factors affectding customer trust in online shopping in vietnam

UNIVERSITY OF ECONOMICS HO CHI MINH CITY

International School of Business

------------------------------

Tran Minh

FACTORS AFFECTING CUSTOMER TRUST IN ONLINE SHOPPING

IN VIETNAM

MASTER OF BUSINESS (Honours)

Ho Chi Minh City Year 2012


UNIVERSITY OF ECONOMICS HO CHI MINH CITY

International School of Business

------------------------------

Tran Minh

FACTORS AFFECTING CUSTOMER TRUST IN ONLINE SHOPPING

IN VIETNAM

ID: 60340102

MASTER OF BUSINESS (Honours)

SUPERVISOR: Dr. NGUYEN HUU LAM


i

Acknowledgement

Apart from the efforts of me, the success of this thesis is depended largely on

the encouragement and guidelines of many others. Especially, Dr. Nguyen Huu Lam

and Associate Prof Dr. Nguyen Dinh Tho have been instrumental in the successful

completion of this study. I would like to take this opportunity to express my gratitude

to them and I really appreciate with their tremendous support and help. I feel motivated

and encouraged every time I attend his meeting. Without his encouragement and

guidance, this project would not have materialized.

Besides, I would like to thank my close classmates and staffs working at

International School of Business UEH including Nguyen Thanh Huong, Huynh Ngoc

Duy, Thai Thi Thu Giang, and Nguyen Thi Ngoc Lien for their guidance and support.

Ho Chi Minh City, Jan 1 st 2013

Tran Minh

ii

Table of Contents Abbreviations............................................................................................................................. iii

List of Tables ............................................................................................................................. iv

List of Figures............................................................................................................................. v

List of Appendix ........................................................................................................................ vi

Chapter One: Introduction .......................................................................................................... 1

1. Background..................................................................................................................... 1

1.1. The Internet in Vietnam.......................................................................................... 1

1.2. Online shopping in Vietnam................................................................................... 1

2. Statement of purpose ...................................................................................................... 2

3. Research question ........................................................................................................... 3

4. Significance of the study................................................................................................. 3

5. Scope of the study........................................................................................................... 3

6. Structure of the study...................................................................................................... 3

Chapter Two: Literature Review ................................................................................................ 5

1. Trust in online shopping ................................................................................................. 5

1.1. Definition of trust in e-commerce........................................................................... 5

1.2. The importance of trust in e-commerce................................................................ 15

2. Trust antecedents identified in the literature................................................................. 16

2.1. Perceived privacy and security protection ............................................................ 16

2.2. Perceived risks and benefits.................................................................................. 18

Chapter Three: Methodology.................................................................................................... 20

1. Participants.................................................................................................................... 20

2. Instruments.................................................................................................................... 20

3. Samples and data collection procedures ....................................................................... 24

4. Data analysis ................................................................................................................. 24

Chapter Four: Results ............................................................................................................... 26

1. Characteristics of the sample population ...................................................................... 26

2. Reliability of measurement instruments ....................................................................... 28

iii

2.1. Validating measures.............................................................................................. 28

2.2. Exploratory factor analysis ................................................................................... 32

3. Tests of regression assumptions ................................................................................... 37

3.1. Test of multicollinearity........................................................................................ 37

3.2. Test of normality of residual & heteroscedasticity............................................... 38

4. Evaluating demographic variables impacts on customers trust ................................. 38

5. Hypotheses testing ........................................................................................................ 39

6. Summary of the results ................................................................................................. 41

Chapter Five: Discussion .......................................................................................................... 43

1. Findings ........................................................................................................................ 43

2. Implications .................................................................................................................. 44

3. Conclusion .................................................................................................................... 45

4. Limitations and directions for future research.............................................................. 45

References................................................................................................................................. 46

iii

Abbreviations

WTO World Trade Organization

APEC Asia-Pacific Economic Cooperation

ASEM Asia-Europe Meeting

SPSS Statistical Package for the Social Sciences

PP Privacy Protection

SP Security Protection

PR Perceived Risk

PB Perceived Benefit

CTIS Customer Trust in Internet Shopping

EFA Exploratory Factor Analysis

TVE Total Variance Extracted

VIF Variance Inflation Factor

iv

List of Tables

Table 2.1. Summary of prior conceptualizations of trust ........................................................... 6

Table 3.1. Privacy protection and security protection scales.................................................... 21

Table 3.2. Perceived risk and perceived benefits scales ........................................................... 22

Table 3.3. Customer trust scale................................................................................................. 23

Table 4.1. Distribution of respondents based on demographic characteristics......................... 27

Table 4.2. Item-Total Statistics................................................................................................. 29

Table 4.3. Total Variance Explained ........................................................................................ 34

Table 4.4. Pattern Matrixa ......................................................................................................... 35

Table 4.5. Item-Total Statistics................................................................................................. 36

Table 4.6. Model Summary ...................................................................................................... 39

Table 4.7. ANOVAb.................................................................................................................. 39

Table 4.8. Coefficients a............................................................................................................ 40

v

List of Figures

Figure 1. Conceptual Model ..................................................................................................... 19

Figure 2. Results of testing the conceptual model .................................................................... 42

vi

List of Appendix

Appendix A. Customer Survey Form ............................................................................51

Appendix B. Graphs.......................................................................................................57

Graph 1. Regression Standadized Residual ...................................................................57

Graph 2. Normal P-P plot of regression standardized residual......................................57

Graph 3. Scatterplot .......................................................................................................58

1

Chapter One: Introduction

1. Background 1.1. The Internet in Vietnam

It has been more than one decade since the Internet started to have been used in

Vietnam. Vietnam connected the world in 2000, the Internet users was a small figures,

just 0.3% of the population in 2000. However, the Internet is growing fast, much faster

than in any other Asian countries in 2011. Over the last ten years 2000-2010, Internet

usage has grown by 12.4 times in Vietnam. This is the highest level of penetration in

the Asian countries. After five years from 2000, this number was up to 12.8%; and

17.9% in 2007; 24.0% in 2008; and 25.7% of Vietnam population in 2009.

Impressively, este et al. (2012) suggest that a large number of Vietnamese Internet

users accounted for 30.8 million at the end of Feb 2012, equivalent to 34% of Vietnam

population. More and more people are online and in Vietnam, they spend a massive

amount of time on the Internet. There is a huge, targetable population of consumers

online. As to Feb 2012, 30.8 million Vietnamese people can be reached on the Internet,

with a strong growth every year. In addition, these are not just the teenagers, but also

more and more also their parents and in general, the household decision makers, an

interesting target audience for marketing activities. They are also increasingly

comfortable with making purchases online.

1.2. Online shopping in Vietnam The internet is changing the way consumers shop and buy goods and services,

and has rapidly evolved into a global phenomenon and even in Vietnam. Many

companies have started using the Internet with the aim of cutting marketing costs,

thereby reducing the price of their products and services in order to stay ahead in

highly competitive markets. Customers use the Internet not only to compare prices,

2

product features, after sale service facilities they will receive, but they can save time

and cost for buying products from a particular store. In 2010, every second Internet

user in Vietnam has already visited sites that offer online shopping, buy and sell

activities or auctions. Este et al. (2012) suggest that the most of customers purchasing

online is just a small piece of big potential e-commerce market and online shopping

activities are mainly common in the north and in big cities, whilst in smaller cities it is

not yet frequent. Hanoi is the undisputed leader in e-commerce with 60 per cent of

Hanoi net citizens using these sites.

To advance its e-commerce to improve businesses competitiveness thus

boosting the countrys industrialization and modernization, Vietnam government

approved a plan on e-commerce for the next 5 years 2011 2015 last year. This

decision helps concretize Vietnams commitments for international integration with

WTO, APEC and ASEM. Although e-commerce purchases in early stage market in

Vietnam, the high young generation population and great coming opportunities closer

promises the strongest growth in online shopping area. However, the major problem in

the area of online shopping is the low confidence in online payment systems. Este et al.

(2012) suggest that one of the key factor to explain for this is that people does not trust

in Internet shopping. Therefore, studying trust is considered as a vital key for

individuals or organizations to maintain and build customers trust so in Internet

shopping that the growth of e-commerce can be speeded up for the coming years in

Vietnam.

2. Statement of purpose This study aims to identify which ones of the four antecedents of trust (privacy

protection, security protection, perceived risk, and perceived benefits) have impacts on

customer trust in online in shopping in Vietnam.

3

3. Research question Is customers trust affected by perceptions about privacy, security protection,

perceptions about the risks and benefits during the transaction on the Internet.

4. Significance of the study In terms of theory, this study provides an empirical understanding role of factor

trust towards online shopping; And in terms of practice, this study presents strategic

implications and directions for the development of online shopping in Vietnam.

5. Scope of the study The study focuses on collecting people having experience in the online shopping

Ho Chi Minh City. The city is selected due to the highest Internet penetration rate. Este

et al. (2012) suggest that the Internet penetration rate is more than 50% the population

have used the Internet already in urban Vietnam. The city is higher than the average

rate of 50% of the population with the rate 62% in 2011.

6. Structure of the study The thesis consists of five chapters. Chapter 1 introduces an overview of the

background, statement of purpose, research question, the significance of the study, and

scope of the study. Chapter 2 reviews existing literature on trust, online customer trust,

and the four antecedents of trust. These literatures summarize briefly the knowledge of

recent studies, describes the conceptual model, and hypotheses. Chapter 3 presents who

participate in this study, instruments used to measure the research constructs, the

description of the samples, data collection procedures and data analysis. Chapter 4

describes characteristics of the sample. In addition, validity and reliability of measures

will be checked by coefficients of Cronbachs Alpha and EFA (Principle Axis

Factoring with Promax). Then testing the assumption of regression, evaluating

4

demographic variables impacts on customers trust, and testing hypotheses are

presented. Chapter 5 presents discussions on the research findings. Theoretical

contributions, practical implications, and limitations of the current research are also

discussed. Suggestions for future research will conclude this dissertation.

5

Chapter Two: Literature Review

1. Trust in online shopping 1.1. Definition of trust in e-commerce

Trust definition in Internet shopping is a quite complicated concept in e-

commerce field. Depending on different contexts, researchers offer different meanings.

As Table 2.1 shows below, trust is viewed as 1). A set of specific beliefs (Doney &

Cannon 1997; Ganesan 1994). 2). A general belief that another party can be trusted

(Gefen 2000; Hosmer 1995; Moorman et al. 1992) 3). Affect reflected in feelings of

confidence and security. 4). A combination of three elements mentioned above. Based

on trust objects, trust has been conceptualized as a specific and general belief. Some of

them describe the specific beliefs as antecedents to the general beliefs (Jarvenpaa and

Tractinsky, 1999; Mayer and Davis, 1999; Mayer et al., 1995; Jarvenpaa and

Tractinsky, 1999) or sometimes conceptualize the specific beliefs as antecedents to

trusting intentions (McKnight et al., 1998). The others conceptualize trust as general

beliefs in e-commerce contexts that leads to behavorial intentions (Gefen, 2000); as a

combination of intergrity and caring that leads to an increase in behavioral intentions to

vulnerability (Javenpaa and Tractinsky, 1999); as a specific belief dealing with

benevolence, competence, and intergrity that results in trusting intentions (McKnight et

al., 2002).

However, the distinction between trust as a set of specific and general belief is

primarly happened dealing with interpersonal trust in organizational settings

(McAllister, 1995; McKnight et al., 1998). However, this distinction is seldom occured

in economic transaction settings because the definition of trust is used in these contexts

is an extension of trust definition rather than the original definition of interpersional

trust (Hosmer, 1995; Williamson, 1985). Consequently, some researchers stated that

actual behavior in ongoing economics alliances is a proxy for trust, defined in that

6

context as confidence or an overall belief (Gulati, 1995). This study has adopted the

conceptualization of trust as a set of specific beliefs because it deals with going

economic relationships (Crosby et al., 1990; Doney and Cannon, 1997; Ganesan, 1994;

Schurr and Ozanne, 1985) and this set of specific beliefs is most widely used in the

literature. Therefore, Trust as a feeling (Rempel et al., 1985) has been previously

studied in the context of interpersonal relationships. It is arguably irrelevant to business

transaction. (see Table 2.1)

Table 2.1. Summary of prior conceptualizations of trust

Study Trust Conceptualization Trust Object Measures

Anderson

and Narus

(1990)

Expectations about the

behavior of the other

company.

Business

relationships

Overall trust

Bustler

(1991)

Two sub-constructs:

1. Attitude affective trust

2. Cognitive specific trust

Organizational Measure of overall

trust

Crosby et

al. (1990)

Confidence that the trusted

party will behave in the

interest of the customer.

Buyer-seller

relationships

Empirical: overall

trust, caring, integrity

Doney and

Cannon

(1997)

Perceived credibility

(integrity) and benevolence.

Buyer-seller

relationships

Honesty, caring,

trustworthy

Source: Gefen, David; Karahanna, Elena; Straub, Detmar W. (2003, p. 56-59)

7

Table 2.1. Summary of prior conceptualizations of trust (Cont.)


Doney et

at. (1998)

Willingness to rely and be

dependable upon another.

This encompasses trust as a

set of beliefs (Fukuyama

1995; Larzelere and Huston

1980; Rotter 1971) and

willingness to behave

(Luhmann 1979; McAllister

1995)

Culture Conceptual

Fukuyama

(1995)

Expectation of regular,

honest, cooperative

behavior.

Business

relationships

Conceptual

Gambetta

(1988)

The subjective probability

that the trusted party will

behave in a way that

warrants cooperation with

them.

Conceptual Conceptual

Ganesan

(1994)

Willingness to rely on a

partner in whom one has

confidence based on belief

in that party's credibility

(integrity and ability) and

benevolence.

Buyer-seller

relationships

Empirical:

1. Credibility (ability

and

reliability/honesty)

2. Benevolence


8



Gefen

(2000)

Willingness to depend. E-commerce Empirical: overall trust

Gefen

(2000a)

Willingness to depend. E-commerce Empirical: overall

trust

Gefen

(2000b)

Willingness to depend based

on beliefs in ability,

benevolence, and integrity.

Business

relationships

Empirical: a single

scale with items

dealing with ability,

integrity, and

benevolence.

Gefen and

Silver

(1999)


on beliefs in ability,

benevolence, and integrity.

Business

relationships

Empirical: a single

scale with items

dealing with ability,

integrity, and

benevolence.

Giffin

(1967)

Reliance on the

characteristics of another in

a risky situation.

Literature

review

Conceptual: integrity,

benevolence, and

ability

Gulati

(1995)

Expectations that alleviate

fears that the other party will

be opportunistic.

Business

relationships

Empirical: indirect

measurement

Hart and

Saunders

(1997)

Confidence about the

behavior and goodwill of

another.

Business

relationships

Conceptual


9



Hosmer (1995)

The expectation of ethical

behavior, related to the

willingness to rely on the

trusted party based on

optimistic expectations that

the trusted party will behave

in a morally correct manner.

Literature

review

Conceptual

Jarvenpaa

et at.

(1998)

Willingness to be vulnerable

based on expectations that

the other party will behave

appropriately even without

monitoring.

Online student

teams

Empirical: overall

trust that is built

through beliefs in

ability, benevolence,

and integrity

Jarvenpaa

and

Tractinsky

(1999)

Willingness to rely when

there is a vulnerability.

E-commerce Empirical: overall

trust combined with

integrity, and caring.

Jarvenpaa

et at.

(2000)

A governance mechanism in

buyer-seller relationships.

E-commerce Empirical: overall

trust combined with

integrity, and caring.


10



Korsgaard

et al.

(1995)

Confidence in the goodwill

of the leader, meaning

honesty, sincerity, and being

unbiased.

Interpersonal

trust in

organizational

settings

Single item

Kumar

(1996)

Belief in dependability and

honesty.

Business

relationships

Conceptual

Kumar et

al. (1995a)

Honesty and benevolence. Business

relationships

Empirical:

1. Trust in honesty

2. Trust in

benevolence

Separate from a

willingness to invest

construct.

Kumar et

al. (1995b)

Honesty and benevolence. Business

relationships

Empirical:

1. Trust in honesty

2. Trust in

benevolence

Separate from a

willingness to invest

construct.


11



Larzelere

and Huston

(1980)

Benevolence and honesty. Interpersonal

trust in close

relationships

Integrity and

benevolence

Luhmann

(1988)

Willingness to behave based

on expectation about the

behavior of others when

considering the risk

involved.

Social life Conceptual

Mayer and

Davis

(1999)

Willingness to be

vulnerable.

Interpersonal

trust in

organizational

settings

Empirical: overall

trust, which is

separate from

trustworthiness that is

defined as ability,

benevolence, and

integrity.

McAllister

(1995)

Willingness to depend upon

another.

Interpersonal

trust in

organizational

settings

Empirical:

1. Cognitive-based

trust (ability, trust,

monitor)

2. Affect-based trust

(share ideas and

feelings, emotional

investment)


12



McKnight

et al.

(1998)

Trusting beliefs dealing with

benevolence, competence,

honesty, and predictability

that leads to a trusting

intention.

Interpersonal

trust in

organizational

settings

Conceptual

McKnight

et al.

(2002)

Based on McKnight et al.

(1998)

E-commerce Empirical:

1. Trust beliefs

dealing with

benevolence,

competence, and

integrity.

2. Resulting in

trusting intentions

measuring

willingness aspects to

interact with an e-

vendor.

Mishra

(1996)


based on belief that the other

party is competent, open,

concerned, and reliable.

Interpersonal

trust in

organizational

settings

Conceptual


13



Mishra and

Morrissedy

(1990)

Two definitions:

1. Integrity, character,

ability of others.

2. Confidence and support

Interpersonal

trust in

organizational

settings

Empirical:

1. Integrity, character,

ability of others.

2. Confidence and

support.

Moorman

et al.

(1992)

Willingness to depend. It is

both a belief about the other

party and a behavioral

intention.

Business

relationships

Empirical: overall

trust

Morgan

and Hunt

(1994)

Willingness to depend on a

party in whom one has

confidence. Sam as

Moorman et at. (192)

Business

relationships

Empirical: overall

trust and integrity.

Pavlou and

Gefen

(2002)

Willingness to depend. Online auctions Empirical: one factor

of being reliable,

honest, and

trustworthy.

Ramaswam

i et al.

(1997)

Faith that the trusted party

will continue to be

responsive.

Interpersonal

trust in

organizational

settings

Empirical: overall

trust


14



Rempel et

al. (1985)


on a generalized

expectation/confidence

about what others will do.

Interpersonal

trust in close

relationships

Empirical: overall

trust, benevolence,

predictability, and

honesty.

Rotter

(1971)

The expectation that one's

word or promise can be

relied upon.

Social life Conceptual

Rousseau

et al.

(1998)


based on confidence in

positive expectations about

the intentions and behavior

will be fulfilled.

Buyer-seller

relationships

Trust was

manipulated in an

experiment. The

manipulation check

dealt with

trustworthiness

combined with

fairness,

dependability, and

openness.

Zaheer et

al. (1998)

The expectation that an actor

will

1. Fulfill its obligations

2. Be predictable

3. Be fair and not

opportunistic

Buyer-seller

relationships

Empirical: fairness,

non-opportunistic,

keep promises, and is

trustworthy.


15



Zaheer et

al. (1998)

The expectation that an actor

will

1. Fulfill its obligations

2. Be predictable

3. Be fair and not

opportunistic

Buyer-seller

relationships

Empirical: fairness,

non-opportunistic,

keep promises, and is

trustworthy.

Zand

(1972)

Trusting behavior is actions

that increase one's

vulnerability.

Experiment

with business

executives

Trust was

manipulated in an

experiment.

Zucker

(1986)

Set of expectations, an

implicit contract.

Business

relationships

Conceptual


1.2. The importance of trust in e-commerce Trust plays such an important role between sell site and buy site, especially these

containing the element risk including interacting with an e-vendor (Reichheld and

Schefter 2000). It is ones belief that the other party will behave in a dependable (Kumar

et al., 1995a), ethical (Hosmer, 1995), and socially appropriate manner (Zucker, 1986).

Trust is also deal with fulfillment (Luhmann, 1979; Rotter, 1971). Lack of trust is one of

the most frequently cited reasons for consumers not shopping on the Internet (Lee and

Turban, 2001). Trust becomes a serious issue in Internet shopping because there is an

absence of proven guarantees. Jarvenpaa and Tractinsky (1999) and Reichheld and

Schefter (2000) suggested that online customers generally stay away from e-vendors who

they do not trust on.

16

2. Trust antecedents identified in the literature This study builds upon previous research by combining several trust antecedents in

order to provide insights to online firms conducting business in different parts of the

world. The model suggests that trust in Internet shopping is directly affected. The model

assumes that their cultural backgrounds influence consumers perceptions (see Table 1).

The results of this study will identify which factors having significant effects and having

an important role in the generation of customer trust in an online environment (e.g.,

McKnight et al., 2002; Lee and Turban, 2001). The literature provides considerable

evidence that a number of factors have strong predictive importance and are therefore

deserving of consideration in any examination of the construct. These factors include the

influence of perceived privacy, security protection, perceived risks and benefits (Lee and

Turban, 2001; Gefen, 2000).

2.1. Perceived privacy and security protection Lallmahamood (2007) define perceived security and privacy as users perception

of protection against security threats and control of their personal data information in an

online environment. On the whole, perceived security and privacy is about the self-belief

that a user has in the system to conclude a transaction securely and to maintain the

privacy of personal information (2007, p. 7).

Privacy protection is widely considered as one of the most important factors in

building e-trust (Hoffman et al. (1999); Jorgensen (2000); Shankar et al. (2002)). The

privacy issue is considered as the major concerns of the online shoppers (Egelman, Tsai,

Cranor and Acquisti, 2004). Customers cannot avoid being leaked out their private

information over the Internet due to risk in the transaction (Monsuwe et al., 2004).

Because of using web to carry out transactions, customers face security, encryption, and

transactional privacy issues (Grewal et al., 2004).

17

Security protection is a great concern to online customers when they make

transactions over the Internet. They concern whether the information they required to

enter on would be intercepted or stolen or not during the transmission on the Internet

(Koufaris, 2004). Riegelsberger and Sasse (2001) find that concerning about whether

information of credit cards gets intercepted and information of the transaction is correctly

transmitted.

Bierhoff and Vornefeld (2004) states that:

Although the Internet is a technical system with strict, built-in security measures, it is managed, maintained, and used by humans and therefore will never be able as a system to guarantee perfect security (p. 48).

Customers would be easier to trust if security is guaranteed. Web vendors have an

ability to provide a secure website; this would play such an important part in

implementation and success of shopping on Internet (Ruppel, Underwood-Queen and

Harrington, 2006). Furthermore, if a virtual store is not able to effectively demonstrate

its commitment to superior data security technologies, few consumers will feel

comfortable entrusting the virtual store with their sensitive information (Chen & Tan,

2004, p. 78).

However, consumers do not have enough ability and resources to make sure their

sensitive and personal information sent to the suppliers servers over the Internet would

be safe and secure during transactions (Monsuwe et al., 2004). Fowell (2000) finds that

consumers raising privacy as a concern invariably mentioned security as well. Therefore,

issues of network security, transactional privacy, and security become a paramount

concern (Grewal et al., 2004, p. 707). Lee and Turban (2001) points out that security and

privacy protection impacts trust in Internet shopping.

Security and privacy in online shopping have a positive association with trust in

Internet shopping (Monsuwe et al., 2004). A high level of security and privacy in online

shopping experience has a positive effect on consumer trust (Ilagan, Sheila de Villa,

2009).

18

H1. Privacy protection of a web has a positive effect on consumers trust in

Internet shopping.

H2. Security protection of a web has a positive effect on consumers trust in

Internet shopping.

2.2. Perceived risks and benefits Ko, Jung, Kim, and Shim (2004) defines perceived risk as the potential for loss in

pursuing a desired outcome when engaged in online shopping (section 1, para. 3). The

concept of risk involves both uncertainty (Lewis and Weigert, 1985) and vulnerability

(Barney and Hansen, 1994). The consumers perception of risk associated with the

transaction will tend to predominate in his/her decision to engage in a transaction

(Salam, Rao, & Pegels, 2003, p. 328).

Some researchers have the same finding the less perceived risks associated with

online buying, the more willingly consumers disclose personal information, and the more

trust a person has in the online store (Corritore et al., 2003; Jarvenpaa et al., 2000; Kim et

al., 2008; Olivero & Lunt, 2004; Salam et al., 2003; Teo & Liu, 2007; Van der Heijden et

al., 2003). Perceived risk has a negative effect on building e-trust (Chen and Tan, 2004).

Ilagan, Sheila de Villa (2009) shows that perceived risk is a significant predictor of trust

in Internet shopping.

H3. Perceived risks have a significant negative effect on consumers trust in

Internet shopping.

Kim, Ferrin, and Rao (2008) define perceived benefits as a consumers belief

about the extent to which he or she will become better off from the online transaction with

a certain Web site (p. 547). These benefits include convenience, time saving because of

finding information about a product within a short time frame and less time spent on

shopping, or having more products to choose. Chen and Tan (2004) note that consumer

trust can only be inspired if the risks associated with online purchases are reduced to a

level that is tolerable to consumers (p. 78). If there are people who stay away from

19

Internet shopping because of the risks, there are also people who engage in it because of

the benefits obtained.

H4. Perceived benefits have a positive effect on consumers trust in Internet

shopping.

Lee and Turbans (2001) propose the conceptual model for customers trust in

Internets shopping but it is modified to accommodate four antecedents of trust and fit the

purpose of the study. On the other hand, this study also examines whether demographic

variables make additional contributions to the prediction produced by the four antecedent

variables of trust.

The model suggests that trust in Internet shopping is directly affected general

perceptions about privacy protection, security protection of the web, and perceived risks

and benefits.

Figure 1. Conceptual model

H2 (+)

Customer Trust in Internet Shopping

(CTIS)

Privacy Perceptions (PP)

Security Protection (SP)

Perceived Risks (PR)

Perceived Benefits (PB) Demographics

(gender/age/ecudcation/income)

H1 (+)

H3 (-)

H4 (+)

20

Chapter Three: Methodology

1. Participants This study used convenience sampling and purposive sampling to recruit

Vietnamese students, white collar workers who had ever bought goods, services online

and used electronic system payments to pay for them in different districts in Ho Chi Minh

City. They had been choosen randomly to answer the questionnaires. The data was

collected from October to mid November 2012.

2. Instruments In order to gather the necessary information, survey questions were adopted from

previous researches and modified for this study. The self administered questionnaires

were divided into two sections including 36 questions that consist of 4 socio-demographic

questions and 32 questions using a 5-point Likert scale measuring the research constructs.

Part I includes 32 questions in term of the independent variables and the dependent

variable. The respondents were required to provide their rating on their perception using

a five-point Likert scale measurement that ranged from 1 = strongly disagree, 2 =

disagree, 3 = neutral, 4 = agree, and 5 = strongly agree . Part II is proposed to collect the

respondents demographic information such as gender, age, highest academic

qualification, average monthly income level.

General perceptions about privacy and security protection

General perceptions about privacy and security protection have the same of six

items used to measure these two scales adopted by Kim et al. (2008) (see Table 3.1). Kim

et al. (2008) states that these scales reached the high level of internal consistency with

coefficient alpha .90 for general perceptions about privacy and .86 for security protection.

21

Table 3.1. Privacy protection and security protection scales

Items Privacy protection (Cronbach's Alpha = 0.900; N of Items = 4)

PP1 I am concerned that unauthorized persons (e.g., hackers) have access to my

personal information.

PP2 I am concerned that Web vendors will share my personal information with

other entities without my authorization.

PP3 I am concerned about the privacy of my personal information during a

transaction.

PP4 I am concerned that Web sites are collecting too much personal information.

PP5 I am concerned that Web vendors will use my personal information for other

purposes without my authorization.

PP6 I am concerned that Web vendors will sell my personal information to others

without my permission.

Items Security protection (Cronbach's Alpha = 0.860; N of Items = 6)

SP1 In general, providing credit card information online is riskier than providing it

over the phone to an offline vendor.

SP2

Internet merchants usually ensure that transactional information is protected

from accidentally being altered or destroyed during a transmission on the

Internet.

SP3 I feel secure about the electronic payment system of Internet merchants.

SP4 Internet merchants implement security measures to protect Internet shoppers.

SP5 I am willing to use a credit card to make purchases online.

SP6 I feel safe making transactions online.

Perceived risks and benefits

Teo and Liu (2007) suggest using four items to measure perceived risks while six

items are used for perceived benefits adopted by Chen et al. (2002). However, the six

items are modified to fit Vietnam context. For instance, the statement: I find the virtual

22

store very useful in my shopping or information seeking was transformed to I find the

virtual store very useful in my shopping. and I find the virtual store very useful in

information seeking. (see Table 3.2). This transformation makes the number of items

increased by twelve items from six items (see Table 3.2). Teo and Liu (2007) state that

four items used to measure perceived risks have a composite reliability .92 and Chen et al.

(2002) support the construct using to measure the perceived benefits scale by giving out

the composite reliability .84.

Table 3.2. Perceived risk and perceived benefits scales

Items Perceived risks (Cronbach's Alpha = 0.920 ; N of Items = 4)

PR1 I believe that the risk of purchasing online is very high.

PR2 There is a high probability of losing a great deal by purchasing from Internet

merchants.

PR3 There is a great uncertainty associated with purchasing from Internet

merchants.

PR4 Overall, I would label the option of purchasing from Internet merchants as

something negative.

Items Perceived benefits (Cronbach's Alpha = 0.840; N of Items = 12)

PB1 Using the virtual store enables me to accomplish shopping more quickly than

traditional stores.

PB2 Using the virtual store enables me to accomplish information seeking more

quickly than traditional stores.

PB3 Using the virtual store improves my performance in shopping (e.g., save

money)

PB4 Using the virtual store improves my performance in information seeking (e.g.,

save time)

PB5 Using the virtual store increases my productivity in shopping (e.g., make

purchase decisions)

23

Table 3.2. Perceived risk and perceived benefits scales (Cont.)

Items Perceived benefits (Cronbach's Alpha = 0.840; N of Items = 12)

PB6 Using the virtual store increases my productivity in information seeking (e.g.,

find product information within the shortest time frame)

PB7 Using the virtual store enhances my effectiveness in shopping (e.g., get the best

deal)

PB8 Using the virtual store enhances my effectiveness information seeking (e.g.,

find the most important information about a product.).

PB9 Using the virtual store makes it easier for me to shop.

PB10 Using the virtual store makes it easier for me to find information.

PB11 I find the virtual store very useful in my shopping.

PB12 I find the virtual store very useful in information seeking.

Customer trust in Internet shopping

Four items adopted by Lee and Turban (2001) are used to measure customer trust

in Internet shopping based on high coefficient alpha .70 (see Table 3.3).

Table 3.3. Customer trust scale

Items Customer trust in internet shopping (Cronbach's Alpha = 0.700; N of Items = 4)

CTIS1 In general, I cannot rely on Internet vendors to keep the promises that they make.

CTIS2 Internet shopping cannot be trusted, there are just too many uncertainties. CTIS3 Anyone trusting Internet shopping is asking for trouble. CTIS4 Internet shopping is unreliable.

24

3. Samples and data collection procedures The research comprised two phases, a pilot study and a main survey, was

conducted in Ho Chi Minh City. The pilot survey was undertaken in two stages,

qualitative and quantitative stage. Four respondents were recruitted to participate in in-

depth interviews to modified and refine the scale items. And then a quantitative pilot

survey was undertaken with a convenience sample. Characteristics of respondents were

gender, age, education level, and monthly average income. This study targeted

respondents age from 17 to 45. Data collectors distributed the questionnaire to customers

directly and via their e-mail addresses with instruction of how to complete the

questionnaire. In order to know who have ever bought good or services online and paid

for them by ATM, credit card, or a digital wallet, data collectors used filter question. In

the other hand, to prevent respondent to choose the number that indicates the level of their

agreement or disagreement, the collector also emphasized that online shoppers could

withdraw from this questionnaire at any time. After completing the questionnaire, the

collector check whether there was a response bias and the questions were answered

without reading. The purpose of this study was to validate measures and to test the

relationship between the four antecedents and customer trust in online shopping.

Statistical package for the social sciences version 19 was used to analyze the data. The

number of questions used to get respondents ideas was 32 not including 4 ones for

demographic variables. Based on this, the minimum size of the sample the study needed

was 160. However, to improve validity and reliability of this study, collectors made

decision to increase the sample size to 250. However, 34 questionnaires were unable to

use for due the high response rates of bias. Hence, the final sample size was 216. See

Table 4.1 for the sample characteristics.

4. Data analysis In terms of data analysis, a descriptive analysis was innitially performed to provide

information pertaining to the demographics of the respondents. Testing for reliability was

25

checked first using reliability coefficients Cronbachs Alpha. Next, the factor analysis

was run to show an association between a number of items and constructs. After that, an

associative analysis in the form of a correlation analysis was conducted to test for

existence of multi-co linearity. The study continued to test regression assumptions before

using OLS method to run a regression. Hierarchical multiple linear regression was used to

check whether demographic variables (gender, age, education, and income) contribute

anything to the prediction produced by the block of trust antecedent variables in the next

step. Subsequently, multiple regression analyses were performed to test the relationship

between the whole set of predictors and the dependent variables under the current study.

Lastly, hypothesis testing continued to conduct in order to determine whether hypotheses

proposed based upon a review from existing literature were supported or not.

26

Chapter Four: Results

1. Characteristics of the sample population The data set used for this study includes 216 (N = 216) completed questionnaires,

accounted for 86.40%, in total 250 ones delivered to respondents who agreed to reply the

questionnaires to data collectors. The respondents required to answer 36 questions divided

into two sections. Section 1 consisted of 32 questions measuring respondents perception

on Internet shopping. Four questions were used for collecting personal information of the

respondents (see Appendix A).

Gender. Of the 216 respondents, there were 138 females, equivalent to 63.9%. The

rest were 78 male respondents, equivalent to 36.1% (see Table 1).

Age. Most respondents reported ages belonged to the range 17 25 years,

accounted for 48.6% and 46.8% for the range 26 35 years while the fewest number of

respondents were 36 45 years old (4.6 %) (see Table 4.1).

Education. More than nine tenth of the respondents (91.2%, n = 197) had

bachelor degrees. In contrast, the percent of the rest who had master degree, high school

and associate degree is 5.1% (n = 11), 2.3% (n = 5), and 0.9% (n = 2) prospectively. No

response was just 0.5% (n = 1) (see Table 4.1).

Income. Out of two respondents (0.9%) who did not report their income, 44.9% (n

= 97) earned between 4 8 million Dong monthly average income, 23.1 % (n = 50)

belonged the range 9 13 million Dong per month, 12.5% (n = 27) of those who had

earned less than or equal four million Dong per month. It was followed by 10.6 % (n =

23) who earned between 14 21 million Dong. The total percent of the others was 7.9 %

(n = 17) belonged to the two ranges 22 35 and 36 more million Dong per month (see

Table 4.1).

Based on the general characteristics of respondents, they were found that

Vietnamese respondents were mostly female who already got bachelor degree. They

27

distributed in two both groups 17 25 and 26 35 years olds with monthly average

income 4 8 million Dong (see Table 4.1).

Table 4.1. Distribution of respondents based on demographic characteristics

Male or Female

Frequency Percent

Valid

Percent

Cumulative

Percent

Valid Female 138 63.9 63.9 63.9

Male 78 36.1 36.1 100

Total 216 100 100

A range of age

Valid From 17 to 25 105 48.6 48.6 48.6

From 26 to 35 101 46.8 46.8 95.4

From 36 to 45 10 4.6 4.6 100

Total 216 100 100

Highest academic qualification

High school 5 2.3 2.3 2.3

Bachelor 197 91.2 91.6 94

Master degree 11 5.1 5.1 99.1

Associate degree 2 0.9 0.9 100

Valid

Total 215 99.5 100

No

answer

1 0.5

Total 216 100

28

Table 4.1. Distribution of respondents based on demographic characteristics

(Cont.)

Highest academic qualification

Monthly average income

4 million VND 27 12.5 12.6 12.64 8 million VND 97 44.9 45.3 57.9

9 13 million VND 50 23.1 23.4 81.3

14 21 million VND 23 10.6 10.7 92.1

22 35 million VND 9 4.2 4.2 96.3

36 million VND 8 3.7 3.7 100

Valid

Total 214 99.1 100

No

answer

2 0.9

Total 216 100

2. Reliability of measurement instruments 2.1. Validating measures

For the perceptions about privacy protection, the six items (items one through six,

see Table 4.2) used to measure for the perceptions about privacy protection had a

Cronbachs Alpha of .885, .642 for security protection (items 1 through 6, see Table 4.2),

.584 for perceived risk (items 1 through 4, see Table 4.2), .856 for perceived benefits

(items 1 through 12, see Table 4.2), and 0.743 for customer trust in Internet shopping

(items 1 through 4, see Table 4.2),

The Cronbachs Alpha of privacy protection, security protection, perceived

benefits, and customer trust in Internet shopping were greater than .600. The only

coefficient of Cronbachs alpha of perceived risk was lower than .600. Their items all

were kept for four constructs accepting item one of the construct security protection (item

one, see Table 4.2) having a low corrected item-total correlation. As it was deleted, the

29

Cronbachs Alpha of the construct security protection would increase and reached 0.704.

Meanwhile, the Cronbachs Alpha of perceived risks was quite low. As one of four items

(see Table 4.2) was delleted, the Alpha could not increase higher. However, these items

were kept for further analysis.

Table 4.2. Item-Total Statistics

Items Code

Scale

Mean if

Item

Deleted

Scale

Variance

if

Item

Deleted

Corrected

Item-

Total

Correlation

Cronbach's

Alpha if

Item

Deleted

Privacy Protection (Cronbach's Alpha = .885; N of Items = 6)

1. I am concerned that unauthorized persons (e.g.,

hackers) have access to my personal information. PP1 19.206 27.826 0.563 0.887

2. I am concerned that Web vendors will share my

personal information with other entities without my

authorization.

PP2 19.318 26.603 0.637 0.876

3. I am concerned about the privacy of my personal

information during a transaction. PP3 19.444 23.234 0.834 0.843

4. I am concerned that Web sites are collecting too

much personal information. PP4 19.621 25.044 0.728 0.862

5. I am concerned that Web vendors will use my

personal information for other purposes without my

authorization.

PP5 19.519 25.105 0.732 0.861

6. I am concerned that Web vendors will sell my

personal information to others without my

permission.

PP6 19.435 24.951 0.707 0.866

30

Table 4.2. Item-Total Statistics (Cont.)

Items Code

Scale

Mean if

Item

Deleted

Scale

Variance

if

Item

Deleted

Corrected

Item-

Total

Correlation

Cronbach's

Alpha if

Item

Deleted

Security Protection (Cronbach's Alpha = .642; N of Items = 6)

1. In general, providing credit card information

online is riskier than providing it over the phone to

an offline vendor.

SP1 14.773 12.815 0.128 0.704

2. Internet merchants usually ensure that

transactional information is protected from

accidentally being altered or destroyed during a

transmission on the Internet.

SP2 14.351 11.534 0.343 0.612

3. I feel secure about the electronic payment system

of Internet merchants. SP3 14.749 11.227 0.528 0.546

4. Internet merchants implement security measures

to protect Internet shoppers. SP4 14.431 11.923 0.359 0.604

5. I am willing to use a credit card to make

purchases online. SP5 14.474 11.422 0.438 0.575

6. I feel safe making transactions online. SP6 14.829 11.295 0.554 0.541

Perceived Risks (Cronbach's Alpha = .584; N of Items = 4)

1. I believe that the risk of purchasing online is very

high. PR1 9.754 5.348 0.285 0.583

2. There is a high probability of losing a great deal

by purchasing from Internet merchants. PR2 9.403 5.727 0.286 0.572

3. There is a great uncertainty associated with

purchasing from Internet merchants. PR3 8.972 4.923 0.513 0.397

4. Overall, I would label the option of purchasing

from Internet merchants as something negative. PR4 8.91 5.301 0.402 0.485

31


Items Code

Scale

Mean if

Item

Deleted

Scale

Variance

if

Item

Deleted

Corrected

Item-

Total

Correlation

Cronbach's

Alpha if

Item

Deleted

Perceived benefits (Cronbach's Alpha = .856; N of Items = 12)

1. Using the virtual store enables me to accomplish

shopping more quickly than traditional stores. PB01 22.374 44.827 0.3 0.865


information seeking more quickly than traditional

stores.

PB02 22.864 43.62 0.624 0.84

3. Using the virtual store improves my performance

in shopping (e.g., save money). PB03 21.949 42.875 0.487 0.848

Perceived benefits (Cronbach's Alpha = .856; N of Items = 12)

4. Using the virtual store improves my performance

in information seeking (e.g., save time). PB04 22.64 43.668 0.536 0.844

5. Using the virtual store increases my productivity

in shopping (e.g., make purchase decisions). PB05 22.126 43.688 0.442 0.852

6. Using the virtual store increases my productivity

in information seeking (e.g., find product

information within the shortest time frame).

PB06 22.519 43.594 0.587 0.842

7. Using the virtual store enhances my effectiveness

in shopping (e.g., get the best deal). PB07 21.621 44.603 0.398 0.854


information seeking (e.g., find the most important

information about a product.)

PB08 22.407 41.566 0.64 0.837

9. Using the virtual store makes it easier for me to

shop. PB09 22.416 41.446 0.731 0.831


find information. PB10 22.673 42.550 0.676 0.836

11. I find the virtual store very useful in my

shopping. PB11 22.21 44.007 0.499 0.847

12. I find the virtual store very useful in information

seeking. PB12 22.673 43.902 0.605 0.841

32


Items Code

Scale

Mean if

Item

Deleted

Scale

Variance

if

Item

Deleted

Corrected

Item-

Total

Correlation

Cronbach's

Alpha if

Item

Deleted

Customer trust in internet shopping (Cronbach's Alpha = .743; N of Items = 4)

1. In general, I cannot rely on Internet vendors to

keep the promises that they make. CTIS1 8.755 5.181 0.579 0.66

2. Internet shopping cannot be trusted, there are just

too many uncertainties. CTIS2 8.736 5.191 0.549 0.677

3. Anyone trusting Internet shopping is asking for

trouble. CTIS3 8.301 5.402 0.483 0.714

4. Internet shopping is unreliable. CTIS4 8.222 5.271 0.535 0.685

2.2. Exploratory factor analysis Constructs all were analysed at the same time using Exploratory Factor Analysis

(EFA) to make sure all of them were suitable for applying in Vietnam context. EFA

explored research concept, omitted disqualified observations, and created homogeneous

measures. During the process of running EFA, this study met the following requirements:

Factor loading () .707 (Nguyen, 2011) and iA iB .30. However, in practice research, is greater than or equal .50 is acceptable. An item with the highest factor loading would be belonged to the factor containing it. Whatever an item does not

meet, the requirement would be omitted out of the construct. An item with the highest

factor loading would be belonged to the factor containing it.

TVE (Total Variance Extracted) .50 and Eigenvalue must be greater than 1, the measure is accepted.

This study used EFA with Principal Axis Factoring and Promax was conducted to

assess the underlying structure for the 32 items on the questionnaire. Six factors were

requested, because the items were designed to index six constructs: privacy protection,

security protection, perceived risks, perceived benefits, and customer trust. After rotation,

33

the first factor accounted for 19.55% of the variance, the second factor accounted for

15.73%, the third factor accounted for 10.1%, the fourth factor accounted for 6.47%, and

the fifth factor accounted for 3.65% (see Table 4.3). Table 4.4 displays the items and

factor loadings for the rotated factors, with loadings less than .50 omitted to improve

clarity.

After checking, the requirement mentioned above, twelve items were taken out of

three constructs. There were five factors explored, all items with corrected item-total

correlation were higher than .5, coefficient of Cronbachs alpha were greater than .7 (see

Table 4.5). The first factor privacy protection loads most strongly on the first six items,

with loadings in the first column. The second factor, named perceived benefits, was

composed of the five items with loadings in column 2 of the table. The third factor,

named customer trust, comprises the four items with loadings in the third column. The

fourth factor, named security protection, was composed of the three items with loadings

in column 4 of the table. The last one, named perceived risk, loads most strongly on the

two items in column 5.

These twenty items of five factors with loading were greater than .5 and TVE

explained 55.51% (> 50%) of variance at Eigenvalue 1.13. The number of factors

extracted was very suitable with the initial literatures.

The EFA results showed that the dependent variable customer trust was still

influenced by four independent variables (see Figure 1). There was no change in items of

the construct. Therefore, research concept achieved particular values, the measures

qualified convergent validity, and EFA model was completely suitable.

However, the number of items of each construct was already changed. Seven items

of PB variable were taken out of its scale. It remained five items for PB including PB02,

PB08, PB09, PB10, and PB12. The items of SP scale were reduced from 6 to 3 remaining

SP3, SP5, and SP6), 2 items in total 4 ones were kept for PR scale (PR3 and PR4).

Meanwhile, there was no change in the number of items of CTIS (CTIS1 through CTIS4)

34

and PP (PP1 through PP6). After deleting these twelve items, the final model had a quite

good fit to the data.

Table 4.3. Total Variance Explained

Initial Eigenvalues Extraction Sums of Squared

Loadings

Rotation

Sums of

Squared

Loadingsa Factor

Total % of

Variance

Cumulative

% Total

% of

Variance

Cumulative

% Total

1 4.343 21.717 21.717 3.910 19.550 19.550 3.644

2 3.515 17.576 39.293 3.147 15.734 35.284 3.123

3 2.472 12.358 51.650 2.020 10.101 45.384 2.496

4 1.771 8.853 60.503 1.295 6.473 51.857 1.637

5 1.131 5.654 66.157 .730 3.650 55.507 1.750

6 .891 4.455 70.612

20 .180 .898 100.000

Extraction Method: Principal Axis Factoring.

a. When factors are correlated, sums of squared loadings cannot be added to obtain a

total variance.

35

Table 4.4. Pattern Matrixa

Factor

1 2 3 4 5

PP3 .909

PP5 .810

PP4 .790

PP6 .757

PP2 .632

PP1 .575

PB10 .886

PB12 .819

PB08 .783

PB09 .763

PB02 .611

CTIS4 .712

CTIS3 .693

CTIS1 .646

CTIS2 .573

SP5 .757

SP6 .722

SP3 .564

PR3 .930

PR4 .519

Extraction Method: Principal Axis Factoring.

Rotation Method: Promax with Kaiser Normalization.

a. Rotation converged in 6 iterations.

36

Table 4.5. Item-Total Statistics

Items Code

Scale

Mean if

Item

Deleted

Scale

Variance

if

Item

Deleted

Corrected

Item-

Total

Correlation

Cronbach's

Alpha if

Item

Deleted

Privacy Protection (Cronbach's Alpha = 0.885; N of Items = 6)

1. I am concerned that unauthorized persons (e.g.,

hackers) have access to my personal information. PP1 14.773 12.815 0.128 0.704

2. I am concerned that Web vendors will share my

personal information with other entities without my

authorization.

PP2 14.351 11.534 0.343 0.612

3. I am concerned about the privacy of my personal

information during a transaction. PP3 14.749 11.227 0.528 0.546

4. I am concerned that Web sites are collecting too

much personal information. PP4 14.431 11.923 0.359 0.604

5. I am concerned that Web vendors will use my

personal information for other purposes without my

authorization.

PP5 14.474 11.422 0.438 0.575

6. I am concerned that Web vendors will sell my

personal information to others without my

permission.

PP6 14.829 11.295 0.554 0.541

Security Protection (Cronbach's Alpha = 0.731; N of Items = 3)

3. I feel secure about the electronic payment system

of Internet merchants. SP3 5.737 3.148 0.502 0.704

5. I am willing to use a credit card to make

purchases online. SP5 5.451 2.758 0.571 0.624

6. I feel safe making transactions online. SP6 5.817 3.037 0.594 0.600

Perceived Risks (Cronbach's Alpha = 0.704; N of Items = 2)

3. There is a great uncertainty associated with

purchasing from Internet merchants. PR3 3.427 1.085 0.543 3.427

4. Overall, I would label the option of purchasing

from Internet merchants as something negative. PR4 3.376 1.047 0.543 3.376

37


Items Code

Scale

Mean if

Item

Deleted

Scale

Variance

if

Item

Deleted

Corrected

Item-

Total

Correlation

Cronbach's

Alpha if

Item

Deleted

Perceived Benefits (Cronbach's Alpha = 0.872; N of Items = 5)


information seeking more quickly than traditional

stores.

PB02 7.486 9.33 0.598 0.868


information seeking (e.g.,find the most important

information about a product.).

PB08 7.032 7.929 0.712 0.844


shop. PB09 7.046 8.407 0.706 0.843


find information. PB10 7.301 8.323 0.787 0.824

12. I find the virtual store very useful in information

seeking. PB12 7.301 8.965 0.711 0.844

Customer Trust in Internet Shopping (Cronbach's Alpha = 0.743; N of Items = 4)

1. In general, I cannot rely on Internet vendors to

keep the promises that they make. CTIS1 8.755 5.181 0.579 0.66

2. Internet shopping cannot be trusted, there are just

too many uncertainties. CTIS2 8.736 5.191 0.549 0.677

3. Anyone trusting Internet shopping is asking for

trouble. CTIS3 8.301 5.402 0.483 0.714

4. Internet shopping is unreliable. CTIS4 8.222 5.271 0.535 0.685

3. Tests of regression assumptions 3.1. Test of multicollinearity

In order to check the correlations among the predictor variables prior to running

the multiple linear regression, Variance Inflation Factor (VIF) of an independent variable

is greater than 10; the variable does not have statistical significance to explain variance of

Y in the model Multiple Linear Regression (Hair & ctg 2006). However, VIFs of four

38

independent variables were lower than 10 (see Table 4.8), it meant that the

multicollinearity did not happen among the predictor variables or there were no

multicollinearity between the independent variables.

3.2. Test of normality of residual & heteroscedasticity Before running multiple linear regressions, the normality of residual and

heteroscedasticity need to be tested in advance.

Based on the result of Graph 1 and Graph 2 graphs (see Appendix B), the

regression standardized residual (Graph 1) and Normal P-P plot of regression

standardized residual (Graph 2) indicate the residuals are normally distributed, the

residual is relatively uncorrelated with the linear combination of predictors, and the

variances of the residuals are constant. Regression standardized predicted values (Graph

3) are distributed randomly. Therefore, the data meet the assumptions for running

multiple liear regressions.

4. Evaluating demographic variables impacts on customers trust Hierarchical multiple regression was used to check whether demographic variables

(gender, age, education, and income) contribute anything to the prediction produced by

the block of trust antecedent variables. The block of four antecedents of trust was entered

first and then one of demographic variables was added to the model to see if it made an

additional contribution to the outcome of prediction

The results showed that there was no significantly additional contribution to the

predicted outcome to CTIS in term of gender (R2 change = .001, p = .617); in terms of

age (R2 change = .001, p = .588); in terms of education (R2 change = .003, p = .406); in

terms of income (R2 change = .000, p = .726)

In general, demographic variables (gender, age, education, and income) didnt

make any significantly additional contribution to the outcome of prediction to CTIS.

39

5. Hypotheses testing The research question asked whether customers trust affected by perceptions

about privacy, security protection, perceptions about the benefits, and significantly

affected by perceptions about the risks during the transaction on the Internet.

The model summary table showed that the multiple correlation coefficient (R),

using all the predictors simultaneously, was equal to 21.7 percent (R2 = .217) and the

adjusted R2 was equal to 20.2 percent (see Table 4.6) reflecting 20.2 percent of variability

in CTIS that could be predicted from PP, SP, PB, PP combined. Table 4.6. Model Summary

Change Statistics Model R R Square

Adjusted R Square

Std. Error of the

Estimate R Square Change

F Change df1 df2

Sig. F Change

1 .466a 0.217 0.202 2.60159 0.217 14.505 4 209 0.000 a. Predictors: (Constant), PerceivedRisks , SecurityProtection , PrivacyProtection , PerceivedBenefits

The ANOVA table (see Table 4.7) shows that F = 14.505 and is significant. This

indicates that the combination of the predictors significantly predicts CTIS. Furthermore,

P value (see Table 4.7) was lower than .001; this study could conclude that the model was

significantly good at building the outcome of customers trust in Internet shopping. Table 4.7. ANOVAb

Model Sum of Squares df Mean Square F Sig.

Regression 392.684 4 98.171 14.505 .000a

Residual 1414.573 209 6.768 1

Total 1807.257 213

a. Predictors: (Constant), PerceivedRisks , SecurityProtection , PrivacyProtection , PerceivedBenefits b. Dependent Variable: CustomerTrust

H1. Privacy protection of a web has a positive effect on consumers trust in

Internet shopping.

Looking at Table 4.8, the p-value on the row marked privacy protection is .004,

which means the p-values less than 0.05. Therefore, the relationship between privacy

40

protection and CTIS was statistically significant. The coefficient of privacy protection ( = .180) also indicated that privacy protection appeared to have a positive relationship with

CTIS. That meant the hypothesis one supported.

H2. Security protection of a web has a positive effect on consumers trust in

Internet shopping.

The Table 4.8 showed that security protection of a web ( = .108, p > 0.05) didnt has a positive effect on consumers trust in Internet shopping. Therefore, hypothesis two

was not supported.

H3. Perceived risks have a significantly negative one with consumers trust in

Internet shopping.

The result of running regression (see Table 4.8) showed that perceived risks ( = -.379, p < 0.05) had a significantly negative effect on consumers trust in Internet

shopping. So, hypothesis three was supported.

H4. Perceived benefits have a positive effect on consumers trust in Internet

shopping.

Based on the result of Table 4.8, perceived benefits ( = .057, p > 0.05) (see Table 4.8) didnt have a significantly positive effect on consumers trust in Internet shopping.

Consequently, hypothesis four was not supported.

Table 4.8. Coefficients a

Unstandardized Coefficients

Standardized Coefficients

Correlations Collinearity

Statistics Model

B Std.

Error Beta

t Sig. Zero-order

Partial Part Toler-ance

VIF

(Constant) 12.822 1.175 10.916 .000

PrivacyProtection .106 .037 .180 2.895 .004 .243 .196 .177 .972 1.028

PerceivedBenefits .047 .051 .057 .923 .357 .091 .064 .056 .970 1.031

SecurityProtection .127 .073 .108 1.747 .082 .121 .120 .107 .974 1.027

1

PerceivedRisks -.606 .099 -.379 -6.093 .000 -.410 -.388 -.373 .970 1.031

a. Dependent Variable: CustomerTrust

41

6. Summary of the results An Enter regression analysis showed that for both privacy protection and perceived

risks contributes significantly to customer trust in Internet shopping. In contrast,

perceived benefits and security protection didnt have significant impacts on customer

trust in Internet shopping from the whole set of predictors.

The beta weights showed that CTIS has the strongest negative relation to perceived

risks ( = -.379, p = .000 < .050), a strong positive relation to privacy protection ( = .180, p = .004 < .050), and no statistically positive relations to security protection ( = .108, p = .082 > .050) and perceived benefits ( = .057, p = .357 > .050). In general, perceived risks and privacy protection were the two significant predictors of CTIS. Of

which, perceived risk is the strongest factor affecting decisions to shop online, but risks

are partially ameliorated by security protection and perceived benefits.

Table 4.9. Results of the testing hypotheses Results

Research question: The question asked whether customers trust affected by

perceptions about privacy, security protection, perceptions about the risks and benefits

during the transaction on the Internet.

Hypothesis 1: Privacy protection of a web has a positive effect on

consumers trust in Internet shopping. Supported

Hypothesis 2: Security protection of a web has a positive effect on

consumers trust in Internet shopping. Not supported

Hypothesis 3: Perceived risks have a significant negative one with

consumers trust in Internet shopping. Supported

Hypothesis 4: Perceived benefits have a positive effect on

consumers trust in Internet shopping. Not supported

42

Figure 2. Results of testing the conceptual model

.180***

.057

Privacy Perceptions (PP)

Security Protection (SP)

Perceived Risks (PR)

Perceived Benefits (PB)

Customer Trust in Internet Shopping (CTIS) (R2 = .217)

Demographics (gender/age/education/income)

-.379***

.108

Siginificant Path (***: p < .01) Non-significant Path (p > .05)

43

Chapter Five: Discussion

1. Findings There has been little doubt that what factors have significant effects on customer

trust in online shopping at the beginning of e-commerce development in Vietnam. The

present study addresses which factors have contributed significantly to the formation of

customer trust. Comparisons among different demographic groups of consumers are also

investigated. The analysis is based on a sample of 216 online shoppers in the university of

Economics Ho Chi Minh and private companies in Ho Chi Minh City. The results show

that the independent variables explain 20.2 % of variance (see Table 4.8) in CTIS. Two of

the four factors influencing customer trust in online shopping are perceived risk and

privacy protection. Not only do they play such main predictors to CTIS, but they also

have significantly negative and positive impacts, respectively. Of which, the strongest

predictor to CTIS is perceived risk ( = -.379, p = .000 < .050). Furthermore, it (zero order coefficient = -.410 < 0) covers relationship between CTIS and security protection,

perceived benefits, and privacy protection. It means that whenever researchers examine

which factors have impact on the formation of CTIS in Vietnam, they cannot but add the

predictor to conceptual model. In contrast, security protection and perceived benefits have

weak correlations with CTIS and they also have impacts on CTIS, but a lesser degree. It is

argued that, in a developing country like Vietnam, people tend to concern risk issues

rather than benefits in the context customers are not familiar with purchasing goods and

serverces online.

Consitent with results found in previous reasearches (Hoffman et al., 1999;

Jorgensen, 2000; Shankar et al., 2002), online shoppers are afraid of being leaked out

their privacy information (Monsuwe et al., 2004; Grewal et al., 2004). It plays such the

second strongest strongest predictor ( = .180, p = .004 < .050) affecting CTIS in conceptual model. However, it is quite supprised that customers concerns privacy

protection rather than security protection. Online shoppers dont concern too much about

44

the information they required to enter such as information of credit cards, and information

of the transaction which might be intercepted or stolen (Koufaris, 2004; Riegelsberger

and Sasse, 2001).

The findings in the multi-group analysis also indicate that what gender customers

are, how old customers are, whatever academic qualifications customers have acquired,

and how much customers earn per month none of them make significantly additional

contribution to the outcome of prediction to CTIS. The findings of this study disagree

with those found in Monsuwe et al. (2004) where gender, age, education, and income are

correlated with customer trust.

2. Implications These findings suggest important practical implications for planning marketing

strategies. Traditional marketing tools such as price promotions, brand advertisements

will not be efficient for converting Internet browsers into real buyers. Instead, perceived

risk should be reduced and privacy protection enhanced. Online shoppers are willing to

purchase a product or service for online merchants that are perceived low risk and high

privacy protection. (e.g., online vendors try to convey customers that their personal

information sent to suppliers over the internet will be safe and secure during

transactions.). Online vendors try to convey customers that their personal information sent

to suppliers over the internet will be safe and secure during transactions.

The findings also indicate that benefits and security protection of online shopping

(e.g., convenience, time saving, more options, secure of transaction information and credit

information) dont ameliorate perceived risk and privacy protection. So, avoiding

advertising them to online shoppers helps businesses save costs and allocate scarce

resources efficiently.

In sum, marketing strategies focusing on reducing perceived risk and enhancing

privacy protection may be more appropriate in persuading online customers.

45

3. Conclusion In this study, Principle Axis Factoring with Promax methods are used to validate

measures help the study refine the supposed research model and increase knowledge of

the four antecedents of trust predicting customers trust response. The model of trust has

both practical and theoretical value in Vietnam context. It not only provides an increased

insight into the nature of trust and provides a refined understanding of the predictors, but

it also provides efficient marketing tools to push up online businesses.

4. Limitations and directions for future research This study has a number of limitations metioned as follows.

Firstly, the conceptual model just considers four antecedents of trust in Lee and

Turbans (2001) proposed model for CTIS and four demographic variables without

adding other controller variables such as online experience, average years of working

experience, etc.

Secondly, demographic variables (gender, age, education, and income) were

investigated, and no significantly additional contribution to the outcome of prediction to

CTIS. However, these variables are necessary for Vietnamese online shoppers. Therefore,

they should be examined in future research.

Thirdly, this study was implemented in Ho Chi Minh City, the highest internet

penetration zone in Vietnam; Consumers in other provinces may exhibit different

concerns toward trust in online shopping. Expanding areas to collect data will be possible

to conduct in future research to generalize findings.

Fourthly, the measurement of perceived risk has Cronbachs alpha lower than .600

(see Table 4.2). Whenever researchers do on the same object, they need to notice this

point to improve the validity and reliability.

Finally, this study has not found out suitable reasons to explain why security

protection and perceived benefits were not supported. Therefore, to find reasons to

explain it will be able to conduct in future researches.

46

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