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Gender differs: assessing a model
of online purchase intentions in
e-tail service
Yu-Bin Chiu
Graduate School of Management, Yuan Ze University, Taoyuan, Taiwan, ROC
Department of Management Information System, Chung-Kuo Institute of
Technology College, Taipei, Taiwan, ROC
Chieh-Peng Lin
Department of Business Administration, Vanung University, Taoyuan,
Taiwan, ROC, and
Ling-Lang Tang
Graduate School of Management, Yuan Ze University, Taoyuan, Taiwan, ROC
Abstract
Purpose – This study proposes a model of online purchase intentions. Four exogenous constructs –
namely, personal awareness of security, personal innovativeness, perceived ease of purchasing, and
perceived usefulness – have not only direct influences on attitudes and online purchase intentions, but
also have indirect influences on online purchase intentions through the mediation of attitudes.
Design/methodology/approach – Data were collected via questionnaires from customers of the
leading internet service provider (ISP) in Taiwan. Structural equation modeling (SEM) was used to
examine the model.
Findings – The influences of personal innovativeness and perceived usefulness on attitudes and
online purchase intentions are similar for males and females. The influences of personal awareness of
security on both attitudes and online purchase intentions are strong for males, while no such effects
exist for females. The influences of perceived ease of purchasing on both attitudes and online purchase
intentions are stronger for females than for males.
Research limitations/implications – There are several limitations. For example, there may exist
common method variance, and the generalisability of the findings might be limited. Besides, this study
is using only one product category, and customers’ online purchase intentions may be only partially
reflected due to the investigation of self-reports.
Practical implications – By learning gender differences, e-tailers and advertisers can better target
right consumers and consequently foster more positive attitudes and online purchase intentions.
Originality/value – This study presents a thorough understanding on a model of online purchase
intentions, and how gender moderates several paths of the model.
Keywords Internet, Purchasing, Electronic commerce
Paper type Research paper
1. Introduction
E-tailers are retailers selling goods via business-to-consumer websites in which
sellers are likely to handle ordering, payment, and shipping as single-order events
The Emerald Research Register for this journal is available at The current issue and full text archive of this journal is available at
www.emeraldinsight.com/researchregister www.emeraldinsight.com/0956-4233.htm
This work was supported by the National Science Council, Republic of China [NSC
93-2416-H-238-008].
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Received 15 July 2004
Revised 2 December 2004
Accepted 16 May 2005
International Journal of Service
Industry Management
Vol. 16 No. 5, 2005
pp. 416-435
q Emerald Group Publishing Limited
0956-4233
DOI 10.1108/09564230510625741
(Bertsch et al., 2002). Most research regarding consumer evaluations of retail services
have examined services that are characterized by face-to-face interactions between
consumers and employees in a physical store (Van Riel et al., 2001). However, the
determinants and outcomes of such research are not readily applied to e-tail service,
which is characterized by interactions with internet technology (Van Riel et al., 2001).
Therefore, to successfully attract internet consumers and benefit from doing so, e-tail
service providers must know more about consumer attitudes towards purchasing via
the internet, online purchase intentions, and their antecedents.
In response to this concern, several studies have put emphasis on the importance of
clarifying individual purchase intentions based on internet usage (Salisbury et al.,
2001; Citrin et al., 2000; Goldsmith, 2002; Bobbit and Dabholkar, 2001), following the
theoretical models such as the technology acceptance model (TAM; Davis et al., 1989),
the theory of reasoned action (TRA; Fishbein and Ajzen, 1975), and the theory of
planned behavior (TPB; Ajzen, 1985). Each of these studies examines different
influences on the formation of beliefs regarding usefulness, ease of use, innovativeness,
security, and so on. Given the belief recurrence in theoretical models of consumer
behavioral intentions towards online purchases, additional work is necessary to
integrate these theories and compare the differences from a gender perspective.
Although the role of gender as a moderator towards buying or IT usage has been
discussed from an attitudinal point of view, the moderating role of gender remains
unknown when buying is performed using IT (e.g. internet). After all, purchasing via
physical stores and purchasing online are two completely different activities.
An understanding of what causes gender differences in perceptions and
attitudes towards buying on the internet and in final formation of online purchase
intentions is valuable not only to e-tail practitioners responsible for implementing
and deploying e-tail service, but also to researchers interested in explaining the
manifestation paths of online purchase intentions. Since gender is frequently a
segmentation variable, a relevant question is whether the influences of exogenous
variables on online purchase intentions are equal for both males and females.
Consequently, a key objective of this study is to provide a basis for tracing the
influence of antecedents (perceived usefulness, perceived ease of purchasing,
personal innovativeness, and personal awareness of security) on attitudes towards
internet purchasing and online purchase intentions by considering gender as a
potential moderator.
This study differs crucially from previous research. First, this study investigates
attitudes toward buying on the internet and purchase intentions from the perspective
of internet application and draws useful inferences for e-tail service industries. Second,
while the previous literature obtained inconclusive results concerning whether the
antecedent variables (e.g. personal awareness of security) influence online purchase
intentions directly or indirectly, this study obtains empirical results pertaining to
the dispute, by examining the possibility of both direct and indirect influences. Third,
this study was one of the first to test a model of online purchase intentions that
includes gender as a moderator due to the neglect of gender role in IT behavioral
research (Gefen and Straub, 1997). Since gender is generally acknowledged to
profoundly influence response to marketing strategies, specifying the moderating
impacts of gender can guide e-tailers in designing different strategies for different
consumers.
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2. Research framework
The conceptual model displayed in Figure 1 is a direct modification of the TAM
(Davis et al., 1989), which received better empirical support in information technology
research than the theory of planned behavior (Ajzen, 1985; Davis et al., 1989). A further
modification of TAM is necessary since TAM was created for a general explanation of
the determinants of computer acceptance, rather than for the specific topic of
behavioral intentions of online purchasing. The model of this study not only includes
variables similar to the ones used in the TAM (Davis et al., 1989), such as perceived
usefulness (Koufaris, 2002; Taylor and Todd, 1995a; Teo, 2001) and perceived ease of
purchasing (Koufaris, 2002; Taylor and Todd, 1995a; Teo, 2001), but also integrates
other critical variables specific to the field of internet shopping, such as personal
awareness of security (Kim and Shim, 2002; Salisbury et al., 2001) and personal
innovativeness (Agarwal and Karahanna, 2000). Restated, this study builds on
previous research by integrating several critical antecedent variables specific to e-tail
service and also fills a gap in existing academic knowledge by empirically testing the
main effects and moderating effects across gender during the formation of online
purchase intentions.
While various studies have indicated that some antecedent variables, for example,
perceived usefulness, may influence online purchase intentions through attitudes
indirectly (Taylor and Todd, 1995b), others have argued for direct effects as well
(Agarwal and Karahanna, 2000). This study obtains empirical results, pertaining to the
dispute, by testing the possibility of both direct and indirect influences. Consequently,
the conceptual model herein hypothesises that four antecedent constructs – namely,
personal awareness of security, personal innovativeness, perceived ease of purchasing,
and perceived usefulness – have not only direct influences on attitudes toward buying
on the internet and online purchase intentions, but also have indirect influences on
Figure 1.
Research framework for
online purchase intentions
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online purchase intentions through the mediation of attitudes toward buying on the
internet. Gender moderates several paths in the model and also has main effects on
those four antecedents, respectively.
2.1 Hypothesis development
Online purchase intentions are measures of the strength of a consumer’s intentions to
perform a specified purchasing behavior via the internet (Salisbury et al., 2001).
Attitudes toward buying on the internet are defined as a consumer’s positive or
negative feelings about performing the purchasing behaviors on the internet
(Schlosser, 2003). Attitude theory (Fishbein and Ajzen, 1975) suggests that intentions
toward the idea of online purchasing are mainly explained by the attitudes toward the
idea. Correspondingly, this study infers that attitudes toward buying on the internet
are the determinants of online purchase intentions. It is therefore hypothesised that
attitudes towards buying on the internet positively influence online purchase
intentions. In order to keep the study more readable, the attitudes toward buying on the
internet are therefore briefly relabeled in this study as attitudes. Hence, the hypothesis
is restated as follows:
H1. Attitudes positively influence online purchase intentions.
Personal awareness of security is conceptualized as the extent to which consumers
believe that the internet is secure for transmitting sensitive information for business
transactions (Kim and Shim, 2002). Personal awareness of security is crucial to
consumer attitudes and purchase intentions (Salisbury et al., 2001), because there may
be a perception of risk involved in transmitting sensitive information such as credit
card numbers across the internet (Janda et al., 2002). A previous study revealed that
security is the most critical concern influencing the attitudes of internet non-purchasers
(Yang and Jun, 2002). Institutionally-established measures to enhance consumers’
personal awareness of security covering internet transactions are essential to the
development of e-tail service (Kim and Shim, 2002), indicating the significant influence
of personal awareness of security on consumer attitudes and online purchase
intentions. Given this likely influence, hypotheses are proposed as follows:
H2. Personal awareness of security positively influences attitudes.
H3. Personal awareness of security positively influences online purchase
intentions.
Personal innovativeness is defined as a consumer who reflects a willingness to try out
any new fashion of consumption, and it can be therefore regarded as an antecedent for
the consumer cognitive attitude for information technology absorption and application
(Agarwal and Karahanna, 2000). Studies in consumer behaviors view innovativeness
as a personality construct that is possessed, to a greater or lesser degree, by all
consumers, since everyone, in the course of their lives, adopts some objects that are
new in the context of their individual experience (Citrin et al., 2000). If there were no
such characteristic as innovativeness, then consumer attitudes and behavioral
intentions would consist of a series of routine buying responses to a static set
of products (Hirschman, 1980). A variety of studies have revealed the association of
consumer innovativeness with attitudes and internet purchase intentions (Citrin et al.,
2000; Goldsmith, 2002). Consequently, the hypotheses are summarized as follows:
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419
H4. Personal innovativeness positively influences attitudes.
H5. Personal innovativeness positively influences online purchase intentions.
From the perspective of e-tail service, perceived usefulness for online purchases can be
defined as the prospective consumer’s subjective probability that using the internet
will efficiently facilitate his or her purchasing (Koufaris, 2002; Taylor and Todd, 1995a;
Teo, 2001). At the same time, perceived ease of use for online purchasing refers to the
degree to which the prospective consumer expects the online purchases to be free of
effort (Koufaris, 2002; Taylor and Todd, 1995a; Teo, 2001). Davis et al. (1989) argues
that the theoretical foundations for perceived usefulness as a predictor of usage
behavior are derived from several diverse research streams, including self-efficacy
theory, a cost-benefit paradigm, and adoption of innovations research.
Factor analyses suggest that the perceived usefulness and perceived ease of use are
statistically distinct dimensions (Davis et al., 1989). Several empirical studies affirm the
significance of perceived usefulness and ease of use beliefs in predicting attitudes and
intentions. For example, Davis et al. (1989) find that usefulness is a highly significant
predictor of attitudes as well as behavioral intentions to implement the new technology
at two different time periods in the context of word-processing software. Similar
studies were replicated by Venkatesh and Davis (1996), Mathieson (1991), Adams et al.
(1992), Szajna (1996). Agarwal and Karahanna (2000) propose and find empirical
support for the modification of a TAM. Despite some differences in these models from
the original conceptualization of TAM, the salience of perceived usefulness and ease of
use in data collected for information technology usage is again supported.
Instead of focusing on the construct of perceived ease of use in general, this study
herein narrows down the scope of such construct and renames the factor as perceived
ease of purchasing so as to fit the current research needs. A better label (the perceived
ease of purchasing) is necessary, because such a construct focuses on the consumer’s
efforts towards the online purchase issues rather than purely on technology usage.
The re-labeling of such a construct is important for further research in the future.
After all, ease of use for the internet itself has been successfully achieved today, while
ease of online purchase still has a long way to go. Based upon the previous literature,
the hypotheses are stated as follows:
H6. Perceived ease of purchasing positively influences attitudes.
H7. Perceived ease of purchasing positively influences online purchase intentions.
H8. Perceived usefulness positively influences attitudes.
H9. Perceived usefulness positively influences online purchase intentions.
2.2 Gender effects
Several analyses of consumer behavior suggest that males and females differ in their
processing of information (Holbrook, 1986; Palmer and Bejou, 1995); particularly, males
and females respond differently to alternative consuming tasks and stimuli (e.g. pictures
versus words) (Meyers-Levy, 1989). Females respond to non-verbal stimuli by evoking
more associative, imagery-laced interpretations and more elaborate descriptions than
males (Gilligan, 1982). This phenomenon suggests that the fundamental gender
differences may contribute to the moderating role for attitudes and online purchase
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intentions, because products promoted online bring about different stimuli and
imagery-laced interpretations versus those of products demonstrated in a physical store.
Accordingly, females are more sensitive to relevant information online than males when
making judgments (Meyers-Levy and Sternthal, 1991), causing subsequent purchase
attitudes and intentions rendered by males and females to vary.
As genders adhere to alternative sex roles, individuals with stronger feminine or
masculine identities make different consumption choices accordingly (Costa, 1994;
Fischer and Arnold, 1994). It has been summarized that males are guided
predominantly by controlling tendencies and stress self-assertion, self-efficacy,
mastery, and avoidance of insecurity and uncertainty (Meyers-Levy, 1988). This
phenomenon implies that the influence of personal awareness of security on attitudes
and online purchase intentions may be moderated by gender, and such an influence is
hypothesised to be stronger for male consumers than for females.
Compared to traditional purchases, online purchasing is an innovative way to
acquire goods and services for consumers. Hence, the personal innovativeness of an
individual consumer becomes critical to his/her attitudes and online purchase
intentions. Specifically, the self-identified innovativeness of females is found to be more
influential than that of males in a previous study (Goldsmith et al., 1987), indicating
that level of innovativeness among females could be expected to lead to more positive
attitudes and online purchase intentions than those of males. This phenomenon also
suggests that the influence of personal innovativeness on attitudes and online purchase
intentions may be moderated by gender, and the influence is hypothesised to be
stronger for female consumers than for males. Based on the above review, the
hypotheses are summarized as follows:
Ha. The relationship between personal awareness of security and online purchase
intentions is stronger for males than for females.
Hb. The relationship between personal awareness of security and attitudes is
stronger for males than for females.
Hc. The relationship between personal innovativeness and online purchase
intentions is stronger for females than for males.
Hd. The relationship between personal innovativeness and attitudes is stronger
for females than for males.
According to Venkatesh and Morris (2000), the influence of perceived ease of use on
intentions to utilize IT is stronger for females than for males, suggesting a greater
impact of perceived ease of purchasing on both attitudes and online purchase intentions
for females than for males. A lower computer aptitude among females (Venkatesh and
Morris, 2000) may make the influences of perceived ease of use on attitudes and online
purchase intentions more salient. Females potentially have higher levels of computer
anxiety (Igbaria and Chakrabarti, 1990) and consequently rate the handiness of service
and physical environment more highly than males (Hofstede, 1980), implying that the
influence of perceived ease of use on IT usage may be moderated by gender, in which it
may be stronger for females than for males (Venkatesh and Davis, 1996).
Compared to females in general, a strong pattern is found of men rating practical
advancement highly (Hofstede, 1980; Moutinho and Goode, 1995; Oumil and Erdem,
1997) by efficiently completing tasks of online purchases, indicating that usefulness
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reflecting a practical sense is more influential during the formation of online purchase
intentions specifically for male consumers. This phenomenon suggests that the
influence of perceived usefulness on online purchase intentions may be moderated by
gender, and the influence is stronger for males than for females. Moreover, in the field
of IT usage, males tend to exhibit more strongly and sensitively the attitudes of
task-oriented and instrumental applications than females (Venkatesh and Morris,
2000), indicating that the influence of perceived usefulness on attitudes via IT usage is
stronger for males than for females. Based on the above review, the hypotheses are
summarized as follows:
He. The relationship between perceived ease of purchasing on online purchase
intentions is stronger for females than for males.
Hf. The relationship between perceived ease of purchasing on attitudes is
stronger for females than for males.
Hg. The relationship between perceived usefulness on online purchase intentions
is stronger for males than for females.
Hh. The relationship between perceived usefulness on attitudes is stronger for
males than for females.
Another question arises as to whether the evaluations (mean scores) of the four
exogenous antecedents differ across gender. In the contrary directions of previous
hypotheses, this study hypothesises that the evaluations of personal awareness of
security and of perceived usefulness are higher for females than for males, while the
evaluations of personal innovativeness and of perceived ease of purchasing are higher
for males than for females. That is, during online shopping, the factors that are not
mainly concerned and focused on by the consumers of a specific gender may be easily
evaluated more highly than those that are of concern. More specifically, for example,
males easily fascinated by IT are more likely to be willing to try IT product than
females, leading to a higher requirement for IT security based on their IT familiarity.
Consequently, male consumers may give lower scores for their awareness of security
towards online shopping.
Another example is that since the level of IT skills in general is higher for males than for
females (Harrison and Rainer, 1992), it is more likely for males than for females to give
higher scores for perceived ease of purchasing. Because males are much familiar with and
less worry about IT usage, the scores given by males are likely to be higher given that
the factor of ease of purchasing is not their main concern (compared to usefulness). On the
other hand, this study also finds that, in the context of the internet, females may take more
time to get used to and familiarize themselves with new internet applications than males
(Straits Times, 1996; Teo, 2001), reflecting the fact thatfemales have greater difficulty than
males in performing an online purchase. Since females are more sensitive about whether
an online purchase requires a lot of mental effort, they are more likely to respond with
lower scores for perceived ease of purchasing given that their benchmark for ease of
purchasing is higher. Consequently, the hypotheses are stated as below:
Hi. Personal awareness of security is scored higher for females than for males.
Hj. Personal innovativeness is scored higher for males than for females.
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Hk. Perceived ease of purchasing is scored higher for males than for females.
Hl. Perceived usefulness is scored higher for females than for males.
3. Research methods
3.1 Subjects
In this study, data is obtained from a large cross-sectional sample of customers of the
largest internet service provider (ISP) in Taiwan, Chunghwa Telecom. Sampling was
conducted in two stages: first, proportional stratified sampling by area, followed
secondly by systematic sampling from Chunghwa Telecom’s customer phone
numbers. Systematic sampling is appropriate as the phone numbers in the database
are neither serial nor acyclic. One thousand questionnaires were mailed, and a follow
up was performed by telephone. Three hundred and seventy-six fully completed
questionnaires were returned to the researchers (response rate of 38 percent), including
males (43 percent single and 57 percent married) as well as females (48 percent single
and 52 percent married). The educational backgrounds for males comprising 22 percent
non-college graduates and 78 percent college graduates and for females comprising
28 percent non-college graduates and 72 percent college graduates reflect the fact that
the average educational level is quite high in Taiwan. Table I also lists the
distributions for age and internet experience across gender.
3.2 Measures
The constructs in this study are measured using five-point Likert scales drawn and
modified from the existing literature. The Appendix includes the construct measures.
Purchase intentions with five items and attitudes with five items are all modified from
Taylor and Todd (1995a, b). Personal awareness of security with six items is modified
from Yang and Jun (2002) and Salisbury et al. (2001). Personal innovativeness with
six items is modified from McKnight et al. (2002). Perceived ease of purchasing with
six items is modified from Venkatesh and Davis (1996). Finally, perceived usefulness
with four items is modified again from Taylor and Todd (1995a, b).
The constructs were all designed by considering online shopping for a particular
category of products in e-commerce, which are computer and communication products.
This is important, because constructs related to online purchase intentions cannot be
precisely measured if the target product of an online purchase is not well announced in
Characteristic Male (N ¼ 190) Female (N ¼ 186)
No % No. %
Age
20 years or less 19 10 20 11
21-40 years 97 51 102 55
41 years or above 74 39 64 34
Internet experience
12 months or less 11 6 9 5
13-24 months 34 18 30 16
25-36 months 42 22 41 22
37-48 months 57 30 73 39
49 months or over 46 24 33 18
Table I.
Characteristics of the
sample
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the measurement. After all, online shopping for computer and communication products
is one thing, but online shopping for large ticket items, such as cars, is quite another
due to much higher prices for cars in comparison with those of computer and
communication items.
3.3 Measurement model
This study follows a two-step procedure of structural equation modeling (SEM) for data
analysis (Anderson and Gerbing, 1988). After considering modification index (MI) used to
select indicator variables (Jo
¨
reskog and Sorbom, 1986), some indicator variables were
removed throughout repeated filtering under the circumstance of the indicator variables
being multidimensional (Hatcher, 1994), given that an indicator variable is actually
influenced by more than one factor (Hatcher, 1994). Since one desires in SEM to use only
factorally simple indicator variables (indicator variables that measure only one factor), a
better choice in this kind of case (e.g. multidimensional) would be to drop it from the
analysis entirely (Hatcher, 1994). This is practical and acceptable given that retaining all
the indicator variables in the model represents an ideal that very often will not be attained
with real-world data even when the measurement model is quite good (Hatcher, 1994).
The indicators retained in each model (total group, male group, and female group)
have to be identical for the comparison. Every construct in the final measurement
model is measured using at least two indicator variables as in Table II. Moreover, the
values of average variance extracted are all larger than the 0.50 criteria recommended
by Fornell and Larcker (1981). The overall goodness-of-fit indices shown in Table III
(chi-square/d.f. smaller than 2.0; RMR and RMSEA smaller than 0.05; CFI, NNFI, NFI,
GFI, and AGFI all greater than 0.9 except one value of AGFI slightly lower than 0.9)
indicate that the fit values of the three models are all satisfactory. The goodness-of-fit
indices herein do not contain any implication, because they are just indices to verify
whether the model fit is acceptable or not (Hatcher, 1994). The chi-square/d.f. in this
study meets the standard criteria well, because chi-square/d.f. must be lower than 2.0
according to the informal rule-of-thumb criteria (Hatcher, 1994). The model fit judged
by only one single indicator (e.g. chi-square/d.f.) is not appropriate since a model’s fit
need not meet all of the criteria ( p value greater than 0.05; chi-square/d.f. less than 2;
CFI, NFI, NNFI, GFI, and AGFI all greater than 0.90, etc.) in order to be deemed
acceptable (Hatcher, 1994).
The reliability in this study is evaluated on the basis of the Cronbach’s alpha. As shown
in Table II, reliabilities (Cronbach’s alpha) for all constructs exceed 0.7 for all three models,
which constitute Nunally’s (1967) threshold level of acceptable reliability. Therefore, the
general requirement of reliability for research instruments is satisfactory. Besides, In this
study, for all three models (total group, male group, and female group) all factor loadings
for indicators measuring the same construct are statistically significant (see Appendix –
Table AII), showing that all indicators effectively measure their corresponding construct
(Anderson and Gerbing, 1988) and support convergent validity.
The chi-square difference test can be used to assess the discriminant validity
(Hatcher, 1994). The constrained model is identical to the unconstrained model, in
which all constructs are allowed to co-vary, except that the correlation between the two
constructs of interest is fixed at 1. Discriminant validity is demonstrated if the
chi-square difference (with 1 d.f.) is significant, meaning that the model in which
the two constructs are viewed as distinct (but correlated) factors is superior.
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Total (N ¼ 376) Male (n
1
¼ 190) Female (n
2
¼ 186)
Construct Indicators
Average variance
extracted
Standardized loading
(t-value)
Standardized loading
(t-value)
Standardized loading
(t-value)
F1 V1 0.618 0.81 (16.47) 0.79 (11.66) 0.83 (12.04)
V2 0.76 (15.27) 0.77 (11.32) 0.73 (10.46)
Cronbach’s alpha 0.76 0.76 0.76
F2 V6 0.575 0.73 (15.15) 0.76 (11.73) 0.69 (9.64)
V9 0.75 (15.83) 0.78 (12.20) 0.69 (9.57)
V10 0.79 (17.02) 0.85 (13.69) 0.71 (9.91)
Cronbach’s alpha 0.80 0.84 0.74
F3 V12 0.731 0.85 (19.52) 0.80 (12.70) 0.90 (15.29)
V13 0.86 (19.84) 0.85 (13.80) 0.86 (14.26)
V15 0.86 (19.75) 0.87 (14.18) 0.85 (13.92)
Cronbach’s alpha 0.89 0.87 0.90
F4 V18 0.550 0.73 (14.85) 0.71 (10.42) 0.73 (10.40)
V20 0.75 (15.51) 0.78 (11.74) 0.74 (10.50)
V21 0.75 (15.45) 0.76 (11.48) 0.73 (10.25)
Cronbach’s alpha 0.79 0.79 0.78
F5 V24 0.666 0.80 (17.33) 0.76 (10.64) 0.78 (11.85)
V25 0.84 (18.50) 0.77 (10.66) 0.84 (13.13)
V26 0.81 (17.57) 0.69 (09.51) 0.84 (13.17)
Cronbach’s alpha 0.85 0.78 0.85
F6 V29 0.520 0.76 (15.00) 0.75 (10.92) 0.74 (10.03)
V30 0.70 (13.81) 0.75 (10.82) 0.67 (09.00)
V31 0.70 (13.79) 0.71 (10.06) 0.68 (09.08)
Cronbach’s alpha 0.76 0.78 0.74
Notes: F1=Online purchase intentions; F2=Attitudes; F3=Personal awareness of security; F4=Personal innovativeness; F5=Perceived ease of
purchasing; and F6=Perceived usefulness
Table II.
Standardized loadings
and reliabilities for male
and female consumers
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By using the Bonferroni method under the overall 0.01 level, the critical value of
the chi-square test is
x
2
ð1; 0:01=15Þ¼11:58: Since the chi-square difference statistics
for every two constructs all exceed 11.58 for each model (see Appendix – Table AII),
discriminant validity is successfully achieved.
3.4 A control variable
Prior IT experience has been found to be important to behavior (Taylor and Todd,
1995c). Specifically, prior internet usage experience may influence consumer norms,
which serve as frames of reference for evaluating attitudes and intentions. In other
words, consumers with much prior IT experience have been posited to have higher
normative standards of service context (Anderson and Sullivan, 1993) such as ease of
purchasing. Consequently, to avoid making any improper inferences, prior IT
experience (under five levels) is included as a control variable using the application of
dummy variables in this study so as to reduce experimental errors.
3.5 Structural model
Following the first step, the second step analyzing the structural models is now
performed. Table IV lists the test results for structural models, indicating that all paths
except two are significant. Further analysis of indirect and direct effects is performed
as shown in Table V.
3.6 Model testing across groups
3.6.1 Analysis for moderating effects across groups. This study uses the analytical
strategy of Singh (1995) to examine the existence of the moderating effects on the
structural model. The
x
2
statistics for the “unconstrained” and the “partially
constrained” models are compared herein. The results to detect moderating effects of
prior experience and gender along with path coefficients are listed in Table VI.
Group
x
2
d.f. p-value NFI NNFI CFI GFI AGFI RMR RMSEA
Total 145.45 104 0.0046 0.95 0.98 0.99 0.96 0.94 0.02 0.03
Male 109.41 104 0.3390 0.93 0.99 0.99 0.94 0.91 0.03 0.02
Female 132.76 104 0.0300 0.91 0.97 0.98 0.93 0.89 0.03 0.04
Table III.
Goodness-of-fit indices
for the measurement
model
Total group
Hypothesis Standardized coefficient t-value
H1 0.55
***
5.61
H2 2 0.01 2 0.06
H3 0.10
*
1.83
H4 0.30
***
3.08
H5 0.45
***
5.26
H6 0.12
**
2.19
H7 0.14
***
2.74
H8 2 0.03 2 0.32
H9 0.30
***
3.91
Notes: *p , 0:10; **p , 0:05 and
***
p , 0:01
Table IV.
Path coefficients and
t-value based on total
group sample
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426
3.6.2 Latent mean testing. This study conducts latent mean difference tests across gender
in SEM. Since the practice of applying MANOVA to data and hypotheses that are actually
reflective of latent-variable systems can be very misleading, SEM produces a highly viable
alternative that provides accurate information on true group differences for many of the
same types of latent-variable systems (Byrne, 2001; Cole et al., 1993). Table VII reports the
results of latent mean testing. Given that the female group is designated as the reference
group and, as such, its factor means are fixed to zero, the values reported here represent the
latent mean difference between the two subgroups. It is found in Table VII that the
significantly positive estimate of the latent mean difference across the subgroups for
construct F5 indicates that the perception about ease of purchasing for online shopping is
significantly higher for the male group than for the female group (Hk is supported). The
Path
Indirect effect to
online purchase
intentions through
attitudes
Direct effect to
online
purchase intentions
% % Total effects
F3 ! F1 0.06 100 0 0 0.06
F4 ! F1 0.25 45 0.30 55 0.55
F5 ! F1 0.08 40 0.12 60 0.20
F6 ! F1 0.17 100 0 0 0.17
Notes:F1¼ Online purchase intentions; F2 ¼ Attitudes; F3 ¼ Personal awareness of security;
F4 ¼ Personal innovativeness; F5 ¼ Perceived ease of purchasing; and F6 ¼ Perceived usefulness
Table V.
Analysis of direct and
indirect effects based on
total group sample
Standardized coefficient (t-value)
Subgroup comparison (unconstrained)
x
2
(304) ¼ 331.95
Hypothesis Male group Female group Constrained
x
2
(305)
x
2
difference Results
Ha (F3 ! F1) 0.23
**
(2.12) 2 0.01 (2 0.16) 335.31 3.36
*
M . F
Hb (F3 ! F2) 0.28
***
(2.87) 0.06 (0.72) 335.16 3.21
*
M . F
Hc (F4 ! F1) 0.28
*
(1.74) 0.29
**
(2.39) 331.97 0.02 M ¼ F
Hd (F4 ! F2) 0.35
**
(2.54) 0.46
***
(4.16) 332.11 0.16 M ¼ F
He (F5 ! F1) 2 0.05 (2 0.67) 0.36
***
(3.53) 339.42 7.47
***
M , F
Hf (F5 ! F2) 0.04 (0.53) 0.34
***
(3.86) 336.05 4.10
**
M , F
Hg (F6 ! F1) 2 0.05 (2 0.41) 0.11 (0.98) 332.84 0.89 M ¼ F
Hh (F6 ! F2) 0.23
**
(2.03) 0.39
***
(3.52) 332.77 0.82 M ¼ F
Notes:1.M¼ Male group; and F ¼ Female group 2. In the constrained models, the target path
coefficients are set to be equal for cross-group datasets.
*
p , 0:10;
**
p , 0:05 and
***
p , 0:01
Table VI.
Path coefficients and
t-value across gender
Construct Estimate S.E. t-value Male versus Female
Personal awareness of security (F3) 2 0.03 0.08 2 0.38 M ¼ F
Personal innovativeness (F4) 2 0.10 0.07 2 1.45 M ¼ F
Perceived ease of purchasing (F5) 0.72
*
0.08 9.02 M . F
Perceived usefulness (F6) 2 0.18
*
0.07 2 2.69 M , F
Notes: M ¼ Male consumers, F ¼ Female consumers.
*
p , 0:01
Table VII.
Latent mean difference
across two groups
Online purchase
intentions
427
significantly negative estimate of the latent mean difference across the subgroups for
construct F6 indicates that the perception about usefulness is significantly lower for the
male group than for the female group (Hl is supported). Lastly, the latent mean differences
for both constructs F3 and F4 are insignificant, indicating that the means of personal
awareness of security and of personal innovativeness are similar for both the male and
female groups (Hi and Hj are not supported).
4. Results
Table IV lists the empirical test results. Based on the entire sample (total group), two
paths are not supported (H2 and H8 are not supported) while the remaining paths are
all significant (H1, H3, H4, H5, H6, H7, and H9 are supported). Further analysis of
indirect and direct effects is performed as shown in Table V. The decomposition in
Table V indicates that the effects of personal awareness of security and of perceived
usefulness on online purchase intentions are purely through attitudes (100 percent)
indirectly, implying that attitudes are the key consideration with respect to personal
awareness of security and perceived usefulness, from the perspective of internet
consumers. The indirect effect of personal innovativeness on online purchase
intentions through attitudes (45 percent) is slightly weaker than the direct effect on
online purchase intentions (55 percent). Finally, the indirect effect of perceived ease of
purchasing on online purchase intentions through attitudes (40 percent) is
substantially weaker than the direct effect on online purchase intentions (60 percent).
Table VI lists further tests of the moderating effect for individual paths. The test
results indicate that the influences of personal innovativeness and perceived
usefulness on both attitudes and online purchase intentions are similar for both the
male and female groups (Hc, Hd, Hg, and Hh are not supported). The influences of
personal awareness of security on both attitudes and online purchase intentions are
strong for the male customers, while no such effects exist for the female customers
(Ha and Hb are supported). Furthermore, the influences of perceived ease of purchasing
on both online purchase intentions and attitudes are stronger for the female group than
for the male group (He and Hf are supported).
Groups Hc, Hd, Hg, and Hh may have failed, because of the well-developed internet
infrastructure. Due to the popularity of internet usage in Taiwan, the majority of internet
users are familiar with different fashions and benefits of e-commerce. A possibility for
the failures of the four hypotheses might be the dichotomous measure of gender
employed, which treats gender as biological sex. As suggested in the previous literature
(Bem, 1981), gender may be conceptualized as psychological constructs. Male and female
consumers are not at bipolar extremes on such dimensions as personal innovativeness,
and thus they might vary based on degrees of femininity or masculinity (Bem, 1981).
5. Discussion and managerial implications
The test results of this study reveal that four antecedent constructs – namely, personal
awareness of security, personal innovativeness, perceived ease of purchasing, and
perceived usefulness – have indirect influences on online purchase intentions via the
attitudes. Two of antecedent constructs (personal awareness of security and perceived
ease of purchasing) also have direct influences on online purchase intentions.
These findings suggest that boosting the four antecedents can simultaneously increase
online purchase intentions and positive attitudes.
IJSIM
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428
Regarding the main effects of gender, the significant difference for the means of two
exogenous constructs (perceived ease of purchasing and perceived usefulness) across the
subgroups leads to following suggestions for e-tailers. To begin with, the significantly
lower mean of perceived ease of purchasing for female consumers compared to males
indicates that female consumers tend to give a lower evaluation than males of the
effortlessness of online purchases. This unique finding is very helpful for e-tailers,
indicating that a user-friendly system which facilitates online shopping should be
emphasised during advertising campaigns. Besides, word-of-mouth broadcasting by
other female consumers may be another important approach to help female consumers
overcome hurdles and barriers to accepting online purchasing, especially during the early
stages of online shopping. Establishing an online consumer community based on gender
and hobby provides another method of improving female perceptions of ease of
purchasing owing to the effective communications among female consumers themselves.
The significantly lower mean of perceived usefulness for males compared to females
indicates that male consumers are less likely to consider the internet useful for making
purchases. Restated, lower perceived usefulness score for male consumers reveals that
male consumers do not perceive a performance-use contingency (Venkatesh and Morris,
2000) from online purchases. Compared to females, males tend to reveal more “masculine”
traits (Bem, 1981), such as being assertive, impatient, and goal-oriented (Venkatesh and
Morris, 2000), indicating that they desire to successfully accomplish efficient online
purchases without distraction. However, due to the overwhelming explosion of online
information, it takes a longer time for consumers to search and make purchase decisions
online for a specific item, causing lower mean scores of usefulness for male consumers.
This notable finding is important for e-tailers. The evidence suggests that male consumers
value task orientation, reflecting an efficient online purchasing that may require search
mechanisms designed to help consumers compare products from different online stores,
thus producing an extensive list of comparative data on products to enable consumers to
rapidly identify attractive product attributes. Features improving usage speed are also
important for male consumers. For example, offering an expert endorsement of the most
popular products on a single page, from which purchasers can reach the checkout via a
single click, would facilitate male consumers (who tend to be goal-oriented shoppers) to
make the decisions efficiently, since they prefer to visit the sites with readily available and
useful information to provide quick advice. To sum up, offering male consumers the right
products whenever they need them and responding to inquiries in a timely fashion can
strengthen the attitudes and purchase intentions of male consumers.
The finding of significant gender differences provides additional support to
psychological theories, stating that males and females have different gender-based
perceptions (Venkatesh and Morris, 2000) which can influence their preferences and
decisions during online shopping. Two critical implications of gender differences are
summarized as follows.
First, a stronger influence of personal awareness of security on both attitudes and
online purchase intentions for males (versus females) is consistent with the male traits
of concern for insecurity-lability (Rierdan et al., 1982). This indicates that, besides
implementing adequate security measures (e.g. SSL) against fraud, it is even more
important for those e-tailers targeting the male consumers to satisfy those consumers
with a trustworthy brand image of the website by providing additional measures of
limitation or risk, such as insurance for online transactions. After all, consumers
Online purchase
intentions
429
(especially males) will have online purchase intentions if they feel that their sensitive
information is safe and insured, regardless of the objective website security of e-tailers.
All the above measurements can be promoted by celebrity endorsers, reducing the
perceived risk to consumers. As soon as the personal awareness of security is
improved, male consumers will respond promptly and with more positive attitudes and
higher online purchase intentions than females.
Second, the stronger influence of perceived ease of purchasing on both attitudes and
online purchase intentions for females compared to males indicates that online
purchase intentions and attitudes are sensitive to female perceptions, given a higher
demand for the physical environment (e.g. in-store) or a stronger desire for the sensory
pleasures associated with touching a product. After all, online and offline
environments cause different shopping perceptions even when the same products
are available for purchase. An online store that is perceived by females as user-friendly
will facilitate online store visits and online purchase intentions more than other online
stores that are seen as difficult to shop in. By applying interactive virtual reality along
with background music, E-tailers can efficiently demonstrate for female consumers
how to use products in a fashionable manner, and engage them with surprising visual
effects, and thus successfully stimulate positive attitudes and increased purchase
intentions. The customerisation of homepages is substantially positive for females
since it increases the sense of consumer freedom and accessibility.
The implications of this study can be applied to the wireless area where consumers
can surf with their cell phones to purchase movie tickets, food, etc. from retailers
similar to e-tailers. E-tailers might consider the importance of corresponding regularly
with consumers via the internet, thus providing consumers with substantive
information from the perspective of gender differences. Learning gender differences is
critical, as e-tailers and advertisers can better target consumers and consequently
foster more positive attitudes and online purchase intentions.
6. Limitations and future research directions
This study suffers from some limitations relating to data collection and results’
interpretation. The first limitation is the possibility of a common method bias by using a
single questionnaire to measure all constructs. Another limitation was that this study has
been conducted in the e-tail service industry of a single country setting (Taiwan). As a
result, the generalisability of the findings might be limited. Additional research across
different countries will be required in order to generalize the findings. The third limitation
in this study is using only one product category (computer and communication products),
which may seem gender-specific given that computer products are considered appealing
to male consumers. The fourth limitation is that this study uses three different levels of
significance ð p , 0:01; p , 0:05; p , 0:10Þ: However, the level of 0.10 significance
should be used with greater caution during the interpretation of online purchase
intentions. The fifth limitation arises from some indicators being removed during the
measurement model testing. However, the indicator removal is acceptable and inevitable
because, to make the cross-group comparison (Singh, 1995), the items retained in both the
male and female models must be the same to perform the Chi-square difference test, which
is why some items significant in one model but insignificant in another model can not be
retained in the final male and female models. Lastly, this study in fact measured subjects’
intentions and attitudes towards behavior rather than actual behavior. However,
IJSIM
16,5
430
intentions may not be necessarily perfect predictors of behaviors. Therefore, a future
study can try to improve such a shortcoming by directly observing the subjects over time.
Future studies can take note of these shortcomings in planning future research work by,
for example, specifying to some different models. It would be also interesting for future
studies to investigate whether other countries also display similar gender differences in
online purchase intentions.
References
Adams, D.A., Nelson, R.R. and Todd, P.A. (1992), “Perceived usefulness, ease of use, and usage of
information technology: a replication”, MIS Quarterly, Vol. 16 No. 2, pp. 227-50.
Agarwal, R. and Karahanna, E. (2000), “Time flies when you’re having fun: cognitive absorption
and beliefs about information technology usage”, MIS Quarterly, Vol. 24 No. 4, pp. 665-94.
Ajzen, I. (1985), “From intentions to actions: a theory of planned behavior”, in Kuhl, J. and
Beckmann, J. (Eds), Action Control: From Cognition to Behavior, Springer-Verlag,
New York, NY, pp. 11-39.
Anderson, J.C. and Gerbing, D.W. (1988), “Structural equation modeling in practice: a review and
recommended two-step approach”, Psychological Bulletin, Vol. 103 No. 3, pp. 411-23.
Anderson, E.W. and Sullivan, M.W. (1993), “The antecedents and consequences of customer
satisfaction for firms”, Marketing Science, Vol. 12 No. 2, pp. 125-43.
Bem, S.L. (1981), “The BSRI and gender schema theory: a reply to Spence and Helmreich”,
Psychological Review, Vol. 88 No. 3, pp. 369-71.
Bertsch, T., Busbin, J. and Wright, N. (2002), “Gaining competitive advantage in e-tailing through
marketing management and value-added uses of technology”, Competitiveness Review,
Vol. 12 No. 2, pp. 49-56.
Bobbit, L.M. and Dabholkar, P.A. (2001), “Integrating attitudinal theories to understand and
predict use of technology-based self-service”, International Journal of Service Industry
Management, Vol. 12 No. 5, pp. 423-50.
Byrne, B.M. (2001), Structural Equation Modeling with AMOS: Basic Concepts, Applications, and
Programming, Lawrence Erlbaum Associates, New Jersey, NJ.
Citrin, A.V., Sprott, D.E., Silverman, S.N. and Stem, D.E. Jr (2000), “Adoption of internet
shopping: the role of consumer innovativeness”, Industrial Management þ Data Systems,
Vol. 100 No. 7, pp. 294-5.
Cole, D.A., Maxwell, S.E., Arvey, R. and Salas, E. (1993), “Multivariate group comparisons of
variable systems: MANOVA and structural equation modeling”, Psychological Bulletin,
Vol. 114 No. 1, pp. 174-84.
Costa, J.A. (1994), Gender Issues and Consumer Behavior, Sage, London.
Davis, F.D., Bagozzi, R.P. and Warshaw, P.R. (1989), “User acceptance of computer technology: a
comparison of two theoretical models”, Management Science, Vol. 35 No. 8, pp. 982-1003.
Fischer, E. and Arnold, S.J. (1994), “Sex, gender identity, gender role attitudes and consumer
behavior”, Psychology and Marketing, Vol. 11 No. 2, pp. 163-82.
Fishbein, M. and Ajzen, I. (1975), Beliefs, Attitude, Intention, and Behavior, Addison-Wesley,
Reading, MA.
Fornell, C. and Larcker, D.F. (1981), “Evaluating structural equation models with unobservable
variables and measurement error”, Journal of Marketing Research, Vol. 18 No. 1, pp. 39-50.
Gefen, D. and Straub, D.W. (1997), “Gender differences in the perception and use of e-mail: an
extension to the technology acceptance model”, MIS Quarterly, Vol. 21 No. 4, pp. 389-400.
Online purchase
intentions
431
Gilligan, C. (1982), In a Different Voice: Psychological Theory and Women’s Development,
Harvard University Press, Cambridge, MA.
Goldsmith, R.E. (2002), “Explaining and predicting consumer intention to purchase over the
internet: an exploratory study”, Journal of Marketing Theory and Practice, Vol. 10 No. 1,
pp. 22-8.
Goldsmith, R.E., Stith, M.T. and White, J.D. (1987), “Race and sex differences in self-identified
innovativeness”, Journal of Retailing, Vol. 63 No. 4, pp. 411-25.
Harrison, A.W. and Rainer, R.K. Jr (1992), “The influence of individual differences on skill in
end-user computing”, Journal of Management Information Systems, Vol. 9 No. 1, pp. 93-111.
Hatcher, L. (1994), A Step-by-Step Approach to Using the SAS System for Factor Analysis and
Structural Equation Modeling, SAS Institute Inc., Cary, NC.
Hirschman, E.C. (1980), “Innovativeness, novelty seeking, and consumer creativity”, Journal of
Consumer Research, Vol. 7 No. 3, pp. 283-95.
Hofstede, G. (1980), Culture’s Consequences: International Differences in Work Related Values,
Sage, London.
Holbrook, M.B. (1986), “Aims, concepts and methods for the representation of individual differences
in aesthetic response to design features”, Journal of Consumer Research, Vol. 13 No. 3,
pp. 337-47.
Igbaria, M. and Chakrabarti, A. (1990), “Computer anxiety and attitudes toward microcomputer
use”, Behaviour and Information Technology, Vol. 9 No. 3, pp. 229-41.
Janda, S., Trocchia, P.J. and Gwinner, K.P. (2002), “Consumer perceptions of internet retail
service quality”, International Journal of Service Industry Management, Vol. 13 No. 5,
pp. 412-31.
Jo
¨
reskog, K.G. and Sorbom, D. (1986), LISREL VI: Analysis of Linear Structural Relationships by
Maximum Likelihood, Instrumental Variables, and Least Squares Methods , 4th ed.,
Scientific Software, Mooresville, IN.
Kim, Y.M. and Shim, K.Y. (2002), “The influence of internet shopping mall characteristics and
user traits on purchase intent”, Irish Marketing Review, Vol. 15 No. 2, pp. 25-34.
Koufaris, M. (2002), “Applying the technology acceptance model and flow theory to online
consumer behavior”, Information Systems Research, Vol. 13 No. 2, pp. 205-23.
McKnight, D.H., Choudhury, V. and Kacmar, C. (2002), “Developing and validating trust
measures for e-commerce: an integrative typology”, Information Systems Research, Vol. 13
No. 3, pp. 334-59.
Mathieson, K. (1991), “Predicting user intentions: comparing the technology acceptance model
with the theory of planned behavior”, Information Systems Research, Vol. 2 No. 2, pp. 173-91.
Meyers-Levy, J. (1988), “The influence of sex roles on judgment”, Journal of Consumer Research,
Vol. 14 No. 4, pp. 522-30.
Meyers-Levy, J. (1989), “Priming effects on product judgments: a hemispheric interpretation”,
Journal of Consumer Research, Vol. 16 No. 1, pp. 76-86.
Meyers-Levy, J. and Sternthal, B. (1991), “Gender differences in the use of message cues and
judgments”, Journal of Marketing Research, Vol. 28 No. 1, pp. 84-96.
Moutinho, L. and Goode, M. (1995), “Gender effects to the formation of overall product satisfaction:
a multivariate approach”, Journal of International Consumer Marketing, Vol. 8 No. 1,
pp. 71-91.
Nunally, C. (1967), Psychometric Theory, McGraw-Hill, New York, NY.
IJSIM
16,5
432
Oumil, A.B. and Erdem, O. (1997), “Self-concept by gender: a focus on male-female consumers”,
Journal of Marketing Theory and Practice, Vol. 5 No. 1, pp. 7-14.
Palmer, A. and Bejou, D. (1995), “The effects of gender on the development of relationships
between clients and financial advisers”, The International Journal of Bank Marketing,
Vol. 13 No. 3, pp. 18-27.
Rierdan, J., Koff, E. and Heller, H. (1982), “Gender, anxiety, and human figure drawings”, Journal
of Personality Assessment, Vol. 46 No. 6, pp. 594-6.
Salisbury, W.D., Pearson, R.A., Pearson, A.W. and Miller, D.W. (2001), “Perceived security and
world wide web purchase intention”, Industrial Management & Data Systems, Vol. 101
Nos 3/4, pp. 165-76.
Schlosser, A.E. (2003), “Experiencing products in the virtual world: the role of goal and imagery
in influencing attitudes versus purchase intentions”, Journal of Consumer Research, Vol. 30
No. 2, pp. 184-98.
Singh, J. (1995), “Measurement issues in cross-national research”, Journal of International
Business Studies, Vol. 26 No. 3, pp. 597-619.
Straits Times (1996), “Finding out who surfs the internet is their business”, Straits Times,
12 November.
Szajna, B. (1996), “Empirical evaluation of the revised technology acceptance model”,
Management Science, Vol. 42 No. 1, pp. 85-92.
Taylor, S. and Todd, P.A. (1995a), “Understanding information technology usage: a test of
competing models”, Information Systems Research, Vol. 6 No. 2, pp. 144-77.
Taylor, S. and Todd, P.A. (1995b), “Decomposition of cross effects in the theory of planned
behavior: a study of consumer adoption intentions”, International Journal of Research in
Marketing, Vol. 12 No. 2, pp. 137-55.
Taylor, S. and Todd, P.A. (1995c), “Assessing IT usage: the role of prior experience”,
MIS Quarterly, Vol. 19 No. 4, pp. 561-70.
Teo, T.S.H. (2001), “Demographic and motivation variables associated with internet usage
activities”, Internet Research, Vol. 11 No. 2, pp. 125-37.
Van Riel, C.R., Liljander, V. and Jurrie
¨
ns, P. (2001), “Exploring consumer evaluations of
e-services: a portal site”, International Journal of Service Industry Management, Vol. 12
Nos 3/4, pp. 359-77.
Venkatesh, V. and Davis, F.D. (1996), “A model of the antecedents of perceived ease of use:
development and test”, Decision Science, Vol. 27 No. 3, pp. 451-79.
Venkatesh, V. and Morris, M.G. (2000), “Why don’t men ever stop to ask for directions?
Gender, social influence, and their role in technology acceptance and usage behavior”,
MIS Quarterly, Vol. 24 No. 1, pp. 115-39.
Yang, Z. and Jun, M. (2002), “Consumer perception of e-service quality: from internet
purchaser and non-purchaser perspectives”, Journal of Business Strategies, Vol. 19 No. 1,
pp. 19-41.
Further reading
Gilroy, F.D. and Desai, H.B. (1986), “Computer anxiety: sex, race and age”, International Journal
of Man-Machine Studies, Vol. 25 No. 6, pp. 711-9.
Goldsmith, R.E. and Goldsmith, E.B. (2002), “Buying apparel over the internet”, The Journal of
Product & Brand Management, Vol. 11 Nos 2/3, pp. 89-100.
Hofstede, G. (1991), Culture and Organizations, McGraw-Hill, New York, NY.
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Appendix
Construct Indicators Source
Online purchase
intentions (F1)
1. I intend to use the internet to buy CCP
2. I plan to use the internet to purchase CCP within
the next few months
4. Overall, I would use the internet to buy CCP I need
5. Buying CCP via the internet is something I would do
Taylor and Todd (1995a, b)
Attitude (F2) 6. Using the internet to buy CCP is a good idea
7. Purchasing CCP via the internet would be unwise
*
8. I like the idea of buying CCP via the internet
9. Using the internet to purchase CCP is a wise idea
10. Using the internet to buy CCP would be pleasant
Taylor and Todd (1995a, b)
Personal awareness of
security (F3)
11. I would feel secure in providing sensitive
information (e.g. credit card number) for
purchasing CCP online
12. It would be no security problem transmitting
sensitive information online for buying CCP
13. I feel the risk associated with online purchase was
low
14. It would be always too risky to buy CCP via the
internet due to the transmission sensitive
information
*
15. Overall, the internet should be used as a secure
way to buy CCP
16. I would feel fine in providing sensitive
information about myself while buying CCP online
Yang and Jun (2002) and
Salisbury et al. (2001)
Personal innovativeness
(F4)
17. I like to explore new websites
18. When I hear about a new website, I often find an
excuse to go visit it
19. Among my peers, I am usually one of the first to
try out new internet sites
20. In general, I am interested in trying out new web
sites
21. When I have some free time, I would explore new
web sites
22. It is fun to visit a variety of websites
McKnight et al. (2002)
Perceived ease of
purchasing (F5)
23. Buying CCP via the internet would not require a
lot of my mental effort
24. I think buying CCP via the internet would be easy
25. I find it easy to buy CCP via the internet when I
need it
26. Using the internet to buy CCP would be easy for
me
27. I would find it easy to buy CCP I want via internet
28. It would be tough for me to become skillful at using
the internet to buy CCP
*
Venkatesh and Davis (1996)
Perceived usefulness
(F6)
29. The internet would be useful in my purchasing
CCP
30. The advantages of buying CCP via the internet
will outweigh the disadvantages
31. Overall, using the internet to buy CCP will be
advantageous for me
32. Buying CCP via the internet will be of no benefit
to me
*
Taylor and Todd (1995a, b)
Notes: CCP stands for computer&communication product;
*
Denotes items requiring reverse scoring
Table AI.
Measures of constructs
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16,5
434
Total (Unconstrained)
x
2
(d.f. ¼ 104) ¼ 145.45
Male (Unconstrained)
x
2
(d.f. ¼ 104) ¼ 109.41
Female (Unconstrained)
x
2
(d.f. ¼ 104) ¼ 132.76
Construct pair Constrained
x
2
(d.f. ¼ 105)
x
2
difference Constrained
x
2
(d.f. ¼ 105)
x
2
difference Constrained
x
2
(d.f. ¼ 105)
x
2
difference
ðF1; F2Þ 201.16 55.71
*
139.07 29.66
*
156.17 23.41
*
ðF1; F3Þ 307.19 161.74
*
152.88 43.47
*
219.70 86.94
*
ðF1; F4Þ 230.16 84.71
*
143.72 34.31
*
184.22 51.46
*
ðF1; F5Þ 315.48 170.03
*
194.13 84.72
*
204.98 72.22
*
ðF1; F6Þ 273.48 128.03
*
170.31 60.90
*
200.69 67.93
*
ðF2; F3Þ 447.50 302.05
*
231.53 122.12
*
249.26 116.50
*
ðF2; F4Þ 253.73 108.28
*
166.31 56.90
*
178.88 46.12
*
ðF2; F5Þ 487.48 342.03
*
250.83 141.42
*
241.98 109.22
*
ðF2; F6Þ 284.05 138.60
*
190.27 80.86
*
188.33 55.57
*
ðF3; F4Þ 400.79 255.34
*
185.10 75.69
*
279.53 146.77
*
ðF3; F5Þ 649.00 503.35
*
253.42 144.01
*
446.03 313.27
*
ðF3; F6Þ 403.15 257.70
*
226.32 116.91
*
249.63 116.87
*
ðF4; F5Þ 462.71 317.26
*
247.95 138.54
*
280.75 147.99
*
ðF4; F6Þ 271.99 126.54
*
159.50 50.09
*
206.69 73.93
*
ðF5; F6Þ 426.63 281.18
*
243.22 133.91
*
258.41 125.65
*
Notes: F1=Online purchase intentions; F2=Attitudes; F3=Personal awareness of security; F4=Personal innovativeness; F5=Perceived ease of
purchasing; and F6=Perceived usefulness.
*
Significant at the 0.01 overall significance level by using the Bonferroni method
Table AII.
Chi-square difference
tests for examining
discriminant validity
Online purchase
intentions
435
