factors affectding customer trust in online shopping in vietnam
Post on 06-Mar-2016
220 Views
Preview:
DESCRIPTION
TRANSCRIPT
-
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
-
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
Ho Chi Minh City Year 2012
-
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.)
Study Trust Conceptualization Trust Object Measures
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
Source: Gefen, David; Karahanna, Elena; Straub, Detmar W. (2003, p. 56-59)
-
8
Table 2.1. Summary of prior conceptualizations of trust (Cont.)
Study Trust Conceptualization Trust Object Measures
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)
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.
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
Source: Gefen, David; Karahanna, Elena; Straub, Detmar W. (2003, p. 56-59)
-
9
Table 2.1. Summary of prior conceptualizations of trust (Cont.)
Study Trust Conceptualization Trust Object Measures
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.
Source: Gefen, David; Karahanna, Elena; Straub, Detmar W. (2003, p. 56-59)
-
10
Table 2.1. Summary of prior conceptualizations of trust (Cont.)
Study Trust Conceptualization Trust Object Measures
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.
Source: Gefen, David; Karahanna, Elena; Straub, Detmar W. (2003, p. 56-59)
-
11
Table 2.1. Summary of prior conceptualizations of trust (Cont.)
Study Trust Conceptualization Trust Object Measures
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)
Source: Gefen, David; Karahanna, Elena; Straub, Detmar W. (2003, p. 56-59)
-
12
Table 2.1. Summary of prior conceptualizations of trust (Cont.)
Study Trust Conceptualization Trust Object Measures
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)
Willingness to be vulnerable
based on belief that the other
party is competent, open,
concerned, and reliable.
Interpersonal
trust in
organizational
settings
Conceptual
Source: Gefen, David; Karahanna, Elena; Straub, Detmar W. (2003, p. 56-59)
-
13
Table 2.1. Summary of prior conceptualizations of trust (Cont.)
Study Trust Conceptualization Trust Object Measures
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
Source: Gefen, David; Karahanna, Elena; Straub, Detmar W. (2003, p. 56-59)
-
14
Table 2.1. Summary of prior conceptualizations of trust (Cont.)
Study Trust Conceptualization Trust Object Measures
Rempel et
al. (1985)
Willingness to depend based
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)
Willingness to be vulnerable
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.
Source: Gefen, David; Karahanna, Elena; Straub, Detmar W. (2003, p. 56-59)
-
15
Table 2.1. Summary of prior conceptualizations of trust (Cont.)
Study Trust Conceptualization Trust Object Measures
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
Source: Gefen, David; Karahanna, Elena; Straub, Detmar W. (2003, p. 56-59)
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
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
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
2. Using the virtual store enables me to accomplish
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
8. Using the virtual store enhances my effectiveness
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
10. Using the virtual store makes it easier for me to
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
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
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
Table 4.5. 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
Perceived Benefits (Cronbach's Alpha = 0.872; N of Items = 5)
2. Using the virtual store enables me to accomplish
information seeking more quickly than traditional
stores.
PB02 7.486 9.33 0.598 0.868
8. Using the virtual store enhances my effectiveness
information seeking (e.g.,find the most important
information about a product.).
PB08 7.032 7.929 0.712 0.844
9. Using the virtual store makes it easier for me to
shop. PB09 7.046 8.407 0.706 0.843
10. Using the virtual store makes it easier for me to
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
References
B. Jorgensen (October 2000). A matter of trust, Electronic Business.
Barney, J.B. and Hansen, M.H. (1994). Trustworthiness as a Source of Competitive
Advantage, Strategic Management Journal 15: 175-190.
Bierhoff, H., & Vornefeld, B. (2004). The social psychology of trust with applications in
the Internet. Analyse and Kritik, 26, 48-62.
Chen, L., & Tan, J. (2004). Technology adaptation in e-commerce: Key determinants of
virtual stores acceptance. European Management Journal, 22, 74-86.
Chen, L., Gillenson, M., & Sherrell, D. (2002). Enticing consumers online: An extended
technology acceptance perspective. Information and Management, 39, 705-719.
Cimigo NetCitizens 2012. (n.d.). Retrieved August 09, 2012, from
http://www.cimigo.vn/en-US/ReportDetail.aspx?ProductId=266
Corritore, C., Kracher, B., & Wiedenbeck, S. (2003). On-line trust: Concepts, evolving
themes, a model. International Journal of Human Computer Studies, 58, 737-758.
Crosby, L. A., Evans, K. R., and Cowles, D. (1990). Relationshiop Quality in Services
Selling: An Interpersonal Influence Perspective,Journal of Marketing (54:7), pp.
68-81.
D. Hoffman, T. Novak and M. Peralta, Building consumer trust online. Communications
of the ACM 42(4) (1999), 8085.
Doney, P.M. and Cannon, J.P. (1997). An Examination of the Nature of Trust in Buyer
Seller Relationships, Journal of Marketing 61(2): 3551.
Elliot, S., & Fowell, S. (2000). Expectations versus reality: A snapshot of consumer
experiences with Internet retailing. International Journal of Information
Management, 20, 323-336.
Egelman, S., Tsai, J., Cranor, L., and Acquisti, A. (2004). Studying the impact of privacy
information on online purchase decisions. Carnegie Mellon University, pp. 2.
Available from: http://cups.cs.cmu.edu/pubs/chi06.pdf
-
47
Ganesan, S (1994). Determinants of Long-Term Orientation in BuyerSeller
Relationships, Journal of Marketing 58: 119.
Gefen, D., Srinivasan Rao, V. and Tractinsky, N. (2003b). The Conceptualization of
Trust, Risk and Their Relationship in Electronic Commerce: The Need for
Clarifications, in Proceedings of the 36th Hawaii International Conference on
System Sciences (HICSS 2003),
http://csdl2.computer.org/comp/proceedings/hicss/2003/1874/07/187470192b.pdf
Gefen, D. (2000). E-Commerce: The role of familiarity and trust, The International
Journal of Management Science 28 (6): 725-737.
Gefen, David; Karahanna, Elena; Straub, Detmar W. (March 2003).Trust and TAM in
Online Shopping, MIS Quaterly; 27 (1); 51.
Grewal, D., Iyer, G., & Levy, M. (2004). Internet retailing: Enablers, limiters and market
consequences. Journal of Business Research, 57, 703-713.
Gulati, R. (February 1995). Does Familiarity Breed Trust? The Implications of Repeated
Ties for Contractual Choice in Alliances, Academy of Management Journal
(38:1), pp. 85-112.
Hair JF, Black WC, Babin BJ, Anderson RE, & Tatham RL (2006), Multivariate Data
Analysis, 6th ed, Upper Saddle River NJ: Prentice-Hall.
Hosmer, L. T. (1995). Trust: the Connecting Link Between Organizational Theory and
Philosophical Ethics, Academy of Management Review (20:2), pp. 379-403.
Ilagan, Sheila de Villa, Exploring the Impact of Culture on the Formation of Consumer
Trust in Internet Shopping. M.A., Department of Communication and Journalism,
May 2009.
Ja
top related