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MASTER'S THESIS The impact of CRM on customer retention in electronic banking Case of Iranian banks Alireza Nili Master of Arts, Master programme Electronic Commerce Luleå University of Technology Department of Business Administration, Technology and Social Sciences

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Page 1: MASTER'S THESIS - DiVA portal1024082/FULLTEXT02.pdf · MASTER'S THESIS The impact of CRM on customer retention in electronic banking Case of Iranian banks Alireza Nili Master of Arts,

MASTER'S THESIS

The impact of CRM on customer retentionin electronic banking

Case of Iranian banks

Alireza Nili

Master of Arts, Master programmeElectronic Commerce

Luleå University of TechnologyDepartment of Business Administration, Technology and Social Sciences

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MASTER'S THESIS

The Impact of CRM on Customer

Retention in Electronic Banking:

Case of Iranian Banks

Supervisors: Dr. Abbas Keramati

Dr. Anis Chelbi

Prepared by:

Alireza Nili

International University of Chabahar

Lulea University of Technology

Department of Business Administration and Management

Division of Industrial Marketing and e-Commerce

MSc PROGRAM IN E-COMMERCE [Joint]

2010

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I dedicate this thesis

with all my love

to my dear parents

Alireza Nili December – 2010

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Abstract

The purpose of this study is to investigate the impact of CRM on customer retention

in Iranian electronic banking. Iranian banks have used CRM more or less and

proceeded to this issue with different rates of success. However, there are not

available studies that investigated the impact of CRM in these banks especially in

electronic environment.

This study first reviews and analyzes the previous researches in the literature that

investigate the relationship between CRM and firm performance and customer

retention. Next, based on the RBV and process-oriented approach, we suggest a

process-oriented framework that links CRM to customer retention. In order to be able

to appropriately measure the extent of the usage of each of the research variables

across organization, suitable dimensions for each of the research variables are defined

based on the literature. In this regard, CRM resources are measured with five

dimensions which are operational CRM technologies, collaborative CRM

technologies, analytical CRM technologies, organizational, and human CRM

resources. CRM processes are studied on both operational and management

dimensions. Finally, customer retention programs of CRM are studied in four

dimensions including: customer service, customization, community of customers, and

loyalty programs. A questionnaire has been designed and used to collect data from

286 top bank managers of two governmental (Refah and Maskan) and two private

(Saman and Pasargad banks) banks‘ branches in the province of Tehran. The gathered

data have been analyzed using quantitative methods based on a survey strategy. The

results of data analysis show the direct effect of all CRM resources including

technological and infrastructural resources on CRM processes and also the direct

effect of CRM processes on customer retention programs of CRM (loyalty programs,

customer service, customization, and creating community of customers) which

themselves have direct effect on customer retention. Another contribution of this

study is that it presents a comprehensive set of customer retention programs of CRM

and it studies the correlation between each of them and customer retention.

Keywords: Customer Relationship Management; CRM; customer retention;

electronic banking; Iran

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Acknowledgement

Completion of this thesis would not have been possible without assistance and support

from many individuals during the process of writing. I would like to take this

opportunity to express my sincere appreciation and thanks to all of them.

I would like to express my gratitude and appreciation to Dr. Abbas Keramati for his

supervision, constant support and valuable guidance from the very early stage of this

research. I would also gratefully extent my acknowledgment to Dr. Anis Chelbi for

his intelligent advice, supervision, and support.

Special thanks also go to all the respondents in Refah, Maskan, Saman, and Pasargad

banks for their contribution and participation in this research.

I also would like to thank all of International University of Chabahar and Lulea

University of Technology faculty members for their support during this research.

Finally, I would like to deeply thank my dear family, especially my wonderful parents

for their never-ending love, encouragements and support throughout my entire life. I

am deeply and forever indebted to them.

Alireza Nili

December 2010

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Table of contents

Abstract………………………………………………………………………..……....3

Chapter 1: Introduction

1.1. Background………………………………………………………………..….10

1.1.1. Definition of Customer Relationship Management (CRM) and scope……..10

1.1.2. Customer retention………………………………………………………….12

1.1.3. The impact of CRM on customer retention ................................................... 13

1.1.4. Electronic banking…………………………………………………………..15

1.2. Research problem………………………………...…………………………...16

1.3. Purpose of the research………………………………………………………..18

1.4. Outline of the thesis…………………………………………………………...18

Chapter 2: Literature Review

2.1. Introduction……………...……………………………………………………20

2.2. Customer Relationship Management (CRM)…………………………………20

2.2.1. Goals of CRM………………………...…………………………………….22

2.3. The impact of CRM on customer retention…………………………………..23

2.4. Resource-based view and process-oriented approach in the CRM value creation

model……………………………………………………………………………….24

2.4.1. CRM and IT…………………………………...……………………………24

2.4.2. Resource-based view of the firm……………………….……….…………..25

2.4.2.1. Technological CRM resources……………………………………………27

2.4.2.1.1. Communicational/collaborative CRM technologies……………………27

2.4.2.1.2. Operational CRM technologies…………………………………………28

2.4.2.1.3. Analytical CRM technologies…………………………………………..28

2.4.2.2. CRM technological activities in e-banking……………………………….29

2.4.2.3. Infrastructural CRM resources……...…………………………………….31

2.4.2.3.1. Human CRM resources……...………………………………………….32

2.4.2.3.2. Organizational CRM resources…………………………………………32

2.4.3. Process-oriented approach…………………………………………………..32

2.4.3.1 The CRM process………………………………………………………….33

2.4.3.1.1 Operational CRM processes……………………………………………..36

2.4.3.1.2. Management CRM processes…………………………………………...36

2.5. CRM process capabilities……………………………………………………..37

2.5.1. Customer retention programs of CRM ……………………………………..38

2.6. Summary of studies linking CRM to customer retention……………………..42

Chapter 3: Frame of Reference

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3.1. Introduction……………………………..…………………………………….47

3.2. Resource-based View of the firm (RBV)……….…………………………….47

3.2.1. Technological CRM resources………………………………………...……48

3.2.2 Infrastructural CRM resources………………………………………………48

3.3. Process-oriented approach…………………………………………………….48

3.3.1. CRM processes…………………………..…………………………….……49

3.4. Customer retention programs of CRM………………………………………..49

3.5. Research model…………………………………………...…………………..51

Chapter 4: Research Methodology

4.1. Introduction………………………..……………………………………….…52

4.2. Research approach……………………………………………………….……52

4.2.1. Qualitative versus Quantitative Approach………………………………….52

4.2.2. Deduction versus Induction Approach……………………………………...53

4.3. Purpose of the Research....................................................................................53

4.3.1. Exploratory research ………………………………………………………54

4.3.2. Descriptive research………………………………………………………...54

4.3.3. Explanatory research ……………………………………………………….55

4.4. Research Strategy……………………………………………………………..55

4.5. Time Horizon………………………………………………………………....57

4.6. Data Collection method………………………………………………………57

4.7. Sample Selection……………………………………………………………...58

4.7.1. Probability Sampling………………………………………………………..59

4.7.2. Non-probability Sampling…………………………………………………..60

4.8. Sample size……………………………………………………………………61

4.9. Questionnaire Design…………………………………………………………63

4.10. Pilot Test…………………………………………………………………….63

4.11. Data Analysis………………………………………………………………..64

4.12. Validity and Reliability……………………………………………………..65

4.12.1. Validity …………………………………………………………………...65

4.12.2. Reliability…………………………………………………………………65

Chapter 5: Data Analysis

5.1. Introduction…………………………………………………………………..67

5.2. Descriptive Statistics…………………………………………………………68

5.3. Inferential Statistics…………………………………………………………..76

5.3.1. Exploratory Factor Analysis………………………………………………..76

5.3.1.1. The results of Exploratory Factor Analysis for the variable of

"technological CRM resources"……………………………………………………76

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5.3.1.2. The results of Exploratory Factor Analysis for the variable of

"infrastructural CRM resources"…………………………………………………...80

5.3.1.3. The results of Exploratory Factor Analysis for the variable of "CRM

processes"…………………………………………………………………………..83

5.3.1.4. The results of Exploratory Factor Analysis for the variable of "customer

retention programs of CRM"……………………………………………………….87

5.3.1.5. The results of Exploratory Factor Analysis for the variable of "customer

retention"…………………………………………………………………………...92

5.3.2. Structural Equation Model for research Hypotheses Tests……………….95

5.3.2.1. Structural Equation Modeling Procedure……………………………….95

5.4. Lateral Analysis…………………………………………………………….103

5.4.1. Study of the correlation between each of the customer retention programs of

CRM and customer retention using Pearson correlation…………………………..103

5.4.2. Study of the difference between the governmental and private banks in

relation to the research variables………………………………………………….104

5.4.3. Study of the difference between the four banks (under study) in relation to

the research variables by using ANOVA…………………………………………106

5.5. Conclusion……………………………………………………………………111

Chapter 6: Conclusion and Implications

6.1. Conclusion and implication………………………………………………...113

6.2. Theoretical Implications……………………………………………………113

6.3. Managerial Implications……………………………………………………115

6.4. Research limitations and suggestions for future research………………….118

References………………………………………………………………………….120

Appendix A…………………………………………………………………………125

Appendix B…………………………………………………………………………127

Appendix C…………………………………………………………………………133

Appendix D…………………………………………………………………………137

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List of figures

Figure 1.1 Outline of the thesis………………………………………………………19

Figure 2.1 The emerged framework investigation of CRM activities in Iranian

banking industry (Keramati et al., 2009a, p. 213) ……………………….31

Figure 2.2 Getting more customer interaction (Winer, 2001, p. 93)…………………35

Figure 2.3 CRM value generation process (Keramati et al., 2010, p. 1176)…………38

Figure 2.4 Customer Retention Programs (Winer, 2001, p. 98)……………………..39

Figure 2.5 Influencing factors of customer loyalty (Adapted from: Juan & Yan, 2009)..40

Figure 3.1 CRM value generation process (Keramati et al., 2010, p. 1176)…………49

Figure 3.2 The research model……………………………………………………….51

Figure 5.1 The distribution of the respondents' age………………………………….68

Figure 5.2 The distribution of the respondents' educational level…………………...70

Figure 5.3 The distribution of the respondents' field of study……………………….71

Figure 5.4 The frequency related to the respondents' job experience…………….….72

Figure 5.5 The frequency related to type of the banks under study…………….……72

Figure 5.6 The frequency related to the belonging to each bank…………………….73

Figure 5.7 The general form of structural equation model…………………………..96

Figure 5.8 Symbolization……………………………………………………………97

Figure 5.9 The research conceptual model…………………………………………..98

Figure 5.10 Standard model………………………………………………………….99

Figure 5.11 Diagram of significance………………………………………………..101

List of tables

Table 2.1 Summary of studies linking CRM to customer retention……………….…43

Table 2.2 Analysis of studies linking CRM to customer retention…………………..45

Table 4.1 Relevant situation for different research strategies (Yin et al., 1994, p.6)…56

Table 4.2 Descriptive statistics in sampling………………………………………….61

Table 4.3 The sample size for each bank…………………………………………….62

Table 5.1 The frequency of the respondents' age...…………………………………..68

Table 5.2 the frequency of respondents' gender……………………………………...69

Table 5.3 The distribution of the respondents' educational level……………………69

Table 5.4 The frequency of the respondents' field of study………………………….70

Table 5.5 the frequency related to the respondents' job experience…………………71

Table 5.6 The frequency related to type of the banks under study…………………..72

Table 5.7 the frequency related to each bank under study in the sample……………73

Table 5.8 The means of the four research variables for bank Refah………………...74

Table 5.9 The means of the four research variables for bank Maskan………………74

Table 5.10 The means of the four research variables for bank Saman………………75

Table 5.11 The means of the four research variables for bank Pasargad……………75

Table 5.12 The means of the four research variables for governmental and private banks..76

Table 5.13 KMO and Bartlett's Test……………………………………….…………77

Table 5.14 Communalities…………………………………………………………............77

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Table 5.15 Total Variance Explained………………………………………………..78

Table 5.16 Rotated Component Matrix………………………………………………79

Table 5.17 KMO and Bartlett's Test…………………………………………….……80

Table 5.18 Communalities…………………………………………………………...80

Table 5.19 Total Variance Explained ………………………………………….…….81

Table 5.20 The communalities after deleting the questions with little communalities….82

Table 5.21 Total variance explained after deleting the questions with little communalities…82

Table 5.22 Rotated Component Matrix………………………………………………83

Table 5.23 KMO and Bartlett's Test………………………………………………….84

Table 5.24 Communalities…………………………………………………………...84

Table 5.25 Total variance explained before deleting the question………….……….85

Table 5.26 The communalities after deleting the question with little communalities…...86

Table 5.27 Total variance explained after deleting the question with little communality.86

Table 5.28 Component Matrix……………………………………………………….87

Table 5.29 KMO and Bartlett's test……………………………………….………….88

Table 5.30 Communalities……………………………………………….…………..88

Table 5.31 Total variance explained before deleting the question…….……………89

Table 5.32 The communalities after deleting the question with little communalities…..90

Table 5.33 Total variance explained after deleting the questions with little communalities.91

Table 5.34 Rotated Component Matrix………………………………………………92

Table 5.35 KMO and Bartlett's Test…………………………………………………92

Table 5.36 Communalities…………………………………………………………...93

Table 5.37 Total variance explained before deleting the question with little communality..93

Table 5.38 The communalities after deleting the question with little communality..94

Table 5.39 Total variance explained after deleting the question with little communality….94

Table 5.40 Component matrix……………………………………………………….95

Table 5.41 Fit indices………………………………………......……………………100

Table 5.42 Pearson correlation values……………………………………………...103

Table 5.43 Group Statistics…………………………………………………………104

Table 5.44 Independent Samples Test………………………………………………105

Table 5.45 ANOVA.…………..…………………………………………………...106

Table 5.46 Multiple Comparisons…………………………………………………..107

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1. Introduction

This chapter presents a research background regarding the concept of Customer

Relationship Management (CRM) and its impact on customer retention in the

electronic banking environment. It continues with problem discussion, the purpose of

the research and the outline of the thesis.

1.1. Background

This section presents the background of this study focusing on the central idea of

CRM and its impact on customer retention in electronic banking.

1.1.1. Definition of Customer Relationship Management (CRM) and

scope

In the mid-twentieth century, increased product availability for consumers by mass

marketing and mass production techniques, changed the purchasing process and

competition view for the firms. Based on Fickel (1999), Chen and Popovich (2003)

argue that as the customers have the ability to choose the items they want among the

wide variety of services and products, companies lost track of market needs. Today,

companies are trying to build better relationships to existing as well as new customers

to increase customer loyalty and long-term retention. Therefore, many of them are

trying hard to be more efficient in using technological resources and strategies of

customer relationship management (CRM). Researchers also point out that

technological applications of CRM link front office functions for example customer

service, marketing, and sales and back office ones such as operations, financial,

human resources and logistics with the customer touch points in the companies such

as call centers, sales, e-mail, fax, and pagers which most of the times are managed by

separate information systems.

Different authors have described CRM in several ways which reflect variety of

viewpoints about its definition. Some of them have described it as a philosophy or

strategy, others as a process to maintain profitable customers (Zablah et al., 2004).

However, according to Chan (2005) and Dimitriadis and Stevens (2008), most authors

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consider CRM as the combination of information systems and strategies in order to

make the organizations more customer-centric. In this view, CRM is not considered as

only technology, but as a tool which enables the organizations to store and analyze the

data about companies and their customers (Keramati et al., 2008).

Customer relationship management is a comprehensive process and strategy for

customer retention and acquisition to create superior value for them and for the

company (Parvatiyar & Sheth, 2004). It can also be defined as a significant

competitive strategy companies can use to focus on their customer's needs in the

market and to integrate customer-centering path in the firms. Other authors regard

CRM as a discipline focusing on development and automation of business process

associated with management of relationships with the customers in management,

sales, customer support and service (Chatterjee, 2000 cited in Shahin & Nikneshan,

2008).

Based on Payne and Frow (2004) and Payne and Frow (2005), Dimitriadis and

Stevens (2008) considers that CRM creates long-term and profitable relationships

with stakeholders and customers by uniting the IT and appropriate strategies for

relationship marketing. It supplies companies with this chance to use data to realize

the customer's needs by cross-functional integration of operations, people, processes,

and marketing capabilities that is enabled through applications, technology, and

information. Regardless of the channel to interact with companies, CRM offers

convenience of transactions, and customization for the customers (Gulati & Garino,

2000).

According to Boulding et al. (2005, p. 156) and Dimitriadis and Stevens (2008), CRM

is the result of integration and evolution of marketing ideas and technological

resources, information, and organizational forms:

"Not only does CRM build relationships and use systems to collect and analyze data,

but it also includes the integration of all these activities across the firm, linking these

activities to both firm and customer value, extending this integration along the value

chain, and developing the capability of integrating these activities across the network

of firms that collaborate to generate customer value, while creating shareholder value

for the firm."

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Dimitriadis and Stevens (2008) argue that all of the definitions stated underline that

the firm's operations and processes are affected by the implementation and design of

customer relationship management (CRM).

In the study done by Kassanoff (2000), he also points out that CRM applications help

companies assess the profitability and loyalty of their customers by some metrics like

the number of repeated purchases and help to answer questions such as: Which

products or services do the customers want? And how should the companies

communicate with them? Also by these applications, customers save time and money

as well as receiving better treatment, information, and efficient service regardless of

the channel to contact the firm such as call centers and Internet.

However, these strategies and applications are effective if possible risks such as

inadequate ROI and budgets are well thought out and the outcome is creating

competitive value for the customers and better quality of services and improved

profitability with more reasonable price in comparison with the ones competitors

offer. In this situation, one can say the organization is moving on the right path and is

capable to be in a better position of the market (Zineldin, 2006).

1.1.2. Customer retention

In the competitive environments, customer retention has been shown to be a very

important managerial issue in especially the markets with decreasing rate of acquiring

new customers. It is admitted as the first goal of CRM because of its capability in

delivering superior value for the customers and firms (Ahmad & Buttle, 2002).

Therefore, it can be considered as the primary goal of the organizations practicing

CRM strategies and applications (Ang & Buttle, 2006).

Although the meaning of customer retention varies between different companies and

organizations, they all have come to the conclusion that they can reach many

economical objectives by focusing on customer retention such as collecting data about

customers to better target and communicate or to customize the interactions with them

(Ang & Buttle, 2006).

As customers remain loyal, volumes of purchased items will grow and the costs of

relationship maintenance reduces because both suppliers and customers know each

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other better than past. Also, these loyal customers pay higher prices than the new

ones, and are less likely to claim for discounts which we should offer to new

customers in order to acquire them. However, to increase the net present value of

these customers, all of the necessary conditions for CRM programs should be

combined effectively (Ang & Buttle, 2006).

Weinstein (2002, p. 259) argues that many firms spend most of their energy, time, and

marketing budgets to gain new customers. However, because the cost of acquiring

new customers is much more than the cost of retaining existing ones, customer

retention is very important to most banks and organizations. ―It can be [up to] ten

times more expensive to win a customer than to retain a customer – and the cost of

bringing a new customer to the same level of profitability as the lost one is up to 16

times more‖ (Lindgreen et al., 2000, p. 295; Ang & Buttle, 2006).

However, some organizational processes which can be associated with retaining the

customers include the processes for: planning, customer satisfaction measurement,

complaints-handling and quality assurance processes (Ang & Buttle, 2006). Finally,

to be successful in implementing this program, any company should consider the

related issues such as type of customers to be retained and the nature of product or

services sold (Ahmad & Buttle, 2002).

1.1.3. The impact of CRM on customer retention

Many authors describe the final goal of CRM as customer retention which is the key

to survive in competitive markets for any firm. Customer satisfaction and surviving in

today's competitive environments require a heart to understand the customers; a brain

to analyze; and hands to deliver the products to them (Ghahfarokhi & Zakaria, 2009).

These researches also explain that customers are the source of revenue because they

are the ones who pay the bills and generate profit. The role of customers is so vital for

organizations and that is why customer relationship management (CRM) is born

based on the recognition of the value customers have.

Today, customers are the most significant factor in management of businesses

because they are now able to choose the items they want from a variety of choices and

therefore change the strategies and programs firms have. Hence, having enough

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information about customer's needs and marketplace is a vital factor to interact with

their unexpected behaviors and then act in such a way which is in the direction of

companies' objectives (Ghahfarokhi & Zakaria, 2009).

Now, firms know that absorbing and maintaining the customers is an art. They have

realized that to reach this purpose using appropriate programs such as CRM which is

a business philosophy that provides the organizations a vision to deal with their

customers better, is inevitable. A CRM strategy delivers this vision by giving shape to

customer service, marketing, and activities to analyze data and most organizations

view the final purpose of this vision, maximization of relationships with their

profitable customers by increasing the value of these relationships (Ghahfarokhi &

Zakaria, 2009).

Previous researches have shown that there is a strong dependency between customer

satisfaction and customer retention (Winer, 2001). Considering customer retention,

any firm needs to improve the relationships with its customers in a way that results in

more customer satisfaction and therefore increased profits (Dwyer et al., 1987; Becker

et al., 2009). CRM tries to retain the customers by its relationship programs whose

final goal is to deliver a high level of customer satisfaction. Therefore, if companies

want to retain their customers, they must develop and efficiently execute a

comprehensive set of relationship programs to deliver a targeted performance

customers expect. These programs include customization, customer service,

community building, and loyalty programs (Winer, 2001).

Based on CSO Insights (2006), Becker et al. (2009) add that most of the times, the

implementation of these programs affect each of the firm's goals in a different way

and may result in poor economical outcomes. For example, although many firms

engage consultants and project members to implement CRM technological systems

such as software applications and databases or to align the firms' structures and

organizations, they can not be sure that focusing on these activities will guarantee

reaching the final goal of CRM which is customer retention. In order to attain this

goal of customer retention, in addition to the investments for CRM technologies,

firms should consider their organizational and human resources and also relationship

between processes and people because they can determine the degree to which CRM

is adopted and supported.

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1.1.4. Electronic banking

Electronic banking project was launched for the first time in the UK in the early

1980s but it failed to gain considerable acceptance among people. However, rapid

growth in electronic services generated renewed interest in them. For example, in the

1990s, UK banks made transactional e-banking services available for people. In fact

during that time, a quarter of the responding banks with fully operational online

systems provided e-services for their customers (Daniel, 1999; Ibrahim et al., 2006).

Daniel (1999) argues that increased competitions in financial institutions have

resulted in realizing the need to improve alternative delivery channels, the most recent

one being electronic banking. He defines electronic banking as the provision of

services or information for customers and the ability to buy products, gain access to

accounts, and executes transactions via TV, computer, telephone, ATM …

He also points out that today; consumers have this ability to change banks only by

pressing of a button. They can access to online intelligent agents which enable them

to compare the services and conditions and therefore reduce the prices. In addition, he

states that numbers of alternative delivery channels are increasing which causes fall in

percentage of customers visiting bank branches. Besides, electronic banking has many

other advantages for customers such as:

- Convenience: Shopping, paying bills, buying, and transferring money from

anywhere at any time.

- Features: Electronic banking can be carried out at any time of the day or night.

- Attractive Rates and Incentives: Banks offer attractive interest rates that are opened

online. Many others also offer incentives, giveaways and special offers to customers

for opening accounts online.

- Consolidated Portfolio Interface: Most banks offer a seamless and consolidated

interface to customers.

Customers can use e-banking services 24 hours a day, without any limitation in

location and standing in lines. These services include Automated Teller Machines,

Personal Digital Assistants, Mobile Branches, Interactive voice recognition,

Internet Banking, Point of Sale Devices, and Cell Phone Banking.

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Although electronic banking has many benefits both for banks and customers, there

are some problems in its implementation such as legal problems in digital signature

and problems occurring for security systems (Alikhanzadeh, 2008).

1.2. Research problem

The interest in focusing on long-term and profitable customers and the need to better

understand their behaviors have changed the view of marketers about the marketplace.

Traditionally, they have been trying to acquire new customers and the currently other

firm's customers, which needed heavy price-oriented promotions and mass

advertising. However, today, customers have access to a variety of services and

products and when they do not meet their needs easily; they can choose those

institutions that provide them with fast and high quality products or services.

Therefore, companies try to use unique strategies to retain their current customers

instead of customer acquisition which needs more investments. To reach this purpose

new and different tools and mindset are required (Winer, 2001). Also considering

human and organizational resources as much as technological capabilities is necessary

to manage good relationships with the customers (Keramati et al., 2008).

As it has been said above, many studies show that the cost of acquiring new

customers is 6 times much greater than the cost that firms pay for retaining current

ones. Other researches also show the same result such as slight increase in customer

retention rates results in substantial growth in income of firms. Also, other researches

argue that the profit loyal customers generate, is over twice more than the profit

generated by new ones (Winer, 2001).

Customer relationship management (CRM) is now adopted as a necessity and its

methods and experiences are applied in many industries because of its great role in

becoming more customer focused to deal with the competitions between companies

and to retain their current and loyal customers to gain more profit and reduce the costs

(Newell, 2000).

CRM is one of the most growing trends in banking industry these days, especially in

electronic environment and high investments have been spent on its technologies in

order to keep the customers satisfied. Also, it is considered as the top banks'

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implementation programs priority which is today more seen in e-banking (Blery &

Michalakopoulos, 2006).

In today's competitive markets, an aggressive competition between banks is seen

more than in the past. They have realized that relationship with customers and its

management are significant factors to win this race especially in e-banking in which

face to face interactions does not exist. The applications of customer-centric strategies

and programs of customer relationship management (CRM) help banks to build long-

term relationships with customers and result in increasing their income. Therefore, in

the banking sector, CRM is of strategic significance because of the effects it has on

customer satisfaction and retention which is the final goal in any successful

businesses (Blery & Michalakopoulos, 2006).

Although many evidences show the strong positive impact of CRM on organizational

performance such as customer retention, there are many reports showing

disappointing results of applying CRM to retain existing customers and developing

relationships with them. By reviewing the previously published studies about CRM

and firm performance, we found out that to find the origin of this problem and to

propose an integrated framework from CRM investment to customer retention,

resource based view (RBV) of the organization is extended into it by some

researchers. In addition, some other authors have applied process oriented approach in

their studies to enhance and improve this path. In this thesis we have also used these

two approaches to gain insight about the real impact of CRM on customer retention

(Richards & Jones, 2008; Keramati et al., 2010)

Based upon the above discussion and on the limited amount of researches available

about CRM and its impact on customer retention in electronic banking especially for

Iranian banks, we have formulated the research problem of this thesis as follows:

"What is the impact of CRM on customer retention in electronic banking in Iranian

banks?"

1.3. Purpose of the research

From the discussions above, the aim of this thesis is to understand the relationship

between CRM resources, CRM processes, customer retention programs of CRM (as

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the CRM process capabilities), and customer retention in electronic banking of Iranian

banks. Therefore, briefly, this study tries to investigate the impact of CRM on

customer retention in Iranian electronic banking based on RBV and process oriented

approach. Iranian banks have used CRM and its strategies more or less and proceeded

to this issue with different rates of success. However, there are not available studies

that investigated the impact of CRM on customer retention in these banks especially

in electronic environment. This research aims at getting a deeper insight to this issue

regardless of size and age of the banks.

1.4. Outline of the thesis

As it is presented in figure 1.1, this research is divided into six chapters. The first

chapter which is the introduction presents the background of the research, the research

problem, and research objectives. Chapter 2 which presents a literature review

provides the reader with an overview on the main previously published papers related

to CRM and customer retention. Chapter 3 deals with the frame of reference. It

presents the adopted models for the study, formulates the research hypothesizes, and

concludes the final research model. The fourth chapter presents the methodology. It

describes the methodological choices made in this work and it also examines its

validity and reliability.

Chapter 5 analyzes the empirical data which are collected by the means of

questionnaire to test the research hypothesizes. This study ends with chapter 6 in

which the conclusions related to the formulated hypotheses will be stated. This

chapter ends with managerial and theoretical implications and further research

perspectives within the area of customer relationship management in electronic

banking sector. Figure 1.1 presents the outline of the thesis.

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Fig. 1.1: Outline of the thesis

Introduction

Literature Review

Methodology

Data Analysis

Data

Presentation

Conclusion

and

Implications

Frame of Reference

Chapter

1

Chapter

2

Chapter

3

Chapter

4

Chapter

5

Chapter

6

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2. Literature Review

2.1. Introduction

In this chapter, based on the research problem presented in the introduction chapter,

we will review an essential part of the literature concerning CRM and its effects on

firm performance; especially, the 'customer retention'. In doing so, we start by

defining CRM and specifying its goals. Next, the two well known approaches namely,

the resource-based view (RBV) of the firm and the process-oriented approach, are

discussed. Then, we examine the CRM process and process capabilities as well as

customer retention programs of CRM. At the end, a summary of the literature linking

CRM to 'customer retention' is provided.

2.2. Customer Relationship Management (CRM)

Increased product availability and mass production techniques have given this chance

to customers to be able to choose the items they really desire among the variety of

products. Therefore, focusing on customers' expectations is the most important factor

for firms to survive in today market places. On the other hand, knowing customer's

needs and problems helps the companies to acquire and retain them easier and with

less cost (Dimitriadis & Stevens, 2008).

Customer relationship management is built on relationship marketing philosophy and

redefines the relationship between companies and their customers. Some researchers

have defined CRM as a competitive strategy companies adopt to focus on their

customer's needs, but others regard it as a discipline to concentrate on development

and automation of business process in companies. However, despite the variety of

definitions of CRM, they all intend to build customer relationship to create superior

value for both the customers and firms (Chatterjee, 2000 cited in Shahin &

Nikneshan, 2008).

Today, companies have realized that the cost of acquiring new customers is far greater

than the cost of retaining existing profitable customers. Therefore, with the help of

CRM strategies, they are trying to build better and customized relationships with

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existing customers in order to increase customer satisfaction and build customer

loyalty (Ang & Buttle, 2006).

Based on Payne and Frow (2004) and Payne and Frow (2005), Dimitriadis and

Stevens (2008) emphasizes that CRM creates long-term and profitable relationships

with stakeholders and customers by uniting IT technologies and appropriate

strategies. Here, the outcome is a mutually beneficial relationship between company

and customers that leads to customer loyalty and therefore more profitability for the

companies.

Winer (2001) considers the customer retention as the final goal of firms practicing

CRM programs. He suggests that if firms want to deliver their customers the

performance they expect, a comprehensive set of relationship programs is a necessity.

He implies that these programs include customization, customer service, rewards

programs, community building, and loyalty programs.

Ghavami and Olyaei (2006) argue that CRM is one of the key processes in any

company and its implementation needs capital investments to integrate marketing,

strategy, and technology. Using CRM, companies can achieve competitive advantage

provided it is well implemented. In case it is not, customers may leave the company

and never come back again.

Now, a key question is: what are the elements of a successful CRM program?

To answer this question, based on Payne (2001), Ghavami and Olyaei (2006) explain

the following four elements:

1) Strategy assessment process

2) Value creation process

3) Multi channel integration process

4) Data repository process

The first process must be done for both business strategy and customer strategy. The

components of a business strategy include business vision and competitive

characteristics and the components of customer strategy include customer

characteristics, choice, granularity, and customers segmentation (Ghavami & Olyaei,

2006).

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The second process consists in the value customers receive, the value organization

receives, and the customer segment life time value analysis. The values customers

receive from a relationship with a company include value proposition and value

assessment. Moreover, in a well planed and customized relationship with customers,

organizations receive the values of acquisition economics and retention economics

(Ghavami & Olyaei, 2006).

The third process is multi channel integration process. In this step, the components

which CRM tries to integrate and manage include e-commerce, m-commerce, direct

marketing, sales force, telephony, and outlets. Finally, the last process contains the

functions of data analysis, information systems, front office applications such as

customer service and back office applications such as human resources and logistics

(Ghavami & Olyaei, 2006).

2.2.1. Goals of CRM

Based on the study done by Swift (2001), Persson (2004, p.11), believes that by

implementing CRM strategies, firms can achieve many goals such as:

- Reducing costs of sales

Because the relationship with customers will be more efficient and current customers

become more responsive, the costs of sales reduce.

- Reducing costs of acquiring new customers

Because of savings on marketing, relationship programs, services, and so on, the cost

of acquiring new customers will be reduced.

- Increased customer satisfaction and profitability

- Decreasing the need to acquire so many new customers

The number of loyal customers increase, therefore the need to gain so many new

customers decreases.

- Evaluating profitability of customers

Companies can know the profitable customers and the ones who might become

profitable. This is a very significant factor, since any business must focus on

acquiring and retaining the profitable customers to generate profit and reduce costs.

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- Higher customer retention rates

Customer loyalty and retention increases, therefore they will stay longer and generate

more profit for firms.

Based on Newell (2000), Persson (2004) discusses that a real value to a firm is the

value it creates for the customers as well as the value its customers deliver back to

that firm. It is necessary to say that this value lies in the customer knowledge and in

the way the firms use this knowledge in managing relationships with their customers.

If CRM is applied in the right way, it can be an efficient tool that generates profits for

firms. By transforming customer data into knowledge intended to build better

relationships with profitable customers, CRM creates more customer loyalty and

therefore more profits for companies. Ultimately, CRM is about creating mutual and

collaborative satisfying relationships between the company and its customers to

increase customer loyalty and satisfaction.

2.3. The impact of CRM on customer retention

Although many practitioners have provided evidences of the positive effects of CRM

on organizational outcome such as customer retention, there are many reports

showing disappointing and poor outcomes (Richards & Jones, 2008; Keramati et al.,

2010). To find the reason of this problem, many academicians and practitioners tried

to identify the origin of this situation and found two problems referred to the

performance of CRM. The first reason is that many firms view CRM as an IT solution

(Reinartz et al., 2004; Keramati et al., 2010). In this regard, academicians believe that

although it is easy to acquire IT in the industry, it does not lead to success in business

strategies and does not confer competitive advantage. This finding is the same with

CRM technology. Companies can buy the same CRM technologies which their

competitors use and not have the same results (Lawson-Body & Limayem, 2004;

Keramati et al., 2010).

The second reason is referred to the concept of CRM meaning that it should not be

considered only in the context of technology. According to Zablah et al. (2004), there

are five main concepts on CRM including technology, strategy, philosophy, process,

and capability which represent an important insight toward an integrated framework

linking CRM to organizational outcome. Indeed, many models have been developed

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to propose how CRM affects the firm performances which are different in

conceptualization of key dimensions of CRM and the relationships among them

(Keramati et al., 2010).

By reviewing the IT and organizational outcome literature, we found out that in order

to explain the productivity paradox of IT and to propose an integrated framework

from CRM investment to customer retention in firms, some academicians have

emphasized on considering the resource based view (RBV) of the organization. Also,

some practitioners have applied process oriented approach in their studies to improve

and develop this path. Indeed, by using these two approaches, firms can be able to

identify important resources in the CRM processes implementations and can find the

appropriate mechanisms of CRM value creation for the firm. Therefore to find the real

impact of CRM on firm performance such as customer retention which is the most

important factor for firms to have better financial outcomes, the consideration of these

two concepts is necessary (Levesque & McDougall, 1996; Keramati et al., 2010).

2.4. Resource-based view and process-oriented approach in the CRM

value creation model

In this section, we discuss theoretical backgrounds as well as adoption and extension

of CRM and IT, resource based view of the firm (RBV), and the process oriented

approach.

2.4.1. CRM and IT

While many authors such as Keramati et al. (2010) consider CRM as a customer

orientation strategy, they all explain that core organizational IT capability is the basis

of CRM and have described IT as ―information-enabled relationship marketing‖. This

can be the reason of this problem that why many practitioners and managers have

seen CRM as only a technology solution (Keramati et al., 2010, p. 1171).

In many recent papers on CRM, we found evident emphasizes on accepting CRM as a

strategy. Payne and Frow (2005) and Keramati et al. (2010, p. 1171) describe CRM as

a continuum which "on the one side, is about the implementation of a specific

technology solution, and, on the other side, is a strategy for managing customer

relationships to create shareholder value". Therefore, CRM can be defined as a

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marketing strategy to develop beneficial relationships between the company and its

customers with the help of IT.

As it has been mentioned before, to answer this question of why investments in

information technology do not lead to expected organizational outcome, some

theories such as a theory based framework which is referred to as the resource based

view (RBV) in the organizations have been developed (Keramati et al., 2010).

The attributes of RBV is relevant to CRM for some reasons. The first reason is that

CRM is rooted in information systems and marketing. Indeed, we can claim that RBV

has important roles in the both mentioned fields. First, in the field of IT, RBV has

been applied to investigate the ability of IT in providing competitive advantage for

organizations. Second, in the marketing field, using RBV, organizations are able to

assess the competitive advantage provided by the processes which transform the

resources to valuable results both for firms and customers. The second reason for the

relevance of RBV to CRM is that the ‗people‘ aspect has gained considerable

attention in the definition of CRM (Reinartz et al., 2004; Keramati et al., 2010) and

RBV also stresses the importance of people in the field of strategic human resources

management for which the most important components are employees' skills and

behavior in the organization. Finally, the third reason is that, the aim of CRM is

creation of superior value both for organizations (from economical aspect) and their

customers (Greve & Albers, 2006; Keramati et al., 2010). According to Keramati et

al. (2010, p. 1171), "The RBV combines the underlying principles of economics with

a management perspective".

2.4.2. Resource-based view of the firm

Resource-based view of the firm (RBV) emphasizes on the firm resources and views

them as valuable firm assets. As mentioned, technology can be easily purchased and

duplicated by any entrants in the industry; therefore it can not provide competitive

advantage for the organizations. Many relevant researches also imply that there is no

correlation between investments in technology and organizational outcomes, or if

there is a correlation, it is negative. This contention can be considered as a reason for

weak effect of CRM technology on firm's efficient and sustainable relationship

building with customers. On the other hand, recent researches such as the one done by

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Keramati et al. (2010) confirm this idea and imply that if firms want to achieve

competitive advantage, they should compete on the basis of unique, rare and valuable

corporate resources. Through these researches, two main tangible and intangible

resources which are complementary to IT namely as human and organizational

resources are identified. Indeed, an efficient use of these resources can lead to firm

capabilities and therefore expected organizational outcomes. In this regard, we should

note that capabilities measure the organization's ability to assemble these resources

effectively to achieve a specific objective (Coltman, 2007; Keramati et al., 2010).

Is the RBV applicable in CRM strategies? Reinartz et al. (2004), Zablah et al. (2004),

Coltman (2007) and Keramati et al. (2010) conclude in their studies that the findings

below imply a positive answer to the mentioned question:

1) The overemphasis on technology investments addressed by the studies about the

applications of IT and CRM.

2) The concept of capability which refers to efficient use of all resources and as the

result firms will be able to form their performance toward customer-focus strategies

and sustainable competitive advantage.

3) In the view of RBV, a selective and path-dependent process can improve the firm

capabilities and this is consistent with the goal of CRM.

The above mentioned literature discusses the usefulness and application of RBV in

the study of CRM. However, to understand why the RBV is better to use than other

theories in this context, some researchers such as Keramati et al. (2010) argue that the

theory of RBV focuses on the main drivers of competitive advantage which is in the

direction of the main objective of CRM that is creation of superior value for the firm

and customers. In this regard, some other references view the RBV as a theory applied

in industrial organization economics. In these studies, RBV is compared with five

theories that have played an important role in the evolution of industrial organization.

These theories include Bain-type IO, neoclassical economics, transaction cost

economics, and the Schumpeterian and Chicago responses. RBV has at least one

similarity and one distinction with the mentioned theories, Keramati et al. (2010, pp.

1171–1172) say that the RBV ―reflects a strong cumulative IO heritage and is at the

same time unique in that it incorporates major departure from each of the five

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theories‖. In addition to this reason, many other authors such as Lockström (2007)

have also based their study on the RBV because of its powerful logic and

comprehensiveness.

2.4.2.1. Technological CRM resources

CRM technologies are the medium and tools which enable the firms to get the

appropriate information to the right person at the right time (Massey et al., 2001).

Based on the firm strategy on CRM program, Keramati et al. (2009b) classifies

technological resources into:

- IT infrastructure including the electronic services across the company.

- The combination of hardware and software to implement business programs by use

of the infrastructure such as the electronic systems for sales and also the analysis

tools.

CRM technologies in themselves are classified into three classes of

‗communicational‘, ‗operational‘ and ‗analytical‘ technologies. As it has been said

previously, communicational technologies make interactions between the firm and its

customers and help driving the sales through communicational channels such as call

centers. Customer facing applications are related to operational segment which

automate customer service, marketing, and sales. And finally, analytical technologies

uses information sources in the best possible way for better gaining insight of

customer behavior patterns and then make more personalized communication with

them (Keramati et al., 2009b; Keramati et al., 2010).

2.4.2.1.1. Communicational/collaborative CRM technologies

Using communicational CRM, communication between the firm and the customers

become possible. This can be done via the e-services provided for customers to

contact the firm. It also enables suppliers to interact with each other effectively.

Moreover, it offers tools and knowledge to employees, partners and suppliers and

helps driving the sales through communicational channels (Keramati et al., 2009a).

The special features of communicational CRM include enabling the firm to

communicate with the customers at any time and any location. They also allow

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solving customer's questions, fast service response to customer's requirements,

increase in quality of services, customer satisfaction and therefore retaining the

current customers (Keramati et al., 2009a; Reinartz et al., 2004).

2.4.2.1.2. Operational CRM technologies

A typical operational CRM is the contact center and its management. To interact with

customers, customer data should be collected through all possible touch points such as

contact center, web, and sales force. Then, these data must be stored in particular

databases. By this contact management system, tracking any information related to

contact with customers becomes easier and more efficient (Keramati et al., 2009a).

It is also established that by using a contact management system, operational CRM

improves marketing, customer services, sales force automation and so on. It

automates customer contact and customer-facing processes. In addition, it supports

their interaction in service, sales, and marketing. Ultimately, operational CRM is a

great help in personalizing relationships with customers and realizing their needs.

This has been improved by deploying Intranet and Extranet to distribute the

information within the organization and between the organization and all the partners

via all touch points (Keramati et al., 2009a; Keramati et al., 2010).

The functions of operational CRM include sales force automation (SFA) to focus

more on sales, customer service and support to provide better and personalized

services for the customers, and also enterprise marketing automation (EMA) to

evaluate profitability of customer segmentations (Keramati et al., 2009a; Deng, 2009).

2.4.2.1.3. Analytical CRM technologies

Academic researchers argue that to increase competitiveness of a firm and reach its

objectives, customer satisfaction is a necessity. To improve it, companies should

ensure customer's expectations are understood and met. Therefore, they need a

measurement system fed by data gained from electronic systems or directly from

customers. In the next step, to support identifying customer behavior and their loyalty

level, these stored data must be analyzed by specific analytical systems (Reynolds,

2002; Keramati et al., 2009a).

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Globally, the most important goal of analytical CRM is to use information sources

properly to better gaining insight of customer behavior patterns and then have more

personalized communications with them. As a result, because customers are offered

more customized products that fit their buying profiles, they become more satisfied

and therefore more loyal to the company (Keramati et al., 2009a; Reinartz et al.,

2004).

Now a key question is the following: what is the relationship between these

characteristics: ‗communicational‘, ‗operational‘ and ‗analytical‘? To answer this

question we can say that for instance, without data gathered through operational

CRM, analytical CRM can not drive strategic and tactical decision making about

customer acquisition or maintenance. Therefore, if firms want to drive the customer

life cycle effectively, they must consider all the characteristics of CRM and the

available business intelligence at the same time (Keramati et al., 2009a; Keramati et

al., 2010).

2.4.2.2. CRM technological activities in e-banking

In the past few years, many banks in the world have applied data mining technologies

and data warehouses to provide clear information about interactions with their

customers and to realize what types of customers they are dealing with. In order to

target right groups of customers, these banks also have segmented their customers

based on their behavior patterns and loyalty (Keramati et al., 2009a).

Iranian banks have also undertaken different CRM aspects. A number of them are

using operational CRM while others use communicational one. Some of these

activities (e.g. check balances and accounts records) are for observation and some

other such as customer service are for account controls. The latter include ordering

checkbooks, changing the accounts, bill payments to third parties, sending messages

and paying credit-card bills, fund transfer, etc. (Keramati et al., 2009a; Reinartz et al.,

2004).

In general, the applications of CRM include management and automation of call

center, marketing activities, contacts, campaign, e-mails, field service, data

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warehouses, SFA, knowledge management, and customization of the products and

services (Reynolds, 2002).

A considerable part of the customer data can be collected from the customers

interacting through different channels. This data is classified into two main parts

which are transactional data and non-transactional ones. The first part is about

transaction time, place and amount, but the second part is more about feedbacks

gained from customer's propositions or complaints. To communicate with customers

more efficiently, both of the mentioned parts should be combined into a customer data

profile (Keramati et al., 2009a; Kim et al., 2010).

Based on the discussion above, Keramati et al. (2009a) divides customer information

into the following three types:

• Customer's information including their transactional and personal

information.

• The information provided for the customers such as the product information

that customers perceived as useful.

• Non-transactional customer feedback information such as customer's

suggestions, complaints and so on.

It should be noted that the collected information through different channels must be

integrated in a proper database. In the next step, with the help of operational CRM

and considering the gained feedbacks, analytical CRM creates various customer

segments to help designing proper customer strategies. All in all, to perform this

process efficiently, all of these activities should be arranged and conducted one after

another (Keramati et al., 2009a; Reinartz et al., 2004).

Using CRM techniques, banks can be more able to find the profitable customers who

have considerable current and future net value for them, know them and understand

their needs better, and retaining and improving the value of their business in a costless

and efficient way (Keramati et al., 2009a). As the final result, banks will be more

capable to improve their customer satisfaction and retention programs; therefore,

customers are more eager to have long-term relationship with them which results in

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higher customer loyalty and profitability; reduced cost of sales; reduced costs of

acquiring new customers and decreased need to acquire so many of them; buying

more products and therefore increased long-term value (Keramati et al., 2009a; Greve

& Albers, 2006).

Keramati et al. (2009a, p. 213) summarize the emerged investigation of

communicational CRM, operational CRM and analytical CRM activities in the

context of electronic banking in Iran in figure 2.1.

Front office Back office

Communicational

Operational Analytical

Fig. 2.1 The emerged framework investigation of CRM activities in Iranian banking industry

Reference: Keramati et al. (2009a, p. 213)

2.4.2.3. Infrastructural CRM resources

As mentioned before, CRM is a marketing strategy and technology is its non-strategic

aspect. On the other hand, the infrastructure of CRM in the organizations is formed

based on the non-technological CRM resources which are called the 'infrastructural

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akin

g

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CRM resources' and are classified into ‗human CRM resources‘ and ‗organizational

CRM resources‘ (Keramati et al., 2009b; Keramati et al., 2010).

2.4.2.3.1. Human CRM resources

Human CRM resources consist of the employees' ability to work well with the

existing CRM programs. In addition, they are about customer facing and non-

customer facing employees' attitudes, technical skills, and ability to convert customer

data to knowledge (Keramati et al., 2009b; Keramati et al., 2010).

Reinartz et al. (2004) suggest that customers would rather communicate with

employees than electronic systems. This implies the reason why academic researchers

claim that among all CRM resources, human resources is the first priority to be

considered. Based on Rigby et al. (2002), Keramati et al. (2009b) confirm this idea by

mentioning that CRM can be implemented more efficiently simply by motivating

employees to be more aware of customer's expectations.

2.4.2.3.2. Organizational CRM resources

Based on Greenberg (2004), Keramati et al. (2009b) argue that many companies

consider CRM as a project of implementing IT technologies. They emphasize that

CRM is a program not a project because projects are implemented in limited time, but

for CRM there is no end.

These researchers add, to implement a successful CRM program, some elements are

required including setting CRM goals, defining incentive systems, having a customer

centric philosophy, training the employees, and top management commitment.

2.4.3. Process-oriented approach

According to Keramati et al. (2008), while several studies have adopted and applied

the RBV approach, it has some limitations, for example, it does not talk about how

and through which mechanism the resources should be applied and what the best

ways of managing them are. In this regard, the process-oriented approach examines

the impact of Information technology on intermediate business process. Mooney et al.

(1996) and Keramati et al. (2008, p. 1280) state that "to evaluate IT business value,

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the key business processes within each core business area must be identified and the

linkages and contributions of IT to those processes should be defined".

Eng (2004) argues that in the context of CRM, the applications of the process-oriented

approach and RBV are relevant. Keramati et al. (2010) confirm this idea stating the

following reasons:

First, a process management orientation is a necessity for a CRM program to be

successful; therefore, to ensure the efficient use of firm resources toward the creation

of expected firm performance, managers must focus on CRM processes effectively.

Second, firms are able to enhance their process capabilities by strategic approaches

and long-term view to their resources.

Mooney et al. (1996) and Keramati et al. (2010) compared organizations which

efficiently deployed IT with other ones to test the ways by which IT influences the

processes leading to process capability in firms. The result was that the firms which

absorbed and applied IT as a differentiated resource have enjoyed better process

capabilities and firm performance out of it. This research result implies the necessity

of aligning the process oriented approach with the resource based view of the firm

(RBV).

2.4.3.1. The CRM process

A process is the way we do a work and in businesses, it is linked sets of activities to

create value for customers. According to Dwyer and Tanner (2005) and Ghavami and

Olyaei (2006), a CRM process involves four steps including market segmentation,

designing a communication strategy, implementing the strategy, and finally the

evaluation of that strategy.

These authors explain that in the first step of a CRM process which is market

segmentation, companies segment and profile the similar groups of consumers and

then they customize the products meeting that group's expectations. Besides, when

segmenting the customer groups, firms should consider the way customers want to

contact them. For example, some buyers in a segment would rather order over the

web, but some others prefer to buy in another way (Dwyer & Tanner, 2005).

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To understand which way of contact the customers in each segment prefer, companies

can track customers‘ response to earlier contacts. This can be done by constructing a

database of information about the customers and then investigate which customers

responded well to which way of contact in the past. For example if some customers

have bought some items frequently online, they are more likely to buy in this way in

the future (Dwyer & Tanner, 2005; Ghavami & Olyaei, 2006).

According to Winer (2001), a business database should contain the following

information:

• Customer Contacts: This includes all customers and the organization initiated

contacts.

• Transactional information: This kind of information includes comprehensive

and detailed customers' purchase history.

• Response to Marketing activities: to know whether or not the customers

responded to direct contacts.

• Descriptive Information: This is more used for customer segmentation and

analyzing the collected information.

This author also argues that in data collection and creating a database, the biggest

challenge is to create opportunities for communication between firms and customers

especially when the firms use intermediaries such as physical stores that prevent

direct contact.

Winer (2001, p. 93) illustrates the general problems in creating a database in figure

2.2. For example, in this figure, banks have many direct communications with

customers; thus, creating a database is an easy for them. On the other hand, this job is

very hard for the lower right-hand quadrant due to the indirect communications these

firms have with their customers.

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Customer Interaction

Direct Indirect

Airlines Banks

High Telecom Packaged goods

Retail Drugs

Interaction

Frequency

Personal

Computers Furniture

Low

Internet Autos

Infrastructure

Fig. 2.2 Getting more customer interaction

Reference: Winer (2001, p. 93)

The collected data in database should then be analyzed through statistical methods

such as cluster analysis or through "lifetime customer value" (LCV), click stream

analysis, etc. The purpose of these analyses, especially LCV, is that each customer

must be analyzed in terms of profitability to the company; therefore, companies can

realize which customers to target more efficiently (Winer, 2001, p. 94).

Different results can be gained through these analyses. For example, if purchasing

behaviors of the customers have been analyzed, the customers in the highest

purchasing rate are the first priority to focus in loyalty programs. The final goal is to

select long-term profitable customers among all existing ones (Winer, 2001).

In the next step, a strategy for communication with customers will be designed. In this

strategy, several different communication channels such as email and direct mail will

be used (Dwyer & Tanner, 2005).

Many academicians have suggested organizations to dialogue with their customers

through these communication channels rather than communicating with them through

mass media such as television or radio. Also, they say Internet can be deployed as a

technological tool to facilitate individual relationship building with customers.

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Meanwhile, based on the customers' profiles and their buying behaviors, companies

should make offers such as purchasing discounts (Dwyer & Tanner, 2005).

In the third step, the strategy is implemented and then in the final step, it is evaluated.

Measures of performance are considered for evaluation. Customer satisfaction is the

most important one. Depending on the results, companies may change their customer

segmentations and strategies (Dwyer & Tanner, 2005).

Keramati et al. (2010) classify CRM processes into operational CRM and

management CRM processes. They explain that operational CRM processes include

the CRM's tasks in a firm's value chain, but management CRM processes are about all

CRM's tasks related to the administration, allocation, and control of all resources in

firms. By dividing CRM processes into these two main classes, organizations become

able to determine the CRM activities under each one more efficiently.

2.4.3.1.1 Operational CRM processes

According to Zablah et al. (2004) and Keramati et al. (2010), those customer

processes found at the operational level of the business are called operational CRM

processes which themselves are classified into operational knowledge processes and

operational interaction processes. The above authors explain that the first sub-process

involves the tasks associated to customer knowledge at the operational level.

Collecting customer data through different channels and disseminating customer

knowledge between the firms and their customers is an important example for this

process. On the other hand, the operational interaction processes consist of different

activities from the activities done in the first process. Based on the studies done by the

mentioned researchers, using these processes, firms consider the available intelligence

to establish and develop relationships with customers.

2.4.3.1.2. Management CRM processes

Based on Greve and Albers (2006) and Keramati et al. (2010), all the activities which

refer to the activities programmed to create market intelligence and improve decision

making in allocating the resources, developing new products, and so on are explained

in the context of management CRM processes. An important example of these

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activities in this context is the realization of customer behavioral pattern which has

significant effects on decision making and therefore on the firm performance.

2.5. CRM process capabilities

Coltman (2007) and Keramati et al. (2010) argue that capabilities are ‗intermediate

transformation ability‘ between all the resources (technological and infrastructural)

and the organization's mission. Capabilities measure the organization's ability in

effective combination of resources to meet its goal; therefore, they approximately

reflect the concept of efficiency. The same authors believe that since capabilities have

mediating effect between firm's resources and performance, they are not observable;

therefore they become hard to imitate.

Process capabilities can also be regarded in the context of CRM. Keramati et al.

(2010, p. 1177) define the CRM process capabilities as: "the process abilities that are

gained by effectively applying CRM resources to CRM processes and that enable

firms to create superior value for their organization as well as their customers.".

Regarding this definition, we can conclude that the best level to assess the CRM

process capabilities is the process level. Like CRM processes, these capabilities are

classified as management CRM process capabilities such as new product development

capabilities and operational CRM process capabilities such as customer support

capabilities.

Keramati et al. (2010, p. 1176) summarize the relationship between CRM resources,

CRM processes, CRM process capabilities, and organizational performance in figure

2.3.

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Fig. 2.3 CRM value generation process

Reference: Keramati et al., (2010, p. 1176)

As previously mentioned, process capabilities have mediating effect between firm's

resources and performance to meet a specific objective. This study investigates the

impact of CRM resources and processes on customer retention (as the organizational

performance); therefore, to attain this objective, firms should implement a

comprehensive set of relationship programs with their customers as their process

capability. Winer (2001) calls these programs as 'customer retention programs of

CRM'.

2.5.1. Customer retention programs of CRM

As it has been discussed before, the overall objective of relationship programs is

delivering a higher level of customer satisfaction than similar companies do.

Therefore, companies must measure their customers' satisfaction levels and improve

their relationship programs which help to deliver products and services beyond the

customer expectation (Winer, 2001; Farquhar, 2004; Arbore & Busacca, 2009) and

therefore contribute to retain the customers.

Winer (2001) believes that if firms want to efficiently retain their profitable

customers, they need to implement a comprehensive set of relationship programs

including: frequency/loyalty programs, customer service, customization, and

community building. He states these programs are determinants of customer

satisfaction which finally leads to customer retention (figure 2.4).

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Fig. 2.4 Customer Retention Programs

Reference: Winer (2001, p. 98)

Loyalty/Frequency Programs

Loyalty program which is also called frequency program provides rewards to targeted

consumers in order to encourage them to buy products repeatedly. These programs

have become competitive necessities for the firms in the marketplace especially those

with a decreasing rate of customer retention. Recent studies have also shown that the

most successful retailers in the top sectors such as department stores, grocery,

drugstores category, and mass merchandisers design these programs (Winer, 2001;

Verhoef, 2003; Juan & Yan, 2009).

Customer loyalty in service industry consists of three main dimensions including

affective loyalty, behavioral loyalty, and cognitive loyalty. Behavioral and affective

loyalties are formed based on cognitive loyalty which itself is influenced by

company's reputation, brand recognition, its public image and most importantly,

corporate social responsibilities. On the other hand, in the intermittent service

industry, customer satisfaction, service quality and customer perceived value (CPV)

Customer

Relationship

Management:

Satisfaction

Frequency/

Loyalty

Programs

Customization

Customer

Service

Community

Building

Rewards

Program

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are the main factors affecting customer loyalty. Through the moderation of customer

satisfaction, both of CPV and service quality affect Customer loyalty indirectly

(Farquhar & Panther, 2008; Juan & Yan, 2009).

In descending order of significance, affective loyalty is influenced by customer

satisfaction, cognitive loyalty, customer perceived value, and service quality.

Moreover, determinants of behavioral loyalty include customer satisfaction, service

quality, affective loyalty, customer perceived value, and cognitive loyalty. And

finally, the only influencing factor of cognitive loyalty is the service quality (Juan &

Yan, 2009). Figure 2.5 illustrates the influencing factors of customer loyalty in

service industry.

Fig. 2.5 Influencing factors of customer loyalty

Adapted from: Juan & Yan (2009, p. 68)

Many academicians believe that there are some considerable problems with loyalty

programs such as the need for high investments and the difficulty to correct mistakes.

There is confusion about whether these programs improve the customer retention or

average spending behavior, and most considerably, it is not easy to gain competitive

advantage by these programs. However, loyalty programs can work effectively if

firms increase the switching costs and build barriers to entry (Winer, 2001; Deng et

al., 2009; Juan & Yan, 2009).

Service Quality

Customer Satisfaction

Customer Perceived Value

Customer

Loyalty

Cognitive Loyalty

Affective Loyalty

Behavior Loyalty

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Customer Service

Generally, a customer service encounter is defined as the contact or "touch points"

that a customer has with a company. It has the potential to improve customer strategy

or to have the opposite effect. Because the targeted customers are most valuable

customers to the companies and they are able to choose their favorite item among a

variety of products in the marketplace, customer service must be the first priority

within each company (Winer, 2001, p. 99; Venetis & Ghauri, 2004; Smith, 2006).

There are two types of services including Reactive and Proactive services by which

companies can enhance their customer service (Winer, 2001; Smith, 2006).

Reactive services are applied when a customer faces problem such as product failure

or question about a bill and contacts the firm to solve it or get more information about

it. Today, most organizations especially the banks have used the technology and also

trained their employee to deal with these situations through e-mails, faxback systems,

telephone, etc. Proactive services are the situations where the firms do not wait for

customers to contact them and are determined to hold a dialogue with their customers

prior to use reactive solutions such as complaint handling. This can be done well

where the sales force or other employees dealing with the customers are trained and

interested to realize customers' expectations (Winer, 2001; Venetis & Ghauri, 2004;

Smith, 2006; Arbore & Busacca, 2009).

A number of systems leveraging the Internet assist both of these two types of services.

For example, some Web-based service providers such as HumanCIick provide their

customers with the ability of real time interactions with service representatives

(Winer, 2001; Smith, 2006).

Community

Using the Web, both online and offline companies can build online networks of

customers in which they are able to exchange information about the products and also

can interact between themselves and the company easier and more personally. This

network of customers is called community. In this situation, the customer is

committed to the company and therefore he or she is less likely to leave the family of

customers. In addition, by creating these communities, companies can gain more

accurate information about specific products in a market and then make it with the

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characteristics or the quality that customers expect (Stauss et al., 2001; Winer, 2001;

Guo et al., 2009).

Customization

Customization goes beyond communicating with customers and is also about the

creation of products for individuals. For example, some companies such as Dell and

Nike have established online processes for creation of customized and personal

products and services based on individual customer's taste. In this way, each customer

is able to choose a product from a list or order the item he or she really wants (Winer,

2001; Ahmad & Buttle, 2002; Farquhar, 2004).

Academicians call such customization, "versioning" which is easy and cheap to do.

The point that should be noticed is that versioning is easier to do for services and

intangible products than for physical products; however companies can use the

additional information gained from customers to tailor at least the appearance of

products (Winer, 2001; Farquhar, 2004; Smith, 2006).

2.6. Summary of studies linking CRM to customer retention

The coordination and integrity between different components of CRM have a

significant effect on CRM value creation for the organizations; therefore, to be able to

investigate the real value generation process of CRM, its different components should

be aligned and linked together.

Many researches have been conducted to empirically and theoretically examine the

value process of CRM such as its impact on firm performance. We have discussed the

concept of 'customer retention' and its importance for firms in the previous chapter

and also in the first part of this chapter. In this section, a summary of our literature

survey of studies linking CRM to customer retention (as the considered firm

performance in this study) is presented. Table 2.1 provides this review. Besides, table

2.2 exhibits the analysis of these studies based on addressed or not addressed

important issues from the viewpoint of this research. As it can be noticed, some of

these addressed issues are more technology-oriented while others are more strategic.

However, to have a broader view, the important issues which have not been addressed

in these studies are brought, too.

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Table 2.1: Summary of studies linking CRM to customer retention

Table 2.1: (continued)

Winer

(2001)

Reference CRM components Mediators Findings

Customer retention

programs of CRM:

- Loyalty programs

- Customization

- Customer service

- Community building

- Customer satisfaction

- Customer retention

- Each of the loyalty programs, customization, customer service,

and community building affects customer satisfaction

significantly.

- Customer satisfaction has a great impact on customer

retention.

- IT knowledge

- Inter-firm cooperation

- Customer-focusing

technology usage

- Organizational size

- There is a positive relationship between IT knowledge and

customer-focusing technology usage.

- Customer-focusing technology affects customer retention.

- The effect of Inter-firm cooperation on performance in large

banks is more than small banks.

- The association of organizational size with the relationship

between customer-focusing technology and customer

relationship outcome (such as customer retention) is negative.

Lüneborg

and Nielsen

(2003)

Verhoef

(2003)

RMIs:

- Loyalty programs

Direct mailing-

- Customer retention

- Commitment has a significant effect on customer retention.

- RMIs (Loyalty programs and direct mailing) affect customer

retention.

Eng (2004)

- Industry attractiveness

- Resource advantage of

customer portfolio

- Long-term value of

customer portfolio

- The correlation between industry attractiveness and resource

advantage of customer portfolio is positive and considerable.

- There is a weak positive correlation between long term value

of customer portfolio and customer retention.

Reinartz et

al. (2004)

Customer-facing level

of CRM process:

- Relationship initiation

- Relationship

maintenance

- Relationship

termination

- CRM technology

- Organizational

alignment

- Only for initiation and maintenance level CRM process

implementation associates better customer retention.

- A CRM-compatible organizational alignment plays the role of

moderator between CRM processes and firm performances

(such as customer retention).

- Using large CRM technology proportion does not affect

customer retention as it is expected.

Wang et al.

(2004)

- Customer value

- Customer satisfaction

- Customer value (customer's perception) is a determinant of

customer satisfaction and customer loyalty.

- There is a positive relationship between customer satisfaction

and customer retention.

Roh et al.

(2005)

- Process fit

- Quality of customer

information

- Efficiency

- Customer satisfaction

- Among CRM initiatives, process fit is the only one that has

direct effect on performance.

- CRM elements influences efficiency and efficiency has

significant impact on customer satisfaction

- Customer satisfaction affects customer retention.

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Table 2.1: (continued)

- CRM technology

- Top management

commitment

- CRM orientation

- Organizational

alignment

- Customer

heterogeneity

- CRM activities

Greve and

Albers

(2006)

- Use of CRM technology affects performance indirectly

(including customer retention).

- Top management commitment and customer orientation only

affect the retention performance.

- Except customer orientation and top management

commitment, all variables affect each of initiation, maintenance,

and retention performances directly.

Coltman

(2007)

- Superior CRM

capabilities

- Reactive and proactive

market orientation

- CRM capability affects customer retention.

- The relationship between CRM capability and customer

retention is partially mediated by proactive market orientation,

but reactive market orientation does not show a significant

mediation impact.

Keramati et

al. (2009b)

CRM resources:

- Technological

resources

- Human resources

- Organizational

resources

- CRM resources (Technological, human, and organizational

resources) positively affect CRM processes.

- The extent of affected CRM processes is directly associated

with improved CRM process capabilities.

- There is a positive association between CRM process

capabilities and firm performance.

Keramati et

al. (2010)

- CRM resources

- CRM processes

- CRM process

capabilities

- Firm performance

(such as customer

retention)

- Firm performance

(including customer

retention)

- Technological CRM resources affect organizational

performance (such as customer retention) when they are aligned

with infrastructural resources.

- Deployment of all CRM resources leads to better customer

retention.

- While the integration of organizational resources and

capabilities (analytical and operational capability, and

information resources) may impact retention process, they are

not enough to provide desired firm performance of acquiring

new customers and improving relationships with them.

- Implementation of

enterprise CRM

- Customer retention

- Relationship

expanding

- Organizational

resources

- Organizational

capability for CRM

implementation

Kim et al.

(2010)

- CRM implementation impacts customer retention only if the

appropriate company stakeholders support it adequately.

- Customer heterogeneity impacts customer retention rate

significantly.

- The effect of technological implementations with employee

support on performance is significant and positive for both the

initiation and the maintenance aspect.

- Customer acquisition

- Customer

maintenance and

retention

- Technological

implementation of

CRM

- Organizational

implementation of

CRM

Becker et

al. (2009)

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Table 2.2: Analysis of studies linking CRM to customer retention

Lüneborg

and

Nielsen

(2003)

- Measures significant IT related capabilities (for example

information analysis skills) from the view of RBV.

- Addresses two levels of organizational outcome. The

first level which is ―customer-focusing technology usage‖

measures these capabilities as ―market orientation‖ and

―frontline support‖.

- While investigates the relationship between adoption of

CRM and customer performance, it does not address the

way of relating this adoption to performance.

- Does not pay attention to CRM processes.

- Many human and organizational aspects are not

considered.

Reference

Addressed issues

Not addressed issues

Eng (2004)

- examines the effects of three significant strategic views

in attaining customer performance (including customer

retention).

- Emphasizes on the scales measuring the specified three

constructs.

- The variable of ―competitive characteristics‖ is

examined as an important indicator of industrial view.

- It does not directly address CRM and its applications.

Reinartz et

al. (2004)

- Specifies and assesses the impacts of attractive three

stages of CRM processes at customer-facing level for

practitioners.

- evaluates the impact of technological CRM resources

on the relationship between CRM processes and

organizational outcome (including customer retention).

- Measures some company performances such as

customer retention.

- Considers industry as a control variable and say that

benefits of CRM such as customer retention are

approximately the same among different industries.

- Evaluates the relationship between CRM processes and

firm outcome (including customer retention) directly.

However, it does not address the benefits gained by CRM

which improve the customer retention.

Wang et al.

(2004)

Winer

(2001)

- Addresses the relationship between customer

satisfaction and customer retention.

- Specifies all the determinants of customer retention and

gives them in the form of a comprehensive set of

customer retention programs.

- Discusses CRM processes broadly.

- Focuses on suggesting a comprehensive set of customer

retention programs; As a result, the significance and priority

of each program have gone unnoticed.

- It does not say that which program affects customer

retention directly and which program affects it indirectly.

- Organizational resources are not addressed enough.

Verhoef

(2003)

- Investigates the impact of relationship marketing

instruments (RMIs) and customers‘ relationship

perceptions (CRPs) on customer retention.

- Tries to find out whether different variables of RMIs

and CRPs affect customer retention.

- Human aspect of CRM especially employees are not

addressed.

- Management CRM processes are not considered.

Roh et al.

(2005)

- Focuses on behavioral variables of CRM performance and

does not mention how they are created and help to retaining

existing customers.

- Measures behavioral outcomes of CRM such as

emotional value from customer's view and relates them

to customer retention.

- The emphasis of the research is on the technological

resources of CRM and as it is obvious, the considered

efficiency aspects are only from technology perspective,

therefore, they are not enough to enhance the customer

retention.

- The study does not address the people aspect in CRM

programs.

- emphasizes on important aspects of CRM (for example

CRM systems and customer information).

- It argues why the CRM system affects profitability and

customer retention by considering efficiency aspects of

the CRM system.

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Table 2.2: (continued)

- Assesses the impact of technological CRM resources

on organizational outcome.

- Moreover, it views top management commitment and

customer orientation as aspects of CRM.

- It measures CRM performance including customer

retention at three stages of CRM processes.

- Evaluates the resource capabilities of CRM based on

RBV.

- By considering the four perspectives of the balanced

scorecard, it assesses organizational outcome.

- Investigates the mediating effect of conversion

feasibility (i.e., the organization's capability in following

the specified path when the CRM resource capabilities

are built) on the benefits gained by CRM.

- It does not pay enough attention to human resources of

CRM. For example it does not consider employee

experiences.

- Determines the factors affecting customer retention, but

it does not say why these effects happen.

Greve and

Albers

(2006)

- Since it evaluates the CRM resources too broadly, some

of CRM aspects about customer retention (such as

efficiency) are not considered.

- CRM processes are not addressed directly.

Coltman

(2007)

- Proposes a framework to investigate the relationship

between CRM resources, CRM processes and process

capabilities, and firm performance based on RBV and

process oriented approach.

- Emphasizes on the importance of human and

organizational resources of CRM and aligning them to IT

and CRM technology.

- Evaluates the effect of all CRM resources on CRM

processes.

- Discusses CRM processes and CRM process

capabilities on both operational and management

processes.

- Customer facing level of CRM process is not discussed

directly.

Keramati et

al. (2009b)

- This study focuses on the role of CRM resources based

on RBV; therefore, it does not pay enough attention to

CRM processes.

- Investigates the role of technological, human, and

organizational resources of CRM on firm performance

including customer retention based on RBV.

- Addresses and discuses the important role of human

and organizational CRM resources and concludes that

without efficient use of these two resources, investments

and large use of IT and CRM technology can not lead to

expected performance.

Keramati et

al. (2010)

- It does not examine CRM and its applications directly

and deeply.

- Considers information resource and analytical

capability as the organizational resources and capabilities

and talks about the impact of integrating them on

customer retention.

- Addresses the technology, process, and people aspects

in implementing a successful customer retention strategy.

Kim et al.

(2010)

- This research emphasizes on the effect of technological

and organizational CRM resources on customer

retention, but does not pay enough attention to human

CRM resources.

- The capabilities of firm in CRM implementation is

discussed enough, but the CRM process capability is not

general discussed.

- Investigates what effect firms can expect CRM to have

on customer retention and how this effect can be

leveraged.

- Presents a conceptual model that tries to find the

relationship between organizational and technological

resources. Also, the impact of management and

employee support on CRM process-related outcome is

addressed.

- Measures performance in terms of the initiation,

maintenance, and retention of gained by CRM.

Becker et

al. (2009)

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3. Frame of Reference

3.1. Introduction

In the previous chapter, we presented a literature survey of several studies linking

CRM to customer retention and elaborated on their important addressed and not

addressed issues from the viewpoint of this study. The two models presented by

Winer (2001) and Keramati et al. (2010) appear as the most suitable ones for this

thesis. In fact, in comparison with other studies, they enable the researcher to test all

CRM resources and comprehensive set of customer retention programs with regard to

firm performance and most importantly customer retention. Also these models are

found to be the best for collecting data from banks employees working in electronic

banking section in Iran. In addition, using these models, measuring the effectiveness

of CRM resources, processes, and retention programs on customer retention in the

mentioned environment becomes easier and more accurate. These models were fully

explained in the previous chapter. We will consider them as a reference to state our

research hypotheses. They are presented again in this chapter to be combined as the

final research model for this thesis.

In this chapter, by gaining insight from the RBV and the process-oriented approach,

we propose a framework to investigate the relationship between CRM resources,

CRM processes, customer retention programs of CRM (as the CRM process

capabilities), and finally customer retention as the result of these programs. Thus, in a

broad view, we will be investigating the impact of CRM on customer retention. The

dimensions of each of the constructs have been specified and the research

hypothesizes are formulated.

3.2. Resource-based View of the firm (RBV)

RBV emphasizes on all the firm resources and sees them as the specific and valuable

assets. According to many academicians if organizations want to achieve competitive

advantage, they should compete based on unique corporate resources. Thus, two

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tangible and intangible resources which are complementary to CRM technology and

also not duplicable by competitors, namely as human and organizational resources are

introduced by researchers and practitioners (Keramati et al., 2010).

3.2.1. Technological CRM resources

As mentioned, CRM technologies are the tools enabling the firms to get the right

information to the right person at the right time and are divided into three parts of

‗communicational‘, ‗operational‘ and ‗analytical‘ technologies (Keramati et al.,

2010).

3.2.2 Infrastructural CRM resources

Based on Keramati et al. (2010), CRM is a marketing strategy and its infrastructure is

based on the non-technological CRM resources which academicians call

'infrastructural CRM resources'. They are classified into ‗human CRM resources‘ and

‗organizational CRM resources‘.

Human CRM resources

Human CRM resources consist of customer facing and non-customer facing

employees' attitudes, technical skills, and ability to convert customer data to

knowledge. Academic researchers claim that among all CRM resources, human

resources are the most important ones (Keramati et al., 2010).

Organizational CRM resources

Some researchers such as Keramati et al. (2010) emphasize that to implement a

successful CRM program, some changes in culture of firms are required. For

example, the firms must consider some elements including training the employees,

and top management commitment.

Our first hypothesis related to this aspect is the following:

H1. CRM resources have direct effect on CRM processes.

3.3. Process-oriented approach

The process-oriented approach examines the impact of information technologies on

intermediate business process. Indeed, a process management orientation is a

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necessity for a CRM program to be successful; therefore, to ensure the efficient use of

firm resources toward the creation of expected firm performance, managers must

focus on CRM processes effectively (Keramati et al., 2010).

3.3.1. CRM processes

According to Keramati et al. (2010), CRM processes can be divided into operational

CRM processes and management CRM processes. They explain that operational

CRM processes include the CRM's tasks in a firm's value chain, but management

CRM processes are about all CRM's tasks related to the administration, allocation,

and control of all resources in firms. Based on this study, the following hypothesis can

be developed:

H2. CRM processes have direct effect on customer retention programs of CRM.

Figure 3.1 illustrates the CRM value creation process and also the relationship

between CRM resources, CRM processes, CRM process capabilities, and the

organizational performance.

Fig. 3.1 CRM value generation process

Reference: Keramati et al., (2010, p. 1176)

3.4. Customer retention programs of CRM

As it has been discussed in the previous chapter, process capabilities have mediating

effect between firm's resources and performance to meet a specific objective. This

research considers the customer retention as the final firm performance and

investigates the impact of CRM resources, processes, and customer retention

programs of CRM (as the CRM process capabilities) on customer retention (as the

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organizational performance). Winer (2001) believes that if firms want to efficiently

retain their profitable customers, they need to implement a comprehensive set of

relationship programs including: frequency/loyalty programs, customer service,

customization, and community building.

Loyalty/Frequency Programs

These programs provide rewards to targeted customers in order to encourage them to

buy or demand for services repeatedly. However, loyalty programs can work more

effectively if firms increase customer switching costs and build barriers to entry

(Winer, 2001).

Customer Service

Customer services are two types including Reactive and Proactive services. Reactive

services are deployed by firms when a customer faces problem such as product failure

or question about a bill and contacts the company to inform and solve them. Proactive

services are used when the firms do not wait for customers to contact them and are

determined in holding a dialogue with their customers prior to use reactive solutions

such as complaint handling (Winer, 2001).

Community

Community is online network of customers that firms build it in order to enable their

customers to exchange information about the products or services and also interact

between themselves and the company easier and more personally (Winer, 2001).

Customization

Winer (2001) states that customization is about the creation of products and services

based on each customer's taste. In this way, each customer is able to choose a product

from a list or order the item with the favorite quality or characteristics. Therefore, the

final formulated hypothesis is the following:

H3. Customer retention programs of CRM have direct effect on customer retention.

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3.5. Research model

Considering the above mentioned approaches, programs, and hypothesizes, figure 3.2

proposes the conceptual model for the impact of CRM on customer retention in

electronic banking of Iranian banks.

Fig. 3.2 The research model

Technological

CRM

Resources

Infrastructural

CRM

Resources

CRM

Processes

Customer

retention

programs of

CRM

Customer

Retention

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4. Methodology

4.1. Introduction

In this chapter, we present and describe the procedure and the methods that have been

used to conduct this work. The research purpose, research approach, and the research

strategy used in this thesis are also described and justified. Furthermore, the data

collection, sample selection, questionnaire design, the ways to evaluate the validity

and reliability of the obtained results will be discussed.

4.2. Research approach

4.2.1. Qualitative versus Quantitative Approach

According to Hair et al. (2007, pp. 151–153), there are two types of research

approaches available to researchers, namely quantitative and qualitative. The data in

quantitative approach are numbers and lends itself to statistical analysis in order to

imply the characteristics of something. An important point to consider is that

structure, representativeness, and providing objectivity are important strengths of

quantitative research. This approach provides objectivity because the respondents are

the ones who provide the numbers; therefore researcher's opinion does not have any

impact on testing the hypothesis. In a general sense, this approach is used in

explanatory researches. Furthermore, it allows generalization and enables the

researcher to predict the future.

The data in qualitative approach is generally collected by observation or unstructured

interviews. They are usually in the form of words, phrases and pictures. It is important

to say that this approach provides a deeper understanding of the phenomenon the

researcher wants to find and therefore furnishes a holistic view. Also, because

judgment has an important role in this approach, it is difficult to replicate the findings.

Using this approach, the researcher can choose small sample sizes. Moreover, the

author will have less concern about the representativeness of the results (Hair et al.,

2007, pp. 151–153; Saunders et al., 2009, p. 482).

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From the above discussion and since the results are presented by digits and they are

analyzed by statistical methods, we can conclude that quantitative approach proves to

be suitable for this research.

4.2.2. Deduction versus Induction Approach

In another classification, there are two main approaches which are: deductive and

inductive approaches. In the deductive approach, the researcher develops a theory or

hypothesis and then designs a strategy to test it. This approach is more discussed in

natural sciences and scientific principals where the explanation, anticipation of

phenomena and the prediction of their occurrence are presented by laws. Deduction is

a highly structured approach in which there is the search to explain causal

relationships; the data are quantitative and they are collected through samples of

sufficiently large size to generalize the results. It is clear that in this situation, the

researcher is independent of the subject under study (Hair et al., 2007; Saunders et al.,

2009, pp. 124–127).

In the induction approach, the collected data are analyzed and as a result, a theory will

be formulated and developed. This approach tries to gain a close understanding of the

concepts humans attach to events. Inductive researches use a more flexible structure

in which the researcher is a part of that research and collects the qualitative data

through a small sample size; therefore there is not much concern with the

generalization (Hair et al., 2007; Saunders et al., 2009, pp. 124–127).

In the present work, we have developed some hypothesizes and designed a strategy to

test them. On the other hand, the data we used are quantitative and they were

collected through a large sample. Hence, we can say that the deductive approach is the

one used for this study.

4.3. Purpose of the Research

The way a researcher asks the research question would result in a descriptive or an

explanatory research, or both of them. Considering the research question, a researcher

definitely must think about the research purpose which is classified as exploratory,

descriptive and explanatory. However; Academicians believe that a research may

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have more than one purpose which may change over time (Zikmund, 2000; Saunders

et al., 2009, p. 139).

4.3.1. Exploratory research

Exploratory research is useful when there is little information about the problem or

when the research questions are vague. This kind of research is also useful to discover

new patterns, ideas, relationships and so on; therefore this research is not appropriate

to test specific hypotheses. It should be mentioned that this design relies more on a

qualitative approach; however, usage of quantitative techniques is possible, too. The

great advantage of an exploratory study is that it is flexible. It means the researchers

using this design, can change their direction when a new insight occurs to them (Hair

et al., 2007, pp. 154–155; Saunders et al., 2009, pp. 139–140).

Based on Saunders et al. (2009, p. 140), there are three main principal ways to

conduct an exploratory research as. They include:

- Searching the literature;

- Interviewing focus group;

- Interviewing the 'experts'.

4.3.2. Descriptive research

Sounders et al. (2009, p. 140) state: "The object of descriptive research is to portray

an accurate profile of persons, events, or situations". Descriptive research is a useful

design to obtain the data describing the characteristics of something; thus the

questions starting with who, when, how, which, why, and what can be answered by

this research design. In this situation, the researcher should know the phenomena very

well before collecting the data (Hair et al., 2007; Saunders et al., 2009).

Using descriptive research, the researcher can test the hypothesis. Also the data is

usually collected through structured observation or interviews asking structured

questions. To analyze these data, descriptive research uses descriptive statistics which

include frequency counts, measures of central tendency, variations and so on. (Hair et

al., 2007, p. 155; Saunders et al., 2009, p. 140).

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4.3.3. Explanatory research

Explanatory researches study a problem or a situation in order to finding out the

relationship between dependent and independent variables. Explanatory research is

the most appropriate design to test whether one variable causes or determines the

value of another. For example, does a change in variable X (CRM) cause a change in

variable Y (customer retention)? (Hair et al., 2007, p. 160; Saunders et al., 2009, p.

140).

Hair et al. (2007, p. 160) explain the four conditions a researcher looks for in testing

these relationships as:

1. The time sequence, which means occurring the cause before the effect.

2. Covariance: which means changing of cause is associated with changing

the effect.

3. Organization association: meaning that the relationship is true and is not

because of another variable affecting the dependent and independent variables.

4. Theoretical support: The existence of the relationship between variables is

supported by a logical explanation.

Hence, considering the purpose and the research question of the present work, we can

claim that this research is mainly explanatory. In addition, because the data is

collected through a questionnaire and is used to find out the relation between the

dependent and independent variables of this study, we can conclude that it is

explanatory.

4.4. Research Strategy

Research strategy is a plan showing the way how the research goes on and how the

research questions will be answered. Different research strategies can be used for

explanatory, descriptive, and exploratory researches. Also, some of them belong to

inductive approach, others to the deductive approach. However, researcher's choice of

a research strategy depends on the research question, available time and resources

(Zikmund, 2000; Saunders et al., 2009, p. 141).

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Yin (1994) has identified five research strategies including experiment, survey, case

study, archival records, and history. He has distinguished these strategies according to

the three criteria: form of research question, the extent of control a researcher has on

actual behavioral events and also the degree of focus on contemporary events. These

conditions for each research strategy are illustrated in table 4.1.

Table 4.1: Relevant Situation for Different Research Strategies (Yin, 1994, p. 6)

Form of research Requires control Focuses on

Strategy question over behavioral contemporary events

events

Experiment How, Why Yes Yes

Who, What, Where, No Yes Survey

How many, How much

Who, What, Where, No Yes/No Archival

Analysis How many, How much

History How, Why No No

Case study How, Why No Yes

The purpose of an experiment strategy is to study the casual relationships. It tends to

be used in explanatory and exploratory research and can answer 'how' and 'why'

questions. The survey strategy is usually used in deductive approach and allows the

researcher to collect quantitative data. It is also the most popular strategy to answer

who, what, where, how many and how much questions. Thus, it tends to be used in

descriptive and exploratory research (Saunders et al., 2009, pp. 142–145).

Another research strategy is 'case study'. Saunders et al. (2009, p. 145) state that case

study is: "a strategy for doing research which involves an empirical investigation of a

particular contemporary phenomenon within its real life context using multiple

sources of evidence". This strategy is very appropriate to answer the 'how' and 'why'

questions and very useful for exploratory and explanatory research.

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Archival research is another research strategy which uses recent as well as historical

documents and records as the principal source of data. Archival research enables the

researcher to answer the research questions which focus upon the past and changes

over time, be they explanatory, exploratory or descriptive and tries to answer the

Who, What, Where, How many, and how much questions (Saunders et al., 2009, p.

150).

History is the final strategy which does not deal with contemporary events and there is

no need to control the behavioral events. It is also an appropriate strategy to answer

the 'how' and 'why' questions (Saunders et al., 2009).

Since in this study we want to investigate the impact of CRM on customer retention in

e-banking of Iranian banks from employee's perspective and also as the main question

of this research is the form of 'what', survey seems to be the best research strategy for

this study in which we are using a questionnaire.

4.5. Time Horizon

In planning a research, the following question is important 'is this research done in a

particular time or it is done over a given period'. According to Sounders et al. (2009,

p. 155), a study of particular phenomena taken at a particular time is a cross sectional

research. This study often employs the survey strategy; however, it may also be used

in qualitative approaches. On the other hand, those studies taking place over time are

called longitudinal studies. Their main strength is their capacity in studying change

and development (Saunders et al., 2009, p. 155).

Investigating the impact of CRM on customer retention in electronic banking is

conducted in a limited period of time; so we can conclude that this study is a cross-

sectional research.

4.6. Data Collection method

Zikmund (2000) and Sounders et al. (2009, p. 256) say there are two classifications

for collected data which are: primary and secondary data. Primary data can be

collected for instance through interview, observation, and questionnaire. On the other

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hand, secondary data is the information collected from the studies done before and

can be collected from the Internet or libraries.

Sounders et al. (2009, p. 360) state that questionnaire is one of the most widely used

techniques to collect data within the survey strategy and since each respondent

answers the same set of questions, it is an efficient technique of gathering responses

from a large sample. For this study, primary data seems to be the most suitable one

and because our research strategy is survey and also the sample is large, we used the

questionnaire as the instrument to collect the primary data in this research. Our

questionnaire has been distributed to the top managers in each selected bank branch.

To increase the response rate, meeting was the first method to give the questionnaires.

Due to the time and accessibility limitations, some of the respondents received the

questionnaires by e-mail along with an explanation letter.

4.7. Sample Selection

Ideally, a researcher would like to obtain the data from all members of the population.

However; it is almost impossible; therefore a small subset of the population which

must be representative of all the members in that population is drawn. This subset

which mirrors the characteristics of population is called sample (Zikmund, 2000; Hair

et al., 2007, p. 170).

According to Hair et al. (2007, p. 171), a set of well defined procedures to obtain a

representative sample is as follows:

1. Defining the population under investigation (The complete group of elements

relevant to the research).

2. Determining the appropriate sample frame (a complete list of all the elements in

that population).

3. Sampling method selection.

4. Calculation of the sample size.

5. Implementation of the sampling plan.

There are two broad categories of sampling methods available including probability

and non-probability methods. Both of these methods are explained in details below.

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4.7.1. Probability Sampling

According to Hair et al. (2007, p. 175) and Saunders et al. (2009, p. 214), drawing a

probability sample is based on this premise that each element in the population has a

known and nonzero probability of being selected. Also, in this method selected

samples are usually large to be representative of the population; therefore, with a

specified level of confidence, the findings can be generalized to the population under

investigation. Probability sampling methods are classified as Simple random

sampling, Systematic sampling, Stratified sampling, Cluster sampling, and Multi-

stage sampling.

Simple random sampling

In the simple random sampling (sometimes called just random sampling), all the

elements in the population have equal chances of being selected. In this method, the

resulting sample is representative of the population if its calculated size is sufficiently

large. It is also an appropriate method for a geographically dispersed area if the

researcher uses an alternative data collection technique such as telephone interviewing

or online questionnaires (Hair et al., 2007, pp. 175–176; Saunders et al., 2009, p.

222).

Systematic sampling

In systematic sampling, an initial starting point on a list is randomly selected and after

that, the researcher selects every nth

element in the sampling frame. Thus, to draw the

sample, we should first calculate the sample size and the sampling interval. This

method is useful for geographically dispersed cases only if face to face contact in not

required. Also, using this sampling method, the researcher can obtain representative

data (Hair et al., 2007, p. 177; Saunders et al., 2009, p. 226).

Stratified sampling

To use this method, the sampling frame must be divided into distinct and relatively

homogeneous subgroups which are called strata. This is usually done by the

researcher's past experience or specified by the client. Then the researcher determines

the size of total sample and the size for each individual strata. The composite of the

samples taken from the strata shapes the stratified sample and to select the elements of

this sample, systematic or simple random samples of the strata of the population must

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be drawn. The strength of this method is that it helps increasing the accuracy of the

sample information (Hair et al., 2007, p. 178).

Cluster sampling

In this method, the researcher views the population as made up of heterogeneous

groups, each of them is called a cluster. A cluster group can be for example

geographic areas, households, firms, and so on. It should be noted that cluster

sampling can produce representative data if it is done properly. It is important to say

that in this method the sampling frame is the list of clusters rather than a list of

individual elements in the population. In order to increase the representativeness of

the sample, the researcher must increase the number of sub-areas (Hair et al., 2007, p.

180; Saunders et al., 2009, p. 230).

Multi-stage sampling

Multi-stage sampling which is also called Multi-stage cluster sampling is a

development of cluster sampling method. This method is useful when the population

is a large geographical region and face to face contact with the elements is needed or

it is hard to construct a sampling frame for that region; however, it can be used for not

geographically based discrete groups (Hair et al., 2007, p. 181; Saunders et al., 2009,

p. 231).

4.7.2. Non-probability Sampling

Hair et al. (2007, pp. 181–182) explain that in this technique, the probability of each

element in the population is not known and the selected sample is not necessarily

representative of the population statistically. In this situation, to select the elements in

the sample the researcher uses expert judgment, experience, and convenience.

Therefore, unlike probability samples, the results can not be generalized to the

population (Hair et al., 2007, pp. 181–182). These authors also discuss that the most

common types of non-probability sampling techniques include Convenience

sampling, Judgment sampling, Snowball sampling, Self selection sampling, and

Quota sampling.

As it has been mentioned before, the population of this survey is all Iranian banks'

managers (the top manager in each branch). However, because of the time limitation,

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we limited this population to all the top banks' mangers in the province of Tehran

which is the heart of financial activities. In addition, according to Bank and Insurance

Manifest agency of Iran (2010), it has the highest rate of e-banking usage. In the next

level, among all of the mentioned banks, we selected four of them including two

governmental and two private banks. Among governmental banks, bank Refah and

bank Maskan and among private banks, bank Pasargad and bank Saman have been

chosen. We chose these banks because they are old and in comparison with other

banks they are more pioneering in e-banking. In addition, they have the greatest

number of branches in Iran and have been recognized as the most effective banks in e-

banking context in Iran.

In the final level, we used simple random sampling to choose the branches of each

bank to distribute the questionnaires to the respondents with equal chances of being

selected. Therefore, according to the definitions of each sampling method in the

previous section, we have used a probability stratified sampling method.

4.8. Sample size:

Determining an efficient sample size is of great significance in any research. This is

because too small samples may lead to inaccurate results, while samples that are too

large may waste time and resources (Hair et al., 2007, p. 182). According to Azar and

Momeni (2005), research populations are divided into two kinds: restricted and

unrestricted. Since banks have restricted number of managers, our research population

will be restricted. Therefore, we have used restricted sampling formula (see below) to

calculate the sampling size. Table 4.2 presents the descriptive statistics in sampling.

In this research, first we run 30 questionnaires (shown with N in table 4.2) and then a

sample size is calculated.

Table 4.2: Descriptive statistics in sampling

Descriptive Statistics

N Minimum Maximum Mean Std. Deviation Variance

TOTAL 30 4.65 6.55 5.3626 .62062 .385

Valid N (listwise) 30

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According to this table, the calculated value for standard deviation (σ) is .62062 and

the value for variance is .385. These values are very important for calculation of

suitable sample size using the formula presented by Azar and Momeni (2005) (see

below).

In this formula, n is the total sample size for this research. Also total population is

shown by N. In this research, the total population is sum of the numbers of all banks'

branches under study. The number of the branches of each bank is presented in table

4.3. Thus, as it can be vividly seen, N is equal to 553. Also, it should be said that, for

the 95% level of confidence, z value is 1.96.

2

2

22

2

2

2

)1(

zN

zNn

28686.2

817.90

0.62062)(96.1)05.0)(1553(

0.62062)(96.1553222

22

n

Therefore, the total sample size based on the formula for this research is equal to 286.

Table 4.3 presents the sample size for each of the four banks (Refah, Maskan,

Pasargad, and Saman). In this table, the sample size for each bank is calculated as

follows:

(The percentages of units in the research population) * (the total sample size) = the

sample size for each bank

Table 4.3: The sample size for each bank

The sample size

for each bank

The total

sample size

based on the

formula

The percentages

of units in the

research

population

Number of

the branches

of each bank

Sampling

method

0.266 * 286 = 76

286

0.266

Refah:147

Stratified 0.333 * 286 = 95 0.333 Maskan: 184

0.287 * 286 = 82 0.287 Pasargad:

159

0.114 * 286 = 33 0.114 Saman: 63

286 286 100 553

Total

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4.9. Questionnaire Design

The main measure instrument used in this study is the questionnaire. In this section,

we describe how the questionnaire is designed and developed. Scales for this survey

were developed through the literature reviews and the research model. Appendix A

exhibits the resources from which the scales were drawn.

The questionnaire includes two types of questions. The first type consists of

demographic questions about the top managers in each branch and the second type

includes the different questions concerning their attitude about the impact of CRM on

customer retention in e-banking in the branch they are working in. These questions

are classified into questions about technological CRM resources, Infrastructural CRM

resources, CRM processes, customer retention programs of CRM, and customer

retention. All of the scales in the second type were measured using 7-point Likert

scales (strongly agree, agree, somehow agree, neutral, somehow disagree, disagree,

and strongly disagree).

In the next step, in order to improve the validity and reliability of the data, the first

version of the questionnaire has been modified after submitting it to eight academic

and bank experts as a pilot test. The final English version of the questionnaire is

shown in appendix B and its Farsi version in appendix C.

4.10. Pilot Test

We conducted a pilot test in order to evaluate the respondent' comprehension of the

questionnaire and estimate the average time to complete it. To do this, first we

translated the questionnaire to Farsi. This version of the questionnaire was distributed

to eight decision makers in banking industry of Iran from Refah bank, Maskan bank,

and Saman bank.

From the results, we realized that there were some modifications that should be

performed for some questions. In addition, the wording and relevancy of questions

were checked and based on that, some other questions were modified, too.

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4.11. Data Analysis

In order to make effective decisions in the business world, managers and decision

makers must be able to use statistical data analysis techniques and methods (Zikmund,

2000). In this research, data analysis is done in two parts by descriptive and

interferential statistics. In the first part, the demography of statistical sample is

investigated and the results are shown in frequency tables and diagrams. However, in

the inferential part, the data are analyzed using a series of steps which have to be

followed. To do so, the first step is doing Kaiser-Meyer-Olkin and Bartlett's test

(Momeni, 2006) to measure the adequacy of the sampling. After that, the exploratory

factor analysis will be performed. Indeed, when using factor analysis the researcher

can gain better results when applying confirmatory factor analysis and structural

equation model. This is because the numbers of factors, total variance explained, and

communalities of the questions can be gained from factor analysis. Thus, the

researcher can delete the questions with little communalities. The final step consists in

studying and testing the research hypothesizes using Structural Equation Model

(Confirmatory Factor Analysis). To do so, the following steps will be followed:

1. Model expression

2. Model estimation

3. Correction of the model (if needed)

4. Hypothesis test

In the structural model we want to understand whether the relationships between the

latent traits which are taken from the theory are confirmed by data collected from the

sample or not. After defining the latent and evident variables (gained after doing the

factor analysis), the research conceptual model will be presented. After that, we will

study the accuracy of the measuring model by its indices and correct the model if it is

needed (Kalantari, 2009). Studying the significance of the relationships with t-value

and studying the correlation are the next steps.

To check whether the model is a proper one (accuracy of the measuring model) or not,

some indices including chi-square relative to its degree of freedom, root mean square

error of approximation (RMSEA), p-value, goodness of fit index (GFI) and also

adjusted goodness of fit index (AGFI), comparative fit index (CFI), and normed fit

index (NFI) will be studied. To investigate the mentioned issues, the standard model

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will be presented. In the next step, we study the significance of the results in the

model. After completing the all mentioned stages in presenting the model, the primary

conceptual model is hereby confirmed. Finally, paying attention to the model with

significant numbers and standard estimation model, the research hypothesizes will be

studied.

4.12. Validity and Reliability

In conducting a research the researcher always tries to minimize the measurement

error. This error is reduced when the variables accurately and consistently represent

and measure the concept. Accuracy is related to the term 'validity' while consistency

is referred to 'reliability' (Hair et al., 2007, p. 240).

4.12.1. Validity

The extent to which a construct measures what it is intended to measure is called

'validity'. No measurement error occurs when the construct has perfect validity (Hair

et al., 2007, p. 246). To ensure the validity of this research the approaches mentioned

below have been adopted:

- To make sure that the measurement scales were adapted appropriately, the

questionnaire has been translated into Farsi.

- The questionnaire has been reviewed by the supervisors of this work and banks

experts to remove and correct the potential problems before sending it to the

respondents (content validity).

- To check the construct validity of the questionnaire and also to find out if all

indicators of each variable (construct) measure what is expected, 'exploratory

factor analysis' (calculated in next chapter) has been used. The calculations for this

section lead to satisfactory results.

4.12.2. Reliability

According to Hair et al. (2007, p. 241), if the repeated application of a survey

instrument results in consistent scores, we can consider it reliable. They also state:

"reliability is concerned with the consistency of the research findings". In other

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words, a research can be considered reliable, if its measuring procedure yields the

same results on repeated trials (Saunders et al., 2009, p. 156). In this research,

Cronbach's alpha has been used to measure the reliability of the items. As it is shown

below, the calculated Cronbach's alpha is equal to .970 which is a very good result.

The calculations for this section are brought in appendix D.

Reliability statistics

Cronbach's Alpha N of Items

.970 63

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5. Data Analysis

5.1. Introduction

In order to analyze the collected data, we should convert them into valuable

information by using statistical tests. Indeed, in any research, data analysis constitutes

one of the most important parts. It is of great significance in studying the accuracy of

hypothesizes. This chapter is drawn up in two parts: descriptive and interferential

statistics. In the descriptive part, the demography of statistical sample is tackled and

in the inferential part the following issues are studied:

1. Kaiser-Meyer-Olkin and Bartlett's test to measure the adequacy of the sampling to

use for exploratory factor analysis (EFA).

2. Exploratory factor analysis for deleting the questions with little factor load and

communalities (for more preparation to do the confirmatory factor analysis).

3. Study and test the research hypothesizes using Structural Equation Model

(Confirmatory Factor Analysis)

4. Lateral Analyses:

- Investigating the correlation between each of the four customer retention programs

of CRM and customer retention by using Pearson correlation.

- Two-sample t-test with independent samples to study the difference between the

respondents' point of view in the two groups of banks (private and governmental

banks)

- Analysis of variance or comparing the means of some communities to investigate

the difference in respondents' point of views in the four groups of banks (for each of

the research variables)

It should be noted that the software packages SPSS 17 and Lisrel 8.5 have been used

to perform the calculations mentioned above.

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5.2. Descriptive Statistics

In this section we have studied the variables of respondents' gender, age, educational

level, field of study, and also the type of the banks and the belonging to each bank in

the research sample. The obtained results have been represented by frequency tables

and graphical methods. In addition, a primary comparison of the four banks under

study in relation to the research variables is done.

Age:

Table 5.1 represents the frequency of the respondents' age. As shown, 0.7% of the

respondents are between 21 to 30, 13.3% are between 31 to 40, 31.8% are between 41

to 50, and 54.2% are over 51. Figure 5.1 illustrates these results.

Table 5.1: The frequency of the respondents' age

Frequency Percentage Cumulative

Percentage

Valid

21 to 30 2 .7 .7

31 to 40 38 13.3 14.0

41 to 50 91 31.8 45.8

x>=51 155 54.2 100.0

Total 286 100.0

Figure 5.1: The distribution of the respondents' age

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Gender:

Table 5.2 represents the frequency of respondents' gender.

Table 5.2: the frequency of respondents' gender

Frequency Percentage Cumulative

Percentage

MALE 286 100.0 100.0

As it can be clearly seen, all the respondents are male.

Educational level:

Table 5.3 represents the distribution of the respondents' educational level.

Table 5.3: The distribution of the respondents' educational level

EDU Frequency Percentage Cumulative

Percentage

Valid

Diploma 23 8.0 8.0

A.D 59 20.6 28.7

BA 191 66.8 95.5

MSc 13 4.5 100.0

Total 286 100.0

As it can be seen in table 5.3, 8% of the respondents have high school diploma, 20.6%

have Associate degree (AD), 66.8% have Bachelor degree, and 4.5% have Master

degree (MSc). Figure 5.2 shows the distribution of the educational level of the

respondents.

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Figure 5.2: The distribution of the respondents' educational level

Field of study

Table 5.4 shows the frequency of the respondents' field of study. As we can vividly

see, 1.7% of the respondents' fields of study are banking, 4.9% are economics, 37.4%

are management, 11.5% are accounting, 3.8% are computer engineering, and 40.6%

are other fields such as statistics and electronic engineering.

Table 5.4: The frequency of the respondents' field of study

Frequency Percentage Cumulative

Percentage

Valid

banking 5 1.7 1.7

economic 14 4.9 6.6

management 107 37.4 44.1

accounting 33 11.5 55.6

computer 11 3.8 59.4

other 116 40.6 100.0

Total 286 100.0

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Figure 5.3 shows the distribution of the respondents' field of study.

Figure 5.3: The distribution of the respondents' field of study

Job experience

According to table 5.5, %28.7 of the respondents' job experience is under 5 years,

15.7% are between 6 to 10 years, 9.8% are between 11 to 15 years, 14.3% are

between 15 to 20 years, and 31.5 are over 21 years.

Table 5.5: the frequency related to the respondents' job experience

Frequency Percentage Cumulative

Percentage

Valid

X<=5 82 28.7 28.7

6 TO 10 45 15.7 44.4

11 TO 15 28 9.8 54.2

15 TO 20 41 14.3 68.5

X>=21 90 31.5 100.0

Total 286 100.0

Figure 5.4 illustrates these findings.

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Figure 5.4: The frequency related to the respondents' job experience

Type of the bank

Table 5.6 represents the frequency related to type of the banks under study and figure

5.5 illustrates these results.

Table 5.6: The frequency related to type of the banks under study

Frequency Percentage Cumulative

Percentage

Valid

Gov 171 59.8 59.8

private 115 40.2 100.0

Total 286 100.0

Figure 5.5: The frequency related to type of the banks under study

Having looked at figure 5.5, we can easily perceive that about 59.8% of the

respondents are working in governmental banks and about 40.2% are working in

private banks.

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The belonging to each bank

Table 5.7 represents the frequency related to each bank in the sample. As this table

reveals, 26.6% of the respondents are working in bank Refah, 33.2% in bank Maskan,

11.5% in bank Saman, and 28.7% are working in bank Pasargad. Figure 5.6 shows

these results.

Table 5.7: the frequency related to the belonging to each bank

Frequency Percentage Cumulative

Percentage

Valid

refah 76 26.6 26.6

Maskan 95 33.2 59.8

Saman 33 11.5 71.3

Pasargad 82 28.7 100.0

Total 286 100.0

Figure 5.6: The frequency related to the belonging to each bank

A primary comparison of the four banks under study in relation to the

research variables

- Means of the four research variables for bank Refah:

The means of the four research variables for bank Refah are represented in table 5.8

(the scores are out of 7)

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Table 5.8: The means of the four research variables for bank Refah

Descriptive Statistics

Refah N Minimum Maximum Mean Std. Deviation

CRM resources 76 4.62 5.13 4.8995 .11671

CRM processes 76 5.33 6.07 5.6390 .18309

Retention

programs of CRM 76 4.57 5.39 5.0170 .15592

Customer

retention 76 5.67 6.67 6.2237 .22698

Valid N (listwise) 76

Means of the four research variables for bank Maskan -

The means of the four research variables for bank Maskan are represented in table 5.9

(the scores are out of 7).

Table 5.9: The means of the four research variables for bank Maskan

Descriptive Statistics

Maskan N Minimum Maximum Mean Std. Deviation

CRM resources 95 4.00 4.49 4.2935 .11625

CRM processes 95 4.53 5.63 5.0274 .20369

Retention

programs of CRM 95 3.74 4.65 4.2756 .19030

Customer

retention 95 5.00 6.00 5.3123 .26547

Valid N (listwise) 95

- Means of the four research variables for bank Saman

The means of the four research variables for bank Saman are represented in table 5.10

(the scores are out of 7).

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Table 5.10: The means of the four research variables for bank Saman

Descriptive Statistics

N Minimum Maximum Mean Std. Deviation

CRM resources 33 5.77 6.18 5.9393 .10263

CRM processes 33 4.33 5.33 4.9000 .22608

Retention

programs of CRM 33 4.60 5.34 4.9801 .15371

Customer

retention 33 5.00 6.33 5.5859 .33365

Valid N (listwise) 33

- Means of the four research variables for bank Pasargad

The means of the four research variables for bank Pasargad are represented in table

5.11 (the scores are out of 7).

Table 5.11: The means of the four research variables for bank Pasargad

Descriptive Statistics

N Minimum Maximum Mean Std. Deviation

CRM resources 82 5.56 6.24 5.8612 .13220

CRM processes 82 5.60 6.47 6.1024 .20176

Retention

programs of CRM 82 5.67 6.30 6.0525 .12710

Customer

retention 82 6.00 7.00 6.4756 .26205

Valid N (listwise) 82

- Means of the four research variables for the two groups of governmental and

private banks

The means of the four research variables for the two groups of banks (governmental

and private) are represented in table 5.12 (the scores are out of 7).

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Table 5.12: The means of the four research variables for governmental and private banks

Group Statistics

Bank type N Mean Std. Deviation Std. Error Mean

CRM resources gov 171 4.5628 .32357 .02474

private 115 5.8836 .12897 .01203

CRM processes gov 171 5.2992 .36146 .02764

private 115 5.7574 .58456 .05451

Retention

programs of CRM

gov 171 4.6051 .40898 .03128

private 115 5.7447 .50547 .04714

Customer retention gov 171 5.7173 .51769 .03959

private 115 6.2203 .49340 .04601

Since all of the scales in the questionnaire were measured using 7-point Likert scales

and all of the scores in table 5.12 are more than the average score (4), we can

conclude that both the governmental and private banks are in favorable condition

concerning the four research variables.

5.3. Inferential Statistics:

5.3.1. Exploratory Factor Analysis

Factor validity is a form of construct validity gained through factor analysis. Factor

analysis is a statistical technique with lots of usage in humanities. In fact, it seems that

usage of factor analysis in the researches in which test and questionnaire are used is

essential (Kalantari, 2009). It should be noted that since number of factors, total

variance explained, and communalities of the questions can be gained from factor

analysis, in this part of the research, we aim at calculating the communalities and

deleting the questions with little communalities. Indeed when using factor analysis;

the researcher can gain better results when applying confirmatory factor analysis and

structural equation Model.

5.3.1.1. The results of Exploratory Factor Analysis for the variable of

"technological CRM resources"

Table 5.13 represents the result of KMO and Bartlett's test for the data related to the

variable of 'technological CRM resources'.

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Table 5.13: KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .958

Bartlett's Test of Sphericity Approx. Chi-Square 5.580E3

df 171

Sig. .000

Since KMO is greater than 0.7 and Bartlett's test significance number is less than 0.05

(sig<0.05), we can say that the data is proper for doing factor analysis. Table 5.14

shows the communalities of the questions related to 'technological CRM resources'.

Table 5.14: Communalities

Initial Extraction

col1 1.000 .627

col2 1.000 .726

col3 1.000 .810

col4 1.000 .764

col5 1.000 .770

col6 1.000 .856

col7 1.000 .808

col8 1.000 .820

col9 1.000 .833

op1 1.000 .880

op2 1.000 .851

op3 1.000 .712

op4 1.000 .615

op5 1.000 .748

anal1 1.000 .871

anal2 1.000 .895

anal3 1.000 .804

anal4 1.000 .733

anal5 1.000 .786

Extraction Method: Principal

Component Analysis.

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Since the numbers of communalities in table 5.14 are greater than 0.05, all the questions

related to the variable of 'technology' are proper in the process of factor analysis; therefore,

no question is deleted. The total variance explained is presented in Table 5.15.

Table 5.15: Total Variance Explained

Component

Initial Eigenvalues Extraction Sums of Squared

Loadings

Rotation Sums of Squared

Loadings

Total % of

Variance

Cumulative

% Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

%

1 10.483 55.176 55.176 10.483 55.176 55.176 6.465 34.027 34.027

2 3.408 17.936 73.112 3.408 17.936 73.112 4.963 26.121 60.148

3 1.018 5.359 78.471 1.018 5.359 78.471 3.481 18.323 78.471

4 .534 2.812 81.284

5 .483 2.541 83.824

6 .361 1.898 85.722

7 .339 1.785 87.507

8 .331 1.742 89.250

9 .294 1.547 90.797

10 .268 1.411 92.208

11 .228 1.201 93.409

12 .216 1.139 94.548

13 .194 1.023 95.570

14 .189 .994 96.564

15 .161 .848 97.412

16 .150 .788 98.200

17 .133 .701 98.901

18 .107 .565 99.466

19 .101 .534 100.000

Extraction Method: Principal Component

Analysis.

The total variance explained in table 5.15 shows that these questions totally form

three factors and these three factors explain and cover about 78.47 % of the variance

of 'technology'. This number indicates the very good power of explanation of research

questions for 'technology'. Later, we will deal with factor loads and naming each

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component (factor loads greater than 0.5 are acceptable). Table 5.16 presents the

Rotated Component Matrix.

Table 5.16: Rotated Component Matrixa

Component

Analytical

Operationa

l

collaborativ

e

col1 .353 .108 .700

col2 .746 .407 .667

col3 .660 .352 .500

col4 .778 .307 .552

col5 .243 .754 .678

col6 .686 .585 .596

col7 .303 .653 .538

col8 -.051 .901 .671

col9 .761 .178 .571

op1 .268 .548 .712

op2 .138 .739 .535

op3 -.017 .747 .393

op4 .149 .766 .077

op5 .184 .571 .623

anal1 .921 -.040 .147

anal2 .946 -.034 .001

anal3 .742 .162 .476

anal4 .625 .186 .555

anal5 .842 -.067 .271

Extraction Method: Principal Component

Analysis.

Rotation Method: Varimax with Kaiser

Normalization.

a. Rotation converged in 6 iterations.

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5.3.1.2. The results of Exploratory Factor Analysis for the variable of

"infrastructural CRM resources"

The result of KMO and Bartlett's Test for the data related to the variable of

'infrastructural CRM resources' is shown in table 5.17.

Table 5.17: KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .892

Bartlett's Test of Sphericity Approx. Chi-Square 1.321E3

df 55

Sig. .000

According to table 5.17, the data is proper for doing the factor analysis. This is

because KMO is greater than 0.7 and Bartlett's test significance number is less than

0.05 (sig<0.05). The communalities of the questions for the variable of 'infrastructural

CRM resources' are presented in table 5.18.

Table 5.18: Communalities

Initial Extraction

HR1 1.000 .384

HR2 1.000 .592

HR3 1.000 .558

HR4 1.000 .686

HR5 1.000 .446

org1 1.000 .757

org2 1.000 .643

org3 1.000 .512

org4 1.000 .649

org5 1.000 .478

org6 1.000 .645

Extraction Method: Principal

Component Analysis.

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Table 5.18 shows the inadequacy of some questions because of a lack of harmony

with the other questions within the process of factor analysis (the first and the fifth

questions of human CRM resources and the fifth question of organizational CRM

resources). As we can clearly see, the communalities of the mentioned questions are

less than 0.50. Total variance explained is brought in table 5.19.

Table 5.19: Total Variance Explained

Component

Initial Eigenvalues Extraction Sums of Squared

Loadings

Rotation Sums of Squared

Loadings

Total % of

Variance

Cumulative

% Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

%

1 4.915 44.683 44.683 4.915 44.683 44.683 4.752 43.201 43.201

2 1.435 13.045 57.728 1.435 13.045 57.728 1.598 14.527 57.728

3 .829 7.538 65.265

4 .763 6.935 72.201

5 .655 5.953 78.154

6 .585 5.317 83.471

7 .528 4.802 88.273

8 .393 3.573 91.845

9 .361 3.286 95.131

10 .305 2.774 97.905

11 .230 2.095 100.000

Extraction Method: Principal Component Analysis.

The total variance explained in above table (before deleting the questions with little

communalities) shows that these questions totally form two factors which explain and

cover about 57.72 % of the variance of 'infrastructural CRM resources'. After deleting

the questions with little communalities (less than 0.5), we will deal with the

mentioned issues again. Table 5.20 represents the communalities of the questions

after deleting the questions with little communalities.

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Table 5.20 Communalities of the questions after deleting the questions with little communalities

Communalities

Initial Extraction

HR2 1.000 .610

HR3 1.000 .736

HR4 1.000 .693

org1 1.000 .769

org2 1.000 .663

org3 1.000 .536

org4 1.000 .702

org6 1.000 .691

Extraction Method: Principal

Component Analysis.

Since the numbers of communalities in table 5.20 is more than 0.5, these questions (the

remaining questions) are suitable in the process of factor analysis. Table 5.21 represents the

total variance explained after deleting the questions with little communalities.

Table 5.21: total variance explained after deleting the questions with little communalities

Component

Initial Eigenvalues Extraction Sums of Squared

Loadings

Rotation Sums of Squared

Loadings

Total % of

Variance

Cumulative

% Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

%

1 4.170 52.120 52.120 4.170 52.120 52.120 3.923 49.036 49.036

2 1.231 15.382 67.502 1.231 15.382 67.502 1.477 18.466 67.502

3 .679 8.488 75.990

4 .576 7.202 83.193

5 .398 4.973 88.166

6 .373 4.661 92.827

7 .332 4.147 96.974

8 .242 3.026 100.000

Extraction Method: Principal

Component Analysis.

The total variance explained in table 5.21 (after deleting the questions with little

communalities) shows that these questions totally form two factors and these two

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factors explain and cover about 67.5% of the variance of 'Infrastructural CRM

resources'. Comparing the two 'total variance explained' tables, it can be easily

perceived that after deleting the questions with little communalities, the powers of

explanation of the questions have risen. This result would be very useful in

confirmatory factor analysis. Later we will deal with factor loads and naming each

component. Table 5.22 shows the Rotated Component Matrix.

Table 5.22: Rotated Component Matrix

Rotated Component Matrixa

Component

Organizational HR

HR2 .433 .651

HR3 .050 .856

HR4 .796 .784

org1 .853 .804

org2 .803 .138

org3 .649 .338

org4 .830 .110

org6 .785 -.272

Extraction Method: Principal

Component Analysis.

Rotation Method: Varimax with

Kaiser Normalization.

a. Rotation converged in 3

iterations.

5.3.1.3. The results of Exploratory Factor Analysis for the variable of

"CRM processes"

Table 5.23 shows the result of KMO and Bartlett's test for the questions related to the

variable of 'CRM processes'.

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Table 5.23: KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .801

Bartlett's Test of Sphericity Approx. Chi-Square 966.344

df 45

Sig. .000

Since KMO is greater than 0.7 and Bartlett's test significance number is less than 0.05

(sig<0.05), we can say that the data is suitable for doing the factor analysis. The

communalities of the questions for the variable of 'CRM processes' are represented in

table 5.24.

Table 5.24: Communalities

Initial Extraction

okp1 1.000 .340

okp2 1.000 .670

okp3 1.000 .771

oip1 1.000 .599

oip2 1.000 .747

mp1 1.000 .688

mp2 1.000 .685

mp3 1.000 .666

mp4 1.000 .685

mp5 1.000 .752

Extraction Method: Principal

Component Analysis.

The above table shows the inadequacy of the first question among the above questions

because of a lack of harmony with other questions within the process of factor

analysis. As it can be clearly seen, the communality of this question is less than 0.50.

Table 5.25 represents the total variance explained before deleting the mentioned

question.

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Table 5.25: Total variance explained before deleting the question

Component

Initial Eigenvalues Extraction Sums of Squared

Loadings

Rotation Sums of Squared

Loadings

Total % of

Variance

Cumulative

% Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

%

1 3.577 35.773 35.773 3.577 35.773 35.773 2.700 27.000 27.000

2 1.977 19.773 55.546 1.977 19.773 55.546 2.084 20.836 47.836

3 1.049 10.489 66.035 1.049 10.489 66.035 1.820 18.198 66.035

4 .797 7.968 74.003

5 .580 5.799 79.802

6 .540 5.401 85.203

7 .476 4.757 89.959

8 .399 3.993 93.952

9 .321 3.210 97.163

10 .284 2.837 100.000

Extraction Method: Principal

Component Analysis.

The total variance explained in table 5.25 (before deleting the questions with little

communalities) shows that these questions totally form three factors and these three

factors explain and cover about 66.03% of the variance of 'CRM processes'. After

deleting the questions which there communalities are less than 0.5, we will deal with

the mentioned issues again. Table 5.26 shows the communalities of the questions after

deleting the question with little communalities.

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Table 5.26: Communalities of the questions after deleting the question with little communalities

Communalities

Initial Extraction

okp2 1.000 .698

okp3 1.000 .777

oip1 1.000 .602

oip2 1.000 .757

mp1 1.000 .686

mp2 1.000 .735

mp3 1.000 .674

mp4 1.000 .688

mp5 1.000 .752

Extraction Method: Principal

Component Analysis.

Since the numbers of communalities of the questions in table 5.26 are acceptable (higher

than 0.5), these questions are suitable in the process of factor analysis. Table 5.27 presents

the total variance explained after deleting the mentioned question.

Table 5.27: Total Variance Explained after deleting the question with little communality

Component

Initial Eigenvalues Extraction Sums of Squared

Loadings

Rotation Sums of Squared

Loadings

Total % of

Variance

Cumulative

% Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

%

1 3.422 38.025 38.025 3.422 38.025 38.025 2.504 27.827 27.827

2 1.906 21.181 59.206 1.906 21.181 59.206 2.044 22.710 50.537

3 1.041 11.568 70.774 1.041 11.568 70.774 1.821 20.237 70.774

4 .582 6.463 77.237

5 .541 6.010 83.247

6 .488 5.425 88.671

7 .402 4.470 93.142

8 .333 3.701 96.843

9 .284 3.157 100.000

Extraction Method: Principal

Component Analysis.

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The total variance explained in table 5.27 (after deleting the question with little

communality) shows that these questions totally form three factors and these three

factors explain and cover about 70.77% of the variance of 'CRM processes'.

Comparing the two 'total variance explained' tables, it can be easily perceived that

after deleting the question with little communality, the powers of explanations of the

questions have been raised. This result would be very useful in confirmatory factor

analysis. Later, we will deal with factor loads and naming each component. Table

5.28 shows the component matrix.

Table 5.28: Component Matrixa

Component

Management

processes

Operational

knowledge

processes

Operational

interaction

processes

okp2 .617 .505 -.250

okp3 .245 .832 -.158

oip1 .406 .644 .650

oip2 .782 .323 .604

mp1 .586 .206 .548

mp2 .752 -.220 -.348

mp3 .687 .199 .403

mp4 .533 -.402 .492

mp5 .735 -.385 -.251

Extraction Method: Principal Component

Analysis.

a. 3 components extracted.

5.3.1.4. The results of Exploratory Factor Analysis for the variable of

"customer retention programs of CRM"

Table 5.29 presents the results of KMO and Bartlett's test for the data related to the

variable of 'customer retention programs of CRM'.

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Table 5.29: KMO and Bartlett's test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .896

Bartlett's Test of Sphericity Approx. Chi-Square 3.475E3

df 171

Sig. .000

Since KMO is greater than 0.7 and Bartlett's test significance number is less than 0.05

(sig<0.05), it can be said that the data is acceptable to do the factor analysis. The

communalities of the questions for the mentioned research variable are presented in

table 5.30.

Table 5.30: Communalities

Initial Extraction

cust1 1.000 .748

cust2 1.000 .466

cust3 1.000 .857

loy1 1.000 .811

loy2 1.000 .785

loy3 1.000 .756

loy4 1.000 .669

cs1 1.000 .732

cs2 1.000 .671

cs3 1.000 .250

cs4 1.000 .702

cs5 1.000 .728

cs6 1.000 .284

cs7 1.000 .452

cs8 1.000 .585

cs9 1.000 .474

com1 1.000 .824

com2 1.000 .840

com3 1.000 .710

Extraction Method: Principal

Component Analysis.

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Table 5.30 shows the inadequacy of the five (colored) questions among the above

questions because of a lack of harmony with the other questions within the process of

factor analysis. As we see, the communalities of these questions are less than 0.50.

Table 5.31 presents the total variance explained before deleting the question.

Table 5.31: Total variance explained before deleting the question

Component

Initial Eigenvalues Extraction Sums of Squared

Loadings

Rotation Sums of Squared

Loadings

Total % of

Variance

Cumulative

% Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

%

1 5.327 30.773 30.773 5.327 30.773 30.773 4.206 20.516 20.516

2 2.339 15.792 46.565 2.339 15.792 46.565 3.022 21.063 41.579

3 2.329 15.492 62.057 2.329 15.492 62.057 3.012 21.062 62.641

4 2.318 14.307 76.364 2.318 14.307 76.364 3.837 13.723 76.364

5 .822 4.328 73.944

6 .698 3.674 77.618

7 .612 3.223 80.840

8 .568 2.990 83.830

9 .429 2.257 86.087

10 .393 2.068 88.155

11 .372 1.959 90.115

12 .340 1.789 91.904

13 .321 1.689 93.593

14 .307 1.616 95.209

15 .225 1.182 96.391

16 .220 1.158 97.549

17 .199 1.050 98.599

18 .142 .749 99.348

19 .124 .652 100.000

Extraction Method: Principal

Component Analysis.

The total variance explained in table 5.31 (before deleting the questions with little

communalities) shows that these questions totally form four factors and which explain

about 76.364% of the variance of 'customer retention programs of CRM'. After

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deleting the questions with little communalities (less than 0.5) we will deal with the

mentioned issues again. Communalities of the questions after deleting the questions

with little communalities are shown in table 5.32.

Table 5.32: Communalities of the questions after deleting the questions with little communalities

Since the numbers of communalities of the questions in table 5.32 is higher than 0.5, these

questions are acceptable for the process of factor analysis. Table 5.33 presents the total

variance explained after deleting the questions with little communalities.

Communalities

Initial Extraction

cust1 1.000 .766

cust3 1.000 .856

loy1 1.000 .813

loy2 1.000 .809

loy3 1.000 .778

loy4 1.000 .701

cs1 1.000 .744

cs2 1.000 .715

cs4 1.000 .731

cs5 1.000 .763

cs8 1.000 .631

com1 1.000 .839

com2 1.000 .855

com3 1.000 .735

Extraction Method: Principal

Component Analysis.

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Table 5.33: Total Variance Explained after deleting the questions with little communalities

Component

Initial Eigenvalues Extraction Sums of Squared

Loadings

Rotation Sums of Squared

Loadings

Total % of

Variance

Cumulative

% Total

% of

Variance

Cumulative

% Total

% of

Variance

Cumulative

%

1 6.227 32.773 32.773 6.227 32.773 32.773 4.506 23.716 23.716

2 3.399 17.892 50.665 3.399 17.892 50.665 4.002 21.063 44.779

3 2.309 17.992 68.657 2.499 17.992 68.657 4.102 22.063 66.839

4 2.218 14.307 64.972 2.718 14.307 64.972 3.837 20.193 87.032

5 .822 4.328 73.944

6 .698 3.674 77.618

7 .612 3.223 80.840

8 .568 2.990 83.830

9 .429 2.257 86.087

10 .393 2.068 88.155

11 .372 1.959 90.115

12 .340 1.789 91.904

13 .321 1.689 93.593

14 .307 1.616 95.209

15 .225 1.182 96.391

16 .220 1.158 97.549

17 .199 1.050 98.599

18 .142 .749 99.348

19 .124 .652 100.000

Extraction Method: Principal Component Analysis.

The total variance explained in table 5.33 (after deleting the question with little

communality) shows that these questions totally form four factors and these four

factors explain and cover about 87.032% of the variance of 'customer retention

programs of CRM'. Comparing the two 'total variance explained' tables, we can

conclude that after deleting the question with little communality, the powers of

explanation of the questions have been raised. Later, we will deal with factor loads

and naming each component. The results of Rotated Component Matrix are presented

in table 5.34.

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Table 5.34: Rotated Component Matrixa

Component

Customization Loyalty

programs Community

Customer

service

cust1 .730 .874 -.041 .407

cust3 .841 .246 .298 .145

loy1 .407 .805 -.015 .652

loy2 .145 .842 .282 .327

loy3 .652 .557 .206 -.425

loy4 .327 .762 .113 -.320

cs1 -.425 .743 .105 .849

cs2 -.320 .085 .778 .779

cs4 .849 -.030 -.093 .885

cs5 .779 .251 -.306 .849

cs8 .756 -.022 .244 .551

com1 -.024 .114 .908 -.024

com2 .485 .191 .764 .485

com3 .259 .026 .816 .485

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.

a. Rotation converged in 5 iterations.

5.3.1.5. The results of Exploratory Factor Analysis for the variable of

"customer retention"

Table 5.35 presents the results of KMO and Bartlett's test for the data related to the

variable of 'customer retention'.

Table 5.35: KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .675

Bartlett's Test of Sphericity Approx. Chi-Square 235.081

df 6

Sig. .000

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Since KMO is between 0.5 and 0.7 and Bartlett's test significance number is less than

0.05 (sig<0.05), it can be said that the data is partially suitable for doing the factor

analysis. The communalities of the questions for the mentioned research variable are

presented in table 5.36.

Table 5.36: Communalities

Communalities

Initial Extraction

CR1 1.000 .120

CR2 1.000 .727

CR3 1.000 .529

CR4 1.000 .709

Extraction Method: Principal

Component Analysis.

Table 5.36 shows the inadequacy of the first question among the above questions

because of a lack of harmony with other questions within the process of factor

analysis. As we can vividly see, the communality of this question is less than 0.50.

Table 5.37 presents the total variance explained before deleting the mentioned

question.

Table 5.37: Total Variance Explained before deleting the question with little communality

Compo

nent

Initial Eigenvalues Extraction Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative %

1 2.084 52.103 52.103 2.084 52.103 52.103

2 .965 24.113 76.216

3 .574 14.342 90.558

4 .378 9.442 100.000

Extraction Method: Principal Component Analysis.

The total variance explained in table 5.37 (before deleting the question with little

communality) shows that these questions totally form one factor which explains and

covers about 52.103% of the variance of 'customer retention'. After deleting the

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question with little communality (less than 0.5), we will deal with the mentioned

issues again. Table 5.38 represents the values of communalities of the questions after

deleting the question with little communality.

Table 5.38: Communalities of the questions after deleting the question with little communality

Communalities

Initial Extraction

CR2 1.000 .717

CR3 1.000 .572

CR4 1.000 .730

Extraction Method: Principal

Component Analysis.

According to table 5.38, the numbers of communalities of the questions are more than 0.5;

therefore, these questions are proper for the process of factor analysis. Table 5.39 presents

the total variance explained after deleting the question with little communality.

Table 5.39: Total Variance Explained after deleting the question with little communality

Compo

nent

Initial Eigenvalues Extraction Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cumulative %

1 2.019 67.286 67.286 2.019 67.286 67.286

2 .598 19.930 87.216

3 .384 12.784 100.000

Extraction Method: Principal Component Analysis.

The total variance explained in table 5.39 (after deleting the question with little

communality) shows that these questions totally form one factor and this factor

explains and covers about 67.28% of the variance of 'customer retention'. Table 5.40

presents the component matrix for the questions related to the variable of 'customer

retention'.

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Table 5.40: Component Matrix

Component Matrixa

Component

1

CR2 .847

CR3 .756

CR4 .854

Extraction Method:

Principal Component

Analysis.

a. 1components extracted.

5.3.2. Structural Equation Model for Research Hypotheses Tests

One of the most suitable and the most powerful methods of analysis in the field of

social sciences is the multi-variable analysis. This is because the nature of these

researches is multi-variable, and cannot be solved by two-variable methods in which

an independent variable is considered with one dependent variable in each time.

Multi-variable analysis is attributed to a series of analysis methods whose main

characteristic is the ability to analyze K independent variables and N dependent

variables simultaneously. It should be said that the covariance structures analysis or

Structural Equation Model is one of the main methods of analyzing complex data

structures.

5.3.2.1. Structural Equation Modeling Procedure:

The process of covariance structures analysis includes a series of steps which have to

be followed. These steps include:

4. Hypothesis test

3. Correction of the

model (if needed)

2. Model

estimation

1. Model

expression

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Figure 5.7 shows the general form of the structural equation model.

Figure 5.7: The general form of structural equation model

In this model Y1 to Y4 and X1 to X7 are observed variables and Ksi1 to Ksi3 and Eta1 to

Eta2 are Latent variables. Ksi1 to ksi3 are exogenous and Eta1 to Eta2 are

endogenous.

In the structural model it is tried to make clear whether the relationships between the

latent traits which are taken from the theory are confirmed by data gathered from the

sample or not. The relationships between the variables in structural equation model

are divided into two general areas: 1) the relationships between hidden and evident

variables and 2) the relationships between hidden variables and hidden variables. The

first group is called measuring model and the second is called structural model

(Kalantari, 2009). The symbolization of the presented path gained by Lisrel software

is also shown in figure 5.8.

X1

X2

X3

X4

X5

X6

X7

Ksi1

Ksi3

Ksi2

Eta1

Eta2

Y1

Y2

Y3

Y4

Measurement

component

Construct

component

Measurement

component

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Figure 5.8: Symbolization

According to what has been said above, the sign oval indicates the main variables

(latent variables) and rectangular signs indicate the research dimensions (evident

variables). In this figure, the one-way arrow from the latent variable (oval) to evident

variable (rectangular) indicates a correlation and two-way arrow between latent

variables shows the relationship between variables which is known as (φ) in the

structural equation model (Kalantari, 2009)

In the conceptual model of the research there are four latent variables (oval)

including: CRM resources, CRM processes, customer retention programs of CRM,

and customer retention. The evident variables are the research dimensions which have

been gained after doing the factor analysis. The research conceptual model is

presented in figure 5.9.

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Figure 5.9: The research conceptual model

In this section the following steps will be followed:

1. Studying the accuracy of the measuring model by its indices

2. Correction of the model if it is needed (Kalantari, 2009)

3. Studying the significance of the relationships with t-value (if they are not

significant, a special number will be determined)

4. Studying the correlation

The first basic question which is raised after modeling is whether the model is a

proper one (accuracy of the measuring model) or not. To check this, some indices

should be used including chi-square relative to its degree of freedom which should be

less than 3, root mean square error of approximation (RMSEA) whose value should

be less than 0.08, p-value which should be less than 0.05, goodness of fit index (GFI)

and also adjusted goodness of fit index (AGFI) which should be more than 0.9. The

standard model presented in figure 5.10 investigates the mentioned issues to find the

accuracy.

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Figure 5.10: Standard model

The mentioned indices indicate the suitability of the measuring model of the related

variables. Indeed, chi-square relative to its degree of freedom is 2.79 (less than 3),

RMSEA is less than 0.08, and the p-value is less than 0.05. The other fitness indices

of the model are presented in table 5.41.

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Table 5.41: Fitness indices

Fit

ness

indices

result

Value

Recommended

value

Indices

Row

suitable

2.79

/df < 3 2

chi-square

relative to its

degree of

freedom

1

suitable 0.00

p-value< 0.05

p-value

2

suitable 0.078

0.08>0.05<RMSEA

root mean

square error of

approximation

3

suitable 0.90 GFI > 0. 9

goodness of fit

index

4

fairly

suitable 0.89 AGFI > 0. 9

adjusted

goodness of fit

index

5

suitable

0.92 CFI> 0.9 comparative fit

index

6

suitable

0.92 NFI > 0.9 normed fit

index

7

Having looked at table 5.41, it can be easily perceived that the fitness of the model is

suitable. In the next step, using the diagram of significance presented in figure 5.11,

we study the significance of the results in the model.

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Figure 5.11: Diagram of significance

It should be noted that since we are looking for hypothesizes test with a level of

confidence of 0.95, the numbers that are significant are not between -1.96 and 1.96

(Kalantari, 2009). In the significance model, all the relationships are significant;

therefore, the research hypothesizes can be studied.

Hence, the primary conceptual model is hereby confirmed. Now, paying attention to

the model with significant numbers and standard estimation model, the following

hypothesizes will be tested.

Hypothesis 1:

CRM resources have direct effect on CRM processes.

H0: CRM resources do not have direct effect on CRM processes.

H1: CRM resources have direct effect on CRM processes.

Based on the significance diagram, the gained number indicates the significance of

the effect of CRM resources on CRM processes (they are not between -1.96 and

1.96). The standard diagram indicates that CRM resources have explained 79% of the

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changes in CRM processes; therefore, the hypothesis H0 is rejected and the

hypothesis H1 which states that CRM resources have direct effect on CRM processes

is accepted. The remaining 21% is related to the factors other than CRM resources

that are not studied in this research.

Hypothesis 2:

CRM processes have direct effect on customer retention programs of CRM.

H0: CRM processes do not have direct effect on customer retention programs of

CRM.

H1: CRM processes have direct effect on customer retention programs of CRM.

Based on the significance diagram, the gained number indicates the significance of

the effect of CRM processes on customer retention programs of CRM (they are not

between -1.96 and 1.96). The standard diagram indicates that CRM processes have

explained 95% of the changes in customer retention programs of CRM; therefore, the

hypothesis H0 is rejected and the hypothesis H1 which states that CRM processes

have direct effect on customer retention programs of CRM is accepted. The remaining

5% is related to the factors other than CRM processes that are not studied in this

research.

Hypothesis 3:

Customer retention programs of CRM have direct effect on customer retention.

H0: Customer retention programs of CRM do not have direct effect on customer

retention.

H1: Customer retention programs of CRM have direct effect on customer retention.

Based on the significance diagram, the gained number indicates the significance of

the effect of customer retention programs of CRM on customer retention (they are not

between -1.96 and 1.96). The standard diagram indicates that customer retention

programs of CRM explain 95% of the changes in customer retention; therefore, the

hypothesis H0 is rejected and the hypothesis H1 which says customer retention

programs of CRM have direct effect on customer retention is accepted. The remaining

5% is related to the factors other than customer retention programs of CRM that are

not studied in this research.

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5.4. Lateral Analysis

5.4.1. Study of the correlation between each of the customer retention

programs of CRM and customer retention using Pearson correlation:

H0: There is no positive and significant correlation between each of the customer

retention programs of CRM and customer retention

H1: There is positive and significant correlation between each of the customer

retention programs of CRM and customer retention

Table 5.42 presents the values gained after calculating the Pearson correlation.

Table 5.42: Pearson correlation values

RETENTION

Customization

Pearson Correlation .802**

Sig. (2-tailed) .000

N 286

Loyalty

programs

Pearson Correlation .815**

Sig. (2-tailed) .000

N 286

Customer

service

Pearson Correlation .877**

Sig. (2-tailed) .000

N 286

Community

Pearson Correlation .858**

Sig. (2-tailed) .000

N 286

According to Hair et al. (2007), the most common measure of correlation is Pearson's

correlation which its value ranges between -1 and +1. It measures the degree of

association between two variables. Indeed, a positive value for the correlation implies

a positive association, but if its value is negative, it implies an inverse association.

They also say that a correlation value above 0.80 is considered pretty high.

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Regarding the significance numbers which all equal to 0.000 (Sig < 0.05), hypothesis

H0 is rejected and hypothesis H1 is accepted. Thus, it can be said that all the

relationships are significant. On the other hand, all the numbers gained by SPSS

software in table 5.42 are positive and above 0.80. It means that there is a positive and

significant correlation between each of the customer retention programs of CRM

(customization, loyalty programs, customer service, and community) and customer

retention. The results in table 5.42 also reveal that the correlation between customer

service and customer retention is the most significant one.

5.4.2. Study of the difference between the governmental and private

banks with respect to the research variables

H0: There is no significant difference between the governmental and private banks

with respect to the research hypothesizes

H1: There is significant difference between the governmental and private banks with

respect to the research hypothesizes

Table 5.43 and table 5.44 present the results of group statistics and the independent

samples test respectively.

Table 5.43: Group Statistics

Type of the bank N Mean Std. Deviation Std. Error Mean

Resources gov 171 4.5628 .32357 .02474

private 115 5.8836 .12897 .01203

Processes gov 171 5.2992 .36146 .02764

private 115 5.7574 .58456 .05451

Retention

programs

gov 171 4.6051 .40898 .03128

private 115 5.7447 .50547 .04714

Customer

retention

gov 171 5.7173 .51769 .03959

private 115 6.2203 .49340 .04601

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Table 5.44: Independent Samples Test

t-test for Equality of Means

t df

Sig.

(2-

tailed)

Mean

Difference

Std. Error

Difference

95% Confidence

Interval of the

Difference

Lower Upper

CRM

Resources

Equal

Means

assumed

-41.590 284 .000 -1.32080 .03176 -1.38331 -1.25829

Equal

Means not

assumed

-48.009 239.850 .000 -1.32080 .02751 -1.37500 -1.26661

CRM

Processes

Equal

Means

assumed

-8.186 284 .000 -.45817 .05597 -.56833 -.34801

Equal

Means not

assumed

-7.496 172.516 .000 -.45817 .06112 -.57881 -.33754

Retention

programs

Equal

Means

assumed

-20.990 284 .000 -1.13963 .05429 -1.24650 -1.03276

Equal

Means not

assumed

-20.146 209.274 .000 -1.13963 .05657 -1.25115 -1.02811

Customer

retention

Equal

Means

assumed

-8.208 284 .000 -.50294 .06127 -.62355 -.38233

Equal

Means not

assumed

-8.286 252.484 .000 -.50294 .06070 -.62248 -.38340

Considering significance number, hypotheses H0 is rejected and hypothesis H1 is

accepted. This means that there is significant difference between the two groups of

private and governmental banks with respect to the research variables from the

respondents' points of view. In addition, since the value of both of the upper and

lower limits are negative, we can conclude that M1-M2<0 ;thus, M1<M2 which

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means that the respondents have assessed the situation of private banks better than

governmental banks in relation to the research variables.

5.4.3. Study of the difference between the four banks (under study) in

relation to the research variables by using ANOVA

H0: There is no significant difference between the four banks (under study) with

respect to the research hypothesizes

H1: There is significant difference between the four banks (under study) with respect

to the research hypothesizes.

Table 5.45 and table 5.46 present the results of ANOVA and the multiple

comparisons respectively.

Table 5.45: ANOVA

Sum of Squares df Mean Square F Sig.

CRM

resources

Between Groups 135.600 3 45.200 3.151E3 .000

Within Groups 4.045 282 .014

Total 139.645 285

CRM

Processes

Between Groups 64.252 3 21.417 532.276 .000

Within Groups 11.347 282 .040

Total 75.599 285

Retention

programs

Between Groups 139.571 3 46.524 1.799E3 .000

Within Groups 7.292 282 .026

Total 146.863 285

Customer

retention

Between Groups 71.093 3 23.698 340.725 .000

Within Groups 19.613 282 .070

Total 90.706 285

Considering the significance number (Sig < 0.05), hypothesis H0 is rejected and

hypothesis H1 is accepted. This means that there is significant difference between the

four banks (under study) with respect to the research hypothesizes.

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Table 5.46: Multiple Comparisons

Tukey HSD

Dependent

Variable (I) bank (J) bank

Mean

Difference (I-J) Std. Error Sig.

95% Confidence Interval

Lower Bound Upper Bound

CRM

resources

Refah

Maskan .60601* .01843 .000 .5584 .6536

Saman -1.03982* .02497 .000 -1.1043 -.9753

Pasargad -.96171* .01907 .000 -1.0110 -.9124

Maskan

Refah -.60601* .01843 .000 -.6536 -.5584

Saman -1.64583* .02420 .000 -1.7084 -1.5833

Pasargad -1.56773* .01805 .000 -1.6144 -1.5211

Saman

Refah 1.03982* .02497 .000 .9753 1.1043

Maskan 1.64583* .02420 .000 1.5833 1.7084

Pasargad .07810* .02469 .009 .0143 .1419

Pasargad

Refah .96171* .01907 .000 .9124 1.0110

Maskan 1.56773* .01805 .000 1.5211 1.6144

Saman -.07810* .02469 .009 -.1419 -.0143

CRM

processes

refah

Maskan .61167* .03087 .000 .5319 .6914

Saman .73904* .04182 .000 .6310 .8471

Pasargad -.46340* .03194 .000 -.5459 -.3809

Maskan

refah -.61167* .03087 .000 -.6914 -.5319

Saman .12737* .04053 .010 .0226 .2321

Pasargad -1.07507* .03024 .000 -1.1532 -.9969

Saman

Refah -.73904* .04182 .000 -.8471 -.6310

Maskan -.12737* .04053 .010 -.2321 -.0226

Pasargad -1.20244* .04135 .000 -1.3093 -1.0956

Pasargad

Refah .46340* .03194 .000 .3809 .5459

Maskan 1.07507* .03024 .000 .9969 1.1532

Saman 1.20244* .04135 .000 1.0956 1.3093

Customer

retention

programs of

CRM

Refah

Maskan .74138* .02475 .000 .6774 .8053

Saman .03695 .03352 .689 -.0497 .1236

Pasargad -1.03549* .02560 .000 -1.1017 -.9693

Maskan

Refah -.74138* .02475 .000 -.8053 -.6774

Saman -.70444* .03249 .000 -.7884 -.6205

Pasargad -1.77688* .02424 .000 -1.8395 -1.7142

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Saman

Refah -.03695 .03352 .689 -.1236 .0497

Maskan .70444* .03249 .000 .6205 .7884

Pasargad -1.07244* .03315 .000 -1.1581 -.9868

Pasargad

Refah 1.03549* .02560 .000 .9693 1.1017

Maskan 1.77688* .02424 .000 1.7142 1.8395

Saman 1.07244* .03315 .000 .9868 1.1581

Customer

retention

Refah

Maskan .91140* .04059 .000 .8065 1.0163

Saman .63783* .05498 .000 .4957 .7799

Pasargad -.25193* .04199 .000 -.3604 -.1434

Maskan

Refah -.91140* .04059 .000 -1.0163 -.8065

Saman -.27358* .05329 .000 -.4113 -.1359

Pasargad -1.16333* .03975 .000 -1.2661 -1.0606

Saman

Refah -.63783* .05498 .000 -.7799 -.4957

Maskan .27358* .05329 .000 .1359 .4113

Pasargad -.88975* .05437 .000 -1.0303 -.7492

Pasargad

Refah .25193* .04199 .000 .1434 .3604

Maskan 1.16333* .03975 .000 1.0606 1.2661

Saman .88975* .05437 .000 .7492 1.0303

*. The mean difference is significant at the 0.05 level.

- Comparing the four banks from the aspect of applying CRM resources:

Comparing banks Refah and Maskan (using table 5.46), the two upper and lower

bounds are positive; therefore, M1-M2>0 which is equivalent to M1>M2. Thus, we

can conclude that bank Refah has applied CRM resources better than bank Maskan.

But, comparing bank Refah with banks Pasargad and Saman, the two bounds are both

negative; therefore, M1-M2<0 and M1<M2. This means that from the aspect of

applying CRM resources, banks Pasargad and Saman are better than bank Refah.

Comparing bank Maskan with the other three banks, both the lower and upper bounds

are negative; therefore, M1-M2<0; thus, M1<M2 which means that banks Refah,

Pasargad, and Saman have applied CRM resources better than bank Maskan.

Comparing bank Saman with the other three banks, both the upper and lower bounds

are positive; thus, M1-M2>0 which is equivalent to M1>M2; therefore, we can say

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that from the aspect of applying CRM resources, the performance of bank Saman has

been better than the performance of the other three banks.

Comparing bank Pasargad with banks Refah and Maskan, both the upper and lower

bounds are positive; therefore, M1-M2>0 (M1>M2); Thus, we can conclude that bank

Pasargad has applied CRM resources better than banks Refah and Maskan. But,

comparing bank Pasargad with bank Saman, the two bounds are both negative;

therefore, M1-M2<0 (M1<M2) which means from the aspect of applying CRM

resources, bank Saman is better than bank Pasargad.

- Comparing the four banks from the aspect of applying CRM processes:

Comparing bank Refah with banks Maskan and Saman, both the upper and lower

bounds are positive; thus, M1-M2>0 which is equivalent to M1>M2; therefore, we

can conclude that bank Refah has applied CRM processes better than banks Maskan

and Saman. On the other hand, comparing bank Refah with bank Pasargad, the two

bounds are both negative. This means M1-M2<0 (M1<M2); thus, from the aspect of

applying CRM processes, bank Pasargad is better than bank Refah.

Comparing bank Maskan with bank Saman, the two upper and lower bounds are

positive; therefore, M1-M2>0 (M1>M2).So, we can say that bank Maskan has applied

CRM processes better than bank Saman. But, comparing bank Maskan with banks

Refah and Pasargad, the two bounds are both negative; therefore, M1-M2<0

(M1<M2). This means banks Refah and Pasargad have applied CRM processes better

than bank Maskan.

Comparing bank Saman with the other three banks, both the upper and lower bounds

are negative; therefore, M1-M2<0 which is equivalent to M1<M2. Thus, it can be said

that from the aspect of applying CRM processes, the other three banks are better than

bank Saman.

Comparing bank Pasargad with the other three banks, both the upper and lower

bounds are positive; thus, M1-M2>0 (M1>M2); therefore, we can conclude that bank

Pasargad has applied CRM processes better than the other three banks.

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- Comparing the four banks from the aspect of applying customer retention

programs of CRM:

Comparing bank Refah with bank Maskan, both the upper and lower bounds are

positive; therefore, M1-M2>0 which is equivalent to M1>M2; Thus, we can conclude

that bank Refah has applied customer retention programs of CRM better than bank

Maskan. But, comparing bank Refah with bank Saman, the lower bound is negative

and the upper bound in positive; therefore, M1=M2 and sig = 0.689 (sig > 0.05); thus,

we can say that there is no significant difference between these two banks in relation

to applying customer retention programs of CRM. On the other hand, comparing bank

Refah with bank Pasargad, both the upper and lower bounds are negative; therefore,

M1-M2<0 (M1<M2). This means that from the aspect of applying customer retention

programs of CRM, bank Pasargad is better than bank Refah.

Comparing bank Maskan with the other three banks, both the upper and lower bounds

are negative; thus, M1-M2<0 (M1<M2). Therefore, we can conclude that the other

three banks have applied customer retention programs of CRM better than bank

Maskan.

Comparing bank Saman with bank Refah, the lower bound is negative and the upper

bound is positive; therefore, M1=M2 and also sig = 0.689 (sig > 0.05); thus, we can

say that there is no significant difference between these two banks in relation to

applying customer retention programs of CRM. On the other hand, comparing bank

Saman with bank Maskan, both the upper and lower bounds are positive; therefore,

M1-M2>0 which is equivalent to M1>M2; thus, we can conclude that bank Saman

has applied customer retention programs of CRM better than bank Maskan. But,

comparing bank Saman with bank Pasargad, both the upper and lower bounds are

negative; thus, M1-M2<0 (M1<M2) which means that from the aspect of applying

customer retention programs of CRM, bank Pasargad has a better performance than

bank Saman.

Comparing bank Pasargad with the other three banks, both the upper and lower

bounds are positive; therefore, M1-M2>0; thus, M1>M2. This means that bank

Pasargad has applied customer retention programs of CRM better than the other three

banks.

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- Comparing the four banks from the aspect of customer retention:

Comparing bank Refah with banks Maskan and Saman, both the upper and lower

bounds are positive; therefore, M1-M2>0; thus, M1>M2. So, we can conclude that

from the aspect of customer retention, bank Refah is better than banks Maskan and

Saman. But, comparing bank Refah with bank Pasargad, the two bounds are negative;

thus, M1-M2<0 (M1<M2). This means that the rate of customer retention in bank

Pasargad is more than the rate of customer retention in bank Refah.

Comparing bank Maskan with the other three banks, both the upper and lower bounds

are negative; therefore, M1-M2<0 that is equivalent to M1<M2. Thus, it can be said

that customer retention in the other three banks is better than customer retention in

bank Maskan.

Comparing bank Saman with bank Maskan, both the upper and lower bounds are

positive; therefore, M1-M2>0 (M1>M2). Thus, we can say that customer retention in

bank Saman is better than customer retention in bank Maskan. But, comparing bank

Saman with banks Refah and Pasargad, both of the upper and lower bounds are

negative; therefore, M1-M2<0; so, M1<M2 which means that from the aspect of

customer retention, banks Refah and Pasargad are more successful than bank

Maskan.

Comparing bank Pasargad with the other three banks, both the upper and lower

bounds are positive; therefore, M1-M2>0; thus, M1>M2. This means that from the

aspect of customer retention, bank Pasargad is more successful than the other three

banks.

5.5. Conclusion

In this research, data analysis is done in two parts by descriptive and interferential

statistics. In the descriptive part, the demography of statistical sample is investigated

and the results were shown in frequency tables and diagrams, but in the inferential

part, the data is analyzed using a series of steps. To do so, first of all we did the

Kaiser-Meyer-Olkin and Bartlett's test to measure the adequacy of the sampling. Next,

the exploratory factor analysis for deleting the questions with little factor load and

communalities was done. Finally, to study and test the research hypothesizes we used

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structural equation model (confirmatory factor analysis). To do so, model expression,

model estimation, correction of the model (if needed), and hypothesis test were

followed respectively. The final results of hypothesis test show that all the formulated

hypothesizes in the third chapter are accepted. Therefore according to the first

hypothesis, all the CRM resources including technological and infrastructural CRM

resources have direct effect on CRM processes. The second hypothesis confirms the

direct effect of CRM processes on all customer retention programs of CRM (loyalty

programs, customer service, customization, and creating community). Also, based on

the third (the last) hypothesis, all of these programs have direct effect on customer

retention.

After testing the research hypothesizes, we did some lateral analyzes. The first one

was investigating the correlation between each of the customer retention programs of

CRM and customer retention. The results of Pearson correlation for this analysis show

that there is a positive and significant correlation between each of the customer

retention programs of CRM (customization, loyalty programs, customer service, and

community) and customer retention. These results also reveal that the most significant

correlation is between customer service and customer retention.

Next, two-sample t-test with independent samples was done to study the difference in

each of the research variables in the two groups of private and governmental banks.

According to this test, we can say that there is significant difference between the two

groups of private and governmental banks with respect to the research variables from

the respondents' points of view. Indeed, the respondents have assessed the situation of

private banks better than governmental banks in relation to the research variables.

Finally, to investigate the difference in each of the research variables in the four

groups of banks, analysis of variance or comparing the means of some communities

was done. The results show the significant difference between the four banks (under

study) with respect to the research variables. The detailed results of this analysis are

shown in the previous section.

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6. Conclusion and Implications

6.1. Conclusion and Implications

This study first emphasized on the fact that the resource based view of the firm (RBV)

and the process-oriented approach can be applied in the context of CRM. Then, it

reviewed and analyzed the previous researches in the literature that investigate the

relationship between CRM and firm performance and customer retention. Next, based

on the RBV and process-oriented approach, we suggested a framework that links

CRM to customer retention. This framework shows that if organizations want to attain

a sustainable competitive advantage through CRM, they should efficiently apply all

the CRM resources to create CRM process capabilities which are unique and can not

be copied by other organizations (Keramati et al., 2010). The way we reviewed the

literature, determined the research variables and dimensions, collected data, and

analyzed them are discussed in section 6.2 which also provides the theoretical

implications related to the conclusions provided by the data analysis in previous

chapter. This chapter continues with managerial implications as well as research

limitations and suggestions for future research.

6.2. Theoretical Implications

The purpose of this study has been to investigate the impact of CRM on customer

retention in Iranian electronic banking based on RBV and process oriented approach

and from employees' perspective. Iranian banks have used CRM more or less and

proceeded to this issue with different rates of success. However, there are not

available studies that investigated this impact in these banks especially in electronic

environment. Thus, in this research we have aimed at getting a deeper insight into this

issue.

To propose a process-oriented framework for investigating the impact of CRM on

customer retention, we reviewed previous studies linking CRM to firm performance

such as customer retention. CRM, e-Banking, customer retention, and model, were the

most used keywords to search the needed literatures. To find the appropriate theories

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and models for this study we used a two-stage process of classifying the searched

literatures. First, we selected the articles related to the effects of CRM on customer

retention by reviewing their titles and abstracts. Then, we extended our research and

found studies done with different views about the impact of CRM on customer

retention in banks and other firms.

Deep interviews with the experts and supervisors of this work were also conducted

during the search through the papers. This was done to distinguish different factors

among several factors some of which had the same concept, but were only given

different titles. In the next step, the research variables were determined; however, in

order to be able to measure the extent of the usage of each of the research variables

across organization precisely, appropriate dimensions for each of the research

variables are defined based on the literature. In this regard, CRM resources are

measured with five dimensions including operational CRM technologies,

collaborative CRM technologies, analytical CRM technologies, organizational, and

human CRM resources. CRM processes are studied on both operational and

management dimensions. Finally, customer retention programs of CRM are studied in

four dimensions including: customer service, customization, community of customers,

and loyalty programs. After determining the variables of the research and their

dimensions, we designed the research model. A questionnaire was also designed, pre-

tested, and distributed to the top manager in each selected branch of two

governmental (Refah and Maskan) banks and two private (Saman and Pasargad)

banks in Tehran province. When all the data were gathered, Kaiser-Meyer-Olkin and

Bartlett's test was done to measure the adequacy of the sampling. Next, we used

exploratory factor analysis for deleting the questions with little factor load and

communalities. Finally, to study and test the research hypothesizes structural equation

model (confirmatory factor analysis) was done. To do so, model expression, model

estimation, correction of the model, and hypothesis test were the steps which were

followed respectively. The final results of hypothesis tests show that all the

formulated research hypothesizes in the third chapter are accepted.

One of the most important implications of this study is to align different studies that

address the CRM value generation process and its impact on customer retention in a

simple integrated framework. In doing so, we considered different views about CRM

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and gained insight from the resource-based view (RBV) of the firm and the process-

oriented approach.

The results of the first research hypothesizes test reveal that all CRM resources

including technological and infrastructural resources have direct effect on CRM

processes. The second hypothesis confirms the direct effect of CRM processes on all

customer retention programs of CRM (loyalty programs, customer service,

customization, and creating community). Also, based on the third (the last)

hypothesis, all of these programs have direct effect on customer retention.

In addition, in this study, we tried to find out by which CRM resources customer

retention is mostly affected. We also proposed a process-oriented framework showing

the path in which CRM affects customer retention of Iranian banks in e-banking.

Finally, although there are some studies which link CRM to firm performance such as

the rate of success in financial aspects or customer satisfaction, in this study we have

investigated the effects of CRM on customer retention. To do this, on one hand, we

have presented a complete path from investments on CRM resources to customer

retention; on the other hand, we presented a comprehensive set of customer retention

programs of CRM and investigated the effect of each of them on customer retention.

After that, we studied the correlation between each of these programs and customer

retention that led to find the most effective retention programs.

6.3. Managerial Implications

The findings reported in this research have led to a number of managerial implications

for the organizations interested in leveraging their spending on CRM. First, managers

should note that the findings of this study are applicable to any other industry. This

result is the same as the result of multi-industry research done by Reinartz et al.

(2004), which implies that the benefits of CRM are approximately the same across

different organizations.

The second main result that managers should consider is that CRM is a

multidimensional phenomenon that should be considered in all levels of the firm;

therefore, if organizations are going to get the most out of value that can be created by

CRM, they should design a suitable organization-wide customer strategy with specific

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goals. According to Zablah et al. (2004) and Keramati et al. (2010), not all the

customers are interested in specific relationships; therefore, if a firm tries to apply the

same relationships as the other ones do, the result can even be a negative rate of

customer retention. As a result, since all the companies can not act based on the same

customer strategies, using the mentioned strategy is the most important step to have a

competitive advantage.

When the customer strategy is designed, managers should commit themselves to it.

Indeed this commitment leads to at least two important results. The first and most

important one is that managers will provide the technological and human resources

properly and at the right time; secondly, when the employees see that their managers

are completely committed to the designed customer strategy, they adopt and apply it

easier and faster.

Third, as it has been previously mentioned, organizations can develop their customer

retention programs of CRM by path-dependent processes. In this research we

suggested a path through which firms can develop their capability in customer

retention. In addition, based on the theory of production, a certain level of output can

be achieved by different combinations of inputs. Therefore, if organizations want to

use CRM to improve their rate of customer retention, they should leverage their

spending on a proper combination of technological, human, and organizational CRM

resources and put them properly in place.

The fourth point is that for designing customer retention programs of CRM, CRM

processes are the prerequisite which use all CRM resources (CRM inputs). Therefore,

to design effective customer retention programs of CRM, companies should organize

specific management and operational CRM processes. When applying both kinds of

processes, managers should control the procedure to ensure that they are working as

designed.

Fifth, in assigning the CRM resources, technology is the last one to consider. Indeed,

organizations should first consider what CRM goals they want to reach, what

processes they want to use, and what benefits they are going to gain and in the next

step, they can provide the proper technological resources.

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The sixth point is that managers should note that among all CRM resources, the

human part is the most important one. Indeed, to manage a CRM program and achieve

its goals including customer retention, technical and behavioral skills and experience

are required. To do so, they need people with the mentioned characteristics; therefore,

they should train their available experts; otherwise, they can provide experienced and

skilled people by employing them from outside the organization. Besides, by showing

the importance of CRM programs to these skilled employees, designing incentive

systems to motivate them, and defining the roles of each one clearly, managers can

implement and control the CRM processes more efficiently.

Seventh, the calculated means in tables 5.8 to 5.12 show that from the respondents'

point of view, all the four considered banks have applied all the research variables

(CRM resources, CRM processes, customer retention programs of CRM, and

customer retention) more than the average scale (4). However, the performance of

private banks (Pasargad and Saman) is better than governmental ones (Maskan and

Refah) for all of these variables. Therefore, a very important point that managers of

governmental banks should notice is that since they have not been successful in

applying CRM resources, CRM processes and programs as much as private banks,

they should improve these variables to have a better performance in customer

retention and therefore a better competitiveness. Also, we realized that between the

two private banks, the performance of Pasargad bank is a little weaker than Saman

bank in applying CRM resources, but for the rest of the research variables, the

situation of Pasargad bank is better than the other three banks and has the highest rate

of customer retention.

The last point is that the results of Pearson correlation suggest that there is a positive

and significant correlation between each of the customer retention programs of CRM

and customer retention. However, these results also reveal that among the four

customer retention programs of CRM, the most significant correlation is between

customer service and customer retention and the lowest belongs to customization and

customer retention. Therefore, we can conclude that to have a higher rate of customer

retention, it is recommended that among the studied customer retention programs of

CRM in this research, banks should invest and focus more on customer service to

improve and develop it.

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6.4. Research limitations and suggestions for future research

This research like any other research has its own limitations from which future

research could start. First, due to the time limitation and since there is a huge number

of bank branches in Iran; we limited the population (all Iranian banks) to all the banks

in Tehran province and then among them two governmental (Refah and Maskan) and

two private banks (Saman and Pasargad) were chosen. Finally, a random sample of

these 4 banks was chosen. Consequently, future researches can focus on different

samples from different Iranian governmental and private banks in different provinces.

The second restriction of this study is that the questionnaires were only sent to the

senior bank managers; therefore other banks' managers' such as IT managers and

marketing managers' views can be considered in future studies. In addition, since the

benefits of CRM are perceived by both the organizations and their customers, this

limitation is related to inaccessibility to the customers' opinions. Therefore,

investigating the customers' views about CRM in e-banking will enable the future

researchers to get more accurate findings.

The correlation between each of the customer retention programs of CRM and

customer retention is studied in this research; however, since on one hand, we have

studied and presented a comprehensive path of CRM value generation in the area of

customer retention; and on the other hand we have studied all the customer retention

programs of CRM, we did not sufficiently focus on the correlation between each of

these programs and customer retention. Therefore, another suggestion for future

researches can be focusing more on these correlations.

Another important limitation for this thesis is that e-commerce and e-banking are

young fields in Iran. Thus, if the knowledge about the applications of e-commerce in

businesses increases, the researches done in this field can gain more and better

findings.

As mentioned before, CRM is an evolving phenomenon whose processes,

characteristics and benefits may change over time. Thus, future researches can help to

recognize these changes.

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As mentioned before and according to Reinartz et al. (2004), the findings of CRM

researches such as this research's findings do not vary greatly among industries; thus,

our findings can be generalized in other organizations in different industries; however,

since future studies enable the researchers to further test the validity and reliability of

this research, it is better that they consider other industries, too.

As a final suggestion, since each governmental and private bank has many branches

allover the country, and the number of most Iranian banks branches' employees is

under 185, another suggestion for future studies is that they can investigate the results

of this research in other enterprises which are located and more integrated in fewer

places. These enterprises are mostly big; however, since, there are fewer managers,

gathering the opinions of all of them is an easier, cheaper, and less time consuming

work.

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Appendix A

References related to the questions in the questionnaire

References related to questions in the questionnaire

References

Measurement

criterions

(indicators)

Model

constructs

Massey et al., 2001; Reynolds,

2002; Greve and Albers, 2006;

Coltman, 2007; Keramati et al.,

2009b; Keramati et al., 2010

Collaborative

technologies

Technological

CRM

Resources

Reynolds, 2002; Zablah et al.,

2004; Greve and Albers, 2006;

Keramati et al., 2009b; Keramati et

al., 2010

Operational

technologies

Reynolds, 2002; Reinartz et al.,

2004; Greve and Albers, 2006;

Keramati et al., 2009b; Keramati et

al., 2010

Analytical

technologies

Rigby et al., 2002; Reinartz et al.,

2004; Keramati et al., 2009b;

Keramati et al., 2010

Human

resources

Infrastructural

CRM

Resources

Greenberg, 2004; Greve and

Albers, 2006; Keramati et al.,

2009b; Keramati et al., 2010

Organizational

resources

Mooney et al., 1996; Winer ,2001;

Eng, 2004; Dwyer and Tanner,

2005; Keramati et al. ,2008;

Keramati et al., 2009b; Keramati et

al., 2010

Operational

knowledge

processes

CRM Processes

Winer, 2001; Eng, 2004; Reinartz

et al., 2004; Dwyer and Tanner,

2005; Keramati et al., 2008;

Keramati et al., 2009b; Keramati et

al., 2010

Operational

interaction

processes

Mooney et al., 1996; Winer, 2001;

Eng, 2004; Dwyer and Tanner,

2005; Greve and Albers, 2006;

Keramati et al., 2010

Management

processes

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Appendix A (Continued)

References related to questions in the questionnaire

Winer, 2001; Ahmad & Buttle,

2002; Farquhar, 2004; Smith,

2006; Coltman, 2007; Keramati et

al., 2010

Customization

Customer Retention

Programs of CRM

Winer, 2001; Verhoef, 2003;

Coltman, 2007; Farquhar &

Panther, 2008; Juan & Yan, 2009

Loyalty

programs

Winer, 2001; Venetis & Ghauri,

2004; Smith, 2006; Coltman, 2007;

Arbore & Busacca, 2009;

Keramati et al., 2010

Customer

service

Stauss et al., 2001; Winer, 2001;

Coltman, 2007; Guo et al., 2009

Community

Winer, 2001; Lüneborg and

Nielsen; 2003; Verhoef, 2003;

Eng, 2004; Reinartz et al., 2004;

Wang et al., 2004; Roh et al.,

2005; Greve and Albers, 2006;

Coltman, 2007; Keramati et al.,

2010

_

Customer Retention

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Appendix B

English Questionnaire

Personal information:

Gender: Male Female

Age: Under 20 20-30 31-40 41-50 51 and over

Educational level: Diploma Associate degree Bachelor degree Master degree Ph.D

Field of study: Banking Economics Management Accounting

Computer engineering others

Information on the bank:

Name of the bank you are working in: Refah Maskan Saman

Pasargad

What is the type of your bank? Governmental Private

Your years of experience: Under 5 years 5-10 11-15 16-20

21 and over

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Appendix B (continued)

Please indicate the extent to which the following technologies have been used by your

bank. Use a 1–7 scale: 1 = not at all; 4 = to some extent; and 7 = strongly.

Code Scales

CRM-TECH Technological CRM resources

TECH-V1 Collaborative CRM technologies

Col 1 1. Telephone banking

Col 2 2. Automated teller machine (ATM)

Col 3 3. POS

Col 4 4. Pin Pad

Col 5 5. Internet banking

Col 6 6. Email Management

Col 7 7. Mobil banking (mobile banking, SMS banking)

Col 8 8. Help desk

Col 9 9. Call center

TECH-V2 Operational CRM technologies

Op 1 1. Information systems in sales activities

(e.g., online distributions, transaction systems)

Op 2 2. Information systems in marketing activities

(e.g., personalized marketing, offerings product information)

Op 3 3. Information systems in customer service and support

activities (e.g., customer satisfaction tracking, customer service)

Op 4 4. Customer database

Op 5 5. Credit cards

TECH-V3 Analytical CRM technologies

Anal 1 1. Tools for customer data collection

Anal 2 2. Tools for customer data storage

Anal 3 3. Tools for knowledge management in customer data

analysis

Anal 4 4. Tools for analyzing customer data

Anal 5 5. Tools for decision making in customer strategy

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Appendix B (continued)

Please indicate the extent to which you agree with the following statements. Use a 1–

7 scale: 1 = strongly disagree; 4 = neither agree nor disagree; and 7 = strongly agree.

Code Scales

CRM-INFRA Infrastructural CRM resources

INFRA-V1 Human resources

HR 1 1. Your employees have shown their commitment to your

customer strategy (later deleted)

HR 2 2. Your employees are well trained in using customer

related technologies

HR 3 3. You have marketing and sales expertise to have a

desired CRM

HR 4 4. You have qualified technical staff to provide technical

support for use of technology to build satisfactory relationships with customers

HR 5 5. In general, your employees accept the changes in your

organization readily (later deleted)

INFRA-V2 Organizational resources

Org 1 1. The employees‘ performance are measured and the

employees who meet customer needs and successfully serve the customers are

rewarded

Org 2 2. You have specific programs to improve your

employees‘ skills on CRM strategies

Org 3 3. There is a good synergy between all departments in

your bank

Org 4 4. The information flow between the front office and the

back office in your bank has been examined.

Org 5 5. You have a clear customer strategy with defined goals

(later deleted)

Org 6 6. Your organizational structure is designed based on

your customers‘ situations

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Code Scales

CRM-PRO CRM processes

PRO-V1 Operational knowledge processes

Okp 1 1. Providing your customers with proper information about

your products and services (later deleted)

Okp 2 2. Continuous collection of customer data in order to

recognize them (e.g., to recognize valuable customers)

Okp 3 3. Customers tracking when they contact your bank through

the touch points

Appendix B (continued)

Please indicate the extent to which the following CRM processes have been

influenced by CRM resources in your bank Use a 1–7 scale: 1 = no effect; 4 =

moderate effect; and 7 = extreme effect.

PRO-V2 Operational interaction processes

Oip 1 1. Welcoming the new customers

Oip 2 2. Different treatment with your customers based on their

value to your bank

PRO-V3 Management processes

Mp 1 1. Studying about the products or services your customers

Need

Mp 2 2. Segmenting customers (e.g., based on their needs)

Mp 3 3. Analyzing your customer data

Mp 4 4. Studying your existing customers' behavioral patterns

(Purchasing behavior, contacting behavior, satisfaction and loyalty, retention and

defection patterns)

Mp 5 5. Tracking your customer satisfaction level

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Appendix B (continued)

Please indicate the extent to which the following customer retention programs of

CRM have been improved in your bank. Use a 1–7 scale: 1 = no improvement; 4 =

moderate improvement; and 7 = extreme improvement.

Code Scales

CUS-RET-PROG Customer retention programs of CRM

RET-PROG-V1 Customization

Cus 1 1. Individualization of marketing messages to the customers

Cus 2 2. Customization of products and services for the customers

(later deleted)

Cus 3 3. Communicating with customers based on the way they desire

RET-PROG-V2 Loyalty programs

Loy 1 1. Offering rewards to motivate customers to request and

make next purchase

Loy 2 2. Offering rewards to long term customers

Loy 3 3. Offering more rewards for more investments

Loy 4 4. Giving some free complementary products or services

RET-PROG-V3 Customer service

Cs1 1. Educating customers about how they can use (or better use)

the products or services

Cs 2 2. Handling the customers‘ complaints responsively

Cs 3 3. Handling customer‘s requests or problems with the product

or services (later deleted)

Cs 4 4. Providing the customers with the ability of real time

interactions with service representatives through proper touch points

Cs 5 5. Holding a dialogue with the customers prior to use a

reactive solution such as complaint handling

Cs 6 6. Safe and error free transactions (later deleted)

Cs 7 7. Providing clear instructions in use of the services (later

deleted)

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Code Scales

CUS-RET Customer retention

Appendix B (continued)

Please indicate the extent to which your bank has improved over the previous two

years on the following statements. Use a 1–7 scale: 1 = no improvement; 4 =

moderate improvement; and 7 = extreme improvement.

CR 1 1. Customers' loyalty to your bank (later deleted)

CR 2 2. Customers have trusted on the e-banking services you

have provided for them.

CR 3 3. Overall customer satisfaction from the e-banking

services you have provided for them

CR 4 4. Overall customer retention

Cs 8 8. Providing user friendly website for the customers

Cs 9 9. Your e-services (such as the website for internet banking)

are easy to use (later deleted)

RET-PROG-V4 Community

Com 1 1. Building online customers networks through which they are

able to exchange information about the products and also can interact between themselves

and your bank easier and more personally.

Com 2 2. Building online customers networks through which they are

able to give their opinions about bank performance.

Com 3 3. Building online customers networks for e-learning purposes.

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Appendix C:

Farsi Questionnaire ب وام خدا

دد ا ن ددي ناصغددشه نپغددنان نش ددشى شهدد دشبعشصددن نبعدد دددش ددنى بددششپا هدد ا د نببنددش دددش هغدد ا دددا دد نب

بدد عدوش ن را دك نبن د نشصدی دش نلپدك د ى بان هغش. هغ ا شى نب هحپط دشک نب نلک اپکن ن انى نصد

ن ب بشهل هجب نپغافد ا چ دپغ ا ن ي بحمپك د نرال شت آى نب خ هد دشک نب نلک اپکن ن انى لدانب

ال م د ذدا نصد د نرال شت ن ي ناصغشه دشهال دن شم دن بش نب نپغدنان نش دشى شهد . خن گافد هن دشع

.خن ع هذدب نص فشن

ه ا د نببنش دش هغ ا ن را ك ن جشن بصدع دشدظ دش خد هشت توش دد : بعا ف ه ا د نببنش دش هغ ا

هغ ا ، دش نب شدن، ولپشت بحلپل ننن ش نرال شت هغ ا شى غپدا هجدب بیدش د دا چد دپغد ا هغد ا شى

دشد دش هدن گدانن دد ردب دد ددش دد دا دضدپشب ن نب پج هش گشب آش صن دپغ ا دان عدادد دش

هحممشى د ا دش ن آى د نب هغد ا شى نب فردش بلدشد ن هپدشى عدادد دش، هصضدشت، صدش هشى دش دشد دش

.دان دمش نپغافد ا چ دپغ ا نصد

ى هان : جىس

دپغ ا 51 41-51 31-41 21-31 صشل 21 ا :سه

ند ان فق لپضشش لپضشش لن پن پلن فق ن : سطح تحصیالت

صش ا نل صشن ه ا د تضشد نب دشهپپ ا دشک نب : یلیزشت تحص

هضکي صشهشى نشصشبگشن بفش :وام باوکی ک دز آن مشغول فعالیت ستید

خصصن نل ن : ووع باوک

ا دپغ 21 16-21 11-15 5-11 صشل 5 ا : سابق فعالیت شما دز ایه باوک

.مقیاس ای ذکس شدي بیان کىید زا با توج بت س جدلل لطفا پاسخ سواال

آى ددا بدش چد تدد نب ددشکن ددد نب دش فددشب فدش دپدشى دپدد دا د ن لط

دد = 1)دشعد هدن 7بش 1همپشس ش ن . دپ د دشب گاف ع نصدفعشلپد هن

دضددپشب = 6خدد ، = 5بددش تدد ه صددط، = 4دددن، = 3خپلددن دددن، = 2ددپو جدد،

(. شلن= 7 خ ،

7

6

5

4

3

2

1

بلفنش . 1

(ATM)نص ش ش خن نانن . 2

POS))نش ش فاط . 3

(Pin Pad)ععن ن نص ش دشبت خنى . 4

(دشک نب ن ا ن)ن اد . 5

ن وپل. 6

(SMS دشک نب ن را ك هدش ل )دشک نب صپشب . 7

8 .help desk (لفن هادز نشصی گ ن د صبت ن ا ن، ب)...

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آى ددا بدش چد تدد نب ددشکن ددد نب دش فددشب فدش دپدشى دپدد دا د ن لط

دد = 1)دشعد هدن 7بش 1همپشس ش ن . دپ د دشب گاف ع نصدفعشلپد هن

دضددپشب = 6خدد ، = 5بددش تدد ه صددط، = 4دددن، = 3خپلددن دددن، = 2ددپو جدد،

(. شلن= 7 خ ،

7

6

5

4

3

2

1

9 .call center

صپضد ن دش ناننخدد ددان هادشل ) صپض ن نرال دشت نب فعشلپ دش فداط . 11

(نرال شتآال ي ب ع

عشصددن صددش دددان هاددشل نب )صپضدد ن نرال ددشت نب فعشلپ ددش دش نب ددشدن . 11

(فعشلپد ش دش نب شدن

خد هشت دان هاشل)نرال شت نب فعشلپ ش هاد د خ هشت هغ ا صپض ن . 12

(نش ن فاط

هاد د هغ ا شى (data base)نش ش نرال شت . 13

دشبت ش ن نشب . 14

ندزنب ال م دان جوع آب ننن نب بحلپل نرال شت هاد د هغ ا شى . 15

ندزنب ال م دان ذخپا صش ننن ش نب بحلپل نرال شت هاد د هغ ا شى. 16

ندزنب ال م دان ه ا د ننظ نب بحلپل نرال شت هاد د هغ ا شى . 17

ندزنب ال م دان آشلپز نرال شت هاد د هغ ا شى. 18

نص انبژ ش هغ ا ندزنب ال م دان بصوپن گپا نب هبن. 19

همپدشس دش . لطفش دپشى دپ بش چ هپزنى دش ا ن هنبن دا هنفدك ضد پ

.دشع هن 7بش 1 ن

7

6

5

4

3

2

1

د نص انبژ ش هاد نجان دش عوش بع عشى بن ضند د دشبه نى. 1

(تذا ع ). هغ ا غشى ننن ن

پبا د خدنپدشبه نى دش عوش دان نص فشن ن بکلژ ش دشه. 2

.آه ط ن ن

.ننبن ض پ بن بشصص ال م نب هپ ش دش نب شدن فاط. 3

ننبناصل فن دشفن نب هپ فانن دانى خ هشت فن دان هغ ا شى بن ن. 4

.ض پ

(تذا ع ) .د هن بن لنل نصش هشنت وش د بنت ن بغپپاعدشبه نى دش . 5

عدددن دددد دشبهددد نن دددد دددد خددددن ولکدددان دشبهددد نى نددد ن گپدددا هدددن. 6

. شننط ننن هپغنند هغ ا شى بن داآبن هن نت پشجشت

نجدددان بهددد نى نبهدددشبت دددش دش دندددن دددد ه دددبداشهددد دددش هعپدددن . 7

. نص انبژ ش ه ا د نببنش دش هغ ا شى بن ننب

.وکشب وش ن جن ننبنعوش هپشى بوشم دشظ ش دش . 8

.عن جا شى نرال شت هپشى دلپ ولپشت دشکن هطشلع دابصن هن. 9

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همپدشس دش . لطفش دپشى دپ بش چ هپزنى دش ا ن هنبن دا هنفدك ضد پ

.دشع هن 7بش 1 ن

7

6

5

4

3

2

1

ن نا هعپن دش انبژ هغشص وانن نص ،نب هپ نببنش دش هغ ا شى. 11

(تذا ع ) . پد پا هنن

.صشخ شب صش هشن دش دا نصشس هلعپد هغ ا شى هن دشع . 11

1

2

3

4

5

6

7

بصط هنبن ذدا ع اش فاآ ن لطفش دپشى دپ بش چ هپزنى ا

7بش 1همپشس ش ن . ال نب دش عوش بحد بششپا لانب گاف ن نب ج نل دش

بش ت = 5ت ه صط، = 4دن، = 3خپلن دن، = 2د پو ج، = 1)هن دشع

(. دضپشب شن= 7 شن، = 6لشدل بج،

(حرف شد) نبن نرال شت هشصب نب هبن هحصالت خ هشت دان هغ ا شى. 1

جوع آب هض وا نرال شت هغ ا شى د ه ب عشخد ال م ن آش. 2

بن شدن هغ ا نب شم بوشس دش دش بصط هاندز بوشس .3

خط آه گ ن بف شب هشصب دش هغ ا شى ج . 4

غشى دان دش بف شب ه فشت دش هغ ا شى دا نصشس نب ع. 5

دابصن خ هشت هحصالبن د هغ ا شى د آش پش ننب . 6

)بمضپن د هغ ا شى . 7 بودشس آدش دان هاشل دا نصشس پش ش غشى ش را مد

(دش دش

بحلپل نرال شت هاد د هغ ا شى. 8

بف شبددش هاددد ددد دددان هاددشل)ش بف ددشب هغدد ا شى فعلددن هطشلعدد نل دد .9

(چ ن بوشس دش دش خا ش

بیش د هغ ا شى ن دش هپزنىن ن گپا .11

لطفش دپشى دپ ا ن هنبن ذدا ع نب ج ل ا بش چ هپزنى نب دشد

.هن دشع 7ش ب 1همپشس ش ن . عوش نپغافد ننع نصد

7

6

5

4

3

2

1

عشصن صش نپشم ش دان هغ ا شى نب ولپشت دش نب شدن . 1

(تذا ع ) نبن خ هشت هحصالت هطشدك دش خنصد صفشبط ا هغ ا . 2

آگش صدشخ ي نردالر بصدشن دد هغد ا شى ن را دك حد نببندش هدبن اللد . 3

...(، بلفي ش SMSدان هاشل ن را ك ن وپل، )آش

نپغددشن نددشننط ددد هغدد ا شى ددد ه ددب ن جددشن ن پددز دددان نبخنصددد هجدد ن . 4

خ هشت هحصالت

نپغشن نشننط د آى نصد ن هغد ا شن دد ددان هد ت ردالن هغد ا دشد . 5

هش ن

نب دش دپغ ا صاهش گذنب ى د ن ن نپغشن نشننط د هغ ا ش. 6

1

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لطفش دپشى دپ ا ن هنبن ذدا ع نب ج ل ا بش چ هپزنى نب دشد

.هن دشع 7ش ب 1همپشس ش ن . عوش نپغافد ننع نصد

7

6

5

4

3

2

1

ش نب نى بن شىنبن خ هشت ش هحصالت هکول د صبت . 7

آه ط هغ ا شى نب هبن نص فشن صحپح ش د ا هحصدل خا د نب عد . 8

ش صا ش نبخنصد ع

هضئالى هادربصط عکش شت هغ ا شىنپ پا هضئال . 9

(تذا ع ) تل هغکالت بصپ گن د نبخنصد هغ ا شى نب هبن خ هشت. 11

فانن دانى نهکشى بوشس آصشى دالنبگ دد ه دب بعشهدل هپدشى هغد ا . 11

ن را ك هاندز بوشس دشظ خ هشت نب دش

پظ ن ا عکش د ن جشب آشگف دش هغ ا شى تل هغکالبغشى ن. 12

(تذا ع ) د صبت نهي د ى خطش( هشلن)نجشم هعشهالت بنشنالت . 13

نبن نص بنلعول ش نیح بعي دان نصد فشن ن خد هشت نلک اپکدن . 14

(تذا ع ) (ن اد خن نانن ش دان هاشل)

ص فشن هغ ا شى صش د دشبدا نض جد ننبن . 15

(تذا ع ) آصشى نصددش عوش خ هشت نلک اپکن ن نص فشن . 16

ن جددشن نجوددي ددش آال ددي ددد بصددط آددش هغدد ا شى لددشنب ددد بنددشنل نرال ددشت . 17

.هاد د خ هشت هحصالت دش ک ا ض

ش هغ ا شى لشنب د ا گ نظشب ا ن جشن نجوي ش آال ي د بصط آ. 18

.نب نببنش دش ولکان دش دش دشبه نى آى دشع

ن جشن نجوي ش آال ي د ه ب آه ط نلک اپکن هغ ا شى. 19

گذعدد دشدد عددوش بددش چدد هپددزنى نب ددا دد ن هددنبن ددا نب رددل ن صددشل

.دشع هن 7بش 1ن شهمپشس . نپغافد ننع نصد

7

6

5

4

3

2

1

(تذا ع ) عوشهپزنى فشننب هغ ا د دش . 1

هفمپد نب جلب نروپشى هغ ا ن خ هشت دشک نب نلک اپکن . 2

خ هشت دشک نب نلک اپکن دش هپزنى بیش د دلن هغ ا شى ن . 3

هغ ا شىنب نب ولکان دلن . 4

نب نش شى ن وکشب عوش نب ن ي بحمپك صشنلش صپشص زنب ن

1

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Appendix D Reliability calculation

To calculate the reliability of the items, 30 questionnaires (N=30) have been

distributed to the respondents. After collecting all the questionnaires, we calculated

the Cronbach's alpha for the items. As it is shown below, the calculated Cronbach's

alpha for 63 items is equal to .970. Then, we deleted the first item and calculated the

Cronbach's alpha again. This is done for the second, third, and all of the items

respectively. The results show that in each of the mentioned steps there is no change

in the value of Cronbach's alpha. Therefore, we can conclude that the designed

questionnaire can be considered reliable.

Case Processing Summary

N %

Cases Valid 30 100.0

Excludeda 0 .0

Total 30 100.0

a. Listwise deletion based on all variables in the

procedure.

Reliability Statistics

Cronbach's Alpha N of Items

.970 63

Item Statistics

Mean Std. Deviation N

col1 5.8000 .76112 30

col2 6.4333 .67891 30

col3 5.3333 .92227 30

col4 6.3333 .47946 30

col5 5.4333 1.10433 30

col6 4.6000 1.19193 30

col7 4.5667 1.19434 30

col8 4.3667 .80872 30

col9 5.0667 1.04826 30

op1 4.5333 1.40770 30

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op2 4.6667 1.06134 30

op3 3.8667 .93710 30

op4 4.6000 .96847 30

op5 5.5000 1.07479 30

anal1 5.5667 1.13512 30

anal2 5.6667 1.06134 30

anal3 5.6333 1.12903 30

anal4 5.5000 1.16708 30

anal5 5.4333 1.19434 30

HR1 6.3000 .53498 30

HR2 5.6667 .66089 30

HR3 5.5667 .56832 30

HR4 5.1667 1.01992 30

HR5 5.3667 .66868 30

org1 5.3000 .83666 30

org2 5.7333 .78492 30

org3 5.4333 .85836 30

org4 5.3667 .92786 30

org5 6.0333 .76489 30

org6 5.9333 .86834 30

okp1 5.8333 .64772 30

okp2 5.0000 .74278 30

okp3 5.1667 1.05318 30

oip1 6.0667 .78492 30

oip2 5.8333 .69893 30

mp1 5.9000 .66176 30

mp2 5.4333 .72793 30

mp3 5.6667 .75810 30

mp4 5.0667 .78492 30

mp5 5.6333 .66868 30

cust1 3.8333 1.74363 30

cust2 5.1333 1.10589 30

cust3 4.9667 1.06620 30

loy1 5.0000 .90972 30

loy2 5.7000 .83666 30

loy3 4.9333 .86834 30

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loy4 5.5333 .57135 30

cs1 5.6667 .60648 30

cs2 6.6000 .49827 30

cs3 6.4667 .50742 30

cs4 5.9000 .80301 30

cs5 5.9000 .75886 30

cs6 6.2333 .43018 30

cs7 6.4333 .56832 30

cs8 5.8667 .68145 30

cs9 5.4333 .50401 30

com1 4.3000 1.93248 30

com2 4.5333 1.87052 30

com3 3.7667 1.92414 30

CR1 6.3333 .47946 30

CR2 5.8333 .53067 30

CR3 5.7667 .50401 30

CR4 5.8000 .61026 30

Item-Total Statistics

Scale Mean if Item

Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha

if Item Deleted

col1 336.5000 1205.431 .616 .969

col2 335.8667 1214.120 .507 .969

col3 336.9667 1192.792 .705 .969

col4 335.9667 1224.654 .408 .970

col5 336.8667 1181.568 .735 .969

col6 337.7000 1168.631 .841 .969

col7 337.7333 1167.375 .855 .968

col8 337.9333 1225.857 .213 .970

col9 337.2333 1186.185 .710 .969

op1 337.7667 1160.461 .794 .969

op2 337.6333 1178.792 .805 .969

op3 338.4333 1183.909 .834 .969

op4 337.7000 1193.666 .657 .969

op5 336.8000 1179.062 .790 .969

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anal1 336.7333 1175.444 .794 .969

anal2 336.6333 1176.723 .834 .969

anal3 336.6667 1184.368 .681 .969

anal4 336.8000 1177.200 .749 .969

anal5 336.8667 1177.223 .731 .969

HR1 336.0000 1218.483 .530 .970

HR2 336.6333 1210.930 .591 .969

HR3 336.7333 1237.444 .020 .970

HR4 337.1333 1185.844 .736 .969

HR5 336.9333 1245.030 -.147 .971

org1 337.0000 1210.897 .463 .970

org2 336.5667 1197.771 .739 .969

org3 336.8667 1202.395 .595 .969

org4 336.9333 1188.340 .772 .969

org5 336.2667 1201.237 .693 .969

org6 336.3667 1189.068 .814 .969

okp1 336.4667 1219.223 .418 .970

okp2 337.3000 1212.976 .484 .970

okp3 337.1333 1187.499 .688 .969

oip1 336.2333 1201.495 .670 .969

oip2 336.4667 1226.533 .236 .970

mp1 336.4000 1214.455 .513 .969

mp2 336.8667 1245.913 -.153 .971

mp3 336.6333 1200.171 .720 .969

mp4 337.2333 1196.806 .758 .969

mp5 336.6667 1230.161 .170 .970

cust1 338.4667 1140.602 .806 .969

cust2 337.1667 1168.833 .906 .968

cust3 337.3333 1180.023 .784 .969

loy1 337.3000 1206.631 .492 .970

loy2 336.6000 1223.559 .244 .970

loy3 337.3667 1197.964 .663 .969

loy4 336.7667 1219.564 .468 .970

cs1 336.6333 1222.654 .366 .970

cs2 335.7000 1227.872 .299 .970

cs3 335.8333 1239.247 -.026 .970

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cs4 336.4000 1210.524 .490 .970

cs5 336.4000 1245.145 -.134 .971

cs6 336.0667 1239.789 -.047 .970

cs7 335.8667 1231.016 .181 .970

cs8 336.4333 1235.771 .049 .970

cs9 336.8667 1225.292 .369 .970

com1 338.0000 1115.103 .928 .968

com2 337.7667 1120.530 .915 .968

com3 338.5333 1116.533 .920 .968

CR1 335.9667 1214.240 .721 .969

CR2 336.4667 1214.326 .648 .969

CR3 336.5333 1216.740 .613 .969

CR4 336.5000 1220.672 .411 .970

Scale Statistics

Mean Variance Std. Deviation N of Items

3.4230E2 1.239E3 35.19321 63