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A Study of Online Transaction Self-Efﬁcacy, Consumer Trust,

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A STUDY OF ONLINE TRANSACTION SELF -EFF ICACY, CONSUMER TRUST,
AND UNCERTAINTY REDUCTION IN ELECTRONIC COMMERCE
TRANSACTION

By

Young Hoon Kim

A THESIS

Submitted to
Michigan State University
in partial fulﬁllment of the requirements
for the degree of

MASTER OF ARTS

Department of Telecommunication, Information Studies, and Media.

2004

ABSTRACT

A STUDY OF ONLINE TRANSACTION SELF -EFF ICACY, CONSUMER TRUST,
AND UNCERTAINTY REDUCTION IN ELECTRONIC COMMERCE
TRANSACTION

By

Young Hoon Kim

Trust has been accepted as a critical element in electronic commerce due to the fact
that online transactions are characterized as a process that involves uncertainty and risk.
Trust also has been thought of as the most effective means of reducing uncertainty and
risk. The effect of measures to build and maintain trust in e-commerce is subject to
person-speciﬁc and situation-related factors that cannot be controlled by web vendors.
Based on the fact that trust is a mechanism to reduce complexity, a trait inherent to online
transactions, this study has explored factors that inﬂuence trust during online transactions.
Self-efﬁcacy, which is an important factor in explaining motives and motivations of
individual behaviors and choices, has an impact on trust building and uncertainty
reduction. By incorporating self-efﬁcacy into electronic commerce research, this study
expands the understanding of what factors play a role in reducing uncertainty. Based on
previous self-efﬁcacy studies on the relationship between general self-efﬁcacy and
domain-speciﬁc self-efﬁcacy, self-efﬁcacy has been handled in two parts; general self-
efﬁcacy and online transaction self-efﬁcacy. The result shows that both categories of
self-efﬁcacy affect trust in the web vendor and, consequently, positively influencing the

respondent’s purchase intentions.

TABLE OF CONTENTS

LIST OF TABLES ................................................................................. v
LIST OF FIGURES ................................................................................. vi
CHAPTER 1
INTRODUCTION ................................................................................... 1
CHAPTER 2
LITERATURE REVIEW ........................................................................... 4
Trust ............................................................................................... 4
Self—Efﬁcacy .................................................................................... 11
CHAPTER 3
RESEARCH MODEL AND HYPOTHESES .................................................. 18
General Self-Efﬁcacy .......................................................................... 18
Online Transaction Self-Efﬁcacy ........................................................... 20
Disposition to Trust ......................................................................... 22
Consumer Trust ................................................................................. 23
Perceived Risk ............................................................................... 24
CHAPTER 4
RESEARCH METHODOLOGY ................................................................. 26
Data Collection ............................................................................. 26
Measurement ................................................................................... 28
Data Analysis and Results ................................................................ 31
CHAPTER 5
CONCLUSION ...................................................................................... 41
Discussion ....................................................................................... 41

Implications 43

iii

Limitations and Suggestions .................................................................. 45

APPENDIX .................................................................................... 48

BIBLIOGRAPHY .................................................................................. 53

iv

LIST OF TABLES

Table 1. Context of Previous Research on Trust ................................................ 6
Table 2. Context of Previous Research on Self-Efﬁcacy .................................. 12
Table 3. Summary of Concepts and Research Variables .................................. 17
Table 4. Degree of Internet Experience ....................................................... 28
Table 5. Measurement Items for Constructs ................................................... 30
Table 6. Factor Structure Matrix of the Latent Variables .................................. 32
Table 7. Overall Model Fit Indices of CFA for Convergent Validity ................... 35
Table 8. Means, Standard Deviations, and Composite Reliabilities ..................... 35
Table 9. Chi-Square Difference Test for Discriminant Validity ............................ 36
Table 10. Comparison of Squared Correlation and Variance Extracted ................. 37
Table 11. Correlations of Latent Variable ....................................................... 38
Table 12. Model Comparison ................................................................... 39

LIST OF FIGURES

Figure 1. The Intersection Effect of Trust and Self-Efﬁcacy ............................... 14
Figure 2. Research Model ......................................................................... 26
Figure 3. SEM Result of the Proposed Model ................................................. 38

vi

1. INTRODUCTION

Trust in online transactions is still crucial to the success of academic and business
e-commerce. Previous research pointed out that the lack of online consumer trust is the
main barrier of consumer participation in e-commerce (Cole, 1998; Jarvenpaa and
Tranctinsky, 1999; Reichheld and Ssheﬁer, 2000; Rose et al,. 1999). Since consumers are
reluctant to make a purchase from unfamiliar web vendors on account of worries (e. g.,
fraudulent charges, difﬁculties of returning defective or wrong products, etc.), trust has
become an important issue in business-to-customer (B2C) electronic commerce. Among
the major concerns that consumers have is how e-commerce companies use their private

information collected during online transactions (Steinﬁeld, 2004).

Previous research that has been made so far may be categorized as follows: 1)
empirically examining the effect of trust on purchase behaviors (J arvenpaa et al., 2000;
Kim et al., 2003b; Kim et al., 2004), 2) exploring antecedents of trust (Gefen, 2000;
Gefen, 2002; Bhattacherjee, 2002; Kim et al., 2003a): familiarity and disposition to trust,
and 3) clarifying the deﬁnition of trust in e-commerce (McKnight and Chervany, 2002;

McKnight et al., 2002).

Previous studies showed that trust was the most signiﬁcant factor in explaining the
process of commerce (Mayer et a1. 1995; Doney and Cannon, 1997; Gefen, 2002). Trust
was thought of as the most effective uncertainty reduction method (Hart and Saunders,
1997; Gefen, 2000). Trust had a signiﬁcant effect on willingness to transact (B = .36, p <.
001, Bhattacherjee, 2002) and purchase (B = .43, p <. 01, Gefen, 2000). The direct effect
of trust on purchase intention or behavior was proven theoretically and even empirically.

The direct path of trust on purchase intention (or behavior) can be thought of a necessary

condition of an uncertainty reduction mechanism. Jarvenpaa et a1. (2000) suggested an
indirect effect of trust on willingness to buy through risk perception and attitude (trust -»

risk perception (and attitude) —’ willingness to buy).

Although much research has been done, there are still questions remaining to be
answered about 1) whether other potential antecedents affecting trust—building and
reinforcement of purchase intention in e-commerce are to be uncovered, 2) exactly what
kind of relationship exists among several antecedents that have been proved in previous
studies. A fuller understanding of the new uncertainty mechanism - beyond the
conventional wisdom that trust directly affects purchase intention - would be possible
only with a systematic investigation into that relationship. Therefore, there still is a need
to clarify the mechanism that affects the trust and purchase intention of the consumer. To
answer these questions, this study attempts to suggest an advanced uncertainty reduction
mechanism of an online consumer by investigating the inter-relationships among
intemal/subjective factors. To do so, this study illuminates the applicability of self-
efficacy in online transactions, which requires decision-making within uncertain contexts
of online transactions. Self-efficacy will be posited as a causal antecedent to trust in
reducing uncertainty inherent in online transactions and inﬂuences trust building.
Whereas trust is a conventional uncertainty reduction method, and self-efﬁcacy will be

considered as a causal antecedent to this uncertainty reduction process.

The objective of this study is to investigate the inﬂuence of subjective/intemal
factors related to individual’s intention to purchase in e-commerce and to explore the
interdependent relationship among these factors that engage in trust building in e-

commerce. To do so, another objective of this study is to introduce self-efficacy as a

signiﬁcant antecedent for trust as an uncertainty reduction mechanism in the realm of e-
commerce. The proposed model examines how self-efﬁcacy affects trust on web vendors,
and how both self-efﬁcacy and consumer trust affect online consumers’ intention to
purchase from a certain web vendor by incorporating socio-cognitive factors like self-
efﬁcacy into the model explaining online purchasing. This work can provide a broader

picture of trust building and reinforcement of purchase intention in e-commerce.

Since the proposal of Bandura’s socio-cognitive theory of self-efﬁcacy, much
research has been done in explaining an individual’s behavioral motivations. While self-
efﬁcacy has been introduced and utilized in a considerable amount of research in
information systems (IS) and behavioral sciences (Compeau and Higgins, 1995;
Venkatesh and Davis, 1996; Marakas et a1. 1998; Agarwal et a1. 2000), little is known of
the applicability of self-efﬁcacy in the domain of e-commerce. In previous IS research,
self-efﬁcacy was thought of as an effort to provide a solid means to advance the multi-
level model by introducing external variables such as perceived useﬁilness and perceived

ease of use (Venkatesh and Davis, 2000).

The self-efﬁcacy factor can be introduced in the domain of e-commerce in the
same way as it has been in other research areas. If consumers feel too much uncertainty
in e—commerce transactions, they are reluctant to make purchases from online retail
websites. If, however, people are conﬁdent that they can handle the problems that may
occur during transaction with web retailers, conﬁdence in e-commerce will increase.
Customers who see themselves as highly self efﬁcacious are more likely to try out
uncertain and possible risky online transaction, whereas those who perceive low self-

efﬁcacy are likely not to engage in less certain and risky situations.

Two research questions addressed in this study are: (1) the signiﬁcance of self-
efﬁcacy in forming and reinforcing purchase intention of online consumers and (2) what
the structural relationship among subjective/intemal factors engaged in trust-building and
reinforcement of purchase intention in B2C e-commerce is. In order to investigate these
research questions, the SEM (Structural Equation Modeling) was used to examine the
proposed model of purchasing intention-behavior reinforcement. Based on the data
gathered, this multivariate analysis that includes relevant latent variables was conducted
to test whether the proposed model showed the structure and the relationships among

these variables.

The remainder of this study is organized as follows. Chapter 1 reviews literature
and theoretical foundations underpinning previous studies. Chapter 2 introduces the
research model and hypotheses of this study. Chapter 3 describes the research methods
used in this study and reports the results. Chapter 4 concludes with a discussion of the

ﬁndings, implications, and suggestions for further research.

2. LITERATURE REVIEW

The main focus of this literature review is trust and self-efﬁcacy. It’s because these
two factors are key concept upon understanding intemal/subjective factors affecting trust

building and trust-reinforcement in e-commerce.
2.1. Trust

Trust has been conceptualized by investigating a relationship among a belief,

attitude, attention, and behavior (Mayer et al., 1995; McKnight et al., 1998). This

direction of research is concerned with deﬁning the trust concept in a management
context, not interpersonal context. Schurr and Ozanne (1985) deﬁned trust as the “belief
that a party’s word or promise was reliable and that a party will fulﬁll his/her obligation
in an exchange relationship.” Certainly this belief leads to behavioral intentions. Mayer et
al. (1995) deﬁned trust as “the willingness of a party to be vulnerable to the actions of
another party based on the expectation that the other will perform a particular action
important to the trustor, irrespective of the ability to monitor or control that other party.”
(p.712). McKnight et al. (2002) deﬁned trust to mean that “one believes in, and is willing
to depend on, another party.” Gefen (2000) suggested that trust was based on previous
interactions, although a supplier’s previous behavior could not guarantee that he/she will
act as expected. In summary, trust refers to the belief that the promise of the other party
can be relied upon and that, in unpredictable circumstances, the other will act with

goodwill and in a benign fashion toward the trustor.

Previous literatures show distinct dimensions of trust: Integrity, motives,
consistency, openness, discreetness, ﬁinctional competence, interpersonal competence,
business sense, judgment (Gabarro 1978); Predictability, dependability, faith (Rempel et
al., 1985); Availability, competence, consistency, discreetness, fairness, integrity, loyalty,
openness, promise fulﬁllment, receptivity (Butler, 1991); Ability, benevolence, integrity
(Mayer et al., 1995); Keeps commitments, negotiates honesty, avoids taking excessive
advantage (Cummings and Bromiley, 1996); Credibility and benevolence (Doney and
Cannon, 1997); Openness, congruity, shared values, autonomy (Hart et al., 1986); Ability,
benevolence, integrity (Jarvenpaa et al., 1998); Benevolence, competence, honesty,

predictability (McKnight et al., 1998).

Some trust dimensions were originally concerned with interpersonal relationship
(peer and work) so these may not be relevant to online transaction. It should be noted that
some trust dimensions are available to e-commerce situation; others are less relevant to

online transactions.

Table l. Context of Previous Research on Trust

 

 

 

 

 

 

Research Context
Gabarro (1978) Interpersonal relationship among corporate executives
Rempel et al. (1985) Spousal relationships
Hart et al. (1986) Relationship of inter-unit at General Motors
Butler (1991) Interpersonal relationship among corporate managers
Mayer et al. (1995) Suggesting conceptual deﬁnition of trust

 

Cummings and Bromiley (1996) Inter-unit (organization)

 

 

 

 

 

Doney and Cannon (1997) Buyer-seller relationship

Jarvenpaa et al. (1998) Interpersonal relationship among virtual team members
McKnight et al. (1998) Individual in organizations

Gefen (2000, 2002) The role of familiarity and trust/ the dimensions of trust
Bhattacherjee (2002) Individual trust in online ﬁrms

 

 

 

 

Based on previous literatures on trust, Mayer et al. (1995) proposed a generic
concept of trust by suggesting three dimensions: ability, benevolence, and integrity.
These three dimensions are conceptually distinct as well as empirically distinct

(Bhattacherjee, 2002; Gefen, 2002).

Ability is deﬁned as “group of skills, competencies, and characteristics that enable
a party to have inﬂuence within some speciﬁc domain.”(Mayer et al., 1995, p.717). It
reﬂects the trustor’s perception of how well trustee is competent and knowledgeable for
the behavior and performance trustor desires. Bhattacherjee (2002) argued that online

consumer’s perception of web retailer’s ability depends on competence and the

knowledge of the retailer required to yield the desired performance. Benevolence is “the
extent to which a trustee is believed to want to do good to the trustor, aside from an
egocentric proﬁt motive” (Mayer et al., 1995, p.718). It refers to the degree to which a
trustee is believed to try to bring some intended results to a trustor. If a web retailer is
benevolent, it is very much likely to show receptivity and empathy of how much it pays
attention to online customers’ concern (Bhattacherjee, 2002). Integrity reﬂects “the
trustor’s perception that the trustee adheres to a set of principles that the trustor ﬁnds
acceptable” (Mayer et al., 1995, p.719). Integrity can be assessed in terms of conducts of
online transactions, policies that relate to customer service, and use of private information

by ﬁrm (Bhattacherjee, 2002).

For online consumers, transaction with online vendors is considered uncertain and
is a risky situation as compared with the conventional buying-selling process. Consumers
are given little opportunity to verify the quality of goods on their own and it is also not
easy to test goods through interaction with the web vendor. When customers make a
purchase from an unfamiliar web vendor, they are unable to judge the quality, and they
do not know whether or not the service is reliable and legitimate. Grabner-Kraeuter
(2002) classiﬁed and characterized two types of uncertainty in e-commerce: system-
dependent uncertainty, which is caused by functional defects or security problems in
technical systems, and transaction-speciﬁc uncertainty, which is explained by an
asymmetry in information between the transaction partners. It is meaningful to clarify the
level of uncertainty embedded in online transaction in that trust is essentially a

mechanism of risk and uncertainty reduction during transaction.

Previous studies showed that trust was the most signiﬁcant factor in explaining the
process of commerce (Mayer et al. 1995; Doney et al., 1998; Gefen, 2002). Trust is
thought of as the most effective uncertainty reduction method (Gefen, 2000; Hart and
Saunders, 1997). Trust plays a key role in purchasing processes where consumers
especially look for credential qualities of goods and services. Trust is one of the factors
that inﬂuence how risk is perceived and assessed by the individual. Kathryn and Mary
(2002) suggested that perceived risk associated with e-commerce was “a function of trust
between a buyer and a seller.” The level of risk inherent in a particular e-commerce
situation is offset by the degree to which trust is maintained by one party. As a result,
trust functions to reduce perceptions of risk. Bhattacherjee (2002) stated that trust was a
signiﬁcant factor of online transactions because it can be characterized by uncertainty,
anonymity, and lack of control, and potential opportunism. Trust was understood as a
control mechanism that prevents opportunistic behaviors, encourages following

transactions, and supports the formations of long-term relationships.

McKnight and Chervany (2002) classiﬁed the deﬁnition of trust into four stages:
disposition to trust, institution-based trust, trusting belief, and trusting intention. They
approached the trust-building process by integrating trust-related constructs within the
framework of the Theory of Reasoned Action (TRA). There has been an issue of how the
dimensionality of consumer trust should be understood. Unlike the approach that
regarded consumer trust as a single dimensional construct (Jarvenpaa and Tractinsky,
1999; Gefen and DeVine, 2001; Gefen and Straub, 2002; McKnight et al., 2000;

McKnight and Chervany, 2002;), Gefen (2002) proposed that trust should be understood

as a multi-dimensional construct in e-commerce and showed that the different sub-

constructs of trust were distinct.

Gefen (2002) conceptualized trust in two ways; one way understands trust as “a set
of speciﬁc beliefs” about the speciﬁc party that are built on beliefs in terms of its
integrity, benevolence, and ability. He studied the analytic framework in which the
speciﬁc beliefs of ability, integrity, and benevolence can constitute trustworthiness. The
other studies considered trust “a general belief” that the speciﬁc party was reliable

because it has ability, integrity, and benevolence.

The willingness of the buyers to provide their credit card number or other personal
information will depend on their assessment of the trustworthiness of the online vendor.
Trust was the result of “a set of trustworthiness beliefs” (Mayer et al., 1995; Gefen, 2000).
Trust dealt with beliefs about the future behaviors and choices of others and these beliefs
or trustworthiness beliefs were inﬂuenced by the individual’s judgment on the other
party’s future actions and trading environments (Gefen, 2000). Thus, it is evident that
trustworthiness beliefs (ability, benevolence, and integrity) can be inﬂuenced and formed
by the individual’s judgment. Given that individuals were eventually motivated through
understanding and controlling their social environment (Gefen, 2000), this study validly
attempts to make use of self-efﬁcacy judgment that engages in the process of

understanding and accepting their social environment.

As McKnight et al. (2002) pointed out, trust was a complex, dynamic and
multidimensional concept that could not be created by applying adequate external
instruments. Deciding on whether or not to trust others depends ultimately on the

individual’s judgment on the other party and the situation. The consumer’s individual

judgment will be inﬂuenced by that individual’s self-efﬁcacy. Consumers eventually
decide on their own, even though they are given any number of external measures
provided by a guarantee instrument (e. g., TRUSTe) and any rating (e. g., the reputation
system of ebay) results offered by any other consumers. There will always be some
consumers who are not conﬁdent that they can ﬁnd a trustworthy web vendor, even if

ratings and reputations provided by other consumers are given to them.

McKnight and Chervany (2002) posited that certain situations tended to have
stronger effects on interpersonal beliefs. But this does not necessarily lead to the
conclusion that situation (including external safeguards or a guarantee system) inﬂuences
interpersonal beliefs. Through three different measures like information policies,
guarantee policies, and reputation policies, web vendors try to make transactions more
efﬁcient and reliable (Grabner-Kraeuter, 2002). However, these instruments do not
necessarily guarantee that these measures make it possible for online consumers to trust
the web vendor. The study (Kimery and McCord, 2002) showed that there was no
signiﬁcant direct impact of a third-party seal on consumer trust in a speciﬁc e-retailer.
It’s because the meaning of a third-party seal has not been known to the consumers; more

importantly, they lack conﬁdence to consider it a mechanism to guarantee trust.

Bhattacherjee (2002) suggested that ﬁrst, individual level trust should be
distinguished from that of group level or business ﬁrm level, that is, trust in personal
relationship or work relationship is distinct from that in inter-organization. Second, trust
can be considered a domain-speciﬁc psychological state. Mayer et a1. (1995) viewed trust
as a general personality trait or a domain-speciﬁc psychological state. They understood

personality traits were subject to developmental factors that are irrelevant to a given

context. And psychological states were considered as affective or cognitive one that can
be inﬂuenced by the interaction between an individual and social situations. However,
Bhattacherjee argued that trust in e-commerce should be viewed as a domain-speciﬁc
psychological state, because the personality trait approach is more likely not to provide
an adequate explanation of why an individual shows less trust toward uncertain and

anonymous Web retailers.

One should note that the impact of trust on uncertainty reduction is not a direct
relationship; rather, any other variables can get involved in the reinforcing relationship
between trust and uncertainty reduction. This study attempts to explore the factor that
plays a signiﬁcant role in inﬂuencing individual’s trust judgment. Thus, self-efﬁcacy has

been introduced in the argument regarding the impact of trust on uncertainty reduction.

2.2. Self-Efficacy

There has been a considerable amount of study on self-efﬁcacy that deals with
work-related performance (Frayne and Latham, 1987; Wood and Bandura, 1989; Gist et
al., 1989; Mitchell et al., 1994; Agarwal et al., 2000; Venkatesh, 2000). These studies
have sought to explore the existence of many cognitive factors that have motivational
effects on human action. In reality, the relationship between self-efﬁcacy and behavior
has been empirically validated in diverse domains such as education, health
organizational life, and organizational tasks. In the realm of information technology, self-
efﬁcacy is regarded as a determinant of individual behavior and performance. Self-
efﬁcacy was shown to understand the psychological aspects of the Digital Divide (Eastin
and LaRose, 2000), individual behavior toward new information technology (Compeau

and Higgins, 1995; Marakas et al., 1998), perceived ease of use in prior research on user

acceptance of technology (Venkatesh and Davis, 1996), and prediction of using a web-
based information system (Yi and Hwang, 2003). It was empirically demonstrated that
application-speciﬁc self-efﬁcacy has a more direct and powerful effect on the ease of use

and perception (Agarwal et al., 2000).

Table 2. Context of Previous Research on Self-Efﬁcacy

 

 

 

Research Context
Frayne and Latham (1987) Employee attendance management
Wood and Bandura (1989) Complex decision making

 

Gist et al.(1989); Mitchell et al. (1994); Computer skill acquisition
Marakas et al. (1998)

 

 

 

 

 

 

Venkatesh and Davis (1996) User acceptance of technology

Agarwal et al. (2000); Venkatesh (2000);

Saks (1995) Job attitude

Stajkovic and Luthans (1998) Job performance.

Chen et al. (2000) Learning goal orientation

Eastin and LaRose (2000) Understanding psychological aspect of digital devide
Yi and Hwang (2003) Prediction of using a web-based information system

 

 

 

 

Self-efﬁcacy was deﬁned as a personal judgment of “how well one can execute
courses of action required to deal with prospective situations” (Bandura, 1982, p. 122).
Bandura (1986) deﬁned self-efﬁcacy as “people’s judgments of their capabilities to
organize and execute courses of action required to attain designated types of
performances” (p.391). And Bandura (1991) also stated that “people’s beliefs in their
efﬁcacy inﬂuence the choices the make, their aspirations, how much effort they mobilize
in a given endeavor, and how long they persevere in the face of difﬁculties and setbacks”

(p.257). As Bandura pointed out (1986), self-efﬁcacy does not reﬂect skills individuals

12

possess but judgment of what they can do with their skills. Self-efﬁcacy should not be
understood as a measure of skill because it concerns with the extent to which individuals
believe they can perform by using their skills (Eastin and LaRose, 2000). It focuses on
what people believe they can accomplish now. Self-efﬁcacy can be understood as
“beliefs in one’s capabilities to mobilize the motivation” needed to manage given

situational tasks (Wood and Bandura, 1989, p.408).

Gist and Mitchell (1992) pointed out that self-efﬁcacy had three aspects. First, self-
efﬁcacy reﬂects individuals’ comprehensive judgment on whether they are capable of
implementing a speciﬁc task. Second, the judgment on self-efﬁcacy changes as the
individual obtains new information and experiences. Third, a self-efﬁcacy judgment

involves a motivational factor that directly mobilizes the individual’s behaviors.

People are neither driven by intemal/subjective forces, nor determined by
extemal/environmental stimuli; rather, the behavior of people can be explained by the
more comprehensive model of reciprocal relationship. Given that human behavior is
explained by the interaction of internal forces and external stimuli, self-efﬁcacy could be
understood as a key mechanism that accounting for this interactive relationship between
internal forces and external stimuli that affects human behavior. As Bandura ( 1986)
pointed out, self-efﬁcacy was a more comprehensive perception process, and it reﬂects
the adaptation of individual’s performance to accomplish under given circumstances.

Thus, people who have the same skills may perform differently during their task.

Individuals who perceive themselves as highly self-efﬁcacious tend to initiate a
sufﬁcient effort that may produce successful outcomes, whereas those who perceive low

self-efﬁcacy are likely to cease their efforts prematurely and fail on the task. In the

13

context of e-commerce transaction, consumers are concerned that they might not be able
to obtain relevant information on the web vendors from whom they are planning to make
online purchase. Consumers worry that if their orders do not function in the intended
manner, they are unable to return it without any problems, especially in case the
merchandise they have purchased online turns out to be a wrong or defective product. For
example, if people are conﬁdent that they are usually able to purchase exactly the item
that they want from web vendors, they are more likely to trust the web vendors. Once
people are conﬁdent that defective products will be taken care of in an orderly and

desirable fashion, they are more likely to be less reluctant to make purchases from them.

TRUST SELF-EFFICACY

 
 
    
       
 

 

0 Existing mechanism of C] New factor of explaining‘~\

intention and choices of ‘.
\

. . I
reducrn uncertain . ' ' .
8 ty , I Reinforcement of online consumer. \

I

  

\
\

[:1 Direct inﬂuence on risk & uncertainty

I
I
- . g .
purchase intentron. .‘ reduction

[3 Component: ability, . I Trust-reinforcement

\
\

D Direct/Indirect inﬂuence
on purchase intention.

  
  

~---‘

C] Component: general self- :
efﬁcacy, domain- ,’

benevolence, integrity. speciﬁc self-efﬁcacy .’

Figure 1. The Intersection Effect of Trust and Self-Efﬁcacy

Figure 1 shows the effect of uncertainty reduction that trust and self-efﬁcacy have
individually and collectively. As mentioned earlier, trust was illuminated as a

conventional uncertainty reduction measure, self-efﬁcacy will be considered as a causal

antecedent to this uncertainty reduction mechanism.

14

How could self-efﬁcacy link to the arguments for trust building in e-commerce?
McKnight et al. (2002) built on the integrative model including institution-based trust,
trusting intentions, trusting beliefs, and disposition to trust. These constructs are
discriminant from each other, representing dispositional, institutional, and interpersonal
typed trust constructs. According to them, institution-based trust derives from a point of
view that an individual’s behavior is not determined by internal factors within the person
but by the environment surrounding him/her. Trusting intentions and trust beliefs reﬂects
thought that social interactions among people and an exchange of cognitive and
emotional responses among them inﬂuence an individual’s behavior. Disposition to trust
reﬂects a general propensity to trust other. The type of trust concept can be distinguished

into three dimensions: interpersonal, institutional, and dispositional.

They also suggested that the process of trust building was going to be dismantled
according to each stage. According to them, ﬁrst, trust building is initiated at an
individual’s intrapersonal communication level. At this level, precondition of trust is
independent of the degree of previous experience of e-commerce. Second,
cognitive/affective beliefs get involved in trust building. This level can be distinguished

into channel-related belief and speciﬁc web vendor-related belief.

They integrated these trust constructs, basing on the Theory of Reasoned Action
(TRA) (F ishbein and Ajzen, 1975). TRA posits that an individual’s belief causes attitude,
which leads to behavior. “To predict the behavior from the initial measure of intention, it
may be necessary to consider other variable in addition to the intention.” (Fishbein and
Ajzen, 1975, p.370). The theory of reason action and the theory of planned behavior

presume that behavioral intension determines behavior. Intentions, in turn, can be

determined by an individual’s attitude toward the behavior. As mentioned earlier, self-
efﬁcacy is understood as people’s beliefs in their capability inﬂuencing their choices and
aspiration. Self-efﬁcacy is one of the important causes to inﬂuence an individual’s beliefs.
As McKnight and Chervany (2002) pointed out, trusting beliefs was “a belief that the
other party has one or more characteristics beneﬁcial to oneself.” Since these trusting
beliefs relate to each individual consumer’s perception, the more positive the consumers’
attitudes are toward an online transaction, the more likely they are to trust a certain web

vendor.

Socio-cognitive theory and research to date have indicated that task-and-situation-
speciﬁc self-efﬁcacy and general self-efﬁcacy are distinct each other (Bandura, 1986;
Eden, 1988; Sherer et al.1982). In line with the socio-cognitive research, in the realm of
information technology, Marakas et al. (1998) found out that computer self-efﬁcacy
(CSE) was a multi-level construct operating at two distinct levels: at the general
computing level (general CSE) and at the speciﬁc application level (application-speciﬁc
self-efﬁcacy). Organizational behavior research (Eden, 1988; Judge et al., 1998; Chen et
al., 2001) demonstrated that general self-efﬁcacy predicted speciﬁc self-efﬁcacy for a
variety of tasks in various environments. Based on the socio-cognitive perspective on the
interdependent relationship between general self-efﬁcacy and domain-speciﬁc self-
efﬁcacy, self-efﬁcacy in this research has been handled in two parts; general self-efﬁcacy

(GSE) and online transaction self-efﬁcacy.

This paper extends the current understanding on the concept of self-efﬁcacy in trust
building in B2C e-commerce. While extant IS research on self-efﬁcacy focused on self-

efﬁcacy judgment in reference to the task domain of computing (Agarwal et al., 2000),

this study focuses on the impact of self-efﬁcacy on trust in the context of purchasing-

related behavior. In order to apply self-efﬁcacy to online transactions, self-efﬁcacy is

redeﬁned as the belief of an individual in his or her ability to organize and execute certain

behaviors necessary for the consumer to achieve certain objectives in online purchasing

(e.g., a successﬁil and satisfactory transaction) under uncertainty. This study also

suggests online transaction self-efﬁcacy as a domain-speciﬁc self-efﬁcacy in the context

of e-commerce.

Table 3. Summary of Concepts and Research Variables

 

 

 

 

 

 

 

 

Main construct Research variables and description Reference
. . “a set of speciﬁc beliefs about the
Ability speciﬁc vendor” Doney and Cannon (1997)
Benevolence M l 1995) G f
“ . . a er et a . , e en
Trust Integrity antecedents of general belief in (2002) Jar-(venpaa and
trust Tranctinsky (1999)
Institution-based trust, drsposrtron to trust, trustmg beliefs, McKnight et al. (2002)
trusting intentions.
Sherer et al. (1982)
General self-efﬁcacy Eden (1988)
(an individual judgment of efﬁcacy across various
situations in everyday life) Judge at al. (1998)
Chen et al. (2001)
S If- ff
6 e ‘cacy Bandura (1986)
Domain-speciﬁc self-efﬁcacy Gist and Mitchell (1992)
(an individual perception of efﬁcacy in engaging in Lee and B°bk°(l994)
speciﬁc situation.) Stajkovic and Luthans
(1998)

 

 

 

l7

 

3. RESEARCH MODEL & HYPOTHESES

3.1. General self-efﬁcacy

Sherer et al. (1982) attempted to provide a measure to assess self-efﬁcacy that is
not tied to speciﬁc situations. They approached self-efﬁcacy as efﬁcacy expectancies
generalize to an individual’s overall behavior other than the speciﬁc behavior. Sherer et
al. (1982) developed a measure for the General Self-Efﬁcacy Scale (GSES), and obtained
a factor structure that had three sub-dimension: “willingness to initiate behavior:
Initiative”; “willingness to expend effort in completing the behavior: Effort”; and
“persistence in the face of adversity: Persistence”. This measure is consistent with
Bandura’s assertion that self-efﬁcacy expectancies determine the initial decision to

initiate a behavior, expend efforts, and persist in the face of adversity (Bandura, 1986).

General self-efﬁcacy consists of trait-like characteristics that are not about
speciﬁc situations or behavior; rather, it can be applied to various situations (Sherer et al.,
1982). They also stated that individual differences in general self-efﬁcacy exist and these
differences correlate with behavior. More recently, researchers have become interested in
what has been termed general self-efﬁcacy (GSE) (Eden, 1988; Judge at al., 1998). GSE
was deﬁned as “generalized trait consisting of one’s overall estimate of one’s ability to
effect requisite performances in achievement situations” (Eden and Zuk, 1995, p.629), or
as “individuals' perception of their ability to perform across a variety of different
situations” (Judge et al., 1998, p. 170). GSE reveals differences among individuals in
their perception of how well they are capable of meeting task demands in various

contexts (Chen et al., 2001).

The accumulation of previous successful experiences is an important antecedent of
GSE (Sherer et al., 1982). GSE is acquired and reinforced as an individual accumulates
successﬁrl experiences and failed one across various situations (Shelton, 1990). Bandura
( 1997) stated: “Powerful mastery experiences that provide striking testimony to one’s
capacity to effect personal changes can also produce a transformational restructuring of
efﬁcacy beliefs that is manifested across diverse realms of functioning. Such personal
triumphos serve as transforming experiences. What generalizes is the beliefs that one can

mobilize whatever effort it takes to succeed in different undertakings.” (p.53)

The importance of GSE to research can be explained in terms of “1) predict SSE
across situations and tasks, 2) predict general and comprehensive performance criteria,
and 3) buffer against the debilitating effects of adverse experiences on subsequent SSE”
(Chen et al., 2000). People with high self-efﬁcacy are likely to choose to perform more
challenging tasks. When problems occur, they maintain their commitment to their goals.
People who believe that they can produce a desired result are more likely to maintain a
more active life. For example, if individuals are conﬁdent that they will be able to
achieve most of the goals that they have set for themselves, they tend not to be reluctant
to try an uncertain situation. And if people are certain that they will accomplish their goal
even when facing difﬁcult tasks, then they perceive less uncertainty (or risk). As a result,
high self-efﬁcacy makes it less uncertain for the individual to perceive the characteristics

of any tasks.

In the context of online transactions, individuals with higher general self-efﬁcacy
are expected to perceive less risk in B2C e-commerce. More speciﬁcally, if people are

conﬁdent that they are usually able to purchase exactly the item that they want from Web

vendors, they are more likely to trust a web vendor and make purchases in the future. If
consumers are conﬁdent that, even if the vendor they made an online purchase from
would not take back a defective product, they are able to solve the problem through the
assistance of a third party (e. g., friends, better business bureaus, or relevant governmental
agencies), then they are more likely to perceive a lower level of risk. Therefore, it can be

hypothesized that:

HI-a: General self-efficacy (initiation) is positively associated with online transaction
self-efﬁcacy.
HI-b: General self-eﬂicacy (effort) is positively associated with online transaction

self-eﬁ'icacy.

HI-c: General self-efficacy (persistency) is positively associated with online

transaction self-eﬂicacy.

3.2. Online Transaction Self-Efﬁcacy

Although Bandura (1997) stated that “an efﬁcacy belief is not a decontextualized
trait” (p.42), it should be noted that his mention does not mean that speciﬁc self-efﬁcacy
generalize to various context (Stajkovic and Luthans, 1998). Previous research
conceptualized and studied self-efﬁcacy as a task-and-situation speciﬁc construct (SSE)
(Bandura, 1986; Gist and Mitchell, 1992; Lee and Bobko, 1994). Despite advances in
theoretical and empirical research on GSE, the utility of GSE has been questioned.
Stanley and Murphy (1997) raised a question of whether GSE is distinct from self-esteem,
which is another important construct in predicting a motivation of an individual’s

behavior.

Stajkovic and Luthans (1998) argued that the practical applicability of GSE was

low. The low predictability of GSE on behavior can be explained in terms of lacking of

20

speciﬁcity matching. To improve the predictability of GSE, the speciﬁcity of construct
measuring the self-efﬁcacy should be matched the speciﬁcity of the performance or task

to be predicted (Chen et al., 2001).

Task-and-situation speciﬁc self-efﬁcacy represents “a dynamic, multifaceted belief
system that operates selectively across different activity domains and under different
situational demands, rather than being a decontextualized conglomerate” (Bandura, 1997,
p.42). Speciﬁc self-efﬁcacy was deﬁned as how well one believed he or she could
perform given the speciﬁc social context and the particular task. (Stajkovic and Luthans,
1998). Speciﬁc self-efﬁcacy has been proven to predict outcomes of performance or task
well (Bandura, 1986). This is because the outcomes to be measured have been highly
speciﬁed. As previous pointed out, SSE predicts outcomes of a target situation (Bandura,
1986, 1997; Marakas et al., 1998; Yi and Hwang, 2003). What is important to SSE is to

acquire speciﬁcity that can be applied to speciﬁc performance situation.

If people are low self-efﬁcacious, they are less conﬁdent that they are able to return
it without any problem, in case the merchandise they have purchased online turns out to
be defective: then, they are not willing to make purchase online. If consumers are
conﬁdent that they are able to take care of the problems on their own, in case their orders
do not come through in a satisfactory manner, they are more likely not to be reluctant to
make a purchase from a web vendor. Consumers who are highly self-efﬁcacious tend to
make a transaction with any web vendor while customers who have low self-efﬁcacy are

more likely not to transact with any web vendor.

SSE is one important outcome of GSE. As pointed out by Chen et al. (2000), GSE

positively inﬂuence SSE across tasks and situations. The individual’s tendency to feel

21

efﬁcacious across situations could be applied to speciﬁc situation. General self-efﬁcacy is
deﬁned as an individual judgment of efﬁcacy across various situations in everyday life,
and online transaction, while self-efﬁcacy is deﬁned as an individual perception of

efﬁcacy in engaging in a certain situation and task.

The more self-efﬁcacious consumers are with a web vendor, the more favorable
expectations they are likely to have, and the more they are likely to trust a web vendor.
This study develops online transaction self-efﬁcacy as a situation-speciﬁc self-efﬁcacy.
This ﬁnding indicates that the online transaction self-efﬁcacy as a speciﬁc self-efﬁcacy is
inﬂuenced by the degree to which one believes in one's overall competence to effect
performances across a wide variety of achievement situations. Consequently, it can be

hypothesized that:

HZ-a: Online transaction self-eﬂicacy is negatively associated with perceived risk.
H2-b: Online transaction self-eﬂicacy is positively associated with consumer trust.

3.3. Disposition to Trust

Disposition to trust was deﬁned as a general willingness based on the formation of
human relationship to depend on others (McKnight et al., 1998; Ridings et al., 2002). It is
a general personality trait that displays a general expectation of how trusting other people
should be. Ridings et al. (2002) applied disposition to trust in investigating the
antecedents and effects of trust in virtual communities. They posited that, if someone is
willing to trust others in general, then s/he should apply this general willingness to other
in the virtual community. Mayer et a1. (1995) suggested that disposition to trust can be
effective when the parties unfamiliar with one another. Ridings et a1. considered

disposition to trust play a role in explaining trust in a virtual community where anyone is

22

not familiar with one another. Therefore, they hypothesized that user’s disposition to trust
will be positively related to their trust in other members’ ability (benevolence and

integrity) in the virtual community.

McKnight et al. (2002) deﬁned that disposition to trust was the extent to which an
individual showed a tendency to be willing to depend on others across various situations
and persons. The more consumers are disposed to trust the other party (i.e., the vendor),
the less amount of risk they are likely to perceive. The present study proposes those

relationships in online transaction contexts with the following hypotheses:

H3-a: Disposition to trust is negatively associated with perceived risk.
H3-b: Disposition to trust is positively associated with consumer ’s trust.

3.4. Consumer Trust

Trust can be deﬁned as the consumer’s belief that the seller, i.e., a ﬁrm or Website,
will fulﬁll its transaction as expected by the consumer. Gefen (2000) showed that
consumer trust inﬂuenced purchase intentions, and Reichheld and Schef’ter (2000) also
showed that consumers who lack trust in a speciﬁc online vendor were not likely to
engage in e-commerce. Gefen (2000) adopted the idea that not only a general belief that
the speciﬁc other party could be trusted but also the speciﬁc beliefs in ability, integrity,
and benevolence with other party serve as antecedents of this general belief in trust.
Jarvenpaa and Tractinsky (2000) suggested that trust was a critical antecedent to
engaging in online transaction because it reduced the idea of risks associated with

purchasing goods and services over the Internet.

In previous research, trust in others’ ability in the virtual community was positively

related to willingness to give information to others ([3: .15, p < .01) and desire to get

23

information from others (B: .28, p < .01). Trust in others’ benevolence and integrity in
the virtual community was positively related to willingness to give information to others
([3 = .29, p < .01) and desire to get information from other ([3: .22, p < .01) (Ridings et al.,
2002). Trust was signiﬁcant predictor of online banking user’s willingness to transact
(B: .36, p < .001) (Bhattacherjee, 2002). Attitudes and behavior of buyer in an Internet
store were shown to be affected by the level of trust by buyer. Trust in an Internet store
affects attitude of buyers positively (B: .59, p < .05) and affect risk perception negatively

([3= -.91, p < .05)
H4: Consumer trust is positively associated with purchase intention.
3.5. Perceived Risk

Some early research suggested that risk perception may play a small role in the
adoption of online shopping (Jarvenparr and Todd, 1996-96), however, consumer’s risk
perception has been deemed to be a substantial barrier to the growth of e-commerce. Risk
affects the attitude and behavior of an individual in dealing with another party. The level
of risk is an important factor in forming the customer’s attitude and behavior in all kinds
of business transactions. Clearly a high level of risk will make it difﬁcult for customers to
utilize e-commerce. Perceived risk was thought of as the degree to which a consumer
perceives a potentially negative outcome from the online transactions (Featherrnan &

Pavlou, 2002).

Kathryn and Mary (2002) suggested that perceived risk represented an individual’s
assessment of the relative probability of positive and negative outcomes of a given

transaction or situation. It was suggested that perceived risk correlated negatively with

24

the degree of purchasing products online (Miyazaki and Fernandez, 2001). They
hypothesized that perceived risk and concerns toward online shopping are negatively
related to the degree of online purchase. As expected, perceived risk toward online
shopping was shown to be negatively associated with online purchase (r = .48, p < .01).
They also suggested four general categories of risk perception: The ﬁrst category,
Privacy, contains “unauthorized sharing of personal information, unsolicited contacts
from the online retailer (e.g. junk e-mail), and undisclosed tracking of shopping
behavior.” The second category, system security, includes “concerns about potentially
malicious individuals who breach technological data protection devices to acquire
consumers’ personal, ﬁnancial, or transaction—oriented information.” The third category,
online retailer fraud, refers to concerns regarding “fraudulent behavior by the online
retailer, such as purposeful misrepresentation or nondelivery of goods.” The fourth
category, inconveniences of online shopping, is about other reasons for limiting online

shopping behavior.

Meanwhile, two types of risk were categorized in the context of Internet
shopping; product category risk and ﬁnancial risk (Bhatnagar, Misra, and Rao, 2000).
Product category risk is about the product itself. This type of risk refers to the consumers’
belief as to whether the product they have purchased would function according to their
expectations. Financial risk is associated with a unwanted situation like the possibility of
losing money via credit card ﬁaud, putting the consumer at risk. Perceived risk was
shown to be negatively associated with willingness to buy in e-commerce ([5= -.29, p

< .05) (J arvenparr et al., 2000).

H5: Perceived risk is negatively associated with purchase intention.

25

Based on above arguments on several intemal/subjective factors, the research
model is developed as follows (Figure 1.) Testing this model is to address two research
questions: whether self-efﬁcacy plays a role in affecting trust-building and reinforcement
of purchase intention, and what kind of the structural relationship among relevant factors.
Ovals indicate each internal factors, which is latent variables and arrows denote the

association between two constructs.

 

 

, ............ Gsnsralﬁelt-sfﬁcasy.------_--_-..
e w G) s
L ................. r .................... :
l-a Hl-b Hl-c
Online transaction H2-a Perceived
Self-efﬁcacy Risk 5
H2-b

    
   
 
   
 

Purchase

Intention

 
    

H3-a
Disposition to

 

H3-b Consumer Trust

  

Trust

 

 

 

Figure 2. Research Model

4. RESEARCH METHODOLOGY

4.]. Data collection

All data were collected through a survey constructed to measure the following
variables: self-efﬁcacy, disposition to trust, perceived risk, consumer trust, purchasing
intentions, and key demographic characteristics. Much of the research in the domain of
computer-mediated communication and information system has conducted experiments

26

in a laboratory setting, however, external validity in these research also has been called
into question (Ridings et al., 2002). A cross-sectional survey was used to test the research
model of the consumer experiencing a B2C retail transaction. The reason was because the
survey is an appropriate method to test an actual consumer’s perception as to a B2C

online transaction, and it is expected to maximize external validity.

An online survey was used to collect data because of its expediency in data
collection, ease of data transformation to another statistical tool. The questionnaire was
made on ‘Angel system’, which is a Web-based class management system accessible via
the Internet. Responses were submitted by the end of the 15th day after the survey request

were used for this study.

The population of interest in this research is consumers involved in an actual BZC
online transaction. In order to elicit each respondent’s experience and perception, all the
respondents were asked to answer each survey question based on their previous on-line
purchasing experiences in the past six months. 218 complete inquiries were collected
from 220 students. Among the students surveyed 48 were women, and 172 were men.

Most respondents were in the group of ages 20-25 (n = 218).

All subjects used the Internet frequently in their everyday life and quite familiar
with using the computer and the Internet. For the questions about to extent to which
respondents have their experiences with computer and web technology (including
searching, browsing, ﬁnding information, etc), the mean is 5.57 and 5.86 respectively.
(e.g. “how would you rate your experience with computer?” (l—Novice/ 7-Expert), and

“how would you rate your experience with web technology?” (l-Novice/ 7-Expert)).

27

They have been shown to spend about 295 minutes on the Web per week and spend about

352 minutes on a computer (including spending on the Web) during a typical week.

All the respondents had previously bought items at the speciﬁc websites, and most
of them were familiar with making purchases items from the speciﬁc websites. It has
been averagely 2.96 years since they began making purchases on websites. The frequency

that they make purchases from e-commerce web site is 5.82 per month.

Table 4. Degree of Internet and Computer Experience among Participants

 

 

 

Construct Mean Median Mode
Experience with computer* 5.57 6.00 6
Experience with Web technology“ 5.86 6.00 6

 

Average time of spending on the Web 295.52 (min) 210.00 (min.) 180 (min)

 

Average time of spending on a computer 352.39 (min.) 300.00 (min.) 300 (min)

 

Duration of previous e-commerce** 2.96 3.00 3

 

Frequency of e-commerce 5.82 (times) 1.00 1

 

 

 

 

 

 

* : l — Novice / 7 -— Expert
** : 1- less than one year/ 2- two years/ 3-three years/ 4-four years/

5- more than ﬁve years.

4.2. Measurement

To construct the survey instrument, existing scales from the literature were
reviewed, and items were carefully adapted or developed for each construct. All of the
items were measured with seven-point Likert-type scales ranging from strongly disagree
to strongly agree. General self-efﬁcacy can be deﬁned as “an individual’s perception of

their ability to perform across a variety of different situations” (Judge et al., 1998, p.170).

28

The general self-efﬁcacy scale (Bosscher and Smit, 1998) was used to measure this
variable (e.g., “When I have something unpleasant to do, I stick to it until I ﬁnish it”). For
online transaction self-efﬁcacy, no existing scale could be found to measure the online
transaction self-efﬁcacy in a Business—to-Consumer e-commerce environment. Thus, a
six-item scale was developed speciﬁcally for this study (e. g., “I am conﬁdent that, in case
my order does not come through in a satisfactory manner, I am able to take care of the
problems on my own”). Disposition to trust can be deﬁned as a customer’s personality
trait that leads to generalized expectation about the trustworthiness. The scale to measure
disposition to trust was assessed by using items adapted from prior studies of trust in
online transaction contexts (Gefen, 2000) (e.g., “I generally trust other people”).
Perceived risk can be deﬁned as a consumer’s belief about the potential uncertain
negative outcomes from the online transaction. A scale measuring the perceived risk of
B2C retail Websites was created to utilize Jarvenpaa and Tractinsky (2000)’s deﬁnition
of perceived risk in the context of online transactions (e. g., “How much risk would you
tolerate when deciding to make a purchase from the web sites?”). The scale to measure
consumer trust was assessed by using items adapted from previous studies on online
transactions (Gefen, 2000; Jarvenpaa et al., 2000) (e.g., “I believe that the web site
vendor has my best interest in mind”). As a dependent variable, purchase intention refers
to the degree to which a consumer intends to buy from a certain Web site. The scale to
measure purchase intention was assessed by using items adapted from previous studies

(Gefen, 2000) (e.g., “I am likely to purchase the product(s) from this web site”)

29

Table 5. Measurements Items for Constructs

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Constructs Measurement Items Mean SD. Loading
INTI lam likely to purchase the product(s) from this web 5 39 1 364 0 918
Purchase srte. . ' '
Intention INT2 Irigiidsilkely to recommend thrs web srte to my 5_40 1.341 0.862
(INT) I . .
am likely to make another purchase from thrs web
INT3 site if I need the product(s) that I am going to buy. 5'50 L317 0'858
Eigenvalue 4.00T
Percent of explained variance 75.298
Purchasing from this web site would involve more
product risk (i.e. not working, defective product)
R181 when compared with more traditional ways of 4'41 L622 08”
Perceived shopping.
Risk Purchasing from this web site would involve more
(RIS) R182 ﬁnancial risk (i.e. fraud, hard to return) compared 4.72 1.593 0.786
with more traditional ways of shopping.
How would you rate your overall perception of risk
RIS3 from this web site? 3.42 1.523 0.813
Eigenvalue 3.000
Percent of explained variance 66.471
The web site vendor gives the impression that it
Consumer TRUI keeps promises and commitments. 5'55 1'078 0'747
Trust TRU2 The web site understands the market it works in. 5.62 1.089 0.745
(TRU) TRU3 gsicrg'eb site knows how to provide excellent 5.11 1.140 0.801
Eigenvalue 3.999
Percent of explained variance 57.280
1 am conﬁdent that I can obtain relevant information
through online sources (e.g., online discussion
OSEI groups, reputation sites, etc) on the Web vendors 5'75 1'20] 0’79]
Online from whom I am planning to make online purchases.
. I am conﬁdent that I am usually able to purchase
Transaction OSEZ exactly the item that I want from Web vendors. 5'66 1'350 0'822
Self- I am conﬁdent that, in case my order does not come
efﬁcacy OSE3 through in a satisfactory manner, I am able to take 5.55 1.347 0.790
(OSE care of the problem(s) on my own.
) I am conﬁdent that I am able to ﬁnd a trustworthy
web vendor based on ratings (e.g., the number of the
OSE4 stars or the smiley faces) provided by other 5'35 L350 0'752
consumers.
Eigenvalue 4.999
Percent of explained variance 59.804
Disposition DISl I generally trust other people. 4.89 1.379 0.893
to Trust D182 I feel that people are generally reliable. 4.46 1.188 0.757
013 I generally trust other people unless they give me
( ) DIS3 reasons not to. 5.33 1.310 0.811
Eigenvalue 3.000

30

 

 

Table 5 (cont’n)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Percent of explained variance 67.841
PERl :theri/ eIthseertn . important goals for myself, I raray 254 1.459 0.768
PCISiStence I do not seem ca able of dealing with most problems
(PER) PERZ that come up in gay “ft 2.16 1.547 0.769
PER3 mrgnvgge‘xgefted problems occur, I don’t handle 2.43 1.397 0.767
Eigenvalue 4.000
Percent of explained variance 57.264
EFF] (ﬁbrin? make plan, I am certain I can make them 542 1.239 0.728
Effort ' . . . .

(EFF) EFFZ gullcan t do a job the ﬁrst time, I keep tryrng untrl l 5.48 1.223 0.847
EFF3 Failure just makes me try harder. 4.89 1.482 0.719
Eigenvalue 5.001
Percent of explained variance 51.111
INIl l131;)tsltlicr';1e(t)hti;1ygitlooks too complicated I will not even 2.78 1.479 0.895
Initiation INI2 I avoid trying to learn new things when they look too 2 50 1 392 0 849

(INI) difﬁcult. ' ' '

When tying to learn something new, I soon give up

[NB if I am not initially successful. 2'67 1'409 0‘842
EigenvMue 3.000
Percent of explained variance 75.198

 

 

 

4.3. Data Analysis and Results

The SEM (Structural Equation Modeling) was used to examine the proposed model

of purchasing intention-behavior reinforcement. Based on the data gathered, this

multivariate analysis that includes relevant latent variables was conducted to test whether

the proposed model showed the signiﬁcant structural relationships among these variables.

The proposed model and hypothesis was tested using AMOS 4.0 Version with the

maximum likelihood estimation method. This technique enables researchers to

simultaneously assess measurement model parameters and structural path coefﬁcients.

Exploratory, principal component factor analysis was conducted to determine the

extent to which the latent variables were discriminant. The principal component factor

31

 

analysis was conducted using a Varimax rotation (refer to table 6). The objective with the
explanatory factor analysis was to cull out measurement items that did not load on the
appropriate latent variable. This was accompanied with checking of the internal
consistency reliabilities (ICR) of the scales. Results suggested that several items be
dropped from the scales in order to achieve a high level of reliability and validity.
Speciﬁcally, an item was drooped if (a) it did not meet the threshold loading of 0.40 on
any factor, (b) its highest loading on an expected factor was not above 0.60, or (c) it

showed a signiﬁcant variance across multiple factors (Hair et al,. 1998).

Table 6. Factor Structure Matrix of the Latent Variables (Result of Explanatory Factor Analysis)

 

 

 

 

 

 

 

 

Components

1 2 3 4 5 6 7 8
Initiation
INIl -0.007 0002 0.837 -0. 102 -0.000 0.166 0.006 0.003
INIZ -0.001 -0.006 0.773 -0.303 0.003 0.289 -0.010 0.007
INI3 -0.005 -0.154 0.758 -0.124 -0.l83 0.139 0.006 0.121
Effort
EFFl 0.002 0.003 0.139 0.778 0.005 —0. 183 -0.000 -0.181
EFF2 -0.006 -0.001 -0.264 0.704 0.171 -0.005 0.004 0.007
EFF3 0.193 0.186 —O.387 0.607 0.005 -0.242 0.134 0.002
Persistence
PERI -0.141 -0.1 19 0.103 -0.007 -0.007 0.780 -0.004 -0.007
PER2 -0.009 -0.006 0.007 -0. 199 0.000 0.697 -0.001 0.256
PER3 -0.002 -0.005 0.348 -0.216 -0. 126 0.643 -0.006 0005
Online Transaction
Self-Efﬁcacy
OSEl 0.158 0.780 -0.125 0.000 0.006 -0.007 0.133 -0.005
OSE2 0.002 0.707 -0.002 0.227 0.178 -0.003 -0.004 -0.245
OSE3 0.341 0.675 —0.004 ~0.001 0.190 -0.009 0.122 -0.006
OSE4 0.247 0.650 -0. 127 0.007 0.293 -0.006 0.101 0.002

 

32

 

Table 6 (cont’n)

 

 

 

 

Disposition to Trust

DISl 0.1 18 0.008 —0.000 0.003 0.149 -0.007 0.838 -0.002
DIS2 -0.003 0.009 0.001 0.006 0.002 -0.003 0.798 -0.004
D1S3 0.002 0.009 0.000 0.003 0.149 -0.000 0.765 0.003
Perceived Risk

RISl -0. 104 -0.003 0.138 -0.003 -0.1 14 -0.000 0.003 0.824
RIS2 -0.209 -0. 148 0.002 -0.000 -0.004 —0.000 -0.001 0.808
R183 -0.336 -0.007 0.001 -0.005 -0.006 0.186 -0.007 0.613
Consumer Trust

TRUl 0.1 14 0.008 —0.003 0.006 0.736 -0. 183 0.122 -0.000
TRU2 0.003 0.198 -0.115 -0.001 0.707 -0.007 0.130 -0.009
TRU3 0.240 0.178 -0.001 0.169 0.675 0.001 0.006 0214
Purchase Intention

INT 1 0.867 0.215 -0.005 0.002 0.135 -0.105 0.006 -0.163
INT2 0.819 0.223 0.000 0.009 0.005 -0.009 0.000 -0. 161
INT3 0.806 0.140 -0. 126 0.006 0.201 —0.006 0.004 -0. 148

 

 

 

 

CFA (Conﬁrrnatory Factor Analysis) was conducted to examine the convergent
validity of each latent variable. Convergent validity means the extent to which the
measures for each variable are measuring the theoretical construct (latent variable)
(McKnight et al., 2002). This process was done by specifying a single factor model for
each of the latent variables. The correspondence between theoretical concept and
empirical data in a CFA model was assessed using goodness-of-ﬁt metrics such as )8.
However, the Chi-square statistic is sensitive to the number of samples and the
probability of model rejection increases as sample size increases and hence the ration of
chi-square to degree of freedom and other model ﬁt indices should be taken into

consideration (Bentler and Bonnet, 1980).

Table 7 shows the overall model ﬁt indices for each CFA model. The CFA model

for three constructs (disposition to trust, perceived risk, and purchase intention) was not

33

signiﬁcant (p-value = .684, .350, and .114 respectively), hence it suggested that these
models ﬁt well. Unlike general hypothesis signiﬁcance tests, it should not be signiﬁcant
to assess that the theoretical model ﬁts actual data well (It should be noted that the null
hypothesis is that the conceptual covariance (23) from the theoretical relationship among

constructs equals to the actual covariance (S) from the actual data).

Even though chi-square statistics for three constructs (general self-efﬁcacy, online
transaction self-efﬁcacy, and consumer trust) are signiﬁcant, these does not well exceed
the ration of chi-square to degree of freedom (3:1). For the CFA model for general self-
efﬁcacy, xz/df was 2.037, suggesting acceptable model ﬁt. Likewise, the ratio of chi-
square to degree of freedom was estimated as 3.462 and 2.082 for the online transaction
self-efﬁcacy and consumer trust, suggesting acceptable model ﬁt. AMOS provides
additional goodness-of-ﬁt measures such as Comparative Fit Index (CF I), Goodness-of-
F it Index (GFI), and Root Mean Squared Error of Approximation (RMSEA). CF I, based
on a comparison between the model and a model that assume independence of all
variables, should exceed .90 for good model ﬁt. GFI is a measure of the relative amount
of variance and covariance in the sample that is jointly explained by the model. If GFI
equals 1, the model ﬁts very well. To ﬁt closely, RMSEA should be .50 or less than .50.
All other model ﬁt indices exceed the recommended level (Byme, 2001; Bhattacherjee,

2002), indicating that the measurement items of each construct are uni-dimensional.

34

Table 7. Overall Model Fit Indices of CFA for Convergent Validity

 

 

 

 

 

 

 

 

Construct 83?; vaiIiIe d.f 8,1316%” GFI CFI RMSEA
Disposition to trust 2.281 .684 4 .570 .982 .981 .066
General self-efﬁcacy 103.882 .000 51 2.037 .948 .973 .048
Online 3:333:30” self' 31.153 .000 9 3.462 .992 .997 .043
Consumer trust 49.968 .001 24 2.082 .966 .981 .059
Perceived risk 2.098 35¢ T 7 .04? .995 .999 .015
Purarase intention 4.348 .1 l4 2 2.174 .990 .995 .073

 

 

 

 

 

 

 

 

Table 8 shows the means, standard deviations, composite reliabilities and AVEs

(average variance extracted) of each construct in this study.

Table 8. Means, Standard Deviations, and Composite Reliabilities

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Number Composite ,
Construct of Items Mean SD. Reliability AVE Adapted from.
3 g; Initiation 3 2.650 1.427 0.897 0.743
:2}. 2 Bosscher and Smit
g 525 Effort 3 5.079 1.408 0.832 0.502 (1998)
g 97*: Persistency 3 2.427 1.483 0.842 0.572
to
Online transaction sell-T .
efﬁcacy (OSE) 4 5.479 1.349 0.881 0.579 New Items.
Disposition to trust (DIS) 3 4.897 1.295 0.862 0.676 (Gefen 2000)
. . (Jarven aa and
Percerved risk (RISK) 3 4.182 1.580 0.854 0.661 Tractins y, 2000)
(Gefen 2000;
Consumer trust (TRUST) 3 5.451 1.124 0.842 0.571 Jarvenpaa et al. 2000)
Purchase intention (Gefen 2002;
(INTENTION) 4 4-897 1-295 ”-924 “752 Jarvenpaa et al. 2000)

 

Convergent validity can be evaluated for the constructs using three criteria

recommended by Fomell and Larcker (1981). They recommended that, ﬁrst, all

measurement factor loading should exceed .70, second, composite reliabilities are all

higher than .80 and average variance extracted, lastly, (AVE) must exceed the variance

due to measurement error for that construct, that is, AVE should exceed .50. Consistent

35

 

 

with criteria recommended by Fomell and Larcker, all measurement factor loadings
exceed .70 (refer to Table 5.) Composite reliabilities ranged from .832 (for effort) to .924
(purchase intention). All composite reliabilities exceed the recommended level of .80.
Variance extracted measures ranged from .502 (for effort) to .752 (for purchase intention),
which exceeds the recommended level of .50. Hence, all scales met the criteria for

convergent validity.

Discriminant validity means to extent to which measures of two constructs are
empirically distinct (Bagozzi et al., 1991). Discriminant validity was tested by using a
chi—square difference test. To do so, the chi-square statistic of the constrained model
where the correlation between the two latent variables was ﬁxed at 1.00 was compared
with that of unconstrained model where the correlation between the two constructs was
freely estimated. Signiﬁcant differences of chi-square statistic between two models
indicate that the pair of constructs is distinct (Bagozzi et al., 1991). If a chi-square
difference is greater than 3.84, it suggests that the two constructs are signiﬁcantly distinct
(Suh and Han, 2002). The discriminant validity among the four latent variables was
assessed: online transaction self-efﬁcacy, initiation, effort, persistency, and disposition to

trust. The results of discriminant validity testing are shown in Table 9.

Table 9. Chi-Square Difference Test for Discriminant Validity

Model with Model with
s uare
ﬁxed correlation Correlation to be estimated di erence

square d'f' coefﬁcient

Chi-square d.f.

 

36

The chi-square differences range from 11.935 (between online transaction self-
efﬁcacy and initiation) to 38.691 (between online transaction self-efﬁcacy and effort).
The differences are much larger than the 3.84 threshold, indicating that each pair of

constructs is distinct.

To reconﬁrm discriminant validity, it was evaluated by comparing the squared
correlation between the two constructs with their respective variance and extracted
measure. Discriminant validity can be demonstrated if the variance extracted measures of
both constructs are greater than the squared correlation. As the table 10 shows, for
instance, the variance extracted measures for the two constructs (.743, .676) are greater

than the squared correlation between initiation and disposition to trust (.009).

Table 10. Comparison of Squared Correlation and Variance Extracted

 

 

 

 

 

 

Squared correlation Their respective variance extracted.
between two constructs
Initiation —- Disposition to trust .009 .743 (initiation), .676 (disposition)
Effort - Disposition to trust .068 .502 (effort), .676 (disposition)
Persistency — Disposition to trust .072 .572 (persistency), .676 (disposition)
Initiation — Effort .633 .743(initiation), .502 (effort)
Initiation — Persistency .462 .743(initiation), .572 (persistency)

 

 

 

 

 

In table 11, diagonal elements are the square root of Average Variance Extracted.
These values should exceed the inter-construct correlations for adequate discriminant

validity.

37

Table 11. Correlations of Latent Variable

 

 

 

 

 

 

 

 

 

 

23:“ Risk Trust 0313 DIS gin“ Effort 32:“
Intention 0.867
Risk -0493 0.813
Trust 0.457 -0.280 0.756
OSE 0.572 -0332 0.570 0.773
DIS 0.142 -0070 0.321 0.305 0.822
Persistence -0355 0.220 -0325 -0295 -0145 0.756
Effort 0.267 -0177 0.375 0.388 0.192 -0519 0.709
Initiation -0221 0.185 -0229 0268 -0043 0.506 -0554 0.862

 

 

 

Overall, the model provides a good ﬁt to the data. The Chi-square statistic was

446.388 with 320 degree of freedom (p <.000). Fit indices of the model are reported in

ﬁgure 3.

 

General Self-efﬁcacy

    
   
    

 

Online transacti . .
Self-efﬁcacy
(R2=.173)

 

 

5621* 1* * Perceived
‘ Risk
_ new
505*... (R2=-17°) '310
.543**

 

Disposition to

Trust

 

 

- Consumer Trust

 

 

.108

(R2=.555)

 
   

.113

ll

-.150 .025 .141

Purchase
Intention
(R2=.457)

 

 

 

F igure3. SEM Result of the Final Model.

38

The ﬁt indices are, in general, within the recommended guideline, indicating that
the model ﬁts the data (Chi-square= 446.388, d.f. =320 (p-val: .000). GFI = .873, AGFI
= .839, CFI = .949, RMSEA = .043). The chi-square test of model ﬁt is signiﬁcant but the
ratio of chi-square to degree of freedom is less than 2 (chi-square/degree of freedom =
1.395), so it is not necessary to conclude that the proposed model doesn’t ﬁt the data well.
Other model ﬁt indices surpassed the recommended threshold by several previous studies
except GFI (Bentler and Bonnett, 1980). The RMSEA value is well below the value
of .06 recommended by the previous research as an upper boundary (Hayduk, 1987;
Etezadi-Amoli and Farhoomand, 1996), so it can be concluded that the proposed model

ﬁts the data well.

To investigate the validity of the hypothesized model (Model 1), an alternative
model was proposed and compared with the original model. The alternative model
(Model 2) encompasses the direct effect of GSE and OSE on purchase intention. Results
show that GSE has no signiﬁcant direct impact on purchase intention while OSE has

signiﬁcant direct impact on purchase intention.

Table 12. Model Comparison

     

 

 

Chi-square A Chi-s—re ’
Model d.f. Sig. GFI AGFI CFI RESEA .
(n=214) d1fferences
Model 1 458.991 324 .000 .870 .837 .946 .044 12(4)=12.6O3,
Model 2 446.388 320 .000 .873 .839 .949 .043 P ('05

 

Note: Model 1: no direct effect of general self-efﬁcacy and online transaction self-
efﬁcacy on purchase intention.

Model 2: direct effect of general self-efﬁcacy and online transaction self-efﬁcacy on
purchase intention.

39

Chi-square differences between an original model and an alternative model (x2(4)
=12.603) are signiﬁcant at signiﬁcant level .05. Results show that the alternative model
including the direct effect of general self-efﬁcacy and online transaction self-efﬁcacy on

purchase intention does ﬁt the date well.

The structural model and hypotheses are tested by examining the signiﬁcance of
the path coefﬁcients, and the variance accounted for the antecedent constructs. Figure 2
provides the results of hypothesis testing. Supporting Hl-a and Hl-b, general self-
efﬁcacy (initiation) and general self-efﬁcacy (persistency) had signiﬁcant effects on
online transaction self-efﬁcacy (6:.275, p<.05, 6:.513, p<.01), but, inconsistent with H1-
0, general self-efﬁcacy (persistency) had no signiﬁcant effect on online transaction self-
efﬁcacy ( =-.002, ns). In the alternative model, general self-efﬁcacy (initiation, effort,
and persistency) had no signiﬁcant effect on purchase intension (6:.141, ns, 6:025, ns,

and B: -.150, ns, respectively)

Supporting H2-a, online transaction self-efﬁcacy had a signiﬁcant effect on
perceived risk (6: -.621, p<.001). Supporting H2-b, online transaction self-efﬁcacy had a
signiﬁcant effect on consumer trust (6:505, p<.001). In the alternative model, online
transaction self-efﬁcacy had a signiﬁcant effect on purchase intention (6:.543, p<.01).
Inconsistent with H3-a and H3-b, disposition to trust had no signiﬁcant effect on
perceived risk ([3 = .123, ns), and this factor had no signiﬁcant effect on consumer trust (6
= .108, ns). Supporting H5, perceived risk had a signiﬁcant effect on purchase intention
(B = -.310, p < .001). The model accounted for substantial variance in online transaction
self-efﬁcacy (R2 = .348), modest variance in perceived risk (R2 = .292) and substantial

variance in consumer trust (R2 = .625).

40

Consumer trust had a signiﬁcant effect on purchase intention (16 = .808, p<.001)
while it had no signiﬁcant effect on purchase intention ((B = .113, us) in the alternative
model. The model accounted for substantial variance in purchase intention (R2 = .488). In
conclusion, the majority of the model tests supported all of the hypotheses except Hl-c,

H3-a and H3-b.

5. CONCLUSION

5.1. Discussion

The results of this study provide support for the model presented in ﬁgure 1 and for
the hypotheses regarding the association among the model variables. This study
illuminates the applicability of self-efﬁcacy in online transactions, which requires
decision-making within uncertain contexts of online transactions. The results of this study
point out the important role of online transaction self-efﬁcacy by negatively inﬂuencing
perceived risk and positively inﬂuencing consumer trust and even purchase intention. As
demonstrated, self-efﬁcacy reduces uncertainty. This model presents an insight as to how
uncertainty reduction and purchase intention can be affected by incorporating a
motivational factor such as self-efﬁcacy into the consumer trust-building. These ﬁndings
extend the scope of previous research on uncertainty reduction and purchase intention in
e-commerce. Consistent with previous research that examined the direct effect of trust on
purchase intention or behavior (Gefen, 2000), the direct impact of trust on purchase
intention also has been proved in this study. Consistent with the ﬁndings that Jarvenpaa

et a1. (2000) revealed, it has been proved that an indirect effect of trust on purchase

41

intention through risk perception and attitude (trust -> perceived risk -> purchase

intention).

Overall, general self-efﬁcacy has a positive impact on online transaction self-
efﬁcacy (6 = .275, p<.05 and [3 = .513, p <.05, respectively) even though persistence
construct does not have a signiﬁcant impact on online transaction self—efﬁcacy. It should
be noted that two general self-efﬁcacy constructs can account for about 35% of variances
of online transaction self-efﬁcacy. Therefore, it follows that this result supports the
statement that GSE is one important causal antecedent of SSE and GSE positively

inﬂuences SSE (online transaction self-efﬁcacy here) across tasks and situations.

Online transaction self-efﬁcacy has been shown to inﬂuence on consumer trust
signiﬁcantly. It is noted that online transaction self-efﬁcacy is more powerful than
disposition to trust in inﬂuencing consumer trust ([3 = .505 for online transaction self-
efﬁcacy, not signiﬁcant for disposition to trust). There are theoretical arguments
(McKnight et al., 2002) and an empirical test (Gefen, 2002) that disposition to trust
affects trust. Gefen (2000) showed that disposition to trust was a signiﬁcant antecedent of
trust (6 = .53, p < .01). But, in the presence of online transaction self-efﬁcacy, disposition
to trust no longer had a signiﬁcant effect on perceived risk, indicating that online
transaction self-efﬁcacy is a stronger predictor of risk perception than disposition to trust
is. Disposition to trust was deﬁned as a general personality trait that encompasses a
general expectation of how trusting one should be (McKnight and Chervany, 1998). This
kind of general trait-related antecedent seems to have a limitation in explaining trust
formation. In the future study, there should be many discussions of whether this construct

should be included as an antecedent of trust.

42

Online transaction self-efﬁcacy has a negative impact on perceived risk ([3 = -.621,
p<.001), that is, online transaction self-efﬁcacy reduce risk perception signiﬁcantly. This
result shows that situation and task-speciﬁc self-efﬁcacy can play a signiﬁcant role in
uncertainty reduction mechanism while an individual would have to make a choice and
behave. The degree of perceived risk is negatively associated with purchase intention.
The higher degree of perceived risk is, the lower purchase intention will be. The low
perception of risk in turn inﬂuences the attitude of the consumer toward the web vendor.
It is noted that perceived risk and consumer trust account for about 49 percent of the

variance of purchase intention (R2 = .488).
5.2. Implications

These ﬁndings have theoretical implications. First, this study has shown that it is
applicable to introduce self-efﬁcacy into the research of trust in online transactions. This
attempt has been presented in the domain of e-commerce for the ﬁrst time. While self-
efﬁcacy has been introduced and utilized in a considerable amount of research in IS and
behavioral sciences, little is known of self-efﬁcacy in online transactions. Understanding
the nature and antecedents of trust has been a major issue for researchers and
practitioners. This study also has explored possible antecedents and examined existing

one of trust building in e-commerce.

Second, this study presents a new perspective in e-commerce research by showing
that self-efﬁcacy can be utilized in elaborating the uncertainty reduction mechanism as a
previous perspective. Trust plays an important role in making it less risky, uncertain and
insecure transaction and, as this study suggests, self—efﬁcacy can play a signiﬁcant role in

reducing uncertainty as well. As shown in Figure 3, without other factor’s intervention,

43

online transaction self-efﬁcacy accounts for as much as 63 percent of the variance of

consumer trust and a modest 29 percent of the variance of perceived risk.

Third, this study has shown that there is a need to explore possible antecedents and
examine existing ones regarding trust building in e-commerce. Disposition to trust was
viewed as a general propensity to trust others, which inﬂuences an individual’s beliefs
and intentions about a Web vendor (McKnight et a1, 2002). Contrary to received wisdom,
at least, as to perceived risk, disposition to trust which has been considered a signiﬁcant
variable has turned out to be an insigniﬁcant factor in the presence of self-efﬁcacy. As
has mentioned earlier, disposition to trust was included as a signiﬁcant antecedent in
examining trust building in online transaction; however, it has turned out that this factor
does not play a role as an antecedent in the presence of more situation-speciﬁc factor like
online transaction self-efﬁcacy. In the study of trust in virtual community, Ridings et a1.
(2002) posited that people’s disposition to trust in the virtual community will affect their
trust of other members and also showed that this hypothesis was statistically signiﬁcant:

disposition to trust affects trust in other’s ability positively (B = .15, p < .01) and trust in

other’s benevolence and integrity ([3 = .18, p < .01). It should be noted that, as another
antecedent of trust, perceived responsiveness and other’s conﬁding personal information
was included in their research. These factors are all more general-type constructs.
Therefore it can be suggested that disposition to trust can play a role in affecting trust,
however, it cannot under the existence of more speciﬁc-situations and task antecedents.

Also, this ﬁnding reﬂects how extensively self-efﬁcacy inﬂuences trust building.

Lastly, in practical respects, a web-vendor and e-business should take into account

the fact that self-efﬁcacy of their customer can be enhanced by the extent to which they

44

have cumulated previous purchase experiences. Although a web-vendor cannot directly
engage in improving the degree of consumer’s self-efﬁcacy, it is obvious that the
individual self-efﬁcacy judgment can be positively inﬂuenced by cumulated experiences

and transactions.
5.3. Limitations & Suggestions

This study has several limitations. First, it has attempted to address the
applicability of self-efﬁcacy in predicting trust building in e-commerce. As early studies
may conﬁont many problems, this research also faced the obstacles that are common in
exploratory studies in terms of measures of constructs and constructions of hypothesis
between self-efﬁcacy and other factors. Although this study built on measures of new
task-and-situation self-efﬁcacy in e-commerce context, more modiﬁcations will be
needed to improve the reliability and validity of this new SSE. The composite reliability
of the construct is .881. Although future studies need to reﬁne their measures, this

reliability is considered acceptable for exploratory studies.

Second, this study asked the respondents to give their responses based on their
previous transaction experiences. It is acknowledged that, although this measuring
method has the advantage of reﬂecting an overall tendency of the respondents’ online
transactions, their answers which are based on long-term (6-months) retrospection runs
may discount the reliability of this study. To overcome this limitation, future studies will

have to focus on a shorter time frame (e.g., a week).

Third, because the web retailer was not given to the respondents, all the web
vendors with whom the respondents have had business transactions were not unfamiliar

to them. If the subjects were given an unfamiliar e-commerce vendor, the effect of online

45

transaction self-efﬁcacy on other constructs could be more ﬁrmly assessed. Therefore,
there needs to be further research to measure the constructs with the web site established

and managed by an unfamiliar web vendor.

Lastly, to enhance external validity of this study, the research ﬁndings should be
tested with samples having different demographic backgrounds. In this study, many of
the participants were frequent user of the Internet and e-commerce, and seemed to have
had somewhat positive experience with the Internet. If older consumers were asked to

show their answers, they might show different responses.

The suggestions for future research are as follows. First, based on the ﬁndings that
internal/subjective factors of trust-building play an important role, extemal/objective
factors (e. g. the presence of a third party seal, privacy protection and security protection,
etc.) should also be taken into consideration in constructing a research model. Recently,
Web vendors have tried to promote trust in the Internet as well as beliefs about the
speciﬁc vendor through seals from agencies such as TRUSTe. This agency assures
consumers secure transactions by getting web vendors to adhere to its privacy policy.
Presence of a third party seal leads to the assurance of Internet vendors. There is a clear
effect of extemal/objective factors on trust building and, thereby, this kind of factor
should be examined together. That will make for a more comprehensive approach to trust

building in e-commerce.

Second, to enhance the validity of the research, the respondents should be given
two or three unfamiliar web vendors and self-retrospective questions should be avoided.
This study allowed the participants to recall any transaction with their web vendor with

which they had have already transacted. As a result, unlike other research which

46

conducted an experiment regarding web vendors having certain retail domains such as
books and travel, this study came to get data across various retail domains. This
procedure, however, likely biased the responses of the participants in a positive direction.
This is related to the nature of the survey methods, which do not limit the extent of
treatment. Future studies are clearly needed to set up an experimental setting to obtain

more reliable data.

Lastly, there is a need to examine the effect of self-efﬁcacy (both general self-
efﬁcacy and online transaction self-efﬁcacy) on other constructs, e. g. purchase intention.
To be more parsimonious research, this effects were not considered, however it would be
necessary to ﬁgure out this association between self-efﬁcacy and purchase intention. This
will contribute to the examination of the direct effect of self-efﬁcacy on purchase

intention and represent fruitful ways of advancing trust research in e-commerce.

47

APPENDIX: Survey Questionnaire

 

 

We are conducting a study on factors related to transactions in BZC retail e-commerce.
We are requesting your voluntary participation in our study. This study will give you the
opportunity to become aware of key factors in e-commerce transaction or experience
transactions. This study asks for your reaction to electronic commerce Web vendors and your

experience with conducting transactions on the Internet.

It will take 30 minutes to complete this questionnaire. Please note that you may withdraw
from participation at any time without adverse consequences. All information you provide will
be held in strictest conﬁdence and only aggregate information will be used for research
purpose. Please be absolutely candid about all your answers. There are no known risks to
participating in this research. Your individual privacy will be maintained in all published and
written results from the study.

Please ﬁll out the questionnaire if you agree to participate in this research study. By
submitting your responses, you are agreeing to participate in this research. If you are ﬁlling out
this questionnaire in connection with a course you are currently enrolled in, enter your person
ID number and answer ALL of question items so that you can receive full credit for your

participation

If you have any questions regarding this project, please do not hesitate to contact Young
Hoon Kim at Ph: 517) 355-3088; or email at kimyou49@msu.cdu or Dr. Kim at ph: 517) 353—6712;
Fax: 517)355-1292 or email at dankimCuimsuedu. Thank you for your participation.

 

Young Hoon Kim
Prof. Dan. J. Kim
Telecommunication, Information Studies, & Media

Michigan State University

 

48

 

 

 

 

GENERAL INSTRUCTIONS

 

 

Most of the scales used in this survey use a rating system with descriptions at either end.
The end points of the scales take the form of statements such as: "strongly disagree" to "strongly
agree", "extremely unlikely" to "extremely likely", "completely disagree" to "completely agree",
and "not at all conﬁdent" to "completely conﬁdent". You are asked to answer by checking one of

the choices provided. See example below.

”Strongly - Mostly _ T Disagree -. M Agree mM—ost’ly‘ — Strongly
. iD' D' S h Neutral S h A
Today 15 a good day. l Isagree rsagree omew at omew at gree Agree

1 2 3 4 5 6 7

1

By selecting the ﬁfth choice, you would be saying you agree somewhat with the statement that
today is a good day.

 

GENERAL DESCRIPTION

 

 

Please recall your actual B2C (Business-to-Consumer) transactions that have happened in the past
three months. If you have never experienced B2C ecommerce, please take time to go to one of
competitive B2C retailer websites e.g., amazon.com, bamesandnoble.com)

Note: Most of the questions in this survey are about the website that you make transaction.
Even if you do NOT buy from the site, please answer all questions.

 

Section 1: We would like to know your opinions about other strongly Neutral Strongly

 

 

 

 

people in general. disagree agree
DTl: I generally trust other people. 1 2 3 4 5 6 7
DT2: I tend to rely on other people. 1 2 3 4 5 6 7
DT3: I generally have faith in humanity. 1 2 3 4 5 6 7
DT4: I feel that people are generally reliable. I 2 3 4 5 6 7

 

DT5: I generally trust other people unless they give me reasons

notto. 1234567

 

Section 2: We would like to know your conﬁdence with your strongly Neutral strongly
ability to handle the problems of every day life. disagree agree

 

GSEl: If something looks too complicated I will not even bother

to try it.

GSEZ: I avord tryrng to learn new things when they look too 1 2 3 4 5 6 7
drfﬁcult.

1234567

 

 

GSE3: When trying to learn something new, I soon give up if I

am not initially successful. 1 2 3 4 5 6 7

 

 

 

49

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

GSE4: When I make plan, I am certain I can make them work. 1 2 3 4 5 6 7
GSES: If I can’t do a job the ﬁrst time, I keep trying until I can. 1 2 3 4 5 6 7
GSE6: When I have something unpleasant to do, I stick to it
. . . l 2 3 4 5 6 7
untrl I ﬁnish It.
GSE7: When I decide to do something, I go right to work on it. 1 2 3 4 5 6 7
GSE8: Failure just makes me try harder. 1 2 3 4 5 6 7
GSE9: When I set 1mportant goals for myself, I rarely achieve 1 2 3 4 5 6 7
them.
GSElO: I do not seem capable of dealing with most problems
. . l 2 3 4 5 6 7
that come up In my life.
GSEll: When unexpected problems occur, I don’t handle them
1 2 3 4 5 6 7
very well.
GSE12: I feel insecure about my ability to do things. 1 2 3 4 5 6 7
Section 3: We would like to know your conﬁdence when you strongly N strongly
. eutral
make a purchase from e-commerce Web vendors. disagree agree
OSEl: I am conﬁdent that I can obtain relevant information
through online sources (e.g., online discussion groups, 1 2 3 4 5 6 7
reputation sites, etc) on the Web vendors from whom I am
planning to make online purchases.
OSE2: I am conﬁdent that I am usually able to purchase exactly 1 2 3 4 5 6 7
the item that I want from Web vendors.
OSE3: I am conﬁdent that, in case my order does not come
through in a satisfactory manner, I am able to take care of the l 2 3 4 5 6 7
problem(s) on my own.
OSE4: I am conﬁdent that I am able to ﬁnd a trustworthy Web
vendor based on ratings (e.g., the number of the stars or the 1 2 3 4 5 6 7
smiley faces) provided by other consumers.
OSE5: I am conﬁdent that, in case the merchandise I have
purchased online turns out to be defective, I am able to return it 1 2 3 4 5 6 7
without any problems.
OSE6: I am conﬁdent that, if the Web vendor I made an online
purchase from would not take back a defective product, I am
able to solve the problem through the assistance of a third party 1 2 3 4 5 6 7
(e.g., ﬁiends, better business bureaus, or relevant governmental
agencies.)
Section 4: We would like to know your perception (expectation)
- . . strongly strongly
of trustworthiness regardrng the web srte(s) that you have made disagree Neutral agree

a purchase.

 

 

PTl: The web site is trustworthy.

 

1234567

 

50

 

 

PT2: The web site vendor gives the impression that it keeps

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

promises and commitments. 1 2 3 4 5 6 7
PT3: I belreve that the web srte vendor has my best Interests 1n 1 2 3 4 5 6 7
mrnd.

PT 4: The web site vendor has little concern for its customers. 1 2 3 4 5 6 7
PTS: In general, I DO NOT trust the purchasing process in the

web site as much as I trust traditional purchasing processes (i.e. 1 2 3 4 5 6 7
thru local stores).

PT6: Promises made by the web site is likely to be reliable. I 2 3 4 5 6 7
PT7: I do not doubt the honesty of the web site. 1 2 3 4 5 6 7
PT8: I expect that the web site will keep promises it makes. 1 2 3 4 5 6 7
PT9: I expect that the web site have good intentions toward me. 1 2 3 4 5 6 7
PTIO: I expect that the web site intentions are benevolent. 1 2 3 4 5 6 7
PTl 1: I expect that the web site is well meaning. 1 2 3 4 5 6 7
PT12: The web site understands the market it works in. 1 2 3 4 5 6 7
PT13: The web site lmows about the items that it deals with. 1 2 3 4 5 6 7
PT14: The web site knows how to provide excellent service. 1 2 3 4 5 6 7
Section 5: We would like to know your perception of risk strongly Neutral strongly
regarding the web site(s) that you have made a purchase. disagree agree
PR1: How much risk would you tolerate when deciding to make

a purchase from the web sites? (1- absolutely no risk / 7- 1 2 3 4 5 6 7
signiﬁcant risk)

PR2: How would you characterize your experience with the web

site as it relates to making a purchase decision? (1- very negative 1 2 3 4 5 6 7
situation / 7- very positive situation)

PR3: What is the likelihood of your getting a good bargain by 1 2 3 4 5 6 7
buying from this web site? (1- very unlikely / 7- very likely)

PR4: Purchasing from this web site would involve more product

risk (i.e. not working, defective product) when compared with 1 2 3 4 5 6 7
more traditional ways of shopping. (1- strongly disagree / 7-

strongly agree)

PR5: Purchasing from this web site would involve more

ﬁnancial risk (i.e. fraud, hard to return) compared with more 1 2 3 4 5 6 7
traditional ways of shopping. (1- strongly disagree / 7- strongly

agree)

PR6: How would you rate your overall perceptron of risk‘from 1 2 3 4 5 6 7

this web site? (1- absolutely no risk / 7- signiﬁcant risk)

 

 

51

 

 

Section 6: We would like to know about your shopping decision
and your willingness to buy, in the future from the web site(s).

strongly Neutral strongly
disagree agree

 

WTl: I am likely to purchase the product(s) from this web site.

 

WT2: I am likely to recommend this web site to my friends.

 

WT3: I am likely to make another purchase from this web site if
I need the product(s) that I am going to buy.

 

WT4: I would be reluctant to purchase any product(s) from this
web site.

 

 

Section 13. (Respondent Demographics)

 

D1. Your age (in years): D2. Your gender:

 

 

D5. In general, how would you rate your experience with
computers? (l-Novice / 7-Expert)

1234567

 

D6. How would you rate your experience with Web technology
(i.e. searching, browsing, ﬁnding information, etc) ? (l-Novice /
7-Expert)

 

D7. Approximately, how many times per week do you use the

 

 

 

 

 

Internet (including, e-mail checking, searching, browsing, etc)? ( ) / week
D8. A roximatel , how man hours or da do ou s end on

the W513? y y p y y p ( hours) / day
D9. Approximately, how many hours per day do you spend ( hours) / da
overall on a computer (include spending on the Web)? y
D10. How long have you been making a purchase from e- ( ears)
commerce Web sites? (pick one) y

D11. Approximately, how many times per month do you make a ( ) / month

purchase from e-commerce web sites?

 

 

52

 

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