Electron Markets (2009) 19:89–98
DOI 10.1007/s12525-009-0007-1
FOCUS THEME
The impact of uncertainty avoidance, social norms
and innovativeness on trust and ease of use in electronic
customer relationship management
Yujong Hwang
Received: 27 August 2008 / Accepted: 13 February 2009 / Published online: 28 February 2009
# Institute of Information Management, University of St. Gallen 2009
Abstract Social and individual factors and their relationships to trust in electronic customer relationship management (eCRM) are important topics for e-commerce
designers and information systems researchers. In spite of
several previous studies of online trust and consumer
behavior, none has adequately examined the influences of
social and individual factors on online trust in eCRM. In
this paper, the relationships among uncertainty avoidance,
social norms, personal innovativeness in IT, and multidimensions of online trust (integrity, benevolence, and
ability) as well as perceived ease of use (PEOU) are tested,
based on a PLS analysis with 209 student samples. Social
norms influence all three dimensions of online trust, while
uncertainty avoidance affects only the benevolence and
ability dimensions. Personal innovativeness in IT affects
PEOU, and PEOU influences all three dimensions of online
trust. Theoretical and practical implications of these
findings beneficial to our understanding of customer
relationships in the electronic marketplace are discussed in
the paper.
Keywords eCRM . Online trust . Uncertainty avoidance .
Social norms . Personal innovativeness in IT
JEL classification M15
Responsible editor: Nicholas C. Romano, Jr.
Y. Hwang (*)
School of Accountancy and MIS, DePaul University,
1 E. Jackson Blvd.,
Chicago, IL 60604, USA
e-mail: [email protected]
Introduction
Online trust in electronic customer relationship management (eCRM) is an important topic for e-commerce
designers and human–computer interaction researchers
(Saeed et al. 2003c; Gefen et al. 2003; Birkhofer et al.
2000). In spite of several previous studies of online trust
and consumer behavior (e.g., Gefen et al. 2003; Grazoli and
Jarvenpaa 2000; Pennington et al. 2004; Jarvenpaa et al.
2000; Ba and Pavlou 2002; Gefen 2002a, b; Pavlou 2003;
Bhattacherjee 2002; Paul and McDaniel 2004), none has
adequately examined the influences of social and individual
factors on online trust in eCRM. Individual factors for
eCRM have been emphasized for the successful implementation of e-commerce (Leonard and Riemenschneider
2008). The role of various social factors involved in online
trust for successful eCRM is an important research issue
(McKnight et al. 2004). Schoder and Haenlein (2004) also
emphasized a trust-based strategy for successful eCRM.
Anecdotal evidence suggests that 30% to 75% of eCRM
initiatives fail because organizations roll them out without
assessing their social aspects (Simpson 2002). Based on a
meta-analysis of online consumer behavior in 42 MIS
articles published between 1995 and 2002, Saeed et al.
(2003c) argued that social context variables and individual
characteristics should be studied further to fully understand
online consumer behavior. Specifically, the complex effects
of social influence, individual characteristics, and personal
innovativeness on multidimensional trust beliefs, such as
integrity, benevolence, and ability, have not been tested in
previous research. By understanding these relationships as
different sources of influence on online trust, e-commerce
system designers and information systems (IS) researchers
can reduce the research gap between the trust theory and
eCRM and improve website functionalities.
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Thus, this study has two primary objectives. The first
objective is to investigate the social and individual
influences on multidimensional online trust beliefs. For
this purpose, the current study includes uncertainty avoidance (Dorfman and Howell 1988) and social norms
(Limayem et al. 2000) as antecedents of multidimensional
online trust beliefs. Since social and environmental factors
in trust building are crucial issues to e-commerce (Gefen et
al. 2003), this is an important empirical study that tests
these specific relationships. Given that uncertainty and risk
are the main constructs related to trust beliefs (Liang and
Huang 1998; Grazioli and Jarvenpaa 2000; Jarvenpaa et al.
2000; Vellido et al. 2000; Liao and Cheung 2001; Ba and
Pavlou 2002), investigating mechanisms to reduce and
avoid uncertainty and risk should yield important insights
into this proposed model.
The second objective is to investigate the effects of
individual characteristics related to information technology
(IT) adoption, such as personal innovativeness in IT (PIIT)
(Agarwal and Prasad 1998), on multidimensional trust
beliefs and perceived ease of use (PEOU) in an online
consumer behavior context. For this purpose, the current
study tests the influence of PIIT on PEOU, and the effects
of PEOU on multidimensional trust beliefs. Although
Gefen et al. (2003) showed the relationship between PEOU
and trust and usefulness, they used the unidimensional trust
construct in the model. Thus, the direct relationships among
PEOU and the three dimensions of trust beliefs are
currently unknown. Given that social norms are social
sources of influence while PIIT is an individual source of
influence on IT adoption (Lewis et al. 2003), knowing the
effects of these different sources of influence on trust
beliefs would be valuable to more fully understand the
nomological networks in the model. The relationship
between social norms and PIIT will be also tested.
The organization of this paper is as follows. “Research
model and hypotheses” section presents the theoretical
foundations of this research model and the hypotheses.
“Method and measures” section outlines the research
methodology and measures. “Data analysis and results”
section describes the data analysis and results. Finally,
“Discussion and conclusion” section concludes the paper
and presents implications for researchers and practitioners.
Research model and hypotheses
Multidimensional online trust beliefs
Figure 1 presents the proposed research model. Gefen
(2002b) provided a multidimensional construct combining
specific beliefs of online trust: integrity, benevolence, and
ability. Integrity is the belief that the trusted party adheres
Y. Hwang
Online Trust
H1-1
Uncertainty
Avoidance
Benevolence
H1-2
Family
H2
H2-1
Media
H1-3
H2-2
H2
Social
Norms
Integrity
H2-3
Friends
H2-4
Ability
PIIT
H3
H4-1
Ease of Use
H4-2
H4-3
Fig. 1 Proposed research model
to accepted rules of conduct, such as honesty and keeping
promises (Mayer and Davis 1999). Benevolence is the
belief that the trusted party, aside from wanting to make a
legitimate profit, wants to do good to the customer. Ability
is the belief about the skills and competence of the trusted
party. Gefen (2002b) developed and validated these three
dimensions of online trust, and Gefen and Straub (2004)
recently found that social presence has positive effects on
integrity (β=0.21, p<0.01) and benevolence (β=0.41, p<
0.01), but not on ability. Thus, this study includes
uncertainty avoidance, social norms, PIIT, and PEOU as
the antecedents of multidimensional trust beliefs, and
investigates these complex relationships. Based on
Limayem et al. (2000), we use family, media, and friends
influences as formative dimensions of social norms in the
model. Detailed hypotheses and supporting literature are
explained in the next section.
Uncertainty avoidance
In this study, uncertainty avoidance, provided by Dorfman
and Howell (1988), is directly linked to multidimensional
trust beliefs. Uncertainty avoidance is defined as “the extent
of feeling threatened by uncertain or unknown situations”
(Dorfman and Howell 1988). Uncertainty avoidance is a
personal value orientation of the extent to which his/her
society avoids risk and creates security. Doney et al. (1998)
showed that uncertainty avoidance is positively related to
all trust-building processes. Srite and Karahanna (2006)
also found that uncertainty avoidance consistently moderates the relationship between social norms and intention to
adopt. Further, much IS and marketing literature suggests
that uncertainty and risk are important mechanisms in order
for trust to influence choice and behavior (Lewis and
Weigert 1985; Schlenker et al. 1973; Doney et al. 1998).
Uncertainty avoidance, social norms and innovativeness
Uncertainty avoidance addresses the concepts of risk, risk
aversion, and a reliance on risk-reducing strategies. Liang
and Huang (1998) found that experienced shoppers are
more concerned about uncertainty in electronic shopping,
and that this uncertainty subsequently increased transaction
cost and reduced acceptance of electronic channels.
Doney et al. (1998) showed that targets with low
uncertainty avoidance may engage in opportunistic behavior, even if doing so risks damaging a relationship.
Uncertainty avoidance determines the value people place
on continuing current relationships, thus affecting the
benevolence dimension of trust beliefs. People with high
uncertainty avoidance would frown on conflict and value
compromise, providing further evidence that targets have
benevolent intentions (Doney et al. 1998). Uncertainty
avoidance also closely parallels the “tightness” and “looseness” concepts articulated in anthropology (Dorfman and
Howell 1988). “Loose” relationships with low uncertainty
avoidance are associated with less regard for stability,
permanence, and benevolence in relationships, and with
greater risk taking. On the other hand, the stability inherent
in “tight” societies permits trustors to develop confidence in
the relationships and believe that the trustees do good to
trustors to keep this relationship. Pelto (1968) notes that
tight societies with high uncertainty avoidance value
durability, permanence, and solidarity, which can be
connected to the benevolence dimension of online trust.
Thus, the current study hypothesizes:
H1-1: Uncertainty Avoidance will positively influence
Benevolence of Online Trust.
People with low uncertainty avoidance do not fear the
future and tolerate risk easily (e.g., Kale 1991; Kale and
Barnes 1992; Nakata and Slvakumar 1996; Ueno and
Sekaran 1992). Thus, low uncertainty avoidance would
undermine the integrity dimension in trust building. They
are willing to sever existing relationships and enter into
opportunistic behavior because of the low cost of such
behavior. People try to mitigate uncertainty by adopting
strict codes of behavior, by establishing formal rules, and
by rejecting deviant ideas and behaviors (Singh 1990).
Moreover, people with high uncertainty avoidance desire to
establish clear rules as to how one should behave, and live
by these rules (Singh 1990; Kale and Mcintyre 1991). This
strong rule-orientation makes it easy for trustors to predict
targets’ behavior in high uncertainty avoidance (Doney et
al. 1998). Thus, the current study hypothesizes:
H1-2: Uncertainty Avoidance will positively influence
Integrity of Online Trust.
People with high uncertainty avoidance would also seek
to mitigate uncertainty and be likely to establish trust based
on evidence of a target’s expertise, ability, or competence
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(Doney et al. 1998). The value placed on a target’s
capability is strongly determined by uncertainty avoidance
(Kale 1991; Doney et al. 1998). In low uncertainty
avoidance, where people deal easily with risk or uncertainty, common sense, rather than target’s expertise, ability, or
competence, would be the dominant trust-building mechanism (Srite and Karahanna 2006). Doney et al. (1998)
explained that there is a high regard or value placed on
experts and expertise in high uncertainty avoidance. Thus,
individuals with high uncertainty avoidance would show
high ability belief in developing online trust belief. We
hypothesize:
H1-3: Uncertainty Avoidance will positively influence
Ability of Online Trust.
Social norms
Online consumer behavior is a voluntary individual
behavior that can be explained by the theory of reasoned
action (TRA) proposed by Fishbein and Ajzen (1975). TRA
argues that behavior is preceded by intentions and that
intentions are determined by the individual’s attitude
toward the behavior and the individual’s social norms.
Social norms are defined as a person’s perception that most
people who are important to him/her think he/she should or
should not perform the behavior in question (Fishbein and
Ajzen 1975). There are several IS studies focusing on social
norms or environmental influence on online consumer
behavior. Limayem et al. (2000) found that social norms
influence purchase intention (β=0.17, p<0.001), using a
formative construct of social norms (family, media, and
friends influences) in online consumer behavior. However,
they did not include in the model an important construct in
online consumer behavior: online trust.
Rogers (1983) also suggests that there are external and
internal sources of social influence affecting technology
adoption belief. External sources were defined as including
mass media, advertising, and other marketing-related
sources, and internal sources were defined as word-ofmouth influence from friends, family, and others (Pathasarathy
and Bhattacherjee 1998; Lekvall and Wahlbin 1973; Rogers
1983). Kraut et al. (1999) highlight that other family
members’ Internet usage is an important factor in an
individual’s Web usage. Pathasarathy and Bhattacherjee
(1998) found that external influence, interpersonal influence, and network externality are the distinguishing factors
between discontinuing and continuing consumers. Expectation of e-commerce usefulness was negatively impacted
by social disturbance (Han and Noh 2000). Slyke et al.
(2002) found that women viewed online shopping as a
social activity rather than technology adoption. Saeed et al.
(2003b) also found that media influence is an important
92
factor in the enhancement of customer value. Recently,
Venkatesh et al.’s (2003) unified theory of adoption and
usage of technology (UTAUT) revealed that social influence in voluntary contexts, such as e-commerce, operates
by influencing perceptions about technology based on
commitment development, such as internalization and
identification processes. In these previous studies, three
sub-dimensions of online trust (Gefen 2002b) have not
been tested with social norms, and it is unknown how these
social norms influence the separate dimensions of trust
beliefs. This study tests the effects of social norms,
formative construct (family, media, and friends influences)
based on Limayem et al. (2000), on the benevolence,
integrity, and ability dimensions of trust. This study
hypothesizes:
H2-1: Social Norms will positively influence Benevolence
of Online Trust.
H2-2: Social Norms will positively influence Integrity of
Online Trust.
H2-3: Social Norms will positively influence Ability of
Online Trust.
Personal innovativeness in IT
Rogers’ (1983) innovation diffusion theory shows that
diffusion is the process by which an innovation is
communicated through certain channels over time among
the members of a social system. An innovation is a special
type of communication, in that the messages are concerned
with new ideas (Rogers 1983). Moore and Benbasat (1991)
extended the set of perceptions proposed by Rogers (1983)
to include seven perceived characteristics of an innovation
as predictors of IT adoption behavior. Agarwal and Prasad
(1998) also provided Personal Innovativeness in IT (PIIT),
the willingness of an individual to try out any new
information technology, as a trait and a relatively stable
predictor of individuals that is invariant across situational
considerations. They provided valid measures of PIIT and
showed that PIIT has a moderating effect between
perceptions about new IT (relative advantage, PEOU and
compatibility) and intention to use new IT.
Limayem et al. (2000) argued that shopping on the
Internet is an innovative behavior that is more likely to be
adopted by innovators than non-innovators. Thus, it is
important to include this construct in order to account for
individual differences. Limayem et al. (2000) included
personal innovativeness and social norms in the model of
online consumer behavior, and found positive relationships
with purchase intention (p<0.001). In their model, personal
innovativeness is a global innovativeness construct based
on Rogers’ (1983) innovation diffusion theory rather than a
domain-specific innovativeness. Global innovativeness,
Y. Hwang
such as a personal innovativeness in Limayem et al.’s
(2000) study, exhibits low predictive power when applied
to any specific innovation adoption decision (Goldsmith
and Hofacker 1991, Leonard-Barton and Deschamps 1988).
Domain-specific innovativeness, such as PIIT (Agarwal and
Prasad 1998), is posited to exhibit a significant effect on
behaviors within a narrow domain of activity (Goldsmith
and Hofacker 1991), and it has been suggested that this trait
also can be measured directly via self-report, in a manner
similar to the measurement of attitudes and other personality traits (Flynn and Goldsmith 1993). While innovativeness has received attention as a determinant of innovation
adoption behavior, marketing research noted that it is
important to conceptually and operationally draw a distinction between global innovativeness and domain-specific
innovativeness (Flynn and Goldsmith 1993; Agarwal and
Prasad 1998). Lewis et al. (2003) explained that domainspecific PIIT is an important source of “individual
influence” on IT adoption, which is different from “social
influences.” They suggested that future study of IT
adoption should investigate the effects of different sources
of influences (social, institutional, and individual) on IT
adoption.
While there is the possibility to link innovativeness in
the IT domain with strong influences from family, media,
and friends, the influence of social norms on PIIT has not
been tested in the previous research. Innovation diffusion
theory (Rogers 1983) clearly shows that there are external
and internal sources of innovation diffusion. External
sources were defined as including mass media, advertising,
and other marketing-related sources, and internal sources
were defined as word-of-mouth influence from friends,
family, and others (Pathasarathy and Battacherjee 1998;
Lekvall and Wahlbin 1973; Rogers 1983). Thus, this study
hypothesizes:
H2-4: Social Norms will influence Personal Innovativeness in IT.
Perceived ease of use
PEOU is mainly based on the cognitive effort the user
needs to invest in order to utilize the system. To investigate
the relationship between an individual’s influence on
technology adoption and innovation, the current study
relates PIIT to PEOU. Kegerreis et al. (1970) showed that
innovative individuals tend to demonstrate higher selfconfidence when performing new tasks. Thatcher and
Perrewe (2002) also found a direct positive effect (p<
0.01) of PIIT on computer self-efficacy, which is an
antecedent to PEOU. A recent study by Lewis et al.
(2003) supported the direct positive effects of PIIT, a source
of individual influence in technology adoption, on PEOU.
Uncertainty avoidance, social norms and innovativeness
This study hypothesizes that PIIT will have a positive effect
on PEOU. If a person is more innovative, he/she will try
out the new system with an increased belief about his/her
ability with technology and ease of use perception. Thus,
this study hypothesizes:
H3: Personal Innovativeness in IT will positively influence Perceived Ease of Use.
Gefen et al.’s (2003) Trust-technology acceptance model
(TAM) posits that, if the user perceives the website easy to
use, the PEOU will positively affect the online trust beliefs.
They showed empirical support with the unidimensional
measure of trust belief (β=0.28, p<0.01). We can posit that
a vendor can reduce the cognitive load of the user by the
vendor’s ability, integrity, and benevolence of the trust
belief. Thus, the perception of little cognitive effort will be
related to the perception that the vendor be able to support
these needs by its online trust beliefs. Although there are
several studies that support the relationship between PEOU
and unidimensional online trust (e.g., Pavlou 2003; Gefen
et al. 2003), the direct relationships of PEOU to multidimensional online trust beliefs were not tested. This study
hypothesizes:
H4-1: Perceived Ease of Use will influence Benevolence
of Online Trust.
H4-2: Perceived Ease of Use will influence Integrity of
Online Trust.
H4-3: Perceived Ease of Use will influence Ability of
Online Trust.
Method and measures
The free experiment with undergraduate business students
in the northern region of the U.S. was implemented with
209 students who voluntarily participated. The free experiment was conducted in an Internet classroom as suggested
by Gefen (2002b). The students were asked to navigate
www.amazon.com, and go through the procedure of
purchasing a book without actually submitting the purchase
transaction. Next, the students were asked to complete the
experimental instrument of an online survey based on their
experiences with the website. We developed an online
survey website and posted this URL to the class management system for the students to access, and 209 students
voluntarily participated. The average age of participants
was 22 years, and 56% were female. 92% of participants
reported having used e-commerce website to buy products
before. 66% reported having used the Internet 4 to 20 h in a
week, while 20% replied more than 20 h. 15% replied that
they used the Internet less than 3 h in a week. There was no
difference between the earlier participants and the later
93
participants in the survey, showing that non-response bias
was not an issue.
Most of the measurement items were adapted and
revised from previous research on online trust, TRA,
TAM, PIIT, and uncertainty avoidance, as we explained in
the hypotheses section. All questionnaire items used a fivepoint Likert-type scale where 1=completely disagree, 3=
neither agree nor disagree, and 5=completely agree. The
online trust beliefs were adapted from Gefen (2002b).
Uncertainty avoidance was operationalized based on
Dorfman and Howell (1988), and social norms were
measured by three formative items adapted from Limayem
et al. (2000). PIIT was adapted from Agarwal and Prasad
(1998), and PEOU was measured by four items adopted
from the instrument developed by Davis et al. (1989). See
the detailed instrument items in the “Appendix”.
Data analysis and results
The proposed model and hypothesis testing was conducted
using Partial Least Squares (PLS) Version 3.0. PLS avoids
many of the restrictive assumptions underlying covariancebased SEM techniques such as multivariate normality and
large sample size (Falk and Miller 1992; Fornell and
Bookstein 1982). Furthermore, PLS enables both formative
and reflective constructs to be tested together in the model
(Chin 1998). Thus, this study, which includes both
formative and reflective constructs, used PLS rather than
the other statistical techniques in data analysis. Our sample
size of 209 is more than adequate for the PLS estimation
procedures.
The measurement model in PLS is assessed by examining internal consistency, convergent validity, and discriminant validity (Barclay et al. 1995; Yi and Hwang 2003).
Internal consistencies (similar to Cronbach’s alpha) of 0.7
or higher are considered adequate (Barclay et al. 1995;
Fornell and Larcker 1982). Convergent and discriminant
validity are assessed by applying two criteria: (1) the square
root of the average variance extracted (AVE) by a construct
from its indicators should be at least 0.707 (i.e., AVE >
0.50) and should be greater than that construct’s correlation
with other constructs (Barclay et al. 1995; Chin 1998;
Fornell and Larcker 1982), and (2) item loadings (similar to
loadings in principal components) should be at least 0.707,
and an item should load more highly on the construct it is
intended to measure than it does on another construct. The
structural model and hypotheses are assessed by examining
the significance of the path coefficients (similar to
standardized beta weights in a regression analysis) and the
variance accounted for by the antecedent constructs.
Table 1 shows the internal consistency reliabilities and
correlations among constructs. As recommended, the
94
Table 1 Internal consistencies
and correlations of constructs
ICR internal consistency reliability, UA uncertainty
avoidance
Y. Hwang
Construct
ICR
UA
Integrity
Benevolence
Ability
PIIT
PEOU
UA
Integrity
Benevolence
Ability
PIIT
PEOU
0.87
0.89
0.91
0.89
0.88
0.93
0.79
0.20
0.29
0.33
0.10
0.34
0.85
0.59
0.50
0.16
0.33
0.87
0.38
0.09
0.29
0.86
0.20
0.38
0.85
0.39
0.88
internal consistency reliabilities were all higher than 0.7
without exception, and the diagonal elements (square root
of the variance shared between the constructs and their
measures) were all higher than 0.707 and also higher than
correlations between target constructs and other constructs
without exception.
Table 2 presents the factor structure matrix of the study
variables. Demonstrating strong convergent and discriminant validity, all items exhibited high loadings (>0.707) on
their respective constructs except for the first item of
uncertainty avoidance, and no item without exception
loaded higher on other constructs than the one it was
intended to measure. We kept all items in the model based
on the high reliability results. Collectively, the psychometric properties of the constructs were considered excellent.
Figure 2 provides the results of hypothesis testing. As
recommended (Chin 1998), bootstrapping (with 500 subsamples) was performed to test the statistical significance of
each path coefficient using t-tests. All of the hypotheses
Table 2 Factor structure matrix
of loadings and cross-loadings
Scale Items
UA1
UA2
UA3
UA4
Benevolence1
Benevolence2
Benevolence3
UA uncertainty avoidance
*p<0.001)
Integrity1
Integrity2
Integrity3
Ability1
Ability2
Ability3
PIIT1
PIIT2
PIIT3
PEOU1
PEOU2
PEOU3
PEOU4
UA
0.673*
0.812*
0.822*
0.852*
0.350
0.131
0.234
0.183
0.113
0.220
0.362
0.307
0.178
0.057
−0.010
0.159
0.272
0.290
0.295
0.351
were supported, except for the relationship between
uncertainty avoidance and integrity (H1-2). All three
formative indicators were significant factors to social
norms. The model explained substantial variance in ability
(R2 =0.25), and integrity (R2 =0.20), and modest variance in
benevolence (R2 =0.16), PEOU (R2 =0.15), and PIIT (R2 =
0.12). Given the complexity of online trust beliefs, R
squares of the three trust beliefs in the model are
reasonable. There were no direct significant effects of PIIT
on multidimensional trust beliefs, showing that all the
effects of PIIT on trust beliefs are mediated by PEOU.
Further discussions about these findings are in the next
section.
Discussion and conclusion
Results of the data analysis point out the three primary
findings of this study. First, although social norms influence
Benevolence
Integrity
Ability
PIIT
PEOU
0.124
0.265
0.219
0.281
0.894*
0.808*
0.911*
0.187
0.110
0.171
0.176
0.551
0.444
0.535
0.264
0.276
0.245
0.274
0.302
0.324
0.376
−0.075
0.079
0.141
0.118
0.106
0.048
0.066
0.215
0.283
0.275
0.308
0.293
0.190
0.251
0.875*
0.851*
0.838*
0.424
0.410
0.461
0.182
0.111
0.104
0.229
0.305
0.338
0.294
0.432
0.438
0.415
0.868*
0.881*
0.827*
0.183
0.093
0.212
0.296
0.301
0.358
0.377
0.433
0.501
0.581
0.312
0.299
0.374
0.084
−0.003
0.123
0.184
0.276
0.254
0.286
0.139
0.173
0.090
0.194
0.083
0.236
0.859*
0.795*
0.883*
0.345
0.297
0.377
0.358
0.300
0.288
0.264
0.392
0.287
0.290
0.310
0.289
0.384
0.841*
0.883*
0.908*
0.896*
Uncertainty avoidance, social norms and innovativeness
95
Online Trust
.21∗∗∗
Uncertainty
Avoidance
Benevolence
n.s.
Family
Media
.60∗∗∗
.34∗∗
.23∗∗∗
23
Social
Norms
.30∗∗∗
.23∗∗∗
Integrity
(.20)
.24∗∗∗
.51∗∗∗
Friends
(.16)
.34∗∗∗
Ability
PIIT
( 25)
(.
.18∗∗
18
( 12)
(.
.25∗∗∗
.39∗∗∗
.26∗∗∗
Ease of Use
(.15)
Fig. 2 PLS results of research model. Note. *p<0.05, **p<0.01,
***p<0.001
all three dimensions of online trust beliefs, uncertainty
avoidance influences only the benevolence and ability
dimensions of online trust. This is an important and
interesting finding, and was only made possible by
including the social and individual sources of influence in
the model and relating them to multidimensional trust
beliefs. The result shows that when people have a high
tendency to mitigate uncertainty by adopting strict codes of
behavior and by establishing formal rules, their belief that
the trusted party honestly adheres to these accepted rules of
conduct is not directly influenced by this tendency. Thus, to
increase the integrity dimension of online trust, IS designers
should emphasize the social normative aspects of a website
(such as feedback mechanisms in the online community or
media) rather than reducing procedural uncertainty by
formal rules of conduct listed on the website. Insights into
these social and individual influences on technology
adoption and online trust beliefs also enhance our understanding of the nomological networks among the different
constructs found in IS literature. Second, the effects of
PIIT—the source of individual influence in IT adoption—
on trust beliefs are mediated by PEOU. Trust beliefs of
the website should be enhanced through the perception
that the target website is easy to use even in the case of
innovative and proactive IT users. The mediating effects
of PEOU between PIIT and trust beliefs should be tested
further, with other samples, in future research to ascertain
the generalizability of the model. Third, in developing
social norms, internal influences (family and friends) are
more important than external influences (media). Social
norms also significantly influence PIIT, as expected. The
model in this paper explains the main social and
individual factors for online trust building in the electronic
marketplace and eCRM. Rogers (1983) suggests that there
are external and internal sources of social influences.
Kraut et al. (1999) also showed that internal sources of
influence are important for e-commerce implementation.
Based on the findings in this study, internal sources, such
as word-of-mouth influence from friends, family, and
others (Pathasarathy and Battacherjee 1998; Lekvall and
Wahlbin 1973), should be emphasized for online trust
building in eCRM.
There are several limitations in this study. First, rather
than including multiple websites or new website unfamiliar
to the consumers only one popular website, Amazon.com,
was used. While this may limit the generalizability of the
findings, selecting one website is essential to control other
industry- or company-specific factors in the model. We also
carefully followed the other studies’ experiment design,
such as Gefen (2002b), to reduce the generalizability
concerns and to design natural experiment settings for the
test. Future studies should test the model with other types
of e-commerce websites, such as online auctions, with the
other samples to enhance our knowledge in different
domains. Second, the model did not include other important
antecedents of trust beliefs, such as situational normality,
structural assurance, propensity to trust (Gefen et al. 2003),
and institutional influence on technology adoption, such as
commitment (Lewis et al. 2003). However, the model in
this study includes important social and individual antecedents, which were not investigated with the detailed
nomological networks in previous literature, based on the
different sources of influence (social and individual). More
complex relationships among the other antecedents of trust
beliefs and the constructs explained in this study should be
further investigated in the future research.
There are several academic implications of this study on
eCRM. First, given that there are different sources of
influence (social and individual) in determining online
consumer behavior, the proposed comprehensive model
used in this study provides valuable insights into the
relationships among the different sources of influence. In
being more inclusive than previous research models, we
can respond to the call to investigate online consumer
behavior with an integrative view (Saeed et al. 2003c).
Second, this study suggests the new and interesting role of
multidimensional trust beliefs in the successful implementation of eCRM. Specifically, the influence of social norms
and PIIT, mediated by PEOU, on multidimensional online
trust beliefs was theoretically and empirically supported.
Future research can investigate the role of uncertainty
avoidance and social norms on actual purchase behavior to
enhance our understanding of eCRM. Further investigation
of this new understanding of trust, such as its relationship
to commitment (internalization and identification) in online
transactions, should be followed in future research. Other
research questions on IS adoption can be investigated based
96
Y. Hwang
on the model provided in this study. For example, how will
the model be applied to mandatory systems adoption
contexts? How will a multinational group develop trust
beliefs in different countries, based on the current model?
How will the electronic government enhance system
adoption with authority norms based on this model? Further
investigation in these directions will be beneficial to more
completely understand these phenomena and to apply this
knowledge to eCRM.
Practitioners such as website designers and e-commerce
managers can draw interesting implications from this study.
Specifically, the social aspects of a website should be
carefully designed and implemented to make website
visitors purchase products or services based on this study’s
results. Li et al. (2006, p. 438) also recently emphasized
this social aspect of website design in IS research by
arguing, “To build social and psychological bonds with
customers, Web site designers should incorporate features
that increase the sense of ‘personal care,’ ‘belonging,’ and
‘community’ for online customers.” One-to-one personal
connections between a consumer and a business representative enabled by real-time communication technologies
such as instant messaging and online communities such as
chat rooms and discussion forums would be practical
applications to enhance eCRM. These applications will
eventually enhance customer loyalty in the electronic
marketplace (Doong et al. 2008). Warner Brothers conducted a study of how customers with different styles and
characteristics differently navigate on a website. In developing a third party assurance seal, these individual
characteristics can also be considered in the design and
implementation stage (McKnight et al. 2002, 2004;
McKnight and Chervany 2002). Saeed et al. (2003a) also
showed that these customer service enhancement efforts
result in a company’s increased profitability. Thus, this
study, recommending that strong consideration be given to
the social issues of online trust and consumer behavior, will
be beneficial to all of these endeavors for successful eCRM
design and implementation.
Appendix. Instrument items
Construct Items
UA1
It is important to have purchase instructions spelled
out in detail so that buyers always know what they
are expected to do.
UA2
Rules and regulations are important because they
inform buyers what the sellers do.
UA3
Standard operating procedures are helpful to buyers
in purchasing behavior.
UA4
Instructions for operations are important for buyers
in purchasing behavior.
SN1
The members of my family (e.g., parents) think
that I should make purchases through the web.
SN2
The media frequently suggest to us to make
purchases through the Web.
SN3
My friends think that I should make purchases through
the Web.
PIIT1
If I heard of a new information technology, I would
look for ways to experience it.
PIIT2
Among my peers, I am usually the first to try out
new information technologies.
PIIT3
I like to experiment with new information technology.
Benev1
I expect that Amazon.com has good intentions toward me.
Benev2
I expect that Amazon.com’s intentions are benevolent.
Benev3
I expect that Amazon.com is well meaning.
Integrity1 Promises made by Amazon.com are likely to be reliable.
Integrity2 I do not doubt the honesty of Amazon.com.
Integrity3 I expect that Amazon.com will keep promises they make.
Ability1 Amazon.com understands the market they work in.
Ability2 Amazon.com knows about products.
Ability3 Amazon.com knows how to provide excellent service.
PEOU1
PEOU2
PEOU3
PEOU4
Learning to use Amazon.com is easy for me.
I find it easy to get Amazon.com to do what I want
it to do.
My interaction with Amazon.com is clear and
understandable.
I find Amazon.com easy to use.
Note. UA: Uncertainty Avoidance, SN: Social Norm,
Benev: Benevolence
References
Agarwal, R., & Prasad, J. (1998). A conceptual and operational
definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204–215.
Ba, S., & Pavlou, P. (2002). Evidence of the effect of trust building
technology in electronic markets: Price premiums and buyer
behavior. MIS Quarterly, 26(3), 243–268.
Barclay, D., Higgins, C., & Thompson, R. (1995). The partial least
squares approach to causal modeling: Personal computer adoption and use as an Illustration. Technology Studies, 2, 285–309.
Bhattacherjee, A. (2002). Individual trust in online firms: Scale
development and initial test. Journal of MIS, 19(1), 211–241.
Birkhofer, B., Schagel, M., & Tomczak, T. (2000). Transaction- and
trust-based strategies in E-commerce—a conceptual approach.
Electronic Markets, 10(3), 169–175.
Chin, W. (1998). The partial least squares approach to structural equation
modeling. In G. A. Marcoulides (Ed.), Modern methods for business
research (pp. 295–336). Mahwah, NJ: Lawrence Erlbaum.
Davis, F., Bagozzi, R., & Warshaw, P. (1989). User acceptance of
computer technology: A comparison of two theoretical models.
Management Science, 35(8), 982–1002.
Doney, P., Cannon, J., & Mullen, M. (1998). Understanding the
influence of national culture on the development of trust.
Academy of Management Review, 23(3), 601–621.
Doong, H.-S., Wang, H.-C., & Shih, H.-C. (2008). Exploring loyalty
intention in the electronic marketplace. Electronic Markets, 18
(2), 142–149.
Uncertainty avoidance, social norms and innovativeness
Dorfman, P. W., & Howell, J. P. (1988). Dimensions of national culture
and effective leadership patterns: Hofstede revisited. Advances in
International Comparative Management, 3, 127–150.
Falk, R. F., & Miller, N. B. (1992). A Primer for soft modeling.
Akron, Ohio: The University of Akron.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and
behavior, an introduction to theory and research. Reading, MA:
Addison–Wesley.
Flynn, L., & Goldsmith, R. (1993). A validation of the Goldsmith and
Hofacker innovativeness scale. Educational and Psychological
Measurement, 53, 1105–1116.
Fornell, C., & Bookstein, L. (1982). Two structural equation models:
LISREL and PLS applied to consumer exit-voice theory. Journal
of Marketing Research, 19, 440–452.
Gefen, D. (2002a). Customer loyalty in e-commerce. Journal of the
AIS, 3, 27–51.
Gefen, D. (2002b). Reflections on the dimensions of trust and
trustworthiness among online consumers. Data Base for Advances in Information Systems, 33(3), 38–53.
Gefen, D., & Straub, D. (2004). Consumer trust in B2C e-commerce
and the importance of social presence: Experiments in e-products
and e-services. Omega, 31, 407–424.
Gefen, D., Karahanna, E., & Straub, D. (2003). Trust and TAM in online
shopping: An integrated model. MIS Quarterly, 27(1), 51–90.
Goldsmith, R., & Hofacker, C. (1991). Measuring consumer innovativeness. Journal of Academic Marketing Science, 19, 209–221.
Grazioli, S., & Jarvenpaa, S. (2000). Perils of Internet fraud: An
empirical investigation of deception and trust with experienced
Internet consumers. IEEE Transactions on Systems, Man, and
Cybernetics, 30(4), 395–410.
Han, K., & Noh, M. (2000). Critical failure factors that discourage the
growth of electronic commerce. International Journal of Electronic Commerce, 4(2), 25–43.
Jarvenpaa, S., Tractinsky, N., & Vitale, M. (2000). Consumer trust in
an Internet store. Information Technology and Management, 1,
45–71.
Kale, S. (1991). Culture-specific marketing communications: An
analytical approach. International Marketing Review, 8(2), 18–30.
Kale, S., & Barnes, J. (1992). Understanding the domain of crossnational buyer–seller interactions. Journal of International
Business Studies, 23(1), 101–132.
Kale, S., & Mcintyre, R. P. (1991). Distribution channel relationships
in diverse cultures. International Marketing Review, 8(3), 31–45.
Kegerreis, R. J., Engel, J. F., & Blackwell, R. D. (1970). Innovativeness and diffusiveness: A marketing view of the characteristics of
early adopters. In D. Kollat, R. Blackwell, & J. Engels (Eds.),
Research in consumer behavior pp. 671–689. New York: Holt,
Rineholt, and Winston.
Kraut, R., Mukhopadhyay, T., Szczypula, J., Kiesler, S., & Scherlis, B.
(1999). Information and communication: Alternative uses of the
Internet in households. Information Systems Research, 10(3),
287–303.
Lekvall, P., & Wahlbin, C. (1973). A study of some assumptions
underlying innovation diffusion functions. Swedish Journal of
Economics, 75, 362–377.
Leonard, L. N. K., & Riemenschneider, C. K. (2008). What factors
influence the individual impact of the web? An initial model.
Electronic Markets, 18(1), 75–90.
Leonard-Barton, D., & Deschamps, I. (1988). Managerial influence in
the implementation of new technology. Management Science, 34
(10), 1252–1265.
Lewis, D., & Weigert, A. (1985). Trust as a social reality. Social
Forces, 63, 967–985.
Lewis, W., Agarwal, R., & Sambamurthy, V. (2003). Sources of influence
on beliefs about information technology use: An empirical study of
knowledge workers. MIS Quarterly, 27(4), 657–678.
97
Li, D., Browne, G., & Chau, P. (2006). An empirical investigation of
Web site use using a commitment-based model. Decision
Sciences, 37(3), 427–444.
Liang, T., & Huang, J. (1998). An empirical study on consumer
acceptance of products in electronic markets: a transaction cost
model. Decision Support Systems, 24, 29–43.
Liao, Z., & Cheung, M. T. (2001). Internet based e-shopping and
consumer attitudes: An empirical study. Information & Management, 38, 299–306.
Limayem, M., Khalifa, M., & Frini, A. (2000). What makes
consumers buy from Internet? A longitudinal study of online
shopping. IEEE Transactions on Systems, Man, and Cybernetics,
30(4), 421–432.
Mayer, R., & Davis, J. (1999). The effects of the performance
appraisal system on trust in management: A field quasiexperiment. Journal of Applied Psychology, 84(1), 123–136.
McKnight, D. H., Kacmar, C. J., & Choudhury, V. (2004). Shifting
factors and the ineffectiveness of third party assurance seals: A
two-stage model of initial trust in a web business. Electronic
Markets, 14(3), 252–266.
McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing
and validating trust measures for e-commerce: An integrative
typology. Information Systems Research, 13(3), 334–359.
McKnight, D. H., & Chervany, N. L. (2002). What trust means in
e-commerce customer relationships: an interdisciplinary conceptual typology. International Journal of Electronic Commerce,
6(3), 35–60.
Moore, G., & Benbasat, I. (1991). Development of instrument to
measure the perceptions of adopting an information technology
innovation. Information Systems Research, 2(3), 192–222.
Nakata, C., & Sivakumar, K. (1996). National culture and new
product development: An integrative review. Journal of Marketing, 60(1), 61–72.
Pathasarathy, M., & Bhattacherjee, A. (1998). Understanding postadoption behavior in the context of online services. Information
Systems Research, 9(4), 362–379.
Paul, D. L., & McDaniel, R. R. J. (2004). A field study of the effect of
interpersonal trust on virtual collaborative relationship performance. MIS Quarterly, 28(2), 183–227.
Pavlou, P. (2003). Consumer acceptance of electronic commerce:
Integrating, trust and risk with the TAM. International Journal of
Electronic Commerce, 7(3), 101–134.
Pelto, P. J. (1968). The difference between “tight” and “loose”
societies. Transaction, 37, 37–40.
Pennington, R., Wilcox, D., & Grover, V. (2004). The role of system
trust in business-to-consumer transactions. Journal of MIS, 20(3),
197–226.
Rogers, E. M. (1983). Diffusion of innovations (3rd ed.). New York: Free.
Saeed, K., Grover, V., & Hwang, Y. (2003a). Creating synergy with a
click and mortar approach. Communications of the ACM, 46(12),
206–212.
Saeed, K., Hwang, Y., & Grover, V. (2003b). Investigating the impact
of web site value and advertising on firm performance in
electronic commerce. International Journal of Electronic Commerce, 7(2), 121–143.
Saeed, K., Hwang, Y., & Yi, M. (2003c). Toward an integrative
framework for online consumer behavior research: A metaanalysis approach. Journal of End User Computing, 15(4), 1–13.
Schlenker, B., Helm, R., & Tedeschi, J. (1973). The effects of
personality and situational variables on behavioral trust. Journal
of Personality and Social Psychology, 25, 419–427.
Schoder, D., & Haenlein, M. (2004). The relative importance of
different trust constructs for sellers in the online world.
Electronic Markets, 14(1), 48–57.
Simpson, L. (2002). The real reason why CRM initiatives fail.
Training, 39, 50–56.
98
Singh, J. (1990). Managerial culture and work-related values in India.
Organization Studies, 11, 75–101.
Slyke, C. V., Comunale, C. L., & Belanger, F. (2002). Gender
differences in perceptions of Web-based shopping. Communications of the ACM, 45(7), 82–86.
Srite, M., & Karahanna, E. (2006). The role of espoused national
cultural values in technology acceptance. MIS Quarterly, 30(3),
679–704.
Thatcher, J. B., & Perrewe, P. L. (2002). An empirical examination of
individual traits as antecedents to computer anxiety and computer
self-efficacy. MIS Quarterly, 26(4), 381–396.
Ueno, S., & Sekaran, U. (1992). The influence of culture on
budget control practices in the U.S. and Japan: An empirical
Y. Hwang
study. Journal of International Business Studies, 23, 659–
674.
Vellido, A., Lisboa, P., & Meehan, K. (2000). Quantitative characterization and prediction of online purchasing behavior: A latent
variable approach. International Journal of Electronic Commerce, 4(4), 83–104.
Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User
acceptance of information technology: Toward a unified view.
MIS Quarterly, 27(3), 425–478.
Yi, M., & Hwang, Y. (2003). Predicting the use of web-based
information systems: Self-efficacy, enjoyment, learning goal
orientation, and the TAM. International Journal of Human
Computer Studies, 59, 431–449.