Trust and commitment in online shopping in Vietnam, antecedents and consequences

LIST OF FIGURES Figure 2.1 Model of determinants of customer loyalty and retention for online shopping research framework ..... Based on such relationship patterns, the research will dis

VIETNAM NATIONAL UNIVERSITY, HANOI

SCHOOL OF BUSINESS

NGUYEN THANH TRUNG HIEU

TRUST AND COMMITMENT IN ONLINE SHOPPING IN VIET NAM,

ANTECEDENTS AND CONSEQUENCES

Major: Business Administration Code: 60 34 05

MASTER OF BUSINESS ADMINISTRATION THESIS

Supervisors: Dr Tran Doan Kim

Hanoi – 2011

TABLE OF CONTENTS

ACKNOWLEGEMENT i

ABSTRACT ii

TÓM TẮT iv

TABLE OF CONTENTS vi

LIST OF TABLES viii

LIST OF FIGURES ix

CHAPTER 1 INTRODUCTION 1

1.1 BACKGROUND 1

1.2 PURPOSES AND RESEARCH QUESTIONS 2

1.3 METHODOLOGY 2

1.4 DEFINITIONS 3

CHAPTER 2: LITERATURE REVIEW 5

2.1 CUSTOMER RELATIONSHIP MANAGEMENT 5

2.1.1 Trust 7

2.1.2 Commitment 11

2.1.3 Loyalty 16

2.1.4 Retention 17

2.1.5 The relationship among commitment, trust, loyalty and retention 17

2.2 METHOD OF STATISCAL ANALYSIS 24

2.2.1 Correlation analysis 24

2.2.2 Multiple Regression 24

CHAPTER 3: METHODOLOGY 30

3.1 RESEARCH STRATEGY 30

3.2 DATA COLLECTION METHOD 32

3.2.1 Sample size 32

3.2.2 Questionnaire design 33

3.2.3 Data collection 36

3.3 DATA ANALYSIS 37

3.3.1 Measurement of variables 37

3.3.2 Independent variables 37

3.3.3 Dependent Variables 38

3.3.4 Methods of data analysis 39

CHAPTER 4: FINDINGS AND CONCLUSION 40

4.1 DESCRIPTIVE STATISTICS 40

4.2 CORRELATIONS 41

4.3 HYPOTHESIS TESTING 42

4.3.1 The determinants of Trust 43

4.3.2 The determinants of Commitment 45

4.3.3 The determinants of Customer Loyalty 48

4.3.4 The determinants of Customer Retention 54

CHAPTER 5: CONCLUSION AND RECOMMENDATIONS 62

5.1 DISCUSSION 62

5.2 IMPLICATIONS 65

5.3 RECOMMEDATIONS 67

5.3.1 Further research 67

5.3.2 For e-commerce in Vietnam 68

REFERENCES 89

LIST OF TABLES

Table 3.1: Independent Variables 38

Table 3.2: Dependent Variables 38

Table 4.1 Descriptive Statistics of Scales 41

Table 4.2 Correlation Matrix 42

Table 4.3 The Determinants of Trust 43

Table 4.4 Regression Diagnostics – Trust 45

Table 4.5 The Determinants of Commitment 46

Table 4.6 Regression Diagnostics – Commitment 48

Table 4.7 The Direct Determinants of Customer Loyalty 49

Table 4.8 Regression Diagnostics – Customer Loyalty 50

Table 4.9 The Determinants of Customer Loyalty 52

Table 4.10 Regression Diagnostics –Customer Loyalty 53

Table 4.11 The Direct Determinants of Customer Retention 55

Table 4.12 Regression Diagnostics - Customer Retention 56

Table 4.13 The Determinants of Customer Retention 58

Table 4.14 Regression Diagnostics –Customer Retention 60

LIST OF FIGURES

Figure 2.1 Model of determinants of customer loyalty and retention for online shopping (research framework) 23

CHAPTER 1 INTRODUCTION

1.1 BACKGROUND

The Internet‟s success over a few decades has changed many things in today‟s social life People now can communicate virtually and can buy virtually anything without going to the stores or supermarket E-commerce in general and shopping online in particular has helped companies gain a new effective distribution channels beyond traditional ones and has also helped customers save their time As a result, shopping online has developed exponentially with the evidence of rapidly increasing the revenues from e-business and the number of trading transactions through Internet

In Vietnam, e-commerce is one of those areas receiving support from the government In the Ministry of Industry and Trading, there is an E-commerce Development Centre which observes, analyze the development of e-commerce and finds various solutions to further develop e-commerce in Vietnam They post annual reports on the Vietnam e-commerce situation The number of enterprises applying for e-business in Vietnam increased gradually from 8% to 12% in the period of 2006 – 2009 according to the reports of E-commerce Development Centre in 2009 In other words, many products will be supplied through Internet and customers in Vietnam can buy goods by searching without going to supermarket or stores like at the present However, traditional buying habits have with many characteristics such as the close relationship between buyers and sellers where buyers can communicate and ask sellers about products or sellers can introduce new products through the many convenience stores in Vietnam These may create barriers to the development of online business in Vietnam Other barriers preventing shopping online in Vietnam such as electronic payment systems, supplied by banks and network systems companies also are mentioned in the media

1.2 PURPOSES AND RESEARCH QUESTIONS

E-commerce in Vietnam has government and commercial enterprise attention but the number of customers shopping online is still limited People are not yet ready to trade online Moreover, with the limited number of current customers, what should companies do to keep them and develop close relationships with them? How can companies ensure customer product repurchase or recommend others people to use them?

Most research in Vietnam focuses on the development of e-commerce from the point of view of the companies applying, but rarely studies how to attract and keep customers shopping online Therefore, the objective of this research

is to explore the relationship between trust, commitment, loyalty and retention, the four main areas identified in various previous studies on customer relationship marketing Based on such relationship patterns, the research will discuss which factors influence customer loyalty and customer retention and recommend how to encourage and keep customers shopping online in Vietnam From the objectives of the research, two research questions emerged and can be stated as following

1 What are the antecedents of trust and commitment?

2 How do trust and commitment influence customer loyalty and customer retention in online shopping in Vietnam?

1.3 METHODOLOGY

The objective of research is to test the hypothesis to find out the relationship between variables in online shopping in Vietnam lending to a deductive research approach and the research purpose is explanatory

The research will start with a literature review which reviews the theories related to the topic researched to investigate often studied areas in customer

relationship in marketing areas and to develop hypotheses for this research According to the recommendations of the research method, a survey strategy with questionnaire is chosen to find the answers to the research questions

A questionnaire is then built for data collection with 28 questions adapted from previous research (the questionaire included many questions from other many previous research- not from only one paper) to measure both

independent and dependent variables A pilot test is used to make sure the participants and researchers understand the meaning of questions

The data collected are analyzed by the SPSS program Firstly, it will be checked whether distribution normally or not and then statistics calculate by running correlations and multiple regressions to determine if the hypotheses stated in this research are supported or rejected which are reflected the relationships among four main areas are the same or different with current studies

1.4 DEFINITIONS

The research is going to tests the relationship between customer trust, customer commitment, customer loyalty and customer retention, stated key components in theories of customer relationship management and these relationships will be tested in one particular business sector, namely; e-commerce and online shopping in particular Several concepts need to be defined for the research

 Payne and Frow (2005) defined Customer relationship management

(CRM) as “a strategic approach that is concerned with creating improved

shareholder value through development of appropriate relationships with key customers and customer segments CRM unites the potential of relationship marketing strategies and IT to create profitable, long term relationships with

customers and other key stakeholders CRM provides enhanced opportunities

to use data and information to both understand customers and co-create value with them This requires a cross-functional integration of processes, people, operations and marketing capabilities that is enabled through information,

technology and applications”

 Turban and King (2003) defined E-commerce (EC) as “the process of

buying, selling, or exchanging products, services, and information via

computer networks, including the Internet”

 Mosuwe et.al (2004) defined ”Shopping online means customer

intentions to shop on the Internet It refers to the way people buy products through Internet from companies having e-business It belongs to the type of business to customers (B2C) within the larger concept of e-commerce”

 Morgan and Hunt (1994) defined Trust as “the perception of confidence

in the exchange partner‟s reliability and integrity”

 Morgan and Hunt (1994) defined “Commitment as an enduring desire to

maintain a valued relationship It is the commitment in the mind of the customer towards the company and the maintainance of the relationship between them”

 Oliver (1999) defined “Customer loyalty as a deeply held commitment to

re-buy or re-patronize a preferred product/service consistently in the future, thereby causing repetitive same-brand or same brand-set purchasing, despite situational influences and marketing efforts having the potential to cause

switching behavior”

 Gerpott (2001) defined Customer retention as “maintaining the business

relationship established between a supplier and a customer”

CHAPTER 2: LITERATURE REVIEW

This chapter reviews literature relating to the research questions and hypotheses of this study First, the chapter begins with a summary of the definition of customer relationship management (CRM) Then, the relationships of four areas of CRM including trust, commitment, loyalty and retention are discussed The theoretical evidence of impact of factors on these four areas is provided After reviewing the literature related to variables in customer relationship management, the research model has been developed as

a research framework The research framework includes two main hypothese

parts: nine main hypotheses (from all types of H1 to H4 hypothese) to test the different relationship between variables and 10 additional hypotheses (from all types of H5 to H6 hypothese) are developed to test the role of mediator of trust and commitment in the model The research framework of this study is the synthesis of the findings of previous authors in order to explain the reserch problems The research of this paper has conducted in Vietnam Therefore, the results of this study will have certain significance

2.1 CUSTOMER RELATIONSHIP MANAGEMENT

Payne and Frow (2005) defined “CRM is a strategic approach that is concerned with creating improved shareholder value through development of appropriate relationships with key customers and customer segments CRM unites the potential of relationship marketing strategies and IT to create profitable, long term relationships with customers and other key stakeholders CRM provides enhanced opportunities to use data and information to both understand customers and cocreate value with them This requires a cross-

functional integration of processes, people, operations, and marketing capabilities that is enabled through information, technology, and applications” This definition is quite complete because it shows the constituent activities of CRM and how CRM is incorporated in companies Since the concept of CRM appears mid-1990s, CRM has gone through three generations in the development of this area According to Kumar and Reinartz (2006) the author has studied many of the CRM noted in his research stated that “The first generation functional CRM‟s approach was used as a way to increase sales and improve the services CRM at that time identified different activities such as sales force automation or customer services and support” And, the next gerenation of CRM is “customer-facing front end approach‟‟ At that time, CRM was considered as the way to fill the gaps in enterprise resource planning (ERP) functionality and the company‟s business needs, namely; the customer-facing front end Customer relationship management through interaction with customers before selling to after sales through the means of communication such as telephone or internet is not achieved goal in 1990s

By the end of 2002, strategic approach to the third generation began when the company had to draw experience from the implementation of unsucssessful of the old version of CRM So, they not only focused on customer-facing front-end, as in second generation, but also paid attention to back-end systems, namely partners and suppliers Evently, they integrated both of them with Internet technology Therefore, CRM at the moment is not only a technology solution but also is the company's strategy As the result, CRM plays as an important role in the growth of the revenue line in a company

2.1.1 Trust

2.1.1.1 Definition of trust

Because uncertainties exist in transactions though the Internet, many researchers have stated that trust is an important factor influences the successful developing of e-commerce So, trust is consider as an important role in many social and economic interactions relating to uncertainty and dependency In addition, There are two important factors influenced by Trust in online transactions, which are the security and privacy, so the concept trust is very important to defined

Besides the convenience of e-commerce by bringing buyers and sellers also have limitations such as lack of direct communication between buyers and sellers, between buyers and goods In order to reduce the barriers, supplier need to develop a trustworthy relationship to increase customer loyalty

Moreover, Teo & Liu ( 2007) considered “Consumer trust as an important aspect of e-commerce, and understanding its antecedents and consequences

is a prime concern for the following reasons First, the antecedents of trust enable us to know the relative importance of factors affecting trust Understanding these factors would play an important role in devising appropriate measures to facilitate trust Second, the consequences of trust would enable us to better understand the importance of trust and its effect

on online buying behaviour”

In term of the organizational trust literature, Mayer et al (1995) who presented “a model realtionship between a trusting party and a party to be trusted” In term of e-commerce, Jarvenpaa et al (2000) “ examined

whether or not customers‟ perceptions of an Internet store's reputation and size affect their trust in the store So, researchers find out that trust has affect on consequences such as: consumers‟ attitudes, intentions, and behaviors”

Morgan and Hunt (1994) defined Trust as “the perception of confidence in the exchange partner‟s reliability and integrity” It means that Trust is one of the most important factors for successful marketing relationships The other definition from Mayer et al (1995, p.712) as “trust is a willingness of a party

to be vulnerable to the actions of another party based on the expectation that the other will perform a particular action important to the trustor, irrespective

of the ability to monitor or control that other party” Based on the definitions

of trust from different previous studies, it can be seen that confidence and reliability are two basic elements

Although both the original concept of the trust, the definition does not include all the dimensions of trust, because it is a very broad concept So when research is therefore trust need to classify based on the belief from analysis and comparison

of definition of trust, different classifications based on different definitions according to factors such as attitudes, beliefs, behaviors and tendencies or to different referents believe in trust, something in someone or trust in a specific characteristic of someone as honest

To sum up, we should need to be summarized into overall definition, so for this study we use the definition of Morgan and Hunt (1994) - Trust as “the perception of confidence in the exchange partner‟s reliability and integrity”

2.1.1.2 Antecedents of trust

To answer one of part of the reasearch question such as: “ What are the

antecedents of Trust? ”, this research find out that there are two foundational antecedents of trust studied through several previous research including e-retailer reputation and Trust which were done by Bennett & Gabriel ( 2001);

Josang et al., (2007) and privacy concern and Trust by Eastlick (2006) Then, this study will discuss about these antecedents of trust and the relationship

among them

E-retailer reputation

There are many factors affecting the decision to participate in electronic commerce, in which the reputation of the retailer is an important factor Following to Bennett & Gabriel (2001), who defined as: “E-retailer reputation is same meaning as brand reputation which includes name, term, symbol, sign or design to realize goods and services of one retailer with others which provide the same items , and reputation is not only related to the image features but also involves an outsider‟s subjective judgment of an organization‟s qualities in terms of its past performance‟‟

Van and Leunis (1999) showed that “brand reputation is one of the factors reducing customers‟ risk concerns when trading online through the Internet” E-retailer reputation is an important factor affecting customer decision to

participate in e-commerce Especilly, Bennett and Gabriel, (2000) and Josang et al (2007) stated “ Other research show the relationship between reputation and trust in which reputation influences positively on customer trust” That is one of theoretical evidence related to this topic in term of building and testing hypotheses

The reputation of a company or an organization, even a category of individuals is a very sensitive and vulnerable to bad because creating a good reputation is very difficult compared with losing it This is a strategic asset

very fragile but easily tarnished or damaged if not carefully protected Therefore, a supplier with good reputation, the more so the higher consciousness to the protection of his reputation before the negative effects

In the traditional marketing literature, reputation has proved to be positive

factors related to the confidence of buyers and sellers Especilly, from research of Teo & Liu (2007), stated that “ In Internet shopping, perceived reputation of a vendor has also been revealed to be significantly related to consumers‟ trust in the vendor” That is one of theoretical evidence related to building and testing the relationship betweene e-retailer reputation and customer trust.(Mentioned in the hypothes H1a)

Privacy concerns

The privacy concerns is used in this study refer to a major source of consumer concern surrounding the personal data, specific individuals, for example, name, address, demographic characteristics, lifestyle, interests, shopping preferences, purchase history of individuals

There is one of barries to online shopping, which related to risk information, important issue when studying the electronic commerce via internet, customer may loss of privacy and the security of personal information.The information

is such as: personal information, collection information, unauthozied secondary used personal information, mistaken personal information The information also refer to information privacy concern issues involving to online marketing and shopping topic

With the development of database technology and the explosion of Internet, the privacy concerns of consumers that would arise when the market research, customers consolidate all customer information Meanwile,

Milne and Boza (1998) checked the ”relationship between privacy concerns

of consumers and trust toward marketing information practices across different industries including direct marketing” They reported that, with

“various industry, consumer reviews at an average of concerns about privacy concern and low levels of trust for the information practices of marketers directly” (A modern marketing which using up existing tools to satisfy every

customer target) The report also said that, “by using multiple regression techniques in statistics, they also found a negative relationship between trust level for an exhibition of direct marketing and potential risk of loss of privacy concern and sercurity of personal information” With regard to online retailing, it is expected that the relationship between privacy concerns and trust in an e-retailer will be similar to that observed for other direct marketers

(privacy concern negative affectly to trust) That is one of theoritical evidence related to this topic in term of building and testing hypotheses about relationship betweene privacy concerns and customer trust (mentioned in the hypothes H1b)

2.1.2 Commitment

2.1.2.1 The definition of commitment

There are different definitions of commitment Morgan and Hunt (1994) defined as : “ Commitment is considered as „an enduring desire to maintain a valued relationship‟‟ However, there is no single general form of

commitment as in the definition but also many forms of commitment that create different actions Though many the related literatire review, there are other three forms commitment such as: Personal commitment, Moral commitment and Structural commitment Moreover, Allen and Meyer (1990) defined commitment into three aspects like: “(1) affective commitment; (2)

normative commitment; and (3) continuance commitment Affective commitment refers to the feeling of belonging and the sense of attachment to the organization Continuance commitment concerned with perceived costs of leaving both financial and non-financial and is perceived from lack of alternatives Normative commitment relates to the obligation that members feel to remain with an organization and build on generalized cultural expectations”

But this is a model of commitment for organizations which does not focus on customer commitment Therefore, this research is going to conduct a test on

an interrelationship among four areas making it excessively complicated if each area is divided into three components Thus for the purposes of this thesis, the definition of commitment by Morgan and Hunt (1994) will be

used- “ Commitment is considered as „an enduring desire to maintain a valued relationship‟‟

Commitment also is considered as the important factor of partnership success and the first vital component of relationship capital According to Morgan and Hunt (1994), “commitment is the motivation to maintain the relationship and

the length of the relationship A longer relationship implies a certain degree

of commitment between the two parties When normative commitment exists

between channel members, firms would share mutual goals and values; therefore, these firms would work closely in order to achieve both their individual and joint goals” Thus, normative commitment may increase the closely held in the channel members

2.1.2.2 Antecedents of commitment

Commitment has three main antecedents consisting of alternative attractiveness, switching cost and customer satisfaction which base on the

previous research of many authors such as : Ping (1993), Whitten and Wake Field (2006), M Porter (1980), Anderson & Narus (1984 ) and Wetzels et al (1998)

Switching costs

In term of economics literature, Whitten and Wake Field (2006) defined “ switching costs are defined as strategic relationship-between buyers and suppliers that may be classified into three categories: learning costs, transaction costs and contractual costs” This paper encompasses these prior definitions to “define switching costs as the economic and relational costs of discontinuing a service relationship”

The study mainly emphasizes economic theory conversion means sharing costs and competitive market However, mathematical models are used to show that switching costs in developed markets lead to increased prices, and

monopoly rents, and prone to competition in the new market In a dynamic model, switching costs are shown to encourage new market entrants even though such entry is inefficient

In buyer–supplier relationships, switching costs are defined as an overall cost

or difficulty of switching, additional cost and effort in changing suppliers, an undefined component of termination and investments that inhibit change switching costs in service relationships include perceptions of time, effort, and money in changing service providers, perceived economic and psychological costs, perceived disutility, and onetime costs associated with switching providers In a sum, the literature defines and operationalizes switching costs in terms of economic (for example: monetary) expenditures and intangible (for example, psychological or relational) costs associated with changing an exchange relationship The categorization of switching costs has evolved from broad descriptions of cost (for example, economic or psychological) to specific costs (for example, search and evaluation costs or set-up costs) to more definitively understand and study the construct

M Porter (1980) defined switching costs as “ the costs of switching from one supplier‟s product to another supplier‟s product” Additional cost are such as: search costs, transaction costs, learning costs, customers‟ habits, emotional

costs and cognitive effort in the switching cost construct Thus” switching costs include both economic and psychological values which affect relationship-between buyers and suppliers in term of customer comitment” That is one of theoritical evidence related to this topic in term of building and testing hypotheses about relationship betweene switching cost and customer commitment (Mentioned in the hypothes H2b)

Customer satisfaction

Methlie and Nyseen (1999) in their studies defined customer satisfaction as “ the perception of customer about how product performing

As such customer satisfaction is a result when comparing customer perception

of product performance and expectations”

Anderson & Narus (1984) defined as “Satisfaction is a positive affective state resulting from the appraisal of all aspects of a firm working relationship with another firm” Consistent with this view, satisfaction encompasses economic

and economic components but the relation between economic and economic components of scales for measuring satisfaction differs considerably among studies Two ways to conceptualize satisfaction exist in the literature: service encounter satisfaction and overall or cumulative satisfaction This study focuses on overall satisfaction that is defined as a buyer's overall appraisal of a product or service provider to date Cumulative satisfaction recognizes that customers rely on their entire experience when forming intentions or making repurchase decisions, therefore it should be a better predictor of customers' intentions and behavior

non-Vasudevan et al (2006) observed the “positive influence of satisfaction on commitment For services, this means that the more satisfied customers are with the service experience the more likely they are to commit to a relationship with a service provider in a study of marketing relationships in business services” and for consumer services observed “that satisfaction positively influences affective commitment In line with these authors we propose a positive influence of satisfaction on commitment”, i.e., in relationships with high satisfaction firms are more motivated to continue the relationship due to liking and identification When customers are satisfied with their overall experience with the service provider, they are likely to feel a positive attitude to the organization, are likely to want to continue the relationship with that provider and are more likely to become committed to that relationship We also propose a positive influence of satisfaction on normative commitment The rationale behind this proposition is that satisfied clients feel a higher moral obligation to continue the relationship with the

provider they are satisfied with In other words, when customers are satisfied with their experience with the service provider, they are likely to feel that they ought to stay with their provider because of the things the provider has done for them

In contrast with Wetzels et al (1998), who found “a positive influence of

satisfaction on calculative commitment, and on the basis of conceptual definitions of commitment components and consistently with previously stated hypotheses in this paper regarding calculative commitment”, we propose a negative relationship between satisfaction and calculative commitment We suggest that when satisfaction increases there are less calculative reasons to continue the relationship In a similar manner as for trust, we propose that when satisfaction increases firms make a direct comparison of the pros and cons of the relationship less frequently, and a lower level of calculative commitment thereby results

That are some theoritical evidences related to this topic in term of building and testing hypotheses about relationship betweene customer satisfaction and customer commitment.(Mentioned in the hypothese H2c)

2.1.3 Loyalty

Loyalty can be calculated by how many times that the buyer will back to visit and is ready to repeat behavior Some researchers refer to a similar understanding as intention behaviors that include resigning the new contract, making recommendations by word of mount or increasing repeat buying behaviors

On the other hand, the definition of Oliver (1999) defined loyalty as “a deeply held commitment to rebuy or repatronize a preferred product/service

consistently in the future, thereby causing repetitive same-brand or same brand-set purchasing, despite situational influences and marketing efforts having the potential to cause switching behavior” That is the point made clearly to understand the difference between two concept commitment and loyalty In this point of view, we can see that commitment like spirit of loyalty because the commitment in the mind of the customer towards the company and the maintainance of the relationship between them And customer loyalty likes deeply commitment performance with company When a company have customer commitment it means that company motivate and maintain good relationship with customers, while loyaty displays as repeat repurchasing behavior or making good republic relationship about company images

2.1.4 Retention

Gerpott (2001) definded “Customer retention is maintaining the business relationship established between a supplier and a customer” However, as mentioned above when defining the concept of loyalty, the difference between loyalty and retention needs to be made clearly in this study Base on some other points of the concept of loyalty are considered one of the phases

of retention management: satisfaction, loyalty and retention Additionally, the retention concept includes the dimensions: repurchasing, cross-buying, recommendation and decreased price sensitivity

2.1.5 The relationship among commitment, trust, loyalty and retention

The relationship between four key areas, trust, maintaining commitment, customer loyalty and customer retention have been studied for customer relations in general and e-commerce customer relationship in particular Morgan and Hunt (1994) stated as “The effect of trust and commitment on customer relationship in general” and Eastlick (2006) also mentioned in

“online service market“ These basic studies have helped to build the key part

of research model (hypothese to test the relationship among these variables )

in which the Trust and Commitment affect the result of online shopping Moreover, in the context of ecommerce the theory has revealed that trust is vitally related to attitude, and attitude positively affects people‟s purchase intention Thereforce, we can see trust as a belief, confidence, or expectation about an exchange partner's intention and/or likely behavior In a word, The theory has been widely accepted and applied to a broad range of disciplines

and contexts” Existing empirical research has revealed that “trust is significantly related to attitude, and attitude positively signifies people's purchase intention and the theory of reasoned action is also applied as the theoretical base in recent studies on trust formation and, especially in the context of e-commerce” Since trust can be seen as a belief, confidence,

sentiment, or expectation about an exchange partner's intention and/or likely behavior, it is posited to be directly related to the attitudes toward purchasing

from a vendor and indirectly related to consumers‟ willingness to buy through purchasing attitudes

That is one of key point as people more trusted , people tend to buy more producs, and tend to maintain good relationship with the sellers (how customet trust affect to customer loyalty and customer retention?) The social

presence of “e-trust affects purchase intentions and the relationship between trust and retention in online-shopping” was tested by Gefen & Straub (2004)

“The relationship between customer trust and customer Loyalty” was showed

by Gefen (2002) when he determined factors of customer loyalty in

e-commerce namely trust, perceived risk with vendor and cost to switch vendor From the theories review, some relationships which has been researched for e-commerce market, but also have some relationship which has not been

mentioned for online customer in existing theories Thus, this research will test and find the relationship between them in e-commerce

Morgan and Hunt (1994) refer to “Trust may be instrumental in B-to-B exchanges due to its influence on commitment which also affects continued purchasing and loyalty behaviors In B-to-B contexts, commitment is

conceptualized as occurring when an exchange partner puts forth maximum efforts to maintain a valued relationship with another party, and in turn, negatively affects propensity to leave the relationship Hypotheses 3 and Hypothese 4 apply these findings to consumers' willingness to enter exchange relationships with e-retailers

Cater & Cater (2010) stated that “Commitment creates positive intentions to maintain and strengthen the relationship, based on which the contention arises

that affective commitment positively influences customer loyalty” (mentioned

in hypothes H4a) The identification the customer feels toward the brand or

the firm frequently translates into positive feelings communicated to others about the brand or firm Thus, the emotional attachment that affective

commitment involves translates into strong attitudinal loyalty This positive effect of affective commitment on attitudinal loyalty is also confirmed in some studies On the other hand, feelings of attachment and identification with the

brand or the firm also contribute to a “partnership” relationship between the customer and the brand or the firm and the immediate resulting effects of such feelings are on customer patronage of the brand or the firm” In line with

previous this study therefore proposes a positive effect of commitment on loyalty, that is one of big question in this study

Turnbull and Moustakatos (1996) considerd trust is an important element of the perceived quality of the service, the research said that “The more the

customer trusts the supplier, the higher the perceived value of the relationship

by the customer; consequently, one can expect that the greater the chances will be that the customer remains in the relationship, as for the customer of B2B services” ( mentioned in the hypothes H3b)

In more detail, According to previous studies testing the different relationship between variables, which are same to variables in this research; nine main hypotheses have been developed in this research to be tested

- The antecedents of Trust are E-retail reputation and privacy:

 Hypothese 1a: E-retailer reputation positively affects customer trust

(mentioned in E-retail reputation & trust: Bennett & Gabriel, 2001;

Josang et al, 2007; Jarvenpaa et al , 2000; Teo & Liu, 2007 )

 Hypothese 1b: Privacy concerns negatively affect customer trust

(mentioned in Privacy Concern & trust: Eastlick, 2006; Milne & Boza,

1998)

- The antecedents of Commitment are Alternative attractiveness,

Switching cost, Customer satisfaction:

Hypothese H2a: Alternative attractiveness negatively affects customer

commitment

(mentioned in Alternative attractiveness & commitment: Ping,(1993) ) Hypothese H2b: Switching cost positively affects customer commitment (mentioned in Switching cost & commitment: Whitten & Wakefield(

2006) )

Hypothese H2c: Customer satisfaction positively affects customer

commitment

(mentioned in Customer satisfaction & commitment: Vasudevan et

al.,(2006); Beatson et al., (2006); Wetzels et al., (1998); Meyer and Allen, (1991); Sharma and Patterson,(2000) )

- The relationship between trust and customer loyalty & retention:

Hypothes H3a: Increasing customer trust leads to higher customer loyalty

(mentioned in Customer trust & Loyalty: Teo & Liu,( 2007); Gefen, (2002) )

Hypothes H3b: Increasing customer trust leads to higher customer retention

(mentioned in Customer trust & Retention: Gefen & Straub, (2004) )

- The relationship between commitment and customer loyalty &

retention:

 Hypothese H4a: Increasing customer commitment leads to higher

customer loyalty

(mentioned in Commitment & Loyalty: Verhoef , (2003); Uncles et al.,(

2003); Morgan & Hunt, (1994); Cater & Cater (2010); Berry & Parasuraman, (1991) )

 Hypothese H4b: Increasing customer commitment leads to higher

 H5e: Commitment mediates the relationship between customer

satisfaction and customer loyalty

 H6a: Trust mediates the relationship between e-retailer reputation and customer retention

 H6b: Trust mediates the relationship between privacy concern and

customer retention

 H6c: Commitment mediates the relationship between alternative attractiveness and customer retention

 H6d: Commitment mediates the relationship between switching cost

and customer retention

 H6e: Commitment mediate the relationship between customer satisfaction and customer retention

This study can be summarized into following logical thinking such as:

H1a: ER + TR H3a: TR + CL

H1b: PC - TR H3b: TR + CR

H2a: AA - CO H4a: CO + CL

H2b: SC + CO H4b: CO + CR

H2c: CS + CO

H5a: ER TR CL H6a: ER TR CR H5B: PC TR CL H6b: PC TR CR H5c: AA CO CL H6c: AA CO CR H5d: SC CO CL H6d: SC CO CR H5e: CS CO CL H6e: CS CO CR

Where ER: E-retailer reputation; PC: Privacy concerns; AA: Alternative attractiveness; SC: Switching costs; CS: Customer satisfaction; TR: Trust;

CO: Commitment; CL: Customer loyalty and CR: Customer retention

Figure 2.1 Model of determinants of customer loyalty and retention for

online shopping (research framework)

The research framework model of this study is the synthesis of the findings of

previous authors and expand to model in order to explain the reserch problems The research of this paper has conducted in Vietnam Therefore, the results of this study will have certain significance

2.2 METHOD OF STATISCAL ANALYSIS

2.2.1 Correlation analysis

The research data analysis starts with a correlation analysis The result from the correlation analysis helps to explore the relationship between the variables

in the study The relationship between the variables will be calculated based

on the standardization of the covariance between variables and in particular

Pearson‟s correlation coefficient (r)

 The correlation coefficient has to lie between -1 and +1

 A coefficient of +1 indicates a perfectly positive relationship; a coefficient

of -1 indicates a perfectly negative relationship, while a coefficient of 0 indicates no linear relationship at all

 The correlation coefficient is a commonly used measure of the size of an effect: values of ±0.1 present a small effect, ±0.3 a medium effect and ±0.5 a large effect

2.2.2 Multiple Regression

Multiple regression is a statistical technique used to analyse the relationship between a dependent variable and several independent variables” All regressions of this study were equations of a dependent variable and several independent variables Therefore, multiple regressions were employed in this study The major procedures to analyze multiple regression in this study are presenting in the following sections

2.2.2.1 Multi-regression

There are three major methods of regression in social science: standard (forced entry) regression, sequential (hierarchical) regression, and statistical (stepwise) regression Standard regression is a method in which all predictors are forced into the model simultaneously In hierarchical regression, predictors are selected based on the researcher who decides the order to enter predictors into the model In stepwise regression, the decision about the order

in which predictors are entered into the model are based on a purely mathematical criterion The main purpose of this study is to test the mediating relationships among variables Predictors were entered one by one under the control of the researcher instead of being putted into the regression

simultaneously Therefore, the method of hierarchical regression was used in this study to test the hypotheses

2.2.2.2 Analysis of regression results

Analysis of regression result in this study was based on major statistics such

as sums of squares (R 2 , adjusted R 2 , and R 2Change) and regression coefficients

(Bi & ßi)

Sum of squares (R 2 ) is the measure of how much of the variability in

the dependent variable is accounted for by the independent variables In other words, it presents the percentage of the variation in the outcome that can be

explained by the model R 2 is calculated by taking residual sum of squares (SSR) divided by model sum of squares (SSM) P-value of the F-ratio is used

to assess the significant of R 2 R2 is significant if this value is less than 05

close to R 2

more independent variables is entered into the equation This value is

significant if the p-value of the R2Change – ratio is less than 05 at Alpha = 05

2.2.2.3 Assessing regression diagnostics

The purpose of assessing regression diagnostics is to answer the question of whether the model fits the observed data well, or if it is influenced by a small number of cases in the sample The way to assess the regression diagnostics is looking for outliers and influential cases

(1) Outliers and residuals

An outlier is a case that differs substantially from the main trend of the data Outliers affect the values of the estimated regression coefficients; thus, they can cause the model to be biased It is therefore important to detect outliers in order to minimize the bias of the model The way to find out the outliers is to look for large difference between the data values that were collected and the

values predicted by the model These differences are known as residuals In

other words, residuals are the differences between the values of the outcome predicted by the model and the values of the outcome observed in the sample These residuals effectively represent the error present in the model If a model fits the sample data well then all residuals will be small In contrast, if a model is a poor fit of the sample data then the residuals will be large The popular residuals used to detect are standardized residuals

According to Field (2005), one general rule for residuals:

(1) “ standard residuals with an absolute value greater than 3.29 are cause

for concern because in an average sample a value high like this is unlikely to happen by chance”;

(2) “if more than 1% of a sample has standardized residuals with an

absolute value greater than 2.58 there is evidence that the level of error

within our model is unacceptable”; and

(3) “if more than 5% of cases have standardized residuals with an absolute

value greater than 1.96 then there is also evidence that the model is a poor

representation of the actual data”

(2) Influential cases

While residuals are used to test for outliers by looking at the error in the model, influential cases is looking at whether certain cases exert undue influence over the parameters of the model Some statistics measures often used to determine influential cases consist of Cook‟s distance, Levarage, Mahalanobis distance, DFBeta, and Covariance ratio (CVR)

Cook’s distance is a statistic that considers the effect of a single case on

the model as a whole The values of Cook’s distance greater than 1 is may be

cause for concern

Mahalanobis distance relates to the leverage values It measures the

distance of cases from the mean (s) of the predictor variable (s) With large

samples (N= 500) and five predictors, values above 25 are cause for concern

In smaller samplers (N = 100) and with fewer predictors (namely three) values greater than 15 are problematic, and in very small sample (N=30)

with two predictors values greater than 11 should be examined

DFBeta is the difference between a parameter estimated using all cases

and estimated when one case is excluded DEBeta is calculated for every case and for each of the parameters in the model Therefore, by looking at the values of the DFBetas, it is possible to identify cases that have a large

influence on the parameters of the regression model A case with

standardiazed DFBeta greater than 2 in absolute values indicates a substantial influence on the coefficients

2.2.2.4 Assessing generalization of model

When a regression analysis is done, the model is correct for the sample of observed values However, it may not be true for a wider population To generalize the model, some underlying assumptions should been met such as independent residuals, normality of residuals, homoscedasticity of residuals, linearity, and multicolinearity

(1) Independent residuals (Durbin-Watson test)

The first assumption of the generalization of model is that for any two observations the residual terms should be uncorrelated This assumption can

be tested with the Durbin-Watson test, which tests for serial correlations between errors The test statistic can vary between 0 and 4 with a value of 2 meaning that the residuals are uncorrelated A value greater than 2 indicates a negative correlation between adjacent residual, whereas a value below 2 indicates a positive correlation The size of Durbin-Watson statistic depends upon the number of predictors in the model, and the number of observations

As a very conservative rule of thumb, values less than 1 or greater than 3

are definitely cause for concern; however, values closer to 2 may still be problematic depending on sample and model

(2) Normality of residuals

This is assumed that the residuals in the model are random, normally distributed variables with a mean of 0 This assumption indicates that the differences between the model and the observed data are most frequently zero

or close to zero, and those differences much greater than zero happen only occasionally This assumption is tested by two graphical methods: histogram and normal probability The histogram plots of the residuals should be similar

to the normal curve with the same mean and standard deviation as the data If the residuals are normally distributed, all points of normal probability plot should lie on the normal distribution line

2.2.2.5 Testing mediating relationship

 The mediating relationship was tested by two steps by hierarchical multiple

regressions The first step was to enter all of the independent variables (Xi) into the prediction of the dependent variable (Y) to determine the total effect (βtxi) of each independent variable

 The second step was to repeat step 1, but to take all mediating variables

(Mj) into consideration to determine the total effect (βmi) of each of the mediating variables and the coefficients (βxi) of each independent variable

 If βtxi is different from βxi, the relationship between Xi and Yi is mediated

by the mediators Moreover, if the coefficient of Xi in the second model is

non-significant, there is a full mediation; however, if this coefficient is reduced but remains significant, there is just a partial mediation

The research using a deductive approach has several important characteristics First, it explains the causal relationship between variables Second, all variables or concepts need to be measured quantitatively Third, the research has to be generalised so that selecting samples of sufficient numerical size is necessary While an inductive approach is when data are collected then the theory is developed as a result of data analysis Research using an inductive approach is concerned with how to understand the meanings humans attach to events Thus, qualitative data are collected and the research is less concerned with the need for generalization

The approach to research in this study is quantitative because this research centralizes on numerical observations and aims at generalizing a phenomenon through formalized analysis of chosen data where statistical indicators play a central role The quantitative method used in this study refers to the survey implemented in the form of a questionnaire which directly focuses on testing

if the data collected is generally able to answer the research questions

The quantitative methodology applied in this study is a cross-sectional survey

or so called “the study if a particular phenomenon at a particular time” A longitudinal or experimental study has not been used to collect data in this study because it requires a lot of time and resources Cross-sectional survey is implemented in this study because the total population of this study is significantly large, therefore, only a sample of the whole population is approached in order to test the theoretical model Additionally, the data is collected just once over a short period of time from different contexts of the population

Survey methodology has four methods: self-administered questionnaire, interview, structure record review, and structured observation This study is mentioned above to collect data on attitudes and orientations of individuals at

a single point of time and in the condition of limited resources Thus using questionnaire is usually advantage to identify and describe respondents‟ attitude or variability in different phenomena Interviewing requires a significant amount of time and resources especially when a sample is large or medium Furthermore, structured record review and structured observation focus on visual and recorded data are not suitable for collecting data on attitudes Self-administered questionnaire is one of the most common methods of collecting data in social science research and satisfies all requirement of the survey Therefore, a self-administered questionnaire that includes questions and individual respondents complete, is considered the best for conducting the survey in this study

3.2 DATA COLLECTION METHOD

3.2.1 Sample size

The research aims to find out how trust and commitment influences customer loyalty and customer retention in online shopping in Vietnam so the respondents must be people with experience of shopping online In Vietnam, shopping online can understand easily as a simple choice, procurement of goods through the website, online payment or send cash when you receive goods This is a special limited characteristic of the development of e- commerce in Vietnam, due to consumers' limited cash and base limited layer information technology

Because a population is huge and there is not an absolute number of customers shopping online in Vietnam, so using a representative sample is necessary to estimate the characteristics for the whole population After analyzing the data collected from the determined sample, the result can be extended to the population as a whole to answer the research questions In addition, sampling will save time as the research undertaking has a strict deadline

The research uses probability samples which is most popular in the

survey-based research strategy Because the research questions is concerned with

online customer

This research aims to test hypotheses by running multi-regression to conclude the result overall, so Green (1991) suggested using the rule of 50+8k In the equation of 50+8k, k is the number of model predictors There are 9 predictors (e-retailer reputation, privacy concerns, alternative attractiveness, switching cost, customer satisfaction, trust, commitment, customer loyalty

and customer retention) so the minimum sample size is 50 +9x8 = 122 The

response rate expected is 10% so the number of questionnaires should be sent

is 1,220

However the most important thing regarding sample size is population

representativeness and reliability A sample of 1,000 people shopping online

in Vietnam (Most of them are often use Internet, students in universities(especilly in National Economics University in Hanoi City), or working officers, they shop online through Internet then pay by credit card or

by cash) participated in this research with an expected response rate of about 10% - 15% To ensure the representativeness of the sample, after having the

statistical results by running a multi regression reflecting the relationship among variables studied, the sample will be studied with three further

statistics, namely; Cook‟s distance, Mahalanobis distances and DFBeta to check whether the sample is biased by some influence cases or not Then, it can be concluded that the sample can generalize the model for whole population or not

3.2.2 Questionnaire design

With a sample size of 1,000, self-administrated questionnaire which participants have to complete questionnaire themselves is chosen to fit with the limitations of time and sources Moreover, to ensure the response rate of 10% to 15% as in the expectation, this research chose the way of handing the questionnaire to participants and collecting it after they had finished it This is called a delivery and collection questionnaire

3.2.2.1 Questionnaire content

The content of the questions focuses on customers‟ assessments expressed by

„extremely disagree‟ to „extremely agree‟ with the statements regarding the last time people went shopping (question 1 to question 14 testing the

determinants of trust and commitment), and to shopping online in general (with question 15 to question 28 testing the determinants of customer loyalty and customer retention and finding out the relationships between the four main areas of trust, commitment, customer loyalty and customer retention) The questionnaire was designed to gather information from Vietnamese online shoppers to discover factors influencing customers‟ online purchasing decisions and to test the hypotheses of relationships between the nine variables; e-retailer reputation, privacy concerns, alternative attractiveness, switching cost, customer satisfaction, customer trust, customer commitment, customer loyalty and customer retention Each variable is measured by several questions in the questionnaire, which are referenced from different studies of previous researchers to ensure the questions are clear and understandable for participants

3.2.2.2 Questionnaire structure

The questionnaire used in this research consists of 28 questions and is divided into two sections Section one has 14 questions asking about the last time the

customer shopped online and finds out which factors affected two predicted

mediate variables namely trust and commitment Section two has 14 questions

collecting customers‟ perceptions about e-retailers in general in the four areas

of trust, commitment, loyalty and retention Section 1 and 2 addresses the following objectives: (1) To find out the relationships among variables and identify the primary factors influencing customer loyalty and customer retention in online shopping in the Vietnamese market; and (2) To investigate the impact of the factors on customer loyalty and customer retention by testing the following hypotheses that: (see table 3.1 Independent variables and table 3.2 Dependent variables for more information)

No Hypotheses Questions

H1a E-retailer reputation

positively affects customer

trust

Questions 1-3 measure e-retailer reputation, questions 15-19 measure customer trust

H1b Privacy concerns negatively

affect customer trust

Questions 4-6 measure privacy concern, questions 15-19 measure customer trust

H2a Alternative attractiveness

negatively affects customer

commitment

Questions 7-9 measure alternative attractiveness, questions 20-22 measure customer commitment

H2b Switching cost positively

commitment

Questions 10-12 measure switching cost, Questions 20-22 measure customer commitment

H3a Increasing customer trust

leads to higher customer

loyalty

Questions 15-19 measure customer trust, Questions 23-25 measure customer loyalty

H3b Increasing customer trust

leads to higher customer

retention

Questions 15-19 measure customer trust, Questions 26-28 measure customer retention