Computers in Human Behavior 26 (2010) 597–601

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Computers in Human Behavior

journal homepage: www.elsevier .com/locate /comphumbeh

Exploring gender differences in online shopping attitude

Bassam Hasan *

College of Business & Economics, Qatar University, P.O.Box 2713, Doha, Qatar

a r t i c l e i n f o

Article history:Available online 8 February 2010

Keywords:GenderOnline shoppingAttitudeAffectCognitionBehavior

0747-5632/$ - see front matter � 2009 Elsevier Ltd. Adoi:10.1016/j.chb.2009.12.012

* Tel.: +974 485 1583; fax: +974 493 0927.E-mail address: bassamsh@qu.edu.qa.

a b s t r a c t

While attitude and gender are important factors that affect online shopping behavior, toward onlineshopping attitude remains a poor understood construct. Moreover, very few studies, if any, have explic-itly addressed gender differences in online shopping attitude. Using attitude as a multidimensional con-cept to include cognitive, affective, and behavioral components, the present study examines genderdifferences across the three attitudinal components. The results of empirical testing demonstrate threedistinct components of online shopping attitude and significant gender differences in all three attitudinalcomponents. The results also show that the largest gender difference is in the cognitive attitude, indicat-ing that females value the utility of online shopping less than their male counterparts do.

� 2009 Elsevier Ltd. All rights reserved.

1. Introduction

The substantial growth and steady increase of online sales stim-ulate great interest in understanding what impacts people’s deci-sions to participate in or refrain from shopping online (Cho,2004; Glassberg, Grover, & Teng, 2006; Kim, Williams, & Lee,2003; Korzaan, 2003; Liao & Cheung, 2001; Ranganathan &Grandon, 2002; Shih, 2004; van der Heijden & Verhagen, 2004;Zahedi & Song, 2009). Among the multitude of factors examinedin past research as potential determinants of online shopping,attitude toward online shopping demonstrated a significant impacton online shopping behavior (Ahn, Ryu, & Han, 2007; Chen & Wells,1999; Lin, 2007; Pavlou & Fygenson, 2006; Sanchez-Franco, 2006;Wu, 2003). Accordingly, better understanding of online shoppingattitude is critical for designing and managing effective websitesthat can help businesses attract and retain online customers.

Although the number of Internet users is equally divided amongthe genders, more men than women engage in online shopping andmake online purchases (Rodgers & Harris, 2003). This gender gap inonline shopping drew attention to the role of gender in onlineshopping and factors that affect men’s and women’s intention tobuy online (Rodgers & Harris, 2003; Sanchez-Franco, 2006; VanSlyke, Comunale, & Belanger, 2002). Gender difference in onlineshopping have been examined from various perspectives such asperceived risk of online buying (Garbarino & Strahilevitze, 2004),website usability and design (Cyr & Bonanni, 2005), and technol-ogy acceptance (Chen, Gillenson, & Sherrell, 2002; Porter & Donthu,2006; Sanchez-Franco, 2006).

ll rights reserved.

While studies of online shopping attitude are widespread in theliterature, studies of gender differences in online shopping attitudeare scarce and reported findings are inconsistent (Cyr & Bonanni,2005; Dittmar, Long, & Meek, 2004). An extensive review of onlineshopping literature by Chang, Cheung, and Lai (2005) shows thatmore men than women buying online in some studies and no sig-nificant gender differences in online shopping behavior betweenthe genders in other studies. Likewise, a more recent review byZhou, Dai, and Zhang (2007) demonstrates conflicting findings per-taining to the impact of gender on online shopping activities. Thus,gender differences in online shopping attitude deserve more atten-tion and better understanding.

Dittmar et al. (2004) suggest that neglecting emotional and so-cial aspects of online shopping may contribute to the mixed andinconsistent findings. While attitude represents a multidimen-sional variable with cognitive, affective, and behavioral compo-nents (Fishbein & Ajzen, 1975), most studies use attitude as aunifactor concept, focusing primarily on its affective aspect. Usingattitude as a multidimensional concept can enhance understandingof gender differences in online shopping attitude. Accordingly, thisstudy aims to fill this void and examine online shopping attitude asa multi-component concept and investigate gender difference inthe cognitive, affective, and behavioral components of online shop-ping attitude.

2. Research background

2.1. Gender difference in computing

Studies of gender differences in various computer-related be-liefs, attitudes, and behaviors are abound in the literature. Table 1presents a snapshot of studies focusing on gender difference in

Table 1Studies of gender differences in computing.

Study Examined belief/behavior

Cyr and Bonanni (2005) Perceptions of website characteristicsDattero and Galup (2004) Preference of programming languagesGarbarino and Strahilevitze (2004) Risks of online shoppingGefen and Straub (1997) Acceptance of an email systemHartzel (2003) Computer self-efficacy and system

usageIlie, Van Slyke, Green, and Lou

(2005)Acceptance of communicationtechnologies

Kay (2006) Computer ability, attitude, and useOng and Lai (2006) E-learning acceptanceSanchez-Franco (2006) Perceptions of website usageSimon (2001) Satisfaction with website designTeo and Lim (2000) Patterns of Internet usageVan Slyke et al. (2002) Perceptions of online shoppingVenkatesh and Morris (2000) Technology useYoung (2000) Students’ computer attitude

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behaviors related to computer and technology settings. The sum-mary in Table 1 shows that: (1) very few studies focused on genderdifferences in online shopping, and (2) even fewer studies, if any,examined gender differences in online shopping attitude.

Despite the widespread proliferation of Internet-based and on-line commercial applications, females and males harbor diverseperceptions and attitudes toward online shopping. In comparingconventional with online shopping, Dittmar et al. (2004) reportthat men’s attitude stay much the same in both shopping environ-ments whereas women’s attitude change substantially and becomeless favorable toward online shopping. In addition, Cyr and Bonan-ni (2005) indicate that men spend more time and money on onlinepurchases than do women.

Van Slyke et al. (2002) point out gender differences in other on-line shopping characteristics such as compatibility, complexity, re-sult demonstrability, and relative advantage. In examining genderdifference in online transaction security, website design, websitetrust, website satisfaction, and e-loyalty, Cyr and Bonanni (2005)show significant gender differences in beliefs regarding websitedesign, website trust, website satisfaction, and e-loyalty, withmen having more favorable perceptions than women.

With a few exceptions, explicit research studies to address gen-der differences in online shopping are scarce (Dittmar et al., 2004).As a result, little is known about males’ and females’ perceptions ofonline shopping and what impacts men’s and women’s decision toengage in or abstain from online shopping (Cyr & Bonanni, 2005).Furthermore, literature reviews of online shopping indicate thatresults concerning gender differences in online shopping environ-ments are mixed and inconsistent (Chang et al., 2005; Zhouet al., 2007).

Zhou et al. (2007) suggest three explanations for gender differ-ences in online shopping. First, women’s shopping orientation isdifferent from that of men. While men prefer convenience over so-cial interaction, women are more motivated by emotional and so-cial interaction. Thus, the lack of face-to-face communication orsocial interaction offered by online shopping may deter more wo-men than men from shopping online (Dittmar et al., 2004). Second,the types and characteristics of products that are available on on-line seem to favor males (Van Slyke et al., 2002). Products that aremore associated with men such as computers and electronics areamply available and can be easily purchased online. Whereas prod-ucts that are more associated with females such as food, homedécor, and clothing may not be widely available online. Thus, wo-men may view online shopping as less compatible and less accom-modating than conventional shopping. Finally, women prefer andenjoy physical evaluation of products such as seeing and feelingthe product before they buy it (Cho, 2004; Dittmar et al., 2004).

Although businesses can provide very clear images and animationsof their products on their website, customers cannot touch or feelthese products.

2.2. Attitude

Attitude is a key concept in the theory of reasoned action (TRA)(Ajzen and Fishbein, 1980). TRA provides theoretical framework tonumerous studies of online shopping behavior (e.g. Cho, 2004; Kor-zaan, 2003; Pavlou & Fygenson, 2006). Attitude refers to a learnedpredisposition to respond in a consistently favorable or unfavor-able manner to an object, event, or stimulus (Fishbein & Ajzen,1975). This definition implies that attitudes develop over time aspeople gain experience with the object or receive knowledge aboutthe object from other sources. Then, the formed attitude stimulatesactions or behaviors toward the object and, based on their attitude,people perform positive or negative actions.

Attitude is a multidimensional construct with cognitive, affec-tive, and behavioral components (Fishbein & Ajzen, 1975). The cog-nitive component refers to what a person knows about an object,e.g. knowing that online shopping is a convenient way for shop-ping. The affective component concerns the extent to which anindividual likes or dislikes the object. Lastly, the behavioral compo-nent pertains to the behavioral intention, covert, or overt actionstoward the object; what type of action a person will take regardingthe given object (e.g. online shopping). A person’s knowledge (cog-nition) and liking (affect) of the object influence his/her behavioralattitude toward the object.

Attitude attracts considerable research attention and plays asignificant role in other online applications such as e-banking(Ahn et al., 2007; Gao & Koufaris, 2006; Liao & Cheung, 2002;Lin, 2007; Pavlou & Fygenson, 2006). In most studies, attitudeshows a positive relationship with online shopping (Chang et al.,2005). Furthermore, Chang et al. (2005) use online shopping atti-tude as the key dependent variable in the framework they devel-oped to synthesize and map findings in online shopping research.

However, the extent of the relationship between attitude andonline shopping is not collectively consistent across studies(Glassberg et al., 2006). For example, Zhou et al. (2007) indicatethat the path coefficient between attitude and online shoppingintention varies in strength from 0.77 in some studies to 0.35 inothers. They attribute the fluctuation in the effect of attitude tothe use of different definitions and conceptualizations of attitude.They point out that some studies focus on the cognitive componentof attitude (i.e. pros and cons of online shopping) whereas otherstudies use the affective component of attitude (i.e. liking or feel-ings toward online behavior). Furthermore, Bruner and Kumar(2002) indicate that available scales for measuring web attitudesare not psychometrically equivalent and different conclusionscan be drawn depending on which scale is used.

Some studies acknowledge that online shopping attitude is amultifactor construct. In the context of using technology accep-tance model (TAM) examine the acceptance and use of web tech-nologies, Glassberg et al. (2006) distinguish between cognitiveand involvement attitudes. They maintain that incorporating acomponent of attitude in TAM enhances the explanatory powerof the model. A factor analysis data collected from 198 universitystaff and students about intention to shop online for 17 differentproducts reveals three underlying components of online shoppingattitude (Sorce, Perotti, & Widrick, 2005). They identify these com-ponents as convenience, information, and shopping experience andfind that the three factors explain more variance in predicting on-line buying. While these studies are among the first studies toacknowledge that online shopping attitude is a multidimensionalconstruct and provide better insights into the impact of attitude

Table 2Rotated component matrix.

Component1 2 3 a

C1 .889 .287 .292 0.94C2 .895 .315 .248C3 .897 .314 .234A1 .322 .856 .250 0.93A2 .270 .862 .330A3 .459 .751 .313B1 .369 .140 .821 0.91B2 .257 .310 .876B3 .136 .446 .804

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on online shopping, they are exploratory in nature and do notexamine gender difference in attitude or its components.

In studying attitude, Breckler (1984) recommends that a priorimethod is vital for classifying measures of cognition, affect, andbehavior. He also suggests that confirmatory, rather than explor-atory, approach is more appropriate to examine components ofattitude. Accordingly, this study intends to address the aforemen-tioned deficiencies and provide better insights into understandingonline shopping attitude by: (1) recognizing online shopping atti-tude as a multifactor construct with three distinct and correlatedcomponents of cognition, affect, and behavior, and (2) explicitlyexamining gender differences in the three subcomponents of on-line shopping attitude.

Table 3Correlation and means of attitude components.

Attitude component 1 2 3 M F C

1. Cognition 1.00 16.2 8.9 12.92. Affect 0.68* 1.00 15.1 12.0 13.73. Behavior 0.66* 0.59* 1.00 18.3 14.6 16.6

M, males’ mean, F, females’ mean, C, combined mean.* p < 0.001.

Table 4Gender differences in attitudinal components.

Attitude Male Female Difference t p-value

Cognitive 16.2 8.9 7.3 9.960 .001Affective 15.1 12.0 3.1 4.469 .001Behavioral 18.3 14.6 3.7 5.253 .001

3. Methodology

3.1. Experimental design

Data were collected from 80 students enrolled in an electroniccommerce course at a Midwestern university. Of the 80 participants,36 (45%) were females and 44 (55%) were males. The average age ofparticipants is 22.54 years with a standard deviation of 1.73 years.All participants have majors within the college of business.

Participation in the study was voluntary and no credit was gi-ven in exchange for participation. Students were assured that thesurvey is anonymous and individual responses could not be iden-tified. It was also made clear to participants that the aggregatesof their responses would be used for data analysis purposes only.Finally, participants were assured that neither their participationin the study nor their responses bear any impact on their perfor-mance in the course.

For more accurate assessments of consumers’ beliefs about on-line shopping, Cyr and Bonanni (2005) recommend using lessknown websites to minimize the compounding effects of brandname and company reputation on examined relationships amongvariables and to ensure that attitude develops upon visiting thewebsite (Bruner & Kumar, 2002). Thus, a careful search was doneto identify a suitable website for the purpose of this study. To thatend, a gender-neutral website that sells skating shoes/accessorieswas deemed appropriate and was selected for this study.

Prior to the experiment, students were asked whether they hadvisited or used the selected website before and they all indicatedthat the website was unfamiliar to them. Then participants wereasked to go to a computer laboratory to navigate the given websiteand search for a kid’s skating shoe (with specific size and color).Once they find the product, participants were asked to performthe first few steps of placing an online shopping order (until theywere required to provide personal or financial information) forthe required products. After finishing the navigation and orderingtasks, participants completed the research questionnaire.

3.2. Measures

Based on the theoretical foundation of the attitude constructand empirical studies, nine statements were adapted and used tomeasure the underlying components of online shopping attitude.Three statements were used to measure the affective component:(1) I do not like to shop online (reversed), (2) Online shoppingmakes me feel happy, and (3) I feel excited when I shop online.The cognitive attitude was measured by the following statements:(1) Online shopping is a wise way to shop, (2) Online shopping isuseful to people, and (3) Online shopping is an effective way toshop. The behavioral attitude was measured by three statements:(1) I plan to buy from this website in the future, (2) I intend tobuy from this website in the future, and (3) I expect my purchases

from this website to continue in the future. Response to all state-ments were measured by a seven-point scale ranging from (1)strongly disagree to (7) strongly agree.

4. Results

Table 2 presents the results of the confirmatory and reliabilityanalyses. All items (in boldface), with the exception of one affectitem, demonstrate high loading (>0.80) on their intended factorand low loading on other factors (<0.50). Cronbach was used toevaluate construct reliability. As Table 1 indicates, all constructsshow high internal reliability (a > 0.80).

Table 3 presents the correlation estimates among the underly-ing components of attitude along with the means. The correlationsamong the three attritional components as are high and significant,with cognition having the strongest correlation with the other twocomponents. All correlation estimates are below the 0.80 thresholdto suspect the presence of multicollinearity (Bryman & Cramer,1994). Furthermore, male’s means are noticeably higher than thoseof females in all three attitudinal components. Females’ cognitiveattitude toward online shopping is the lowest and behavioralintention to shop online is high among males and females.

Gender differences in attitudinal components were tested usingt-tests and Table 4 presents the results of the t-tests. All gender dif-ferences across the three attitudinal components are significant,with men’s demonstrating more favorable online shopping atti-tudes than women. The largest gender difference is in cognitiveattitude and the lowest is in affective attitude.

5. Discussion

The present study distinguishes among the underlying compo-nents (cognition, affect, and behavior) of online shopping attitude

600 B. Hasan / Computers in Human Behavior 26 (2010) 597–601

and examines gender differences in the three attitudinal compo-nents. The results provide strong support for both objectives.Empirical results identify three distinct and valid components ofonline shopping attitude and reveal significant gender differencesacross the three attitudinal components.

Overall, men’s cognitive, affective, and behavioral online shop-ping attitudes are higher than those of women, suggesting that on-line shopping may not be as attractive or appealing to women as itis to men. These results corroborate past studies which report sim-ilar findings. For instance, Dittmar et al. (2004) indicate that wo-men have more positive attitude toward conventional thanonline shopping and men’s attitudes do not differ significantly be-tween conventional and online shopping.

Females’ cognitive attitude toward online shopping is markedlylower than that of men. According to attitude theory, cognition ofan object or stimulus plays a key role in affect and behavioralintention toward that object. Thus, women’s low cognitive attitudemay explain their low affection toward online shopping and theirmodest online shopping activities. Since cognitive attitude pertainsto understanding pros and cons of an object (Zhou et al., 2007), thisfinding suggests that females are still unconvinced or skepticalabout the benefits of online shopping. Similarly, this finding maysuggest the females are still concerned and apprehensive aboutthe risks and threats associated with online shopping (Garbarino& Strahilevitze, 2004). Thus, businesses aiming to attract more fe-male online consumers need to focus their efforts on increasing fe-males’ awareness of the benefits associated with online shopping.Likewise, mitigating females’ uneasiness about online shopping ispivotal to attracting and retaining female online shoppers. For in-stance, implementing online referral mechanism or offering dis-counts for first-time users or visitors may help consumersovercome their negative perceptions of and alleviate their doubtsabout online shopping.

Affective attitude of females is lower than that of males. This find-ing provide empirical support for Dittmar et al. (2004) study whichreports that females place greater emphasis on the emotional andpsychological experiences linked to online shopping. They maintainthat women prefer conventional shopping over online shopping be-cause of the lack of social and interpersonal interactions in onlineshopping whereas men prefer online shopping because of its func-tional benefits such as convenience, economy, and efficiency.

The affective attitude of females toward online shopping is nothigh as that of males. This is not surprising given that cognitionrepresents a strong antecedent to affective attitude. Thus, in addi-tion to attempts to enhance females’ cognition of the utility of on-line shopping, additional efforts are needed to enhance females’liking of online shopping. For example, businesses can utilize on-line forums, chat rooms, and provide incentives for consumers toshare their experiences with other online consumers to enhancesocial and interpersonal experiences in online shopping (Zhouet al., 2007). As Dittmar et al. (2004) suggest, while functional ben-efits of online shopping act as facilitators for women to shop on-line, social and emotional factors act as barriers. Thus, increasedfocus on the emotional aspects of online shopping such as provid-ing dynamic or instant feedback or information on products mayimprove women’s online social experiences and enhance theiraffection to online shopping.

Affection in online environments can be enhanced by carefuldesign of websites (Zhang & Li, 2005). For example, Cober, Brown,Keeping, and Levy (2004) show that affective reactions to an em-ployer website play a role on the extent to which a job seeker be-comes attracted to an employer. They also suggest that affectivefeelings toward a website can be greatly enhanced by improvedwebsite design such as unity (consistency) and increasing users’sense of playfulness when interacting with the website. Organiza-tional attempts to enhance website design and make it more

attractive and appealing to its target audience may foster moreaffective feelings and enhance attitude toward website.

Men demonstrate higher behavioral intention to shop onlinethan women. Attitude theory presents the behavioral componentof attitude as a function of the cognition and affect components.Since females show lower cognitive and affective attitudes thanmales, their behavioral intention to shop online is lower. This find-ing is consistent with the suggestion that attitude plays a strongerrole in women’s online shopping behavior than in men’s (Sanchez-Franco, 2006). Accordingly, greater understanding the value of on-line shopping (cognition) or improvements in social and emotionalexperiences in online shopping (affection) are likely to boost onlineshopping behavior among consumers. Finally, the results of thisstudy provide empirical support for classifying motives for onlineshopping into goal-oriented (cognition) and fun (liking) factors(Sorce et al., 2005).

For research, this study attempts to fill avoid in the literature byaddressing an issue that has received very little or no attention inpast research. For instance, Glassberg et al. (2006) maintain that atti-tude is not adequately examined in past studies and much less atten-tion investigates its underlying dimensions. On the contrary, mostpast studies in information system literature focus on the cognitiveaspect of attitude and studies in online shopping literature focus onthe affective and behavioral aspects attitudes. Furthermore, Fishbeinand Ajzen (1975) suggest that complete description of attitude re-quires that all three components of attitude be evaluated for a fairtest of the relationship between attitude and behavior. Consistentwith these recommendations, this study recognizes and empiricallytests the three attitudinal components.

The present study reveals that the three components of onlineshopping attitude are conceptually and empirically distinguishableand extends past studies of attitude into online shopping behavior.Glassberg et al. (2006) assert that the power of online attitude to pre-dict behavioral intention can be greatly enhanced by expanding theattitude construct to include cognitive and involvement attitudes.Furthermore, the results of the current study provide additionalempirical support for the three components of attitude (Breckler,1984). Thus, this study makes theoretical and empirical contribu-tions to enhance understanding of online shopping attitude.

6. Limitations and future research

This study has a few limitations that need to be pointed out andrecognized when interpreting the results. One obvious limitation isthe use of students as a proxy for online consumers. However, theuse of student samples and educational settings is widespread in e-commerce and online behavior research (e.g. Cyr & Bonanni, 2005;Dittmar et al., 2004; Gao & Koufaris, 2006; Glassberg et al., 2006;McElroy, Hendrickson, Townsend, & DeMarie, 2007; Zhang & Li,2005) and the patterns of findings in online shopping behaviorare similar among student and non-student samples (Ahuja, Gupta,& Raman 2003). More recently, Zahedi and Song (2009) used stu-dent participants to study the impact of website design on beliefsin e-commerce.

Another potential limitation is the use of behavioral intention tomeasure the behavioral component of attitude (Chang & Cheung,2001). While numerous studies show strong positive relationshipbetween intention and actual online buying behavior (e.g., So,Wong, & Sculli, 2005), future studies need to employ covert andovert online shopping behaviors such as the number of online pur-chases. Using data about one website which sells one type of prod-ucts may present another limitation of the results. Past researchsuggests that product characteristics impact consumers’ accep-tance of online shopping (Lian & Lin, 2008; Shih, 2004). Thus, fu-ture studies should consider using other websites and differenttypes of products.

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In addition to research addressing the limitations pointed outabove, several worthwhile areas for future research emerge fromthis study. First, future research may investigate which componentof online shopping attitude has the strongest impact on onlineshopping behavior and under what conditions. For instance, Fazioand Zanna (1981) argue that the impact of the affective componentof attitude on behavior will be stronger in situations in which atti-tude develops through direct personal experience. Thus, a valuablearea for future research is to examine conditions under which thethree components of attitude will exert the greatest impact onbehavior. This line of research helps business to better allocate re-sources and focus their efforts on the attitudinal component that ismore relevant to their environment in order to yield better returnsand increase online sales.

Other demographic characteristics such as age, education, andincome (Porter & Donthu, 2006) and personal characteristics suchas consumer life style and shopping preferences (Wu, 2003) havesignificant effects on Internet usage. Thus, future research can con-sider these examine the impact of other demographic characteris-tics and examine their impact on online shopping attitude to helpbusinesses develop more accurate profiles of their intended con-sumer base.

Finally, each attitudinal component has unique antecedents(Breckler, 1984). Therefore, identifying antecedents of online shop-ping attitudinal components is a worthwhile effort. Attitude to-ward online shopping is a prevalent construct in electroniccommerce (Bruner and Kumar, 2002) and several reviews of factorsaffecting online shopping attitude are available in the literature(e.g., Chang et al., 2005). Such reviews and summaries provide a so-lid foundation on which to build future studies.

References

Ahn, T., Ryu, S., & Han, I. (2007). The impact of Web quality and playfulness on useracceptance of online retailing. Information and Management, 44, 263–275.

Ahuja, M., Gupta, B., & Raman, P. (2003). An empirical investigation of onlineconsumer purchasing behavior. Communications of the ACM, 46(12), 145–151.

Ajzen, I., & Fishbein, M. (Eds.). (1980). Understanding attitudes and predicting socialbehaviour. New Jersey: Prentice-Hall.

Bryman, A., & Cramer, D. (1994). Quantitative data analysis for social scientists. NewYork: Routledge.

Breckler, S. J. (1984). Empirical validation of affect, behavior, and cognition asdistinct components of attitude. Journal of Personality and Social Psychology, 47,1191–1205.

Bruner, G. C., & Kumar, A. (2002). Similarity analysis of three attitude-toward-the-website scales. Quarterly Journal of Electronic Commerce, 3(2), 163–172.

Chang, M. K., & Cheung, W. (2001). Determinants of the intention to use Internet/www at work: A confirmatory study. Information and Management, 39, 1–14.

Chang, M. K., Cheung, W., & Lai, V. S. (2005). Literature derived reference models forthe adoption of online shopping. Information and Management, 42(4), 543–559.

Chen, L. D., Gillenson, M. L., & Sherrell, D. L. (2002). Enticing online consumers: Anextended technology acceptance perspective. Information and Management,39(8), 705–719.

Chen, Q., & Wells, W. D. (1999). Attitude toward the Site. Journal of AdvertisingResearch, 39(5), 27–37.

Cho, J. (2004). Likelihood to abort an online transaction: Influences from cognitiveevaluations, attitudes, and behavioral variables. Information and Management,41, 827–838.

Cober, R. T., Brown, D. J., Keeping, L. M., & Levy, P. E. (2004). Recruitment on the net:How do organizational web site characteristics influence applicant perceptions?Journal of Management, 30, 623–646.

Cyr, D., & Bonanni, C. (2005). Gender and website design in e-business. InternationalJournal of Electronic Business, 3(6), 565–582.

Dattero, R., & Galup, S. D. (2004). Programming languages and gender.Communications of the ACM, 47(1), 99–102.

Dittmar, H., Long, K., & Meek, R. (2004). Buying on the Internet: Gender differencesin on-line and conventional buying motivation. Sex Roles, 50(5–6), 423–444.

Fazio, R. H., & Zanna, M. P. (1981). Direct experience and attitude–behaviorconsistency. In L. Berkowitz (Ed.), Advances in experimental social psychology(14, pp. 162–202). New York: Academic Press.

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: Anintroduction to theory and research. Reading, MA: Addison-Wesley.

Gao, Y., & Koufaris, M. (2006). Perceptual antecedents of user attitude in electroniccommerce. The Data Base for Advances of Information Systems, 37(2–3), 42–50.

Garbarino, E., & Strahilevitze, M. (2004). Gender differences in the perceived risk ofbuying online and the effects of receiving a site recommendation. Journal ofBusiness Research, 57(7), 768–775.

Gefen, D., & Straub, D. W. (1997). Gender differences in the perception and use of e-mail: An extension to the technology acceptance model. MIS Quarterly, 21,389–400.

Glassberg, B. C., Grover, V., & Teng, J. T. C. (2006). Information system research withan attitude. The Data Base for Advances in Information Systems, 37(2–3), 76–85.

Hartzel, K. (2003). How self-efficacy and gender issues affect software adoption anduse. Communications of the ACM, 46(9), 167–171.

Ilie, V., Van Slyke, C., Green, G., & Lou, H. (2005). Gender differences in perceptionsand use of communication technologies: A diffusion of innovation approach.Information Resource Management Journal, 18(3), 16–31.

Kay, R. (2006). Addressing gender differences in computer ability, attitudes anduse: the laptop effect. Journal of Educational Computing Research, 34(2),187–211.

Kim, S., Williams, R., & Lee, Y. (2003). Attitude toward online shopping and retailwebsite quality: A comparison of US and Korean consumers. Journal ofInternational Consumer Marketing, 16(1), 89–111.

Korzaan, M. L. (2003). Going with the flow: Predicting online purchase intentions.Journal of Computer Information Systems, 43(4), 25–31.

Lian, J. W., & Lin, T. M. (2008). Effects of consumer characteristics on theiracceptance of online shopping: Comparisons among different product types.Computers in Human Behavior, 24(1), 48–65.

Liao, Z., & Cheung, M. T. (2001). Internet-based e-shopping and consumer attitudes:An empirical study. Information and Management, 38(5), 299–306.

Liao, Z., & Cheung, M. T. (2002). Internet based e-banking and consumer attitudes:An empirical study. Information and Management, 39(4), 283–295.

Lin, H. F. (2007). Predicting consumer intentions to shop online: An empirical test ofcompeting theories. Electronic Commerce Research and Applications, 6(4),433–442.

McElroy, J. C., Hendrickson, A. R., Townsend, A. M., & DeMarie, S. M. (2007).Dispositional factors in internet use: Personality versus cognitive style. MISQuarterly, 31(4), 809–820.

Ong, C. H., & Lai, J. Y. (2006). Gender differences in perceptions and relationshipsamong dominants of e-learning acceptance. Computers in Human Behavior,22(5), 816–829.

Pavlou, P. A., & Fygenson, M. (2006). Understanding and predicting electroniccommerce adoption: An extension of the theory of planned behavior. MISQuarterly, 30(1), 115–143.

Porter, C. E., & Donthu, N. (2006). Using the technology acceptance model to explainhow attitudes determine internet usage: The role of perceived access barriersand demographics. Journal of Business Research, 59(9), 999–1007.

Ranganathan, C., & Grandon, E. (2002). An exploratory examination of factorsaffecting online sales. Journal of Computer Information Systems, 42(3), 87–93.

Rodgers, S., & Harris, M. A. (2003). Gender and e-commerce: An exploratory study.Journal of Advertising Research, 43(3), 322–329.

Sanchez-Franco, M. J. (2006). Exploring the influence of gender on the Web usagevia partial least squares. Behaviour and Information Technology, 25(1), 19–36.

Shih, H. P. (2004). An empirical study on predicting user acceptance of e-shoppingon the Web. Information and Management, 41(3), 351–368.

Simon, S. J. (2001). The impact of culture and gender on web sites: An empiricalstudy. The Data Base for Advances in Information Systems, 32(1), 18–37.

So, W., Wong, T., & Sculli, D. (2005). Factors affecting intentions to purchase via theinternet. Industrial Management and Data Systems, 105(9), 1225–1244.

Sorce, P., Perotti, V., & Widrick, S. (2005). Attitude and age differences in onlinebuying. International Journal of Retail and Distribution Management, 22(2),122–132.

Teo, T. S. H., & Lim, V. K. G. (2000). Gender differences in internet usage and taskpreferences. Behaviour and Information Technology, 19(4), 283–295.

van der Heijden, H., & Verhagen, T. (2004). Online store image: Conceptualfoundations and empirical measurement. Information and Management, 41(5),609–617.

Van Slyke, C., Comunale, C. L., & Belanger, F. (2002). Gender differences inperceptions of web-based shopping. Communications of the ACM, 45(8), 82–86.

Venkatesh, V., & Morris, M. G. (2000). Why don’t men ever stop to ask fordirections? Gender, social influence, and their role in technology acceptanceand usage behavior. MIS Quarterly, 24(1), 115–139.

Wu, S. I. (2003). Relationship between consumer characteristics attitude towardonline shopping. Marketing Intelligence and Planning, 21(1), 37–44.

Young, B. (2000). Gender differences in student attitudes toward computers. Journalof Research in Computing in Education, 33, 204–217.

Zahedi, F. M., & Song, J. (2009). Do websites change customers’ beliefs? A study ofbelief prior-posterior beliefs in e-commerce. Information & Management, 46(2),125–137.

Zhang, P., & Li, N. (2005). The importance of affective quality. Communications of theACM, 48(9), 105–108.

Zhou, L., Dai, L., & Zhang, D. (2007). Online shopping acceptance model – A criticalsurvey of consumer factors in online shopping. Journal of Electronic CommerceResearch, 8(1), 41–62.