and organizational support – tools (0.815). It appears that

employees attribute the most weight to these four dimensions when assessing the quality

of their work environment. The second group is comprised of teamwork (0.626),

empowerment (0.679), work design (0.658) and rewards and recognition (0.644). None

of the confidence intervals around the factor loading parameter estimates from the first

group overlap with those from the second group. For example, the 95% confidence

interval around the factor loading parameter estimate for training and coaching is (0.812,

0.861); the interval around the teamwork factor estimate is (0.583, 0.669). Because these

two intervals do not overlap, it can be concluded that training and coaching is a more

salient shaper of employees’ perceptions of their internal service quality than teamwork

is. This argument can be extended to the rest of the elements in each of the two groups.

152

The measurement portion of the employee model makes two very important

contributions. First, it is the most thorough development of a comprehensive internal

service quality construct. The literature review in Chapter 2 is the most scrupulous

examination of internal service quality literature to date, integrating theory from several

different disciplines. This review provides for an eight dimensional representation that

exhibits high degrees of both content and face validity. We also provide the most

rigorous statistical construction of the internal service quality factor. Section 4.4 details

the reliability, uni-dimensionality and discriminant validity of each individual element;

the first such study to do so. Moreover, Section 5.2.2 explores the nomological and

predictive validities of the internal service quality construct by examining its relationship

with employee satisfaction and employee loyalty. Again, this study is the first to explore

thoroughly these relationships using a second order factor model.

The measurement portion of the employee model also gives practicing managers a

valuable tool that can be used in resource allocation. The second order factor loadings of

the eight internal service quality dimensions indicate the weightings that employees place

on each individual dimension. Managers can use these weights, when faced with limited

budgets, as an allocation instrument. If limited funds are available to improve working

conditions, a manager will want to select any of the four first group items: training and

coaching, goal management, organizational support – management and organizational

support – tools. Improving any of these four areas will yield the greatest overall increase

in internal service quality.

153

5.2.2. Linking internal service quality to satisfaction, loyalty and productivity

As discussed in Chapter 2, the following hypotheses are all embedded within the

employee portion of the service profit chain:






Structural equations will test each of these hypotheses independently as well as testing

the overall fit of the employee portion of the service profit chain.

The structural framework tested along with the results are illustrated in Figure

5.4. Due to spatial limitations, the eight internal service quality dimensions, along with

their indicators, are omitted from the diagram, however, their equations are included in

the model (i.e. internal service quality is still a second order factor made up of eight first

order factors all made up of their individual indicators). The paths that are significant are

so at the p<.001 level. The two paths that are not significant are not so at the p<.05 level.

As noted earlier, the variances of all latent variables are set to 1.0 for identification

purposes, rectangles represent indicators and ovals represent latent variables. Table 5.2

154

which is presented immediately after Figure 5.4 details the point estimate, 90%

confidence interval, standard error and t-value for each path parameter.

Figure 5.4. Structural equation results for employee model


1.0

EmployeeLoyalty


1.0

1.0

eelf

eepf

EL1 EL2 EL3

eel1 eel2 eel3

EP1 EP2 EP3

eep1 eep2 eep3


ees1

.696

EL4

eel4

EP4

eep4

.213

.600

ns

ns

.800 .861 .863 .870

.705 .885 .868 .720


1.0

EmployeeLoyalty


1.0

1.0

eelf

eepf

EL1 EL2 EL3

eel1 eel2 eel3

EP1 EP2 EP3

eep1 eep2 eep3

ees1

.696

EL4

eel4

EP4

eep4

.213

.600

ns

ns

.800 .861 .863 .870

.705 .885 .868 .720

155

Table 5.2. Structural equation results for employee model

156

Before discussing model fit each hypothesis is treated individually. H1a theorizes

that there is a positive relationship between internal service quality and employee

satisfaction. The beta coefficient for the path between these two constructs is 0.697, 90%

confidence interval of (0.665, 0.730). The standard error of the path estimate is 0.020.

The t value associated with the path is 35.46 which is significant at the p < .001 level.

These results provide empirical validation of hypothesis H1a – there is a positive

relationship between internal service quality and employee satisfaction. The

confirmation of this hypothesis demonstrates that high quality support services and

organizational policies, such as goal management, support – management, support –

tools, rewards and recognition, etc., lead to employee satisfaction. In other words,

employees notice and value the developmental HR practices of their organizations.

These findings resemble those in closely related fields. Whether the research has used

the term organizational culture (Schneider, 1990; O’Rielly et al, 1991; Sheridan, 1992),

organizational climate (Schneider et al, 1980; Rogg et al, 2001), high performance work

systems (Huselid, 1995), high commitment human resource management (Arthur 1992,

1994; Whitener, 2001), innovative human resource practices (MacDuffie, 1995), quality

of work life (Havlovic, 1991; Lau et al, 2001) or perceived organizational support

(Eisenberger et al, 1986; Rhoades and Eisenberger, 2002) the results have been the same

– employees are grateful for the efforts of the organization’s commitment to provide

them an excellent working environment that not only treats them with respect but also

develops their capabilities.

A similar result occurs when testing hypothesis H1b – Internal service quality is

positively associated with employee loyalty. The path coefficient between these two

157

variables is 0.212, 90% confidence interval of (0.150, 0.274). The standard error of the

coefficient is 0.038, yielding a t value of 5.62 which is significant at the p < .001 value.

These findings do lend support for Hypothesis H1b: internal service quality is positively

associated with employee loyalty. The same arguments made above, linking internal

service quality to employee satisfaction, can be made here. Employees do recognize and

value an excellent working environment where their potential is utilized and developed.

As social exchange theorists argue, employees will feel a certain degree of reciprocity for

their organization’s support – one way the reciprocity will reveal itself is through a

heightened sense of commitment (Homans, 1961; Blau, 1964; Schneider et al, 1980;

Wayne et al, 1997; Rhoades and Eisenberger, 2002).

Before moving to the next hypothesis, a special comparative note should be made

in regards to the direct effect of internal service quality on employee satisfaction and

employee loyalty. The magnitude of the effect of internal service quality on employee

satisfaction is over three times greater than the magnitude of the effect of internal service

quality on employee loyalty, beta weights of 0.697 versus 0.212. This is the first research

that explicitly allows for this comparison. We believe there are two underlying reasons

for this result. First, there are probably fewer contextual effects that mediate the

relationship between internal service quality and employee satisfaction than there are that

mediate the effect between internal service quality and employee loyalty. For example,

an employee who is working in a retail environment while pursuing a college degree may

value and be highly satisfied with their organization’s internal work environment but

their career goal of pursuing a more permanent job in specialized field may lead to a less

pronounced effect of their working environment on their intent to remain with the

158

organization in a similar position, i.e. loyalty. Second, from a temporal sense, internal

service quality may have a more immediate effect on satisfaction than it does loyalty.

Giving employees a better working environment may dramatically increase their

immediate satisfaction, but it may take a certain amount of time for that satisfaction to

yield an increase in a more long term concept like loyalty. This concept is partially

accounted for by exploring the indirect path from internal service quality to employee

loyalty through the employee satisfaction variable.

The third hypothesis embedded within the service profit chain is H1c which states

that there is a positive association between employee satisfaction and employee loyalty.

The beta coefficient between these two variables is 0.601, 90% confidence interval of

(0.547, 0.656). The standard error for this coefficient is 0.033 resulting in a t value of

18.10, which is significant at the p < .001 level. These results lend support for hypothesis

H1c and reflect the findings a vast amount of previous research suggesting the link

between employee satisfaction and employee loyalty is quite strong (see section 2.3 for a

review of this literature). Indeed meta-analyses by Petty et al (1984) and Griffeth et al

(2000) concluded that employee satisfaction is the most significant predictor of employee

loyalty – the high beta weight, 0.601, of this study, serves to validate this concept.

The result of the structural equation model indicates that there is no evidence of

the expected positive relationship between employee satisfaction and employee

productivity or of the relationship between employee loyalty and employee productivity;

hence, hypotheses H1d and H1e are not supported. These results contradict a vast

amount of previous research into these relationships, see section 2.3 and Appendix E for a

review of this research. We believe that our results stem not from a truly insignificant

159

relationship among the three variables but rather from the construction of the employee

productivity measure. In the main survey instrument, four items are used to measure

employee productivity:

• I feel that I am a productive associate.• Within my store I am a top seller.• My average sales per hour is among the best in the store.• My productivity has increased the longer I have worked in the

store.

Although these questions have proven to be valid in previous studies (Denison et al,

1995; Huselid, 1995; Spreitzer et al, 1997; Silvestro and Cross, 2000) after deeper

investigation of the operating policies of the specialty retailer used in this study, it

appears they may not be appropriate measures for this specific setting. First, the specialty

retailer employs associates whose sole job responsibilities are stocking the front room

and inventory control in the back room. As such, questions pertaining to sales are not

applicable. Second, sales associates work in store zones and are instructed to hand

customers over to other associates when the customer leaves a zone. Due to this teaming

approach, individual sales figures are not tracked, therefore, employees do not actually

know their average individual daily sales. Jointly these two store operating

characteristics, along with the fact that anonymous surveys are utilized, precluding the

possibility of using manager’s perceptions of employee productivity, may be the

underlying cause of the insignificance among the three employee measures: satisfaction,

loyalty and productivity. At the very least, this finding warrants future investigation and

research.

The difficulty in measuring employee productivity in the service industry is a

common one to service management scholars (Vuorinen et al, 1998; Van Looy et al,

160

1998), see Nachum (1999) for a review. The measurement difficulty stems from the lack

of uniform inputs and outputs. An employee’s knowledge or helpfulness is not

necessarily captured in traditional manufacturing type output related productivity scores.

One could argue that the only true way to measure a service employee’s productivity is to

use a customer’s perception of the service quality provided by the employee as a

surrogate.

Due to the reasons listed above, and until the validity of an employee productivity

construct can be better ensured, the factor will be dropped from the remaining portions of

this research. The final structural equation model tested is presented in Figure 5.5. Note,

indicators and error terms are left off for spatial considerations. All individual paths are

significant at the p<.001 level. Details of the point estimates, 90% confidence intervals,

standard errors and t-values associated with each path can be found in Table 5.3

immediately following the illustration of the structural model.

161

Figure 5.5. Structural equation results for employee model, revised

Training &Coaching

Goal Mgmt

Teamwork

Empowerment

WorkDesign

Support –Mgmt

Support --Tools


EmployeeLoyalty


.696

.600

.213

.837

.843

.626

.679

.658

.807

.815

.644

Training &Coaching

Goal Mgmt

Teamwork

Empowerment

WorkDesign

Support –Mgmt

Support --Tools

Rewards &Recognition


EmployeeLoyalty


.696

.600

.213

.837

.843

.626

.679

.658

.807

.815

.644

162

Table 5.3. Structural equation results for employee model, revised

In order to be as comprehensive as possible, and to follow established

recommended guidelines (Hu and Bentler, 1998), in assessing the overall fit of the model,

many different fit indices are calculated - see Section 4.1.2 for a complete discussion of

163

each fit measure. Table 5.4 summarizes all the values of the fit indices for the employee

model. An RMSEA of .057 is very close to Hair et al’s (1998) suggestion of .05

indicating good fit and well within the .08 cut-off of reasonable fit. A second absolute fix

index, RMR, also suggests that the model fits the data well. The model’s RMR of 0.05 is

well below the suggested 0.10 cutoff (Chau, 1997). The last absolute fit measure, a

gamma hat value of 0.90, is at the suggested 0.90 cutoff indicating the model fits the data

well (Steiger, 1989). All six of the incremental fit measures are above or near the 0.90

recommended cutoffs: NFI, NNFI, BL86, BL89, RNI and CFI. Taken together, these

nine fit measure thoroughly demonstrate that the proposed employee model does fit the

data well.

164

The fit findings support Heskett et al’s (1994, 1997) notion that investing in

employees and supporting their efforts to service customers will generate increased

employee satisfaction and ultimately employee loyalty. Moreover, the efforts of the

organization can be in programs that enhance their own business practices, not just in

BTB TTT )( −

)1/()//( −−BBTTBB dfTdfTdfT

)//()//( BBTTBB dfTdfTdfT −

)/()( BBTB dfTTT −−

)/()]()[( BBTTBB dfTdfTdfT −−−−

]0),(),max[(

/]0),max[(1

BBTT

TT

dfTdfT

dfT−−

−−

)]1/()[(2/{ −−+ NdfTpp TT

To dfF /^

residuals

n

nresidualsresiduals

/1

∑

Chi-square = 2,318.192 Sample Size = 872d.o.f. = 619 Fo = 1.983

Notes on formulae:TB = Chi-square statistic for baseline (null) modelTT = Chi-square statistic for target (specified) modeldfB = degrees of freedom for baseline modeldfT = degrees of freedom for target modelp = number of manifest variablesFo= Estimate of population discrepancy function

.90Gamma Hat

.91CFI

.91RNI

.92BL89

.88BL86

.91NNFI

.89NFI

.05RMR

.057(.054,.059)

RMSEA

ValueFormulaFit Index

BTB TTT )( −





]0),(),max[(

/]0),max[(1

BBTT

TT

dfTdfT

dfT−−

−−

)]1/()[(2/{ −−+ NdfTpp TT

To dfF /^

residuals

n

nresidualsresiduals

/1

∑



.90Gamma Hat

.91CFI

.91RNI

.92BL89

.88BL86

.91NNFI

.89NFI

.05RMR

.057(.054,.059)

RMSEA




.90Gamma Hat

.91CFI

.91RNI

.92BL89

.88BL86

.91NNFI

.89NFI

.05RMR

.057(.054,.059)

RMSEA




.90Gamma Hat

.91CFI

.91RNI

.92BL89

.88BL86

.91NNFI

.89NFI

.05RMR

.057(.054,.059)

RMSEA


Table 5.4. Fit indices for employee model

165

those programs that directly benefit the employee, such as increased compensation. For

example, the results show that organizations who train their employees and give them

tools to serve the customer will find that employees become more fulfilled and satisfied

with their work. It would be very difficult to argue that training programs have no direct

effect on the service delivery quality that the organization delivers. In summary, the fit

findings support the revised portion of the service profit chain model.

The results of the employee model make several important contributions to

service management literature. Most importantly, the findings support Heskett et al’s

(1994, 1997, 2001) theory that employees are valuable resources to be maximized rather

than value-less costs to be constrained. Rather than reducing front-line service jobs to

mundane, repetitive tasks, organizations can achieve competitive advantage by

broadening job descriptions and developing their employees through commitment

enhancing human resource practices. Supporting the growth of employees can have far

reaching benefits that extend well beyond keeping them happy. As hypothesized in the

service profit chain model, increased employee satisfaction leads to an increase in

employee retention. Increased retention leads to familiarity with the service process and

an enhancement in capability to service unique customer needs. Furthermore, as

Schlesinger and Heskett (1991) demonstrate in their work on the “satisfaction mirror”,

employee satisfaction is one of the best predictors of customer satisfaction. And as

various researchers have shown (e.g. Cronin and Taylor, 1992; Shemwell, 1998; Taylor

and Hunter, 2002), customer satisfaction leads to customer loyalty and overall

improvement in business performance.

166

The employee model also provides evidence for the nomological validity of the

internal service quality construct; something which previous research has yet to establish.

When developing constructs it is not enough to show that they are reliable, convergent

and divergent. It must also be shown, that post refinement, they behave in accordance

with accepted theory in relation to other well established constructs. The structural

equation results show that the eight dimensional representation of internal service quality

used in this study does behave in a manner consistent with human resource theory: it is

positively associated with employee satisfaction and employee loyalty. These results

can be used as justification for the nomological validity for both the eight dimensional

factors as well as the higher, second order internal service quality factor.

5.3. Customer model

In their service profit chain work, Heskett et al (1994, 1997) hypothesize that

external service quality, which we call total retail experience, drives value and customer

satisfaction, which, in turn, drive customer loyalty. As pointed out by Kaplan and Norton

(1991, 2002), in their work on the balanced scorecard, customer satisfaction and loyalty

are leading indicators of overall business performance. Similar to the employee model, a

second order construct, total retail experience, drives this entire causal framework.

Following the organization of section 5.2 we will first discuss the second order

measurement model, we will then explore the relationship among the rest of the

variables. The generic model to be tested is given in Figure 5.6.

167

As discussed in Chapter 3, all the links in the chain are predicted to be positive.

SYSTAT 10.2’s structural equation modeling software, RAMONA, is used to perform

the analysis.

5.3.1. Composition of total retail experience

The measurement portion of the customer model consists of constructing a second

order total retail experience factor. As discussed in section 3.1, a five dimensional

representation of total retail experience is used. The five dimensions include: product

quality, product availability, service quality, store layout and servicescape. The results of

the second order factor construction are illustrated in Figure 5.7. All the variables with

lowercase lettering are error terms. Ovals represent latent variables, rectangles represent

manifest variables. First and second order factor loadings are presented. The variances

of all the latent variables are set to 1.0 for identification purposes resulting in

standardized path coefficients. Finally, all path coefficients are significant at the p<.001

level.

Total RetailExperience

Value CustomerSatisfaction

CustomerLoyalty

+ + +

+



CustomerLoyalty



CustomerLoyalty

+ + +

+

Figure 5.6 Generic representation of customer model

168

Figure 5.7. Total Retail Experience composition

M2em2

M3em3

M4em4

M5em5

M6em6

M1em1

M7esq7

M8esq8

SQ2esq2

SQ3esq3

SQ4esq4

SQ5esq5

SQ1esq1

SQ8esq8

SQ9esq9

SQ7esq7

SS3ess3

SS5ess5

SS7ess7

SS8ess8

SS2ess2

SS10ess10

.773

.637

.777

.847

.888

.845

.697

.770

.790

.843

.941

.913

.854

.834

.831

.786

.757

.837

.729

.748

.583

.641

SS6ess6.773

ProductQuality

ProductAvail.

ServiceQuality

StoreLayout

Service-scape


.730

.737

.716

.740

.791

1.0

1.0

1.0

1.0

1.0

1.0

epqf

epaf

esqf

eslf

essf

M2em2

M3em3

M4em4

M5em5

M6em6

M1em1

M7esq7

M8esq8

SQ2esq2

SQ3esq3

SQ4esq4

SQ5esq5

SQ1esq1

SQ8esq8

SQ9esq9

SQ7esq7

SS3ess3

SS5ess5

SS7ess7

SS8ess8

SS2ess2

SS10ess10

.773

.637

.777

.847

.888

.845

.697

.770

.790

.843

.941

.913

.854

.834

.831

.786

.757

.837

.729

.748

.583

.641

SS6ess6.773

ProductQuality

ProductAvail.

ServiceQuality

StoreLayout

Service-scape


.730

.737

.716

.740

.791

1.0

1.0

1.0

1.0

1.0

1.0

epqf

epaf

esqf

eslf

essf

M2em2

M3em3

M4em4

M5em5

M6em6

M1em1

M7esq7

M8esq8

SQ2esq2

SQ3esq3

SQ4esq4

SQ5esq5

SQ1esq1

SQ8esq8

SQ9esq9

SQ7esq7

SS3ess3

SS5ess5

SS7ess7

SS8ess8

SS2ess2

SS10ess10

.773

.637

.777

.847

.888

.845

.697

.770

.790

.843

.941

.913

.854

.834

.831

.786

.757

.837

.729

.748

.583

.641

SS6ess6.773

ProductQuality

ProductAvail.

ServiceQuality

StoreLayout

Service-scape


.730

.737

.716

.740

.791

1.0

1.0

1.0

1.0

1.0

1.0

epqf

epaf

esqf

eslf

essf

169

Several interesting items emerge from the results illustrated above. It appears that

customers do form a holistic impression of their shopping experience that is multi-faceted

in nature. Total retail experience is a much more comprehensive construct of the

shopping experience than traditional service quality scales such as SERVQUAL and

SYSTRA-SQ. The results indicate that variables that are often overlooked in empirical

studies, for example, servicescape, store layout and merchandise accessibility, do play a

meaningful role in shaping customer perceptions of the retailer. Intuitively, this result

makes sense. It is of no use to customers if a retailer excels in service quality and has

tremendous product quality but lacks product availability and selection.

It also appears that the five dimensions are nearly equally weighted by customers

when forming impressions of the retailer; the second order factor loadings range from

0.716 to 0.790. Only one pair of confidence intervals do not overlap – servicescape, 95%

confidence interval of (0.765, 0.817), is weighted more heavily than service quality, 95%

confidence interval of (0.686, 0.746). This result suggests that resource allocation should

be spread fairly evenly across the five dimensions. For this particular retailer, excessive

investment in one dimension should not come at the expense of another.

The analysis of total retail experience yields two major contributions to the

literature. First, it provides a comprehensive statistical analysis of the composition of the

second order construct. Terblanche and Boshoff (2001 a, b) are the first to study the

concept but they do so only at a dimensional level. They fail to test whether their

dimensions of total retail experience converge to form a second order factor. This

weakness leaves the reader guessing whether Terblanche and Boshoff’s (2001 a, b)

170

dimensions do indeed form a convergent higher level factor or whether they are simply

independent, uncorrelated dimensions. Moreover, the dimensions used in each of the two

studies are not consistent. Furthermore, the customer portion of the service profit chain

used in this study will show the causal structure behind the relationship between internal

service quality, value, customer satisfaction and customer loyalty. Examining this

structure will provide deeper insight into the nomological network of customers’

impressions of the retail experience.

The second contribution is the validation, as a whole, of a more comprehensive

model of customers’ perceptions of their retail experience. In general, service

management literature is lacking just such a factor. Researchers have been using service

quality constructs as a surrogate, but the constructs are too narrowly defined to capture

the essence of the total retail experience. Using hierarchical regression, we test whether

our five total retail dimensions can better explain variance in customer satisfaction than

traditional service quality measures. If the added dimensions can explain significantly

more variance than the traditional measures then it may be a worthwhile pursuit for future

customer satisfaction researchers. At the first level of the hierarchy, customer

satisfaction (dependent variable) is regressed on Gronroos’ (1984, 1997) traditional

product quality/process quality definition of service quality, represented in our model by

the product quality and service quality constructs. The second stage of the hierarchy

involves adding the unique total retail experience dimensions: product availability, store

layout and servicescape. The regression equations for the two stages are listed below:

Step 1: Customer Satisfaction = _1 * Product Quality + _2 * Service Quality + _

171

Step 2: Customer Satisfaction = _1 * Product Quality + _2 * Service Quality +_3 * Servicescape + _4 * Store Layout + _5 * Product Availability andSelection + _

Scores for the five total retail experience factors and the customer satisfaction factor

needed to be created. Scores are generated using both a summated scores approach and a

factor scores methodology. Results are similar using both methodologies, thus only the

factor score method results are provided. The results are given in Table 5.5.

Before performing hierarchical regression, it is necessary to test for multi-

collinearity between the independent variables. The most common methodology used is

calculating variance inflation factor for each variable. This method measures how much

variance in each independent variable can be explained by the group of other independent

variables that are to be used in the regression analysis. A variance inflation factor of over

10.0 indicates that there may be problems with multi-collinearity (Myers, 1990). The

variance inflation factors for each of the independent variables used in this study are:

1.538 for product quality, 1.639 for service quality, 1.501 for product availability, 1.911

for store layout and 1.909 for servicescape. These values are well below 10.0 and

indicate there is no cause for concern of multi-collinearity.

172

The stage 1 regression model indicate that the two traditional service quality

constructs, product quality and service quality, can explain 60.1% of the variation in

customer satisfaction. The three unique total retail experience dimensions are added at

stage 2: servicescape, product availability and store layout. These three dimensions can

explain another 6% of the variation in customer satisfaction beyond that explained by

product quality and service quality. This increase is significant as evidenced by the .000

significance level for the F change statistic. The results indicate that the full five

dimensional total retail experience factor is a better predictor of customer satisfaction

then the traditional two dimension service quality construct (i.e. product quality and

service quality). As such, researchers investigating precursors of customer satisfaction

should consider using the more comprehensive total retail experience construct.

.000.060.000.661Servicescape

ProductAvailability

Store Layout

2

.000.601.000.601ProductQuality

ServiceQuality

1

Sig. FChange

RSquaredChange

Sig. FRSquared

VariablesEntered

Stage

.000.060.000.661Servicescape

ProductAvailability

Store Layout

2

.000.601.000.601ProductQuality

ServiceQuality

1

Sig. FChange

RSquaredChange

Sig. FRSquared

VariablesEntered

Stage

Table 5.5. Hierarchical regression results – Total Retail Experience

173

5.3.2. Linearity between customer satisfaction and customer loyalty

One of the assumptions of structural equation modeling is that the relationships

between all variables in the model are linear. There has been much research into one

specific link in the chain that indicates the possible existence of non-linearity – the link

between customer satisfaction and customer loyalty. Some researchers believe this

relationship is non-linear in that very high levels of customer satisfaction can lead to

abnormally high levels of customer loyalty. Jones and Sasser (1995) conducted research

for the Xerox company and found that “very satisfied” customers are six times more

likely to re-purchase then “satisfied” customers. Fornell (1992) has similar findings in

the retail clothing industry. Anderson and Mittal (2000) use consumer information

search theory to explain the potential non-linear effect in their research, in essence,

claiming that once satisfaction reaches a certain level, consumers will no longer consider

alternative suppliers, resulting in dramatic increases in future purchase patterns. There is

roughly an equal amount of research that investigates the relationship between

satisfaction and loyalty and conclude that it is linear (Soderlund, 1998; Taylor and

Hunter, 2002).

Because there has yet to be a definitive conclusion to this debate, this research

will take an overtly conservative approach and test whether the link between customer

satisfaction and customer loyalty is indeed linear in the data obtained at the primary data

collection stage. The most common way to test whether a relationship between two

variables is linear is to use hierarchical regression. Specifically two forms on non-

linearity will be tested – a quadratic effect and a cubic. Prior evidence has shows that

174

both of these effects can occur. The quadratic effect is shown in Fornell (1992),

Anderson and Sullivan (1993), Jones and Sasser (1995), and Ittner and Larcker (1998).

Johnstone (1995) and Anderson and Mittal (2000) believe that the effect may be cubic in

that only changes near the end of the customer satisfaction spectrum, i.e. very low and

very high satisfaction levels, will cause changes in customer loyalty. The left half of

Figure 5.8 illustrates what a quadratic effect would look like, the right half, what a cubic

effect would look like.


Customer Loyalty


Customer Loyalty


Customer Loyalty


Customer Loyalty

Figure 5.8. A potential non-linear effect

In order to use hierarchical regression to test for linearity, scores must be made

for both the customer satisfaction and customer loyalty variables. Two methods are used

to calculate these scores: factor scores and summated scores. For all tests carried out, the

results are similar, therefore, only the results of the summated scores approach are shown.

The first step in the regression procedure is simply to regress customer satisfaction on

customer loyalty. As hypothesized in the service profit chain and supported in the

literature review in Chapter 3, the model is significant and a positive effect is seen, all

results are reported in Table 5.6. At the next stage of the hierarchy, the customer

175

satisfaction variable is squared. When this variable is entered into the equation, it is

found to be insignificant, p value = 0.275. Since only one additional variable is added at

this stage, the p value of 0.275 also refers to the significance of the change in the R-

squared value between models, therefore, it can be concluded that that model with the

additional squared customer satisfaction term cannot explain any more variance in

customer loyalty then can the model with the simple linear term. A similar result is

found when a cubic customer satisfaction term is introduced into the regression equation.

The cubic term itself and the change in the R-Squared value are both insignificant, p

values of 0.544. The results of the hierarchical regression models do demonstrate that the

effect between customer satisfaction and customer loyalty can be best described as linear.

0.369

1.191

1411.42

F-Value

-.607

-1.092

37.569

T-value

.544.000.001.000Customer Satisfaction Cubed

3

.275.000.004-.005Customer Satisfaction Squared

2

.000.569.024.754Customer Satisfaction

1

P-ValueR2

ChangeStandard Error

BetaVariable AddedStage

0.369

1.191

1411.42

F-Value

-.607

-1.092

37.569

T-value

.544.000.001.000Customer Satisfaction Cubed

3

.275.000.004-.005Customer Satisfaction Squared

2

.000.569.024.754Customer Satisfaction

1

P-ValueR2

ChangeStandard Error

BetaVariable AddedStage

Table 5.6. Hierarchical regression results – Tests for non-linearity

5.3.3. Linking total retail experience, value, customer satisfaction and customer loyalty

As discussed in Chapter 3, the following hypotheses are all embedded within the

customer portion of the service profit chain:

176


H2b: Total retail experience is positively associated with customersatisfaction.


H2d: Customer satisfaction is positively associated with customerloyalty.

The structural equations will test each of these hypotheses independently as well as

testing the overall fit of the customer portion of the service profit chain.

The structural framework tested along with the results are illustrated in Figure

5.9. Due to spatial limitation the individual indicators for the five total retail experience

dimensions are omitted from the diagram. As noted earlier, the variances of all latent

variables are set to 1.0 for identification purposes, rectangles represent indicators and

ovals represent latent variables. All path coefficients are significant at the p<.001 level.

More detailed results are presented in tabular format in Table 5.7.

177

Figure 5.9. Structural equation results for customer model

ProductQuality

ProductAvail.

ServiceQuality

StoreLayout

Service-scape

1.0

1.0

1.0

1.0

1.0


essf

eslf

esqf

epaf

epqf

1.0

Value

CustomerSatisfaction

CustomerLoyalty

1.0

1.0

1.0

evf

ecsf

eclf

V1 V2 V3

ev1 ev2 ev3

CL1 CL2 CL3

ecl1 ecl2 ecl3

CS1 CS2 CS4

ecs1 ecs2 ecs4

.730

.737

.716

.740

.791

.721

.733

.227

.762

.894 .879 .866

.922 .914 .843

.875 .957 .888

ProductQuality

ProductAvail.

ServiceQuality

StoreLayout

Service-scape

1.0

1.0

1.0

1.0

1.0


essf

eslf

esqf

epaf

epqf

1.0

Value

CustomerSatisfaction

CustomerLoyalty

1.0

1.0

1.0

evf

ecsf

eclf

V1 V2 V3

ev1 ev2 ev3

CL1 CL2 CL3

ecl1 ecl2 ecl3

CS1 CS2 CS4

ecs1 ecs2 ecs4

.730

.737

.716

.740

.791

.721

.733

.227

.762

.894 .879 .866

.922 .914 .843

.875 .957 .888

178

Table 5.7. Structural equation results for customer model

Before discussing overall model fit, each individual hypothesis is treated

independently. The first hypothesis incorporated within the customer portion of the

service profit chain model, H2a, theorizes that that total retail experience is positively

associated with value. The structural equation results fully support this hypothesis. The

179

path coefficient between value and total retail experience is 0.721, its associated 90%

confidence interval is (0.688, 0.754). The standard error of the path estimate is 0.020,

resulting in a t value of 36.17, which is significant at the p < .001 level. This finding

supports Terblanche and Boshoff’s (2001 a, b) preliminary work on total retail

experience. Customers assess the overall quality of their shopping experience, captured

in the total retail experience construct, and use it as a basis of judging the value they

receive. If you recall, the most common definition of value is total benefits divided by

total costs. The five dimensions of total retail experience can all be viewed as adding

value to the customer’s shopping experience, hence improving the benefits to cost ratio.

Each one of these dimensions has been individually linked to value/satisfaction in the

past, however, this work, along with Terblanche and Boshoff’s work (2001 a, b) begins to

show how the five dimensions interact and collectively influence customer’s service

experience assessments.

The second hypothesis embedded within the customer model, H2b, is similar to

the one treated above, namely, that total retail experience has a positive association with

customer satisfaction. The results of the structural equation model support this

hypothesis. The path coefficient between the two variables is 0.733, with a 90%

confidence interval of (0.683, 0.783). The standard error of this path estimate is 0.030

resulting in a t value of 24.30 which is significant at the p < .001 level. Again, much

previous research has linked the five dimensions of total retail experience to customer

satisfaction, but this research is one of the first to take such a comprehensive view of

customers’ assessments of their shopping experience.

180

Taken together these two initial findings lend validity to the fairly new concept of

total retail experience. Although still in its infancy stage, the total retail experience

construct draws from many proven service management/marketing concepts: store

shopping experience (V10), store image/personality (Martineau, 1958; Lindquist, 1974;

Stanley and Sewell, 1976; Zimmer and Golden, 1988), servicescape (Bitner, 1990) and

service quality (Gronroos, 1984). These findings demonstrate that tangible aspects of the

shopping experience (e.g. product quality, servicescape) combine with tacit aspects (e.g.

service quality) to influence not only customer assessment of the service organization, i.e.

value received, but also customer attitude, i.e. satisfaction, and as we shall see in the

latter hypotheses, ultimately, customer behavior, i.e. loyalty.

A positive relationship also exists between value and customer satisfaction, as

indicated by the 0.227 path coefficient between the two variables, 90% confidence

interval of (0.173, 0.281). The standard error of this path estimate is 0.033, yielding a t

value of 6.93 which is significant at the p < .001 level. This finding provides full support

for hypothesis H2c. The results confirm that which relationship value marketing

proponents argue: providing a high quality total retail experience, is not enough; it must

also be done at a reasonable cost (Heskett et al, 2001). Consumers are only willing to

pay so much for advancements in quality or servicescape. There will eventually become

a point where improvements to the service delivery process and/or consumer benefit

package come at too high an expense. However, it is also important to note, that in a

specialty niche retail environment, like the one studied in this research, value is not

nearly as effective a predictor of customer satisfaction as the shopping experience itself

is, as evidenced through the 0.733 coefficient versus the 0.227 coefficient. This result

181

was certainly expected for this retailer. In this retail environment, servicescape and

service quality are considered to be the key order winners with value/price playing a

much more subservient order qualifying role.

This research validates one of the most well established links in marketing, the

relationship between customer satisfaction and customer loyalty. As evidenced by the

path coefficient of 0.762, the relationship between these two factors is very strong; in

fact, it is the strongest relationship of all those studied in this research. The 90%

confidence interval for this path estimate is (0.737, 0.787). The standard error of the

estimate, 0.015, results in a t value of 51.14, which is significant at the p < .001 level.

These findings lend support for hypothesis H2d. Customers who are satisfied with their

shopping experience are likely to remain loyal to the organization and exhibit behaviors

such as continued purchases of primary products, increased purchases of ancillary

products and a willingness to recommend the organization to family and friends. Indeed,

in past research, customer satisfaction has been shown to be the best predictor customer

loyalty (Mittal and Lasser, 1998).

Model fit is again assessed through the comprehensive guidelines suggested by

Hu and Bentler (1998). Table 5.8 summarizes the values of several different fit indices

for the customer model.

182

The three absolute fit indices all suggest the model fits the data well. An RMSEA of .053

is very near the .05 suggestion that Hair et al (1998) suggest for good model fit and

certainly well below the .08 threshold of reasonable fit. An RMR value of .05 is well

BTB TTT )( −





]0),(),max[(

/]0),max[(1

BBTT

TT

dfTdfT

dfT−−

−−

)]1/()[(2/{ −−+ NdfTpp TT

To dfF /^

residuals

n

nresidualsresiduals

/1

∑

Chi-square = 1,806.837 Sample Size = 1,076d.o.f. = 455 Fo = 1.285


.93Gamma Hat

.95CFI

.95RNI

.95BL89

.93BL86

.94NNFI

.93NFI

.05RMR

.053(.051,.056)

RMSEA


BTB TTT )( −





]0),(),max[(

/]0),max[(1

BBTT

TT

dfTdfT

dfT−−

−−

)]1/()[(2/{ −−+ NdfTpp TT

To dfF /^

residuals

n

nresidualsresiduals

/1

∑



.93Gamma Hat

.95CFI

.95RNI

.95BL89

.93BL86

.94NNFI

.93NFI

.05RMR

.053(.051,.056)

RMSEA




.93Gamma Hat

.95CFI

.95RNI

.95BL89

.93BL86

.94NNFI

.93NFI

.05RMR

.053(.051,.056)

RMSEA




.93Gamma Hat

.95CFI

.95RNI

.95BL89

.93BL86

.94NNFI

.93NFI

.05RMR

.053(.051,.056)

RMSEA


Table 5.8. Fit indices for customer model

183

below Chau’s (1997) recommended value of 0.10. The gamma hat value for this model

is 0.93, above the suggested 0.90 cutoff (Steiger, 1989). The three absolute measures

indicate that the model is able to reproduce adequately the sample covariance matrix. All

six of the incremental fit measures are above the suggested 0.90 threshold; in fact, the

lowest value is 0.93. The incremental models suggest that the proposed employee model

can fit the data better than a null or baseline model. Collectively, these nine measures

thoroughly demonstrate that the proposed customer model does fit the data well.

It is from this analysis that we find support for the customer portion of the service

profit chain. Specifically, the results support Heskett et al’s (1994, 1997, 2001) assertion

that customers form a holistic impression of their shopping experience. The key

components of the shopping experience include product quality, service quality, product

availability, store layout and servicescape. A customer’s shopping experience has both a

direct influence on the customer’s satisfaction as well as an indirect effect through its

relationship with perceived value. Customer satisfaction then in turns drives customer

loyalty. And if we are to incorporate the extensive research into theories such as

defensive marketing (Fornell & Wernerfelt, 1987), customer lifetime value (Rust et al,

2001), the balanced scorecard (Kaplan and Norton, 1992, 2001), defection analysis

(Reicheld and Sasser, 1990; Reicheld, 1996) and the service profit chain itself (Heskett et

al, 1994, 1997, 2001) the entire customer model will drive business results.

The structural equation analysis of the customer portion of the service profit chain

provides several contributions to service management literature. It is the most

comprehensive study to date of the linkages between total retail experience, value,

customer satisfaction and customer loyalty. It expands upon relationship value

184

management literature in its broad operationalization of total retail experience. Much of

the value literature takes a more simplistic approach to defining value, simply using cost

and quality to define a ratio. In a similar vein, most quality researchers omit the value

variable from their studies altogether, assuming quality improvements do not need to be

justified on a cost basis.

In validating the latter half of Heskett et al’s (1994, 1997) service profit chain

model, the results of this study demonstrate that in a specialty niche retail environment,

total retail experience is a much stronger predictor of customer satisfaction than value is.

Customers are clearly willing to “pay” extra for an enjoyable shopping experience. In

this service setting orders are not won through low cost, but rather, through

individualized service within a pleasant surrounding. As the factor’s label suggests,

customer orders are won by their total experience of shopping at the retailer. Service

management literature has yet to identify specific settings were this phenomenon occurs.

This study can serve as the grounds for future researchers to expand upon, replicating the

study in other types of service settings.

Another major contribution this research makes is presenting evidence for the

nomological validity of two newly constructed scales: servicescape and total retail

experience. The analysis in chapter 4 shows that the constructs are reliable and that they

exhibit several types of validity. The analysis presented in section 5.3.2 shows that both

of these constructs behave in a predictable fashion within a service management network.

Total retail experience, hence all its components as well, is positively associated with

customer satisfaction and value, as would be expected. The relationships among these

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variables indicate that the constructs exhibit nomological validity; something which

previous literature has yet to establish.

5.4. Summary

The first section within this chapter, section 5.1, discusses the reasons why

structural equation modeling was chosen as the data analytic tool for this research. The

primary reasons include the ability of structural equation modeling to incorporate latent

variables, to allow for a single variable to act as both a dependent and independent

variable and to determine both direct and indirect structural effects. A primary concern

when using structural equation modeling is to obtain a large enough sample size to

achieve reasonable power. As discussed at the end of section 5.1, the power of both of

the models used in this research approaches 1.0.

Section 5.2 investigates the first portion of the service profit chain, a portion we

call “the employee model”. Five hypotheses are embedded within the employee model:


H1b: Internal service quality is positively associated with employee loyalty.

H1c: Employee satisfaction is positively associated with employee loyalty.



Before testing these hypotheses it was necessary to analyze the second order

measurement model that comprised internal service quality. Section 5.2.1 demonstrates

that internal service quality is an eight dimensional factor consisting of: training and

coaching, goal management, teamwork, empowerment, support - management, support –

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tools, and rewards and recognition. All eight dimensions exhibit significant loadings

onto the second order internal service quality construct.

Section 5.2.2 uses structural equation modeling to test the five hypotheses

described above. There is empirical evidence to support the first three hypotheses, H1a,

H1b and H1c, but no evidence for the latter two, H1d and H1e. In summary, internal

service quality is positively associated with employee satisfaction and employee loyalty.

Employee satisfaction, in turn, is positively associated with employee loyalty. No

relationships are found between employee satisfaction and employee productivity nor

between employee loyalty and employee productivity. As such, the employee

productivity is dropped from the analysis and the overall fit of the revised model is

assessed. Chapter 6 expounds upon these findings.

The organization of section 5.3 closely resembles that of section 5.2 only it looks

at the second half of the service profit chain – the customer model. Embedded within this

portion of the service profit chain are four hypotheses:





The first sub-section, section 5.3.1, shows the construction of the second order construct

total retail experience. This factor is shown to be five dimensional: product quality,

service quality, product availability and selection, store layout and servicescape.

Hierarchical regression demonstrates that this five dimensional construct is a stronger

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predictor of customer satisfaction than Gronroos’ (1984) traditional measure of service

quality.

In section 5.3.3, the structural equation results confirm all four hypotheses

embedded within the service customer portion of the service profit chain. Moreover, the

overall fit indices indicate that the service profit chain model fits the survey data well.

The section ends with a summary of both the academic and managerial contributions of

testing the customer model.

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CHAPTER 6

SUMMARY AND FUTURE RESEARCH

6.1. Research objectives

The primary objective of this research is to determine the most important drivers

of specialty retail store performance and to illuminate the path through which these

drivers affect operational and marketing performance measures. The study uses the

service profit chain as an organizing framework to research the drivers, and this work

will thus empirically test the validity of the service profit chain theory. In order to meet

the primary objective, several supporting objectives had to be developed. First, two new

multi-dimensional second order factors had to be created: internal service quality, a

factor measuring the internal working environment of the retail outlets, and total retail

experience, a comprehensive customer assessment factor. Second, as well as creating

these new higher order factors, this study must incorporate constructs that have been

developed outside of the service management discipline, e.g. support – management,

work design, value, etc. Finally, before a survey could be constructed to measure the

concepts laid forth in the service profit chain an extensive interdisciplinary literature

review was needed. Extent theory from various disciplines, e.g. marketing, human

resource management, personnel psychology, etc, was analyzed and parallels were drawn

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as to how well-established theory from these other disciplines can be used to verify the

content validity of the service profit chain itself.

6.2. Overview of this study

This study represents one of the first attempts to empirically validate the service

profit chain theory within one setting. Despite an impressive body of theoretical and

anecdotal evidence supporting service profit chain theory, there has been little headway

made into empirically validating its tenets. Of the empirical studies to date, this research

builds the most comprehensive measurement model (Loveman, 1998; Silvestro and

Cross, 2001; Kamarkura et al, 2002). It is also the first to adapt well-established, highly

validated scales from other disciplines in order to build the service profit chain model.

The introductory chapter begins with a visual illustration of the service profit

chain model. Each of the hypotheses embedded within the model are briefly treated from

a managerial perspective. A list of anecdotal support, Table 1.1, helps give some

intuitive insights into the theory itself. The chapter then discusses the primary motivation

for this research: to help a women’s specialty fashion retail chain determine the most

important drivers of retail store performance. From this practitioner-oriented motivation,

an academic motivation is born – the desire to fill a notable gap in academic literature on

service management. As pointed out earlier, previous empirical studies of the service

profit chain have not used comprehensive and refined measurement scales. Section 1.2

details the objectives that stem directly from the motivations; they are also summarized

above in section 6.1. Nine research hypotheses, all extracted directly from the service

profit chain model, are extended in section 1.3. Research methodology, from instrument

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development to data analysis, is then summarized in section 1.4. Chapter 1 ends with a

summary of the layout of the remainder of the dissertation.

Because of the fullness of the task, two chapters are dedicated to literature review.

This first chapter, chapter 2, begins with a discussion of the literature that examines the

entire service profit chain model collectively. A review of theoretical and anecdotal work

is followed by a discussion of empirical research. The remaining substantive sections of

chapter 2, sections 2.2 through 2.3, review research into what Heskett et al (1994) call the

“operating strategy and service delivery system” –in this work simplified to “employee

model.” These sections explore the following constructs: internal service quality,

employee satisfaction, employee loyalty and employee productivity. Each construct is

first looked at independently and, when possible, parallels are drawn to similar constructs

from other disciplines. Research dedicated to analyzing the links between constructs is

then reviewed. The chapter ends with a brief summary, including a restatement of the

first five hypotheses.

Chapter 3 is dedicated to reviewing the second half of the service profit chain, or

as Heskett et al (1994) call it, the “Service Concept” and “Target Market” sections, in this

research referred to as the “customer model”. The first section within this chapter

reviews the total retail experience construct. Since the construct is still in its infancy,

parallels are drawn to other customer assessment/valuation frameworks, such as

Gronroos’ (1984) technical / functional quality schema. Each of the five dimensions of

total retail experience is then analyzed independently. The next section of the review

explores research focused on customers’ perceptions of service value. Customer

satisfaction and loyalty literature is then detailed. The chapter ends with a summary that

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ties a thread through all the constructs and summarizes the four testable hypotheses

embedded within the customer model.

The first portion of chapter 4 is devoted to describing the population frame and

sampling plans of this research. One large women’s specialty fashion merchandiser is

chosen as the population frame. This selection has both advantages and disadvantages, as

described in section 4.2. Five retail locations in Ohio were chosen to partake in the pilot

study. In total, fifty employee surveys were gathered, and sixty two customer surveys

were collected, resulting in response rates of 77% and 25% respectively. Ninety stores

located throughout the entire U.S. represent the main sampling frame. In total, 872

employee surveys and 1,076 customer surveys were collected from these stores, yielding

response rates of 65% and 24%. Before the measurement model is addressed,

exploratory data analysis is conducted. Factor development is then done on the pilot

study constructs – reliability assessment, uni-dimensionality, and convergent and

divergent validity. A few minor modifications are made. The changes appear successful

when the factors are analyzed using the main data. The chapter ends with a summary that

includes the managerial and academic contributions of the measurement model.

Chapter 5 begins with a description of structural equation modeling, including a

discussion of why it is most appropriate for this study. The remaining portion of the

chapter is dedicated to describing the results of two structural equation models – the

employee model and the customer model. The individual paths within each model are

analyzed, in essence testing the nine hypotheses introduced in the introductory chapter.

The overall model fit of the two models is then discussed, testing the two halves of the

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service profit chain model from a more macroscopic view. The chapter ends with a

discussion of the contributions, both academic and applied, of this research.

6.3. Summary of research findings

The summary of the research findings will be organized in a similar way to their

presentation in the body of this dissertation: the findings of the main study measurement

models will be presented first, followed by a review of the structural equation modeling

results.

Internal Service Quality Construct -- As stated in chapter 4, two new second order factors

needed to be developed for this research – internal service quality and total retail

experience. An exhaustive literature review identifies the eight most important operating

policies that can impact employee satisfaction, productivity and loyalty: training and

coaching, goal management, empowerment, work design, support – management, support

– tools, and rewards and recognition. Each of these eight dimensions is developed

individually through both a pilot study and a main study. Furthermore, this research is

the first to test rigorously whether these eight dimensions converge to form a single

reliable and valid second order construct, section 5.2.1. Of the eight dimensions, the

following four proved to have the most significant impact in terms of magnitudue on

employee satisfaction: training and coaching, goal management, organizational support –

management and organizational support – tools. So when faced with limited budgets,

firms will find that increasing any of these four areas will have the greatest impact on

employee outcome measures such as satisfaction and loyalty.

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Total Retail Experience Construct – The discussion put forward in chapter 3 notes a

significant lack of development in the service management literature in reference to a

comprehensive customer assessment construct, as such, a new construct is developed –

total retail experience. Much previous research focused exclusively on product and

process quality within service organizations. This research expands upon that view by

incorporating three additional concepts into the customer assessment framework: product

availability and selection, store layout and servicescape. The measurement model in

chapter 4 demonstrates that the five total retail experience dimensions are individually

reliable and valid. Moreoever, section 5.3.1 shows that the five dimensions do converge

to form a single reliable and valid second order construct. Although the three additional

constructs added into the assessment mix in this study have been thoroughly examined

and validated in other research (e.g. Bitner’s (1992) work on the servicescape), this study

is the first to show how they interact with the traditional service quality / product quality

framework. Furthermore, the hierarchical regression results demonstrate that the total

retail experience construct can explain a significantly larger percentage of variance in

customer satisfaction then traditional assessement frameworks such as the technical /

functional quality schema developed by Gronroos (1984). Service management

researchers can incorporate the total retail experience construct into future studies that

explore how customes assess a firms product and service offering – the assessment is

clearly more comprehensive than traditional scales lead one to believe.

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In addition to assessing the overall fit of the service profit chain models, nine

embedded hypotheses were tested. Of the nine, the following seven were fully supported.


Service organizations can boost the overall satisfaction level of their employees by

providing their employees with an excellent working environment, valuing the

employees’ contributions and investing in their development. This finding is key

because it shows that employee satisfaction can be improved by programs aimed at

increasing business performance (e.g. training and coaching, goal management, etc) and

not just programs aimed at short term employee satisfaction, such as increased pay. As

Heskett et al (1994, 1997) suggest, employees are happier when they can truly help

customers.

H1b: Internal service quality is positively associated with employee loyalty. Human

resources practices such as enhanced training efforts, focus on teamwork, employee

empowerment programs (and the rest of the internal service quality elements) signal to

employees that their organizations’ value their personal development and as such feel

induced to return the benefits they have received (Homans, 1961; Blau, 1964). One way

employees repay the organization is through increased loyalty, manifested in work

tenure. Much like the results of hypothesis H1a, this finding indicates that service

organizations can reap both direct and indirect benefits from investing in employee

development.

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H1c: Employee satisfaction is positively associated with employee loyalty. Quite simply

employees who are satisfied with their working environment are more likely to remain

with organization than employees who are dissatisfied. Intuitively, this result makes

sense and supports much of the satisfaction literature reviewed in Chapter 2 – satisfaction

has always been shown to be the best direct predictor of loyalty (Petty, 1984).

H2a: Total retail experience is positively associated with value. Basically this theory

asserts that customers’ perceptions of value are based on their assessments of five

different store / service firm characteristics: product quality, service quality, product

availability and selection, store layout and servicescape. An improvement in any of

these five areas will have a positive effect on the perceived benefits that the customer

receives. As the benefits a customer receives increases, the ratio of benefits to cost, the

most common definition of value, will increase, hence, total retail experience increases a

customer’s perceived level of value received.


Increases in product quality, service quality, product availability and selection, store

layout and/or servicescape will increase a customer’s overall level of satisfaction.

Moreover, as evidenced through their second order factor loadings, all five are nearly

equally weighted by customers. These results indicate that service organizations have

several options when trying to improve their operations; options that extend far beyond

improving their product’s quality or process quality.

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H2c: Value is positively associated with customer satisfaction. Value is shown to be a

positive predictor of customer satisfaction. As customer’s benefit to cost ratio increases,

their satisfaction increases. It is interesting to note, that in the specialty retail

environment studied in this research, value plays a less important role in shaping

customers’ satisfaction than total retail experience does, as evidenced by the magnitude

of the path coefficients -- .227 vs. .733. This effect was not unexpected as the retail

chain’s strategy is NOT to win orders based on cost, but rather, to focus on issues such as

service quality, servicescape and brand image. The effect confirms what recent service

management strategists have suggested in their work on the “experience economy”,

namely, that competitive advantage can no longer be won simply by providing high

quality goods and/or friendly service, the total shopping experience must be superior

(Hill et al, 2001; Dahlke, 2002)

H2d: Customer satisfaction is positively associated with customer loyalty. As

relationship value marketing scholars have expounded for nearly two decades now,

keeping customers satisfied is the best way to ensure that they continue buying from you

in the future (Gronroos, 1997; Payne et al, 2001). Satisifed customers are less likely to

search for alternatives before making their next purchase. And as defensive marketing

theorists argue, customer loyalty is the best predictor of future business performance.

No empirical support was found for the following two hypotheses.



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We believe the lack of support for these hypotheses stems not from a truly insignificant

relationship among the variables but rather from the construction of the employee

productivity construct, section 5.2.2 details the limitations with the employee

productivity factor construction. Because of the difficulty with this construct, it is

dropped from the employee structural model before overall fit is assessed.

The overall fit statistics of the two service profit chain models are

comprehensively assessed using nine different fit indices. It is determined that both the

revised employee model and the customer model fit the data well – indicating support for

the underlying linkages within the service profit chain. Creating a supportive work

environment for employees, one that develops their skills and advancement potential, will

not only create more satisfied employees but also employees who are likely to remain

with the organization for extended periods of time. Furthermore, providing an

outstanding total retail experience will lead to an increase in customer perceptions of

value as well as customer satisfaction. Satisfied customers are then more likely to exhibit

loyalty traits such as continued future purchases and increased word of mouth referrals.

6.4. Research contributions

The contributions of this research will be divided into two separate categories:

academic contributions and managerial contributions. This separation is used strictly as

an organizing mechanism, we realize that there is substantial overlap between these two

categories but feel the benefit of added clarity outweighs all potential disadvantages.

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6.4.1. Managerial contributions

In developing an internal service quality construct, section 5.1.1., this study can

be used as a resource allocation instrument for managers faced with limited budgets. The

study shows that employee satisfaction and loyalty can be increased by creating a

positive internal working environment. Moreover, the factor loadings associated with the

eight internal service quality dimensions can be interpreted as the weightings that specific

human resource practices have on shaping an employee’s overall satisfaction as well as

the employee’s intent to remain with the organization. Specifically, this research

demonstrates that employees value the following four internal service quality dimensions

most heavily: training and coaching, goal management, support – management and

support – tools. As such, managers faced with constrained resources will achieve the

biggest gains in employee satisfaction and loyalty by focusing on those four areas of

internal service quality. Furthermore, the structural equation results of section 5.2.2

verify Schlesinger and Heskett’s (1991) work on the employee cycle of failure / success.

Namely that employees are resources to be valued and maximized as opposed to costs to

be aggressively constrained. Rather than reducing front-line service jobs to mundane,

repetitive tasks, organizations can achieve competitive advantage by broadening job

descriptions and developing their employees through commitment enhancing human

resource practices.

Similar insights can be gleamed when deconstructing the total retail experience

construct. This research has demonstrated that when forming global beliefs about their

satisfaction customers will place nearly equal weights on all five of the total retail

experience dimensions: product quality, product availability and selection, service

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quality, store layout and servicescape. As such, organizations in a specialty retail market

such as the one studied in this research, should invest in the five areas to a similar degree.

Investments in one of the five areas should not dominate the investments in any of the

other areas. This finding is important because traditional service management literature

has tended to focus research efforts on investigating the effects of product and process

quality on customer outcomes. This research shows that customer satisfaction is also

shaped by “experiential” store characteristics such as layout and servicescape. It shows,

as Collier (1994) argues, that customers are looking for a total “benefits package”.

The structural equation results of section 5.3.3 indicate that customer satisfaction

is more heavily influenced by a customer’s perception of their total shopping experience

than it is by the value their perceived value of the merchandise itself – as evidenced by

the .733 path estimate between total retail experience and customer satisfaction versus the

0.227 path coefficient between value and customer satisfaction. This result indicates that

in a niche, specialty industry such as women’s apparel, price plays a more minor role

than the five total retail experience dimensions. As such, managers can use this finding

as justification for increasing elements of the total retail experience knowing that a

reasonable price increase to pay for such improvements will be tolerated by the customer.

Customers are indeed willing to pay for a nice servicescape as much as they are willing to

pay for increases in product quality, a tenet that has received little empirical attention in

service management literature.

A final contribution is demonstrating the strength of the path estimate between

customer satisfaction and customer loyalty, 0.762. This finding illustrates to managers

that the best way to generate future purchases is to increase the satisfaction level of your

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current customer base, a common component of what service marketing theorists call

defensive marketing. And as described above, the best way to increase customer

satisfaction is to provide a superior total retail experience. As previous research has

indicated, customers who are extremely satisfied with their retail experience are less

likely to search for alternatives decreasing the firm’s overall cost of serving their

customer base, increasing store profitability. Moreover, satisfied and loyal customers are

more likely to recommend the store to others and purchase secondary, ancillary products,

increasing market share (see chapter 3 for a full elucidation of this argument).

6.4.2. Academic contributions

This section will be organized by the focus and depth of the contribution,

specifically, contributions with narrow scope will be described first. The broader

contributions will be detailed last. In terms of construct development and validation this

study provides many contributions to the service management literature. It has taken well

established scales from other disciplines and validated them in a retail setting context:

value (marketing), support – management (human resource management), work design

(psychology) etc. Furthermore, it has developed more rigorously two second order

constructs: internal service quality and total retail experience. As noted in section 2.2.,

although the internal service quality construct has found its way into much academic

literature, to date no one has comprehensively developed the second order factor

(Hallowell, 1991; Edvardsson et al, 1997; Silvestro and Cross, 2000; and Kamakura et al,

2002). This research has scanned several disciplines to propose an eight dimensional

representation of internal service quality. Each of the eight dimensions is independently

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shown to be reliable and valid, section 4.4.2. Furthermore, and herein lies the major

contribution, the eight dimensional second order factor is shown to be valid, section

5.2.1. The structural equation results also begin to show the nomological validity of the

internal service quality construct – the first such study to do so. The internal service

quality construct developed in this study can be used as a foundation upon which future

studies into both employee satisfaction and the service profit chain can be based.

In a similar vein, this research provides the most rigorous development of a total

retail experience construct. It has shown our five dimensional rendering to be valid.

Furthermore, the hierarchical regression results demonstrate that this five dimensional

total retail experience construct can explain significantly more variance in customer

satisfaction than traditional customer assessment constructs used in service management

literature, such as, Gronroos’ (1984) technical / functional breakdown of service quality.

The more comprehensive total retail experience construct can be used in future research

exploring customer reactions to service offerings. It is the first study to incorporate well

received, empirically validated scales such as servicescape and store layout with the

traditional service management scales of product and process quality. In doing so, it has

shown how all these scales converge to form one grand second order scale. Moreover,

the research is also the first to demonstrate the nomological validity of the construct by

showing that it behaves in accordance with well accepted theory in relation to other

constructs, such as value and customer satisfaction.

The inter-disciplinary nature of the theory underlying the service profit chain

required an extensive literature review. The literature review surveys research in several

different disciplines, service/operations management, marketing, human resource

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management, organizational behavior, personnel psychology, just to name a few.

Chapters 2 and 3 identify many parallels to service profit chain theory, as such adding to

its content validity. The review provides service management researchers a framework

for finding the most salient related theory in other fields. These references can be used to

not only build more service management theory but also as a grounds of selecting well-

established and validated constructs to incorporate into modeling attempts. Furthermore,

the literature review adds to the content and face validities of not only the service profit

chain itself, but also the constructs contained therein.

Finally, and most significantly, this study provides the most comprehensive test of

the service profit chain to date. It is the only study to develop rigorously all of the

constructs that Heskett et al (1994, 1997, 2001) discuss in their original works on the

service profit chain. It also uses more sophisticated data analytic techniques than earlier

studies. As opposed to using simple correlation tests, which does not control for

intermediate effects, structural equation modeling is employed in order to elucidate the

paths, both direct and indirect, through with the hypothesized effects occur. As such,

managerial observations, like those in section 6.4.1, can be made. This research lays the

path upon which future service profit chain researchers can build, as described

throughout the next section.

The employee portion of our model has verified the service profit chain notion

that firms receive both direct and indirect benefits from investing in and developing their

labor force. Furthermore, the developmental practices are not limited to practices that are

strictly aimed at instantly “gratifying” employees, such as pay increases. Practices that

allow employees to serve customers more fully also enhance employee satisfaction – in

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essence showing that employees do indeed care about the service quality they deliver to

customers. If we blend this research findings with previous research, as discussed in

chapter 2, we can state the internal service quality will have a positive effect on employee

satisfion and loyalty which will be manifested through reduced turnover (lower recruiting

expenses, lower training expenses, etc) and increased on the job performance.

The customer portion of our model demonstrates that customers do indeed form

comprehensive assessments of the retailer far beyond those which traditional service

management scales can account for. In forming their assessements of a retailer, nearly

equal weight is given to five areas: product quality, process quality, servicescape, store

layout and product availability and selection. These five areas converge into one second

order factor which has a positive effect on customer’s perceptions of value and customer

satisfaction. In spirit it shows that managers have five key ways to influence customers,

not just the two that are traditionally researched (product quality and process quality).

Furthermore, as shown in the path diagram, total retail experience is a much better

predictor of employee outcomes, such as satisfaction and loyalty, than perceived value is.

This confirms recent work on the “experience” economy that posits that customers look

beyond the product itself and even the help employees give when assessing a retailer

(Hill et al, 2001). Tacit components such as servicescape and store layout play a vital

role as well in shaping customer beliefs. Please note, it is a significant contribution to the

service quality literature that this study incorporated a value construct into the structural

model. The majority of early service quality literature did not do so, and by failing to

include a value variable or talk about value at all, researchers in essence were saying that

quality should be enhanced at any cost. It is not until the value variable is included that

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quality enhancement projects must be held responsible from a financial standpoint (Rust

et al, 1995).

Staying within the customer model, the strength of the relationship between

customer satisfaction and customer loyalty lends credence to defensive marketing

theorists (Reichheld, 1990), see chapter 3 for a review of this theory. The key to future

market share is found in customer satisfaction. Satisfied customers are less likely to

search switch to a competitor, more likely to purchase ancillary products, more likely to

refer other customers, etc leading to increases in future revenue performance (e.g. sales

growth) and future margin increases (e.g. profitability).

Although our population frame precluded us from testing the definitive service

profit chain model that Heskett et al (1994, 1997) propose, very strong foundations were

built. Specifically, this research shows that two of the major variables in the service

profit chain are second order variables – internal service quality and total retail

experience. It has also shown that scales from other disciplines can be validly adapted to

a service management framework (e.g. perceived organizational support, value, etc). The

measurement model proposed in this research goes well beyond models used in service

profit chain research attempts. Although it confirms the difficulty in performing service

profit chain research (sample size required, employee productivity construct, etc) it does

show that the research is possible and gives a firm base to build upon.

6.5. Limitations and future research

Like any study, this research has limitations. These limitations offer opportunities

for improvements, thus suggesting ideas for future research. The first item that needs to

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be addressed is the generalizability of this research. The population frame of this study

was one large retail organization in women’s specialty fashion. This frame was chosen

for a variety of reasons (see section 4.2 for a complete review), one of the primary being

that is can be classified as a relatively high contact service process – meaning there is

much interaction between service provider and customer. As Schlesinger and Heskett

(1991) point out, “the service model [service profit chain] will not appeal to all segments,

especially those seeking little or no human interaction.” A drawback to this selection is

conjecturing just how much this study can be generalized across all service industries.

As Schlesinger and Heskett (1991) observe, the model likely applies to other high contact

industries but exactly which industries is still unclear. Future researchers can use service

classification schemes, such as the one proposed by Lovelock (1983), to see which

service categories are most likely to benefit by the management practices underlying the

service profit chain.

Researchers can address the basic question of whether or not the service profit

chain management model is applicable to all industries. Furthermore, within those

industries where the service profit chain is applicable, insights could be gleaned from

further comparative analysis. For example, do the same eight dimensions load onto the

internal service quality construct across all industries? Do the same five dimensions

make up total retail experience? If so, do the weightings of the dimensions change?

Previous research has shown that the relative importance of product quality compared to

service quality changes between industries (Gronroos, 1984; Patterson and Spreng, 1997;

Nowak and Washburn, 1998; Mittal and Sasser, 1998; etc). Does this same phenomenon

occur for the five dimensions of total retail experience? Is servicescape more important

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in some industries than others? If so, what are the characteristics of the environments

that lead to the difference? Additionally, value played a fairly minor role in this study,

possibly because specialty retail is a niche industry, but under what conditions does value

play a more important role?

Another limitation to the population frame used in this study is whether the results

are generalizable to other customer and employee segments. In this study, 3.2% of

employees are male; the remaining 96.8% are female. Although these proportions are

indicative of the entire national workforce for the firm we studied, the largest proportion

of firms within the U.S. will not have this gender distribution pattern. It is known that

gender is a potential mediating effect between employee satisfaction and employee

loyalty (Spreitzer et al, 1997; Griffeth et al, 2000; Moshavi and Terborg, 2002); future

researchers can study whether the service profit chain model is applicable in male-

dominated work environments, as well as gender balanced environments. Will the eight

dimensions of internal service quality, and their respective importance weights, remain

constant? Will the relationships among internal service quality, employee satisfaction

and employee loyalty all remain the same?

The same questions can be investigated on the customer portion of the service

profit chain. Since the merchant in this study sells primarily women’s specialty apparel,

its customer market is dominated by women – nearly 99% of the respondents to the

survey are women. It has been shown that gender can mediate the relationship between

external service quality and customer satisfaction (Odekerken-Schroder et al, 2001) and

between customer satisfaction and customer loyalty (Gremler and Brown, 1999; Mittal

and Kamakura, 2001). Gender has also been shown to influence specific variables, such

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as the importance customers place on the different dimensions of service quality

(Odekerken-Schroder et al, 2001). What role will gender play in influencing the service

profit chain as a whole? Would results similar to ours be found even if the customer base

was male dominated?

Another fertile area of research is to try to piece together the two halves of the

service profit chain and test the entire theory at once. Our selection of population frame

precluded us from this possibility (see section 4.2 for a full discussion of this matter).

Undertaking this kind of research will be very difficult. Two potential data collection

methods that might prove fruitful include administering a simultaneous survey of

particular service encounters and trying to survey many different firms (as opposed to the

one large chain used in this study). Each will be briefly discussed.

The first potential data collection method to facilitate a single model of the

service profit chain is to survey employees and customers immediately after a service

encounter. This method would clearly allow the researcher to link a customer to a

particular store, thus allowing the researcher to test the link between employee outcomes,

such as satisfaction, loyalty and productivity, and customer perceptions, such as total

retail experience, satisfaction and loyalty. Unfortunately, this type of research would also

have to overcome limitations. Namely, many of the constructs in the service profit chain

are abstract concepts that develop over time – e.g. employee and customer loyalty

(Cronin and Taylor, 1992; Storbacka et al, 1994). Loyalty, although it can be measured

at a particular point in time, is a product of many service encounters. One possible way

around this limitation is to ask questions referring to a customer’s perceived change in

loyalty, i.e. how a specific service encounter changed their intention to shop at the store

208

again. If this method is undertaken, a control for initial reference point may have to be

incorporated. The only other possibility would be to drop all constructs that involve

perceptions that have been developed over time. Although doing this would reduce the

model, it would still provide great benefit in that the two halves of the chain would be

tested together.

The second data collection technique that would facilitate studying the entire

model simultaneously is surveying many different service firms. The data collection

required for this methodology might be too exhaustive to permit an application. A

collection of customers and employees would have to be surveyed for each firm. Then,

for each organization, the customer responses and employees responses would be

averaged to get one “score” for each firm. Each firm would then represent one row in the

data matrix. Because the service profit chain is so extensive, including approximately 18

first order factors, each potentially with four to six indicators, a large number of firms

would be needed to achieve even moderate power. Using the requirement of five

observations for each estimated path as a rule of thumb, one would need to collect data

from upwards of 1,000 firms ([36 * 5] * 5 + 20 * 5). Again, for each of these thousand

firms, sets of customers and employees would need to be surveyed.

Our choice of methodology, namely structural equation modeling, is a very

powerful tool that provides many advantages of over other possible methodologies,

section 5.1 details these advantages. However, one note should be made in this regard –

structural equation modeling does not strictly confirm theory. It simply measures how

well a model can reproduce a covariance matrix. It does not provide for a definitive test

of the model itself. In light of this, future researchers can test alternative models, ie.

209

derivations, of the service profit chain. For example, a derivative model could be

completely linear in nature. Internal service quality would influence only employee

satisfaction. In turn, employee satisfaction would influence only employee loyalty. The

linearity would be carried all the way through the model with the last link being customer

loyalty to business performance. Although the service profit chain model tested in this

research does fit the data well, this linear type of model, or any alternative model, could

possibly fit the data better.

A potential bias enters into this research from the sampling plan used to collect

customer surveys. The research team focused solely on distributing surveys to customers

who were in the process of shopping in the store. This method has a potential bias

towards customers who are satisfied and loyal to the store to begin with. There was no

way to contact individuals who had shopped at the retail chain but were so dissatisfied

they did not come back. As such, there were very few low scores on some of the

customer outcome variables – service quality, customer satisfaction, customer loyalty.

The question that results from this potential bias is whether the service profit chain

applies to dissatisfied customers as well. Or does the model need to be altered, and if so,

how?

Finally, two variables within the original service profit chain that Heskett et al

(1994) proposed a decade ago are not found in our final models – employee productivity

and business performance. As discussed in section 5.2.2 an employee productivity

construct is originally included in this study. However, because of measurement issues

within the particular population frame studied, this construct is dropped. In industries

where productivity can be objectively assessed, e.g. sales per employee / units produced

210

per employee, these measurement errors may be overcome. In such situations, the

construct can be included into the service profit chain model so as to test the original

theory. Likewise, because of measurement issues with the retail chain studied, this

research was not able to include any store performance metrics in the customer model.

As discussed earlier in this section, researchers whose data points are distinct firms,

rather than outlets of a single firm, may be able to overcome this limitation.

211

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APPENDIX A

COMPOSITION OF INERNAL SERVICE QUALITY

Training Teamwork EmpowermentOrganizational Support Tools

Goals & Info Sharing Workload Rewards

Havlovic (1991)Schneider & Bowen (1985)

Schneider & Bowen (1985)


Schneider & Bowen (1985) Havlovic (1991)



Huselid (1995)Griffith, et al (2000) Huselid (1995)

Schneider, et al (1980)

Schneider, et al (1980) Huselid (1995)

Griffith, et al (2000)


Rogg, et al (2001) Havlovic (1991) Lau (2000) Allen, et al (2003) Huselid (1995) Lau (2000) Huselid (1995) Allen, et al (2003)

Rust, et al (1996) Lau (2000)Schlesinger & Bowen (1985)

Griffith, et al (2000) Rust, et al (1996)

Schlesinger & Bowen (1985) Rust, et al (1996)

Griffith, et al (2000)

Schlesinger & Bowen (1985) Rogg, et al (2001)

Tornow & Wiley (1991) Havlovic (1991) Zemke (2002)

Tornow & Wiley (1991)

Schlesinger & Bowen (1985) Havlovic (1991)

Wayne, et al (1997)

Schlesinger & Bowen (1985) Ulrich, et al (1991) Rogg, et al (2001)

Moshavi & Terborg (2002) Ulrich, et al (1991)

Tornow & Wiley (1991) Huselid (1995)


Tornow & Wiley (1991) Zemke (2002) Rust, et al (1996)

Schneider & Bowen (1993) Arthur (1994) Ulrich, et al (1991) Lau (2000)

Ulrich, et al (1991) Ulrich, et al (1991) Arthur (1994)Schlesinger & Bowen (1985)

Wright & Boswell (2002)

Schneider & Bowen (1993) MacDuffie (1995) Rogg, et al (2001)

Zemke (2002) Arthur (1994) MacDuffie (1995)Wayne, et al (1997)

Hallowell, et al (1996)


Moshavi & Terborg (2002) Rust, et al (1996)

Arthur (1994) MacDuffie (1995) Sheridan (1992)Tornow & Wiley (1991) Koys (2001) Koys (2001)


Schlesinger & Bowen (1985)

MacDuffie (1995)Moshavi & Terborg (2002)

Spreitzer, et al (1997) Ulrich, et al (1991)

Anderson & Mittal (2000)

Heskett, et al (1994) Sheridan (1992)

Wayne, et al (1997)

Schneider & Bowen (1993) Sheridan (1992)

Wright & Boswell (2002) Zemke (2002)

Heskett, et al (1994)

Schlesinger & Heskett (1991a) Varca (1999)


Sheridan (1992)Wisner & Feist (2001) Koys (2001)

Moshavi & Terborg (2002)

Schlesinger & Heskett (1991c) Loveman (1998)

Wright & Boswell (2002) Ulrich, et al (1991)




Schneider & Bowen (1993) Loveman (1998) Wiley (1991)

Rogers, et al (1994) Zemke (2002)

Whitener (2001)Hallowell, et al (1996) Rucci, et al (1998) Sheridan (1992) Wiley (1991) Meyer, et al (1999) Koys (2001) Arthur (1994)

Hallowell, et al (1996) Koys (2001)

Schlesinger & Heskett (1991a) Whitener (2001) Meyer, et al (1999)

Zeithaml, et al (1988)

Anderson & Mittal (2000) MacDuffie (1995)

Koys (2001)Anderson & Mittal (2000)

Shore & Tetrick (1991)


Chenet, et al (2000)


Koustelios & Bagiatis (1997)



Heskett, et al (1994) Loveman (1998) Koys (2001)


Hayes & Hill (2001)



Heskett, et al (1994) Rucci, et al (1998) Wiley (1991)

Huselid & Day (1991)


Kamurka, et al (2002) Loveman (1998)


Schlesinger & Heskett (1991a)

Schlesinger & Heskett (1991a) Hart, et al (1990)

Rhoades, et al (2001)

Kamurka, et al (2002)

Silvestro & Cross (2000) Wiley (1991) Whitener (2001)

Schlesinger & Heskett (1991c)

Shore & Tetrick (1991) Meyer, et al (1999)

Eisenberger, et al (2001)

Silvestro & Cross (2000) Meyer, et al (1999)


Shore & Tetrick (1991) Loveman (1998)



Gremler, et al (1993)

Chenet, et al (2000) Koys (2001)

Loveman (1998) Wiley (1991)Schneider, et al (1998)

Anderson & Mittal (2000) Bitner, et al (1990)


Rhoades, et al (2001)

Wiley (1991) Meyer, et al (1999)Kamurka, et al (2002)


O'Reilly, et al (1991)



Hart, et al (1990)Zeithaml, et al (1988)

Silvestro & Cross (2000) Rucci, et al (1998)

Chatman & Jehn (1991)



235

Training Teamwork EmpowermentOrganizational Support Tools

Goals & Info Sharing Workload Rewards

Meyer, et al (1999)Schneider, et al (1998)

Rhoades & Eisenberger (2002)


Silvestro & Cross (2000) Rucci, et al (1998)








Silvestro & Cross (2000) Bitner, et al (1990)


Hom & Kinicki (1992)




Liden & Wayne (2000)



Schlesinger & Heskett (1991c)

Silvestro & Cross (2000)

Babakus, et al (2003)

Kraimer, et al (1999)






Babakus, et al (2003) Loveman (1998) Loveman (1998)



O'Reilly, et al (1991) Wiley (1991) Wiley (1991)

Bitner, et al (1990)Chatman & Jehn (1991) Meyer, et al (1999) Hart, et al (1990)


Chenet, et al (2000) Meyer, et al (1999)















Bitner, et al (1990)Chatman & Jehn (1991)

Erdogan, et al (2002)

Liden, et al (2003)

Liden & Maslyn (1998)

236

APPENDIX B

SUMMARY OF EMPIRICAL EVIDENCE RELATING INTERNAL SERVICEQUALITY TO OTHER SERVICE PROFIT CHAIN VARIABLES

Employee Satisfaction Employee Loyalty

Employee Productivity

External Service Quality Value

Customer Satisfaction Customer Loyalty


see Appendix A

Goldstein & Schweikhart (2002)


Zeithaml, et al (1988) Meyer, et al (1999) Meyer, et al (1999) Meyer, et al (1999)

Wayne, et al (1997)

Wayne, et al (1997)


Goldstein & Schweikhart (2002) Rogg, et al (2001)

Roth & Jackson (1995)

Sheridan (1992) Sheridan (1992) Meyer, et al (1999) Rogg, et al (2001)Tornow & Wiley (1991)


Huselid & Day (1991)



Schlesinger & Zornitsky (1991) Zeffane (1994) Huselid (1995)

Rhodes, et al (2001) Huselid (1995)



Kamurka, et al (2002) Lau (2000)


Schlesinger & Zornitsky (1991)









Rust & Zahorik (1993)


Fornell & Wernfelt (1987) Arthur (1994) MacDuffie (1995)

Schneider & Bowen (1985) Wiley (1991)


Havlovic (1991) MacDuffie (1995)Schneider & Bowen (1993)



Huselid (1995)Spreitzer, et al (1997)

Wisner & Feist (2001)


Lau (2000) Varca (1999) Zeffane (1994)Silvestro & Cross (2000)





Tornow & Wiley (1991) Whitener (2001)

Kamurka, et al (2002) Wiley (1991)

Arthur (1994)Silvestro & Cross (2000)

Silvestro & Cross (2000) Bitner, et al (1990)

Rust, et al (1996)Eisenberger, et al (1986) Wiley (1991)


Griffeth, et al (2000)

Shore & Tetrick (1991) Bitner (1990)



Rhodes & Eisenberger (2002)



Kerr and Slocum (1987)


Babakus, et al (2003)O'Reilly, et al (1991)Kerr and Slocum (1987)

237

APPENDIX C

SUMMARY OF EMPIRICAL EVIDENCE RELATING EMPLOYEE SATISFACTIONTO OTHER SERVICE PROFIT CHAIN VARIABLES

Internal Service Quality Employee Loyalty





see Appendix A Sheridan (1992) Sheridan (1992)Tornow & Wiley (1991)









Schneider & Bowen (1985) Koys (2001)



Schneider, et al (1980) Moshavi & Terborg (2002)



Allen, et al (2003)Schlesinger & Zornitsky (1991)




Tornow & Wiley (1991) Koys (2001)


Spreitzer, et al (1997)


Hom & Griffeth (1991) Varca (1999)Schneider & Bowen (1985)


Nagy (2002) Petty, et al (1984)Schneider, et al (1980)


Rust, et al (1996)Schneider, et al (1980)

Koys (2001)Silvestro & Cross (2000)



Silvestro & Cross (2000)Shore & Tetrick (1991)

Erdogan, et al (2003)Babakus, et al (2003)

238

APPENDIX D

SUMMARY OF EMPIRICAL EVIDENCE RELATING EMPLOYEE LOYALTY TOOTHER SERVICE PROFIT CHAIN VARIABLES

Internal Service Quality

Employee Satisfaction





Goldstein & Schweikhart (2002) Sheridan (1992)

Wayne, et al (1997)

Chenet, et al (2000)



Schneider & Bowen (1985) Huselid (1995)

Wayne, et al (1997)

Eisenberger, et al (2001) Sheridan (1992)





Sheridan (1992)Eisenberger, et al (1990)



Huselid & Day (1991) Allen, et al (2003)


Rhodes, et al (2001)

Schlesinger & Zornitsky (1991) Huselid (1995)





Hom & Griffeth (1991) Arthur (1994)

Fornell & Wernfelt (1987)



Havlovic (1991) Nagy (2002)Babakus, et al (2003)

Huselid (1995)Schneider, et al (1980)

Lau (2000) Rust, et al (1996)Schlesinger & Zornitsky (1991) Koys (2001)Tornow & Wiley (1991)


Arthur (1994)Silvestro & Cross (2000)

Rust, et al (1996)Shore & Tetrick (1991)


Erdogan, et al (2003)




Rhodes & Eisenberger (2002)Eisenberger, et al (2002)Hom & Kinicki (1992)Erdogan, et al (2003)

Liden, et al (2003)Kraimer, et al (1999)

O'Reilly, et al (1991)Kerr and Slocum (1987)

239

APPENDIX E

SUMMARY OF EMPIRICAL EVIDENCE RELATING EMPLOYEEPRODUCTIVITY TO OTHER SERVICE PROFIT CHAIN VARIABLES






Goldstein & Schweikhart (2002) Sheridan (1992)

Wayne, et al (1997) MacDuffie (1995)



Silvestro & Cross (2000) Huselid (1995)

Wayne, et al (1997)

Eisenberger, et al (2001) Sheridan (1992)




Sheridan (1992)Eisenberger, et al (1990)






Huselid (1995)Tornow & Wiley (1991) Huselid (1995)




Tornow & Wiley (1991) Varca (1999) Arthur (1994)

Arthur (1994)Wisner & Feist (2001)


MacDuffie (1995) Petty, et al (1984)Babakus, et al (2003)



Varca (1999)Schneider, et al (1980)



Whitener (2001)Liden & Masyln (1998)



Eisenberger, et al (1986)Shore & Tetrick (1991)

Liden & Masyln (1998)Babakus, et al (2003)

O'Reilly, et al (1991)Kerr and Slocum (1987)

240

APPENDIX F

SUMMARY OF EMPIRICAL EVIDENCE RELATING EXTERNAL SERVICEQUALITY TO OTHER SERVICE PROFIT CHAIN VARIABLES



Employee Productivity Value





Chenet, et al (2000) MacDuffie (1995)

Bolton & Drew (1991)

Cronin & Taylor (1992)







Patterson & Spreng (1997)

Aldaigan & Buttle (2002)


Goldstein & Schweikert (2002)

Meyer, et al (1999)Schneider, et al (1980)




Sureshchandar, et al (2002)





Fornell, et al (1996)





Winnie & Kanji (2001)

Goldstein & Schweikert (2002)




Parasuraman, et al (1994)





Sweeney & Soutar (2001)



Anderson, et al (1994)

MacDuffie (1995) Kerin, et al (1992)Schneider & Bowen (1985)





Shemwell, et al (1998) Hallowell (1996)




Van der Weile, et al (2002)

Zeffane (1994)Silvestro & Cross (2000)

Fornell, et al (1996) Wiley (1991)







Anderson & Sullivan (1993) Meyer, et al (1999)


Anderson & Sullivan (1993)



Wiley (1991)Rust & Zahorik (1993)

Mittal & Lassar (1998)

Odekerken-Schroder, et al (2001)

Mittal & Lassar (1998) Hallowell (1996)


Voss, et al (1998) Wiley (1991) Ward, et al (1992)

Hallowell (1996)Taylor & Hunter (2002)



Wiley (1991)Sharma & Patterson (1999)

Nowak & Washburn (1998)

Brown, et al (1993)

Taylor & Hunter (2002) Meyer, et al (1999)

Terblance & Boshoff (2001)

Odekerken-Schroder, et al (2001)

Winnie & Kanji (2001) Lee, et al (2000)Brown, et al (1993)

DeRuyter, et al (1998)

241



Employee Productivity Value



Lassar, et al (2000)

Shemwell, et al (1998)

Meyer, et al (1999)McDougal & Levesque (2000)

Parasuraman, et al (1994) Bitner (1990)


Ridgway, et al (1990)

Spreng & Mackoy (1996)

Dawson, et al (1990)

Lee, et al (2000)Sureshchandar, et al (2002)Shemwell, et al (1998)

McDougal & Levesque (2000)Wakefield & Blodgett (1994)Wakefield & Blodgett (1996)Bitner (1990)

Surprenant & Solomon (1987)Spreng & Mackoy (1996)Ridgway, et al (1990)Dawson, et al (1990)

242

APPENDIX G

SUMMARY OF EMPIRICAL EVIDENCE RELATING VALUE TO OTHERSERVICE PROFIT CHAIN VARIABLES




External Service Quality
















Fornell, et al (1996) Voss, et al (1998)

McDougal & Levesque (2000)

Winnie & Kanji (2001) Sirohi, et al (1998)Parasuraman, et al (1994)




Sweeney & Soutar (2001)


Kerin, et al (1992)

243

APPENDIX H

SUMMARY OF EMPIRICAL EVIDENCE RELATING CUSTOMER SATISFACTIONTO OTHER SERVICE PROFIT CHAIN VARIABLES




External Service Quality Value Customer Loyalty


Meyer, et al (1999)Schlesinger & Zornitsky (1991)




Bolton & Drew (1991) Lee, et al (2000)










Rogg, et al (2001)Moshavi & Terborg (2002)


Fornell, et al (1996) Teas (1994)




Bolton & Drew (1991) Voss, et al (1998)



Tornow & Wiley (1991) Koys (2001)

Goldstein & Schweikert (2002) Sirohi, et al (1998)







Sureshchander, et al (2002) Fornell (1992)










Ittner & Larcker (1998)











Silvestro & Cross (2000) Hallowell (1996)



Mittal & Kamakura (2001)

Van der Wiele, et al (2002)

Wiley (1991)Anderson, et al (1994) Fornell (1992) Wiley (1991)

Bitner, et al (1990)Anderson & Sullivan (1993)


Yeung, et al (2002)



Ittner & Larcker (1998) Fornell (1995)

Bitner (1990)Mittal & Lassar (1998) Bolton (1998)


Voss, et al (1998)Anderson, et al (1994) Martin (1998)

Hallowell (1996)Anderson & Sullivan (1994)

Mazvancheryl, et al (1999)


Rust & Zahorik (1993) Ward, et al (1992)

Wiley (1991)Mittal & Lassar (1998)

Fornell & Wernerfelt (1987)

244




External Service Quality Value Customer Loyalty


Nowak & Washburn (1998) Hallowell (1996)Taylor & Hunter (2002) Wiley (1991)Terblance & Boshoff (2001) Soderlund (1998)


Strauss & Neuhaus (1997)

Brown, et al (1993)

Taylor & Hunter (2002)

Lassar, et al (2000)


Meyer, et al (1999)McDougal & Levesque (2000)


Wakefield & Blodgett (1994)


Wakefield & Blodgett (1996)

Finn, et al (1996)Fornell & Wernerfelt (1987)

Reynoso & Moores (1995)

Frost & Kumar (2000)Lee, et al (2000)Sureshchandar, et al (2002)


McDougal & Levesque (2000)Wakefield & Blodgett (1994)Wakefield & Blodgett (1996)Bitner (1990)Surprenant & Solomon (1987)Spreng & Mackoy (1996)

Ward, et al (1992)

Dawson, et al (1990)

245

APPENDIX I

SUMMARY OF EMPIRICAL EVIDENCE RELATING CUSTOMER LOYALTY TOOTHER SERVICE PROFIT CHAIN VARIABLES

Internal Service

Quality

Employee

Satisfaction Employee Loyalty

Employee

Productivity

External Service

Quality Value

Customer

Satisfaction

Business

Performance

Meyer, et al (1999)Tornow & Wiley (1991)




Bolton & Drew (1991) Lee, et al (2000)

Odekerken-

Schroder, et al (2001)

Rogg, et al (2001)Schneider & Bowen (1985)






Tornow & Wiley

(1991)

Silvestro & Cross

(2000)

Sureshchandar, et

al (2002)

Winnie & Kanji

(2001) Teas (1994)

Kamurka, et al

(2002)

Zeffane (1994)

Bolton & Drew

(1991)

McDougal &

Levesque (2000)

Cronin & Taylor

(1992)

Silvestro & Cross

(2000)



Aldaigan & Buttle (2002) Fornell (1992)



Sureshchander, et al (2002)

Fornell, et al

(1996)





Wiley (1991)Kamurka, et al (2002)


Gremler & Brown (1999)

Shemwell, et al

(1998)

Schneider &

Bowen (1985) Hallowell (1996)


Kamurka, et al (2002) Wiley (1991)

Fornell, et al

(1996)

Silvestro & Cross

(2000)

Fornell &

Wernerfelt (1987)

Anderson, et al

(1994)

Mittal & Kamakura

(2001)

Anderson & Sullivan (1993) Fornell (1992)





Hallowell (1996) Bolton (1998)

Wiley (1991)Anderson, et al (1994)

Taylor & Hunter (2002)

Anderson & Sullivan (1994)

Winnie & Kanji

(2001)

Rust & Zahorik

(1993)

Sharma & Patterson (1999)


Brown, et al

(1993) Hallowell (1996)

Meyer, et al (1999) Wiley (1991)

Odekerken-

Schroder, et al (2001) Soderlund (1998)

Lee, et al (2000)Strauss & Neuhaus (1997)

DeRuyter, et al

(1998)

Taylor & Hunter

(2002)





246

APPENDIX J

FINAL SURVEY INSTRUMENTS

Limitedbrands FISHERCOLLEGE OF BUSINESSOHIO STATE UNIVERSITY

Ohio State University and Victoria's Secret have undertaken a study together to improve customer satisfactionat Victoria's Secret stores. Since we are trying to create a better shopping experience for our customers, webelieve the major source of our improvement efforts should be the opinions of our valued customers.

With this in mind we have designed the following questionnaire and ask that you take a few minutes to give usyour opinion on our stores and products. Please take time to review each question carefully. Indicate your response to each statement by placing an "X" in the appropriate box on the right hand side of the survey. Toshow our appreciation for your time, you will be entered into a drawing to win a $100 gift certificate to be usedat any Victoria's Secret store.

Strongly StronglyDisagree Neutral Agree

MERCHANDISE 1 2 3 4 5 6 7Victoria's Secret (VS) offers merchandise of very high quality. . . . . . . . . . . . . . . . . . . . . . . .

The quality of merchandise at VS is higher than similar merchandise at other stores. . . . . . .

VS merchandise holds up well after repeated washings . . . . . . . . . . . . . . . . . . . . . . . . . . . .

The merchandise I buy from VS is of a consistent quality. . . . . . . . . . . . . . . . . . . . . . . . . . . .

VS merchandise always meets my quality standards. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

The quality of merchandise at VS consistently meets my expectations. . . . . . . . . . . . . . . . .

VS always has the product I want in stock. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VS has a wide selection of merchandise. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VS products have a very good brand image. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VS sets a standard of excellence for the retail industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

BUYING PROCESSVS associates have the skills necessary to help me . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

I receive prompt service when I shop at VS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VS associates give caring and individual attention . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VS associates are willing to go out of their way to help me . . . . . . . . . . . . . . . . . . . . . . . . . .

VS associates are consistently courteous and friendly . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

I do not have to wait in long lines at VS checkouts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

The layout of VS stores allows me to take any path I like when browsing . . . . . . . . . . . . . . .

There is ample space between displays to browse comfortably . . . . . . . . . . . . . . . . . . . . . . .

All merchandise at VS stores is easily accessible . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

I use the drawers beneath the displays to find my size if one is not out . . . . . . . . . . . . . . . . .

CUSTOMER SATISFACTION SURVEY

STORE OPERATIONS

247

Strongly StronglyDisagree Neutral Agree1 2 3 4 5 6 7

STORE ENVIRONMENTVS associates have a neat and professional appearance . . . . . . . . . . . . . . . . . . . . . . . . . . .

VS facilities are always kept neat and attractive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

I find the décor at VS attractive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VS stores have attractive signs and displays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VS stores have attractive posters and models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

The lighting at VS is set at a good level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

The aromas and scents in VS stores are soothing and pleasant . . . . . . . . . . . . . . . . . . . . . .

I enjoy the background music that VS plays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

I truly enjoy the overall shopping environment at VS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

The VS store does not seem old and dated. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VALUEVS offers merchandise at good value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Given the quality of merchandise, VS offers good prices . . . . . . . . . . . . . . . . . . . . . . . . . . . .

VS offers better value than other stores that sell the same merchandise . . . . . . . . . . . . . . . .

SATISFACTIONI am very satisfied with shopping at VS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

I am delighted with the shopping experience that VS offers . . . . . . . . . . . . . . . . . . . . . . . . . .

Of all the stores that sell similar types of merchandise, VS is my first choice . . . . . . . . . . . .

I have good feelings when shopping at VS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

LOYALTYI consider myself a loyal customer of VS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

I intend to remain a VS customer long into the future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

I recommend VS to my friends and family . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

I buy the largest portion of my undergarments and nightwear from VS . . . . . . . . . . . . . . . . .

248

On a scale of 1 to 10 (10 being the highest), please tell us which factors influence most your decision to shop at VS:

Brand image Store shopping experience Merchandise selectionProduct quality Value AdvertisingPrice Feel beautiful Unique products

PLEASE TELL US A LITTLE BIT ABOUT YOURSELF

AGE: under 21 21 to 25 25 to 29 30 to 4040 to 50 over 50

GENDER: male female

EDUCATION (check box with highest degree):High School College Graduate Degree

MARITAL STATUS: single married

How long have you been a VS customer? years

If you would like to be entered into a drawing to win a $100 gift certificate please fill out the following:

Name

Street Address

City State Zip

249

Associate Survey

The Ohio State University and the Limited Brands have joined up to undertake a survey designed towards improving theperformance of Victoria's Secret stores. Our goal is to increase customer satisfaction by improving store operations.In this aim, we would like you to fill out the following questionnaire. At Victoria's Secret, associates' opinions are valuedand used as the primary driver for improvement efforts. Please take time to evaluate each question carefully.

We would like you to be candid and honest in response to these question; therefore, we have designed the survey to be100% CONFIDENTIAL . The survey results can not be traced to any individual. You do not have to give your name andonly the Ohio State Research Team will have access to your responses. This point is extremely important so let us reiterate, the survey is 100% CONFIDENTIAL.

The survey is broken down into eight major sections. For each statement indicate your response by circling the appropriate number on the right hand side of the survey. If you strongly agree with the statement circle 7. If you strongly disagree with the statement circle 1. Answer 4 if you are neutral. Please answer every question.

Please answer the following questions regarding the TRAINING AND COACHINGthat Victoria's Secret provides.

STRONGLY

DISAGREE NEUTRAL

STRONGLY

AGREE

1. Within the first two months of being hired, I received 1 2 3 4 5 6 7the training necessary to fulfill my job requirements.

2. Since originally being hired and trained, I have 1 2 3 4 5 6 7received additional training when necessary.

3. VS gives me a lot of feedback on how to improve 1 2 3 4 5 6 7my job performance.

4. VS's training programs are of high quality. 1 2 3 4 5 6 7

5. VS's management provides good on-the-job 1 2 3 4 5 6 7coaching.

6. VS does an excellent job of hiring the best 1 2 3 4 5 6 7people.

7. VS gives me a lot of useful feedback on how I 1 2 3 4 5 6 7am performing my job.

Please answer the following questions regarding the GOALS of Victoria's Secret.

STRONGLY

DISAGREE NEUTRAL

STRONGLY

AGREE

1. As a company, VS clearly communicates its 1 2 3 4 5 6 7store goals.

2. Store goals are in line with customer needs. 1 2 3 4 5 6 7

3. VS management is good at sharing and 1 2 3 4 5 6 7explaining its goals.

4. I get early notification about future changes 1 2 3 4 5 6 7that will affect my job and/or store performance.

5. VS communicates clear priorities and 1 2 3 4 5 6 7relevant information in a timely manner.

250

Please answer the following questions regarding TEAMWORK at Victoria'sSecret.

STRONGLY DISAGREE NEUTRAL

STRONGLY AGREE

1. VS associates are urged to work in teams. 1 2 3 4 5 6 7

2. VS associates often give each other help. 1 2 3 4 5 6 7

3. VS associates communicate well with each 1 2 3 4 5 6 7

other.

4. Throughout the working day I will help 1 2 3 4 5 6 7associates in other work zones when needed.

5. VS associates help train new hires. 1 2 3 4 5 6 7

6. I give help at the cash wrap when the 1 2 3 4 5 6 7store is very busy.

7. Employees from Limited's home office have 1 2 3 4 5 6 7

a good idea of what customer's require of us.

Please answer the following questions regarding JOB DESIGN at Victoria's Secret.


STRONGLY AGREE

1. I have been given enough authority to serve 1 2 3 4 5 6 7

customers to the best of my ability.

2. I have enough latitude in my job to serve 1 2 3 4 5 6 7customers to the best of my ability.

3. I have enough independence to meet each 1 2 3 4 5 6 7customer's unique needs.

4. I do not have to check with my manager before 1 2 3 4 5 6 7

making any decision to help serve a customer

5. I have enough latitude to follow up on client sales 1 2 3 4 5 6 7

leads as required.6. My job requirements often conflict with 1 2 3 4 5 6 7

customer needs.

7. I find my job stressful. 1 2 3 4 5 6 7

8. My workload is too heavy. 1 2 3 4 5 6 7

9. My job requirements are not clear to me. 1 2 3 4 5 6 7

Please answer the following questions regarding the SUPPORT you receivefrom Victoria's Secret to help your do you job properly.


STRONGLY AGREE

1. As a company, VS cares about my well being. 1 2 3 4 5 6 7

2. As a company, VS values my opinions. 1 2 3 4 5 6 7

3. My immediate supervisor values me as an 1 2 3 4 5 6 7

associate.

4. Help from management is widely available if 1 2 3 4 5 6 7needed.

5. Store technology supports my ability to meet 1 2 3 4 5 6 7customers needs.

6. I am given the necessary tools to satisfy 1 2 3 4 5 6 7

251

STRONGLY

DISAGREE NEUTRAL

STRONGLY

AGREE

7. I have access to information I need in order 1 2 3 4 5 6 7to better serve customers.

8. Store policies and procedures support my 1 2 3 4 5 6 7ability to meet customer needs.

9. In general, I have the resources I need to 1 2 3 4 5 6 7help customers to the best of my ability.

Please answer the following questions regarding the REWARDS & RECOGNITIONthat Victoria's Secret offers its associates.1. VS pays as well or better than other retailers. 1 2 3 4 5 6 7

2. VS provides good associate benefits. 1 2 3 4 5 6 7

3. Over time, my compensation is linked to my 1 2 3 4 5 6 7sales performance.

4. I get personal recognition when I do a great 1 2 3 4 5 6 7job.

5. VS provides good opportunities for advancement. 1 2 3 4 5 6 7

6. When I do a great job, VS management 1 2 3 4 5 6 7acknowledges it and thanks me.

Please answer the following questions regarding the CAPABILITY you have as a VS associate to meet customer needs.


STRONGLY AGREE

1. I feel that I am a productive associate. 1 2 3 4 5 6 7

2. Within my store I am a top seller. 1 2 3 4 5 6 7

3. My average sales per hour is among the 1 2 3 4 5 6 7best in the store.

4. My productivity has increased the longer 1 2 3 4 5 6 7I have worked at the store.

5. Our store is kept clean and organized. 1 2 3 4 5 6 7

6. My VS store follows daily replenishment 1 2 3 4 5 6 7procedures to ensure product is on the floor.

7. VS schedules key associates to work on 1 2 3 4 5 6 7busy and high priority days.

8. My VS store is excellent at managing 1 2 3 4 5 6 7merchandise flow.

9. My VS store ensures that all merchandise 1 2 3 4 5 6 7and displays are kept neat and organized.

10. The more times you interact with the customer 1 2 3 4 5 6 7the higher the sales will be.

11. A good measure of associate productivity is 1 2 3 4 5 6 7sales dollars generated.

12. Assigning associates to zones increases 1 2 3 4 5 6 7store sales.

252

Please answer the following questions regarding your JOB SATISFACTION.STRONGLY DISAGREE NEUTRAL

STRONGLY AGREE

1. Overall, I am satisfied with my job at VS. 1 2 3 4 5 6 7

2. I am satisfied with my compensation. 1 2 3 4 5 6 7

3. I am satisfied with my opportunities for 1 2 3 4 5 6 7 promotion at VS

4. I am satisfied with the relationship I have 1 2 3 4 5 6 7with my supervisor.

5. I am satisfied with the relationship I have 1 2 3 4 5 6 7with my co-workers.

6. I am satisfied with the amount and type 1 2 3 4 5 6 7of job responsibilities that I have.

7. I intend to keep working at VS long into the 1 2 3 4 5 6 7future.

8. I often think about quitting my job. 1 2 3 4 5 6 7

9. I am actively looking for another job. 1 2 3 4 5 6 7

10. As soon as I can find another job I am 1 2 3 4 5 6 7going to leave VS.

11. I am willing to put in a great deal of effort beyond 1 2 3 4 5 6 7that normally expected in order to help VS.

12. I would accept almost any type of job assignment 1 2 3 4 5 6 7to keep working for VS

13. I really care about the fate of this organization 1 2 3 4 5 6 7

Please tell us a little bit about yourself (check one box for each question):

AGE: under 21 21 to 25 25 to 30 30 to 4040 to 50 over 50

GENDER: Male Female

How long have you worked at Victoria's Secret? Years Months

EDUCATION: (check box associated with highest degree obtained)

High School College Degree Graduate Degree

WORK CLASSIFICATION: Part Time Full Time

THANK YOU for taking time to answer these questions, your opinions are truly valued.

Please list any additional comments in the space provided below:

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APPENDIX K

LIST OF STORES USED IN MAIN DATA COLLECTION

I.D. # Store Location District I.D. # Store Location District1 Eastview W NY State 41 Fiesta Phoenix2 Mckinley W NY State 42 Paradise Valley Phoenix3 Eastern Hills W NY State 43 Fashion Square Phoenix4 Boulevard W NY State 44 Biltmore Phoenix5 Market Place W NY State 45 Chandler Fashion Phoenix6 Walden Galleria W NY State 46 Kierland Commons Phoenix7 Greeceridge W NY State 47 Willow Grove N. Philadelphia8 Arnot W NY State 48 The Court N. Philadelphia9 Chautauqua W NY State 49 Springfield N. Philadelphia

10 Cross Creek E North Carolina 50 Exton Square N. Philadelphia11 Independence E North Carolina 51 Montgomery N. Philadelphia12 Crabtree Valley E North Carolina 52 Plymouth Meeting N. Philadelphia13 Northgate E North Carolina 53 Suburban Square N. Philadelphia14 Cary Towne Center E North Carolina 54 Granite Run N. Philadelphia15 Piedmont E North Carolina 55 King of Prussia N. Philadelphia16 Jacksonville E North Carolina 56 Coventry N. Philadelphia17 Cameron Village E North Carolina 57 Southern Park NE Ohio18 Streets At Southpoint E North Carolina 58 Eastwood NE Ohio19 Triangle Town E North Carolina 59 Millcreek NE Ohio20 The Falls Miami 60 Belden Village NE Ohio21 Dadeland Miami 61 Chapel Hill NE Ohio22 Miami International Miami 62 Summit NE Ohio23 Bayside Marketplace Miami 63 River Valley NE Ohio24 Cocowalk Miami 64 Richland NE Ohio25 Shops @ Sunset Place Miami 65 Colony Sq NE Ohio26 Lincoln Rd Miami 66 Indian Mound NE Ohio27 Dolphin Mall Miami 67 Towne East Wichita/Kansas28 Village Of Merrick Park Miami 68 West Ridge Wichita/Kansas29 Highland Austin/El Paso 69 Battlefield Wichita/Kansas30 Cielo Vista Austin/El Paso 70 Northpark Wichita/Kansas31 Sunland Park Austin/El Paso 71 Bradley Fair Wichita/Kansas32 Barton Creek Austin/El Paso 72 Central Wichita/Kansas33 Arboretum Austin/El Paso 73 Towne West Wichita/Kansas34 Lakeline Austin/El Paso 74 Manhattan TC Wichita/Kansas35 Bassett Austin/El Paso 75 Boynton Beach Palm Beach36 Mesilla Valley Austin/El Paso 76 Town Center Palm Beach37 Midland Park Austin/El Paso 77 Palm Beach Palm Beach38 Temple Austin/El Paso 78 Treasure Coast Palm Beach39 Killeen Austin/El Paso 79 The Gardens Palm Beach40 Sunset Austin/El Paso 80 Indian River Palm Beach

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I.D. # Store Location District81 Mall at Wellington Gr. Palm Beach82 Bellevue N. Seattle83 Northtown N. Seattle84 Alderwood N. Seattle85 Bellis Fair N. Seattle86 Northgate N. Seattle87 Redmond Town N. Seattle88 Spokane Valley N. Seattle89 Everett N. Seattle90 Cascade N.Seattle

Documents

A SERVICE PROFIT CHAIN PERSPECTIVE DISSERTATION