This article was downloaded by: [Newcastle University] On: 23 April 2014, At: 00:28 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK Journal of Internet Commerce Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/wico20 Perceived Quality of Online Shopping: Does Gender Make a Difference? Rose Sebastianelli a , Nabil Tamimi a & Murli Rajan a a Kania School of Management, University of Scranton , Scranton, PA Published online: 12 Dec 2008. To cite this article: Rose Sebastianelli , Nabil Tamimi & Murli Rajan (2008) Perceived Quality of Online Shopping: Does Gender Make a Difference?, Journal of Internet Commerce, 7:4, 445-469, DOI: 10.1080/15332860802507164 To link to this article: http://dx.doi.org/10.1080/15332860802507164 PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or
Transcript
This article was downloaded by: [Newcastle University]On: 23 April 2014, At: 00:28Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH,UK
Journal of Internet CommercePublication details, including instructions forauthors and subscription information:http://www.tandfonline.com/loi/wico20
Perceived Quality of OnlineShopping: Does Gender Make aDifference?Rose Sebastianelli a , Nabil Tamimi a & Murli Rajan aa Kania School of Management, University ofScranton , Scranton, PAPublished online: 12 Dec 2008.
To cite this article: Rose Sebastianelli , Nabil Tamimi & Murli Rajan (2008) PerceivedQuality of Online Shopping: Does Gender Make a Difference?, Journal of InternetCommerce, 7:4, 445-469, DOI: 10.1080/15332860802507164
To link to this article: http://dx.doi.org/10.1080/15332860802507164
PLEASE SCROLL DOWN FOR ARTICLE
Taylor & Francis makes every effort to ensure the accuracy of all theinformation (the “Content”) contained in the publications on our platform.However, Taylor & Francis, our agents, and our licensors make norepresentations or warranties whatsoever as to the accuracy, completeness,or suitability for any purpose of the Content. Any opinions and viewsexpressed in this publication are the opinions and views of the authors, andare not the views of or endorsed by Taylor & Francis. The accuracy of theContent should not be relied upon and should be independently verified withprimary sources of information. Taylor and Francis shall not be liable for anylosses, actions, claims, proceedings, demands, costs, expenses, damages,and other liabilities whatsoever or howsoever caused arising directly or
indirectly in connection with, in relation to or arising out of the use of theContent.
This article may be used for research, teaching, and private study purposes.Any substantial or systematic reproduction, redistribution, reselling, loan,sub-licensing, systematic supply, or distribution in any form to anyone isexpressly forbidden. Terms & Conditions of access and use can be found athttp://www.tandfonline.com/page/terms-and-conditions
Perceived Quality of Online Shopping:Does Gender Make a Difference?
Rose SebastianelliNabil TamimiMurli Rajan
ABSTRACT. Data from a sample of U.S. consumers are examinedfor gender-based differences in perceptions about factors affectingthe perceived quality of online retailers. Seven electronic retailing(e-tailing) quality dimensions (reliability, accessibility, orderingservices, convenience, product content, assurance, and credibility) arederived empirically using factor analysis. We find that women placesignificantly more importance on assurance than do men. This dimen-sion, dealing with privacy and security, is closely related to trust. Wefind no gender-based differences in the frequency of online browsingor purchasing, but do find differences in the types of products womenand men prefer to buy online.
Rose Sebastianelli is Alperin Chair and Professor of Operations andInformation Management in the Kania School of Management, Universityof Scranton, Scranton, PA. Nabil Tamimi is Professor of Operations andInformation Management in the Kania School of Management, Universityof Scranton, Scranton, PA. Murli Rajan is an Associate Professor ofFinance in the Kania School of Management, University of Scranton,Scranton, PA.
Address correspondence to Rose Sebastianelli, Alperin Chair andProfessor of Operations and Information Management, 423 Brennan Hall,Kania School of Management, University of Scranton, Scranton, PA18510. E-mail: [email protected]
Journal of Internet Commerce, Vol. 7(4) 2008# 2008 by The Haworth Press. All rights reserved.
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KEYWORDS. B2C, e-tailing, gender, online shopping, quality,survey data
INTRODUCTION
The growth in electronic commerce (e-Commerce) has beenextraordinary, with many of today’s business transactions beingconducted through the Internet. Although the projected annualgrowth in online retail sales is increasing at a decreasing rate, it is stillprojected to reach $271.1 billion by 2011 (Forrester Research, 2006).Consequently, determining what creates a quality shopping experi-ence for online consumers has become increasingly important forelectronic retailers (e-tailers) attempting to meet and=or exceed cus-tomer expectations and stay competitive in the virtual marketplace.
Improved quality has long been recognized as a means to increaseprofitability and ensure long-run survival in a constantly changingbusiness environment (Deming, 1986). From Total Quality Manage-ment (TQM) to newer initiatives such as Six Sigma, firms continue toimplement quality improvement programs in their efforts to increaseprofitability and attain higher levels of customer satisfaction.Although the importance of quality to firms and consumers is wellestablished, there is no universal definition of quality. This hasprompted researchers to concentrate on identifying the specificdimensions of quality. In his comments about developing competitivestrategies based on quality, Garvin (1987, p. 104) states ‘‘managersmust break down the word quality into manageable parts. Only thencan they define the quality niches in which to compete.’’ Toward thisend, he proposed the now well-known eight-dimensional frameworkfor product quality: performance, features, reliability, conformance,durability, serviceability, aesthetics and perceived quality (Garvin,1984, 1987). Similarly, Parasumaran, Zeithaml, and Berry (1985)identified an analogous framework for service quality that includedten dimensions: access, communication, competence, courtesy, credi-bility, reliability, responsiveness, security, tangibles, and understanding=knowing the customer. This was later condensed into the five deter-minants (tangibles, reliability, responsiveness, assurance, and empa-thy) used in the SERVQUAL measurement instrument (Berry &Parasumaran, 1991). Both of these models have been widely adoptedfor measuring the quality of products and services.
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Given the significant impact of e-Commerce on the global econ-omy, it follows that researchers have turned their attention to identi-fying the dimensions that define quality in the e-Commerce arena(i.e., e-quality dimensions). Our study fits in this line of research.Specifically, we focus on electronic retailing (e-tailing) and the factorsthat affect consumers’ perceptions of the quality of online retailers(i.e., e-tailing quality dimensions). Using data from a sample ofU.S. online consumers, we first derive a set of e-tailing quality dimen-sions empirically using factor analysis. We then examine these datafor differences in perceptions about e-tailing quality dimensionsbetween women and men. In addition, we investigate whether gender-based differences exist with regard to online shopping behaviors andpurchase preferences. Therefore, the research objectives of our studyare to (1) derive empirically a set of e-tailing quality dimensions,(2) examine whether gender differences exist in the perceptions ofe-tailing quality based on these dimensions, and (3) determinewhether gender makes a difference in behaviors associated withonline shopping and online purchase preferences.
LITERATURE REVIEW
Quality and e-Commerce
Several studies examining quality in the e-Commerce arena(e-quality) used service quality dimensions as a starting point. Forexample, Cox and Dale (2001) argued that while the lack of humaninteraction during an online experience makes some service qualitydimensions irrelevant for virtual operations, some do apply. Theseincluded accessibility, communication, credibility, understanding,and appearance. On the other hand, van Iwaarden et al. (2003) foundthat all previously defined service quality dimensions are applicableto Web sites. Their survey results on the quality aspects perceivedto be important in Web site design and Web site use correspondeddirectly to SERVQUAL (Berry & Parasumaran, 1991). Likewise,Long and McMellon (2004) organized consumers’ comments aboutexperiences with online retailers and found them to be comparableto SERVQUAL.
Madu and Madu (2002) borrowed dimensions from both productand service quality and proposed the following conceptual
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fifteen-dimensional framework for e-quality: performance, features,structure, aesthetics, reliability, storage capability, serviceability,security=system integrity, trust, responsiveness, product=servicedifferentiation and customization, Web store policies, reputation,assurance, and empathy. Some of these dimensions have been rede-fined for e-Commerce; for example, reliability, a dimension of bothproduct quality and service quality, here refers to ‘‘consistency ofWeb site performance.’’ Others are unique to virtual operations, forinstance, trust, which includes attributes that affect the willingnessof users to disclose personal information or make purchase decisionsthrough the Internet, and security=system integrity, which refers tothe ability to safeguard and protect confidential information. Simi-larly, the updated DeLone and McLean model of Information Sys-tems (IS) Success added e-Commerce success measures within itssix dimensions that include systems quality (e.g., reliability), infor-mation quality (e.g., security), service quality (e.g., empathy), use(e.g., navigation patterns), user satisfaction (e.g., repeat purchases)and net benefits (e.g., time savings; DeLone and McLean, 2003).They stressed the importance of ‘‘service quality’’ as a dimension ofIS success in the e-Commerce arena.
Among the researchers taking an empirical rather than conceptualapproach, several have explored the relationship between e-qualityand the issue of satisfaction in e-Commerce (or e-satisfaction). Thelink between quality and customer satisfaction has been well docu-mented in the literature (e.g., Cronin & Taylor, 1992). In developingan e-Commerce user-consumer satisfaction index, Cho and Park(2002) considered a number of attributes related to purchasing pro-ducts over the Internet. These included product information, cus-tomer service, purchase result and delivery, site design, purchasingprocess, product merchandizing, delivery time and charge, paymentmethods, ease of use, and additional information services. Alongthe same lines, Kim and Eom (2002) used a twenty-seven-item surveyto gauge perceived satisfaction of online retail shopping. Their scaleitems also included attributes related to physical retailing. Theyfound that issues such as guaranteed on-time delivery and hassle-freereturn, and specified policies or explanations of these issues presenton the Web site, affected satisfaction.
While the above studies focused on linking attributes of e-qualityto an outcome measure such as satisfaction, several researchers havecarried out empirical studies in an attempt to derive the underlying
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constructs (or dimensions) of e-quality. These studies have producedseveral scales to measure e-quality. SITEQUAL (Yoo & Donthu,2001) identified the following four e-quality dimensions: (1) ease ofuse, (2) aesthetic design, (3) processing speed, and (4) security of per-sonal and financial information. These four dimensions were basedon nine items and data collected from convenience samples of stu-dents. Using information gathered from Web site designers and con-sumers, as well as undergraduate students’ ratings of e-Commercesites, Loiacono, Watson, and Goodhue (2002) developed WebQual.They identified these twelve dimensions: informational fit-to-task,interactivity, trust, response time, ease of understanding, intuitiveoperations, visual appeal, innovativeness, flow=emotional appeal,consistent image, online completeness, and better than alternativechannels. Both SITEQUAL and WebQual focused on the Web siteinterface.
Given that a consumer’s experience with electronic retailers(e-tailers) goes beyond the Web site interface, Wolfinbarger and Gilly(2003) considered all aspects of purchasing via the Internet indeveloping their scale eTailQ. In a multistage study that involvedfocus groups, sorting exercises, structured conceptualization, andan online survey of Internet shoppers, 40 attributes related toe-tailing quality were reduced to four underlying dimensions: (1)fulfillment=reliability, (2) Web site design, (3) customer service and(4) security=privacy. Fulfillment=reliability encompassed the accuratedisplay and description of the product online as well as delivery of thecorrect product within the promised time. Web site design includednavigation, information search, and order processing. Customerservice involved responsiveness, and security=privacy dealt withensuring that payments are secure and information is kept confi-dential. Wolfinbarger and Gilly found that the first two dimensions,fulfillment=reliability and Web site design, were the strongest predic-tors of e-tailer quality and customer satisfaction.
Equally comprehensive in their approach is the work of Parasu-maran, Zeithaml, and Malhotra (2005) in developing E-S-QUAL.Through the use of focus groups, attributes related to e-Commercequality were identified and categorized into 11 dimensions: reliability,responsiveness, access, flexibility, ease of navigation, efficiency,assurance=trust, security=privacy, price knowledge, site aesthetics,and customization=personalization. Potential items for the E-S-QUAL scale were selected from these attributes. Based on data
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collected from a random sample of Internet users, the 22 itemsof E-S-QUAL yielded the following four dimensions: efficiency(ease of using the site), fulfillment (extent to which the site’s promisesare fulfilled), system availability (correct technical functioning),and privacy (degree of protection). Further, the 11 items ofE-RecS-QUAL revealed three dimensions: responsiveness (effectivehandling of problems), compensation (degree consumers are compen-sated for problems), and contact (availability of assistance). TheE-RecS-QUAL, consisting of dimensions related to customer service,is separate from E-S-QUAL as the researchers considered customer ser-vice relevant only when online consumers seek resolution to a problem.Using data collected about amazon.com and walmart.com, Parasu-maran, Zeithaml, and Malhotra assessed the psychometric propertiesof their scales. Consistent with Wolfinbarger and Gilly, they found thatthe E-S-QUAL dimensions of efficiency and fulfillment had significantpositive effects on perceived e-tailing quality, value, and loyalty.
Gender and e-Commerce
Marketers have long realized the importance of understandingdemographic differences for segmenting the population and develop-ing effective targeted strategies for attracting consumers. Gender hasalways been an important demographic variable. It is no surprise,then, that examining (and understanding) gender differences withregard to Internet usage and purchasing has received some attention.
Early research studies that examined gender-based differences inInternet usage found that males were more likely to use the Internetthan females. Sexton, Johnson, and Hignite (2002) concluded thatthis was probably due to ‘‘a long history of cultural bias in areas ofscience and technology’’ (p. 407). In a study of mostly males conduc-ted in Singapore, Teo (2001) found that men were more likely to usethe Internet for downloading and purchasing compared with women.Using a regional sample of consumers from the southeastern UnitedStates, Korgaonkar and Wolin (2002) found that heavy users of theWeb tended to be male while light users tended to be female.
However, recent statistics reveal that the gender gap with regard toInternet usage and online shopping is virtually nonexistent. The latestreport on online shopping from the Pew Internet & American LifeProject indicated that 75% of American adults use the Internet withthe percentage of women online catching up with the percentage of
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men online (74% compared with 76%, respectively). Furthermore, ofthose who use the Internet, 66% have purchased products online,with slightly more females (51%) than males (49%) having done so.While gender-based differences were found to exist for some typesof online activities (e.g., men were more likely than women to partici-pate in online auctions, pay to download digital content, and tradestocks online), the survey indicated no gender-based differences withregard to researching a product online or using the Internet to booktravel (Horrigan, 2008).
In light of the above discussion, we suggest the first hypothesis tobe investigated in our study as
H1: Women and men do not differ in terms of behaviors asso-ciated with online shopping (e.g., Internet experience andfrequency of online purchasing).
With regard to the types of products purchased online, evidencesuggests that gender matters. An early survey in 2000 indicated thatmen were more likely to purchase computers, electronics, and videosonline while women were more likely to use the Internet to purchaseclothing, health and beauty aids, and toys (Clickz.com, 2000). Laterstatistics (2004) showed that the online retail categories with the high-est proportion of female shoppers still include fragrances and cos-metics, jewelry and luxury goods, toys, and apparel (Shop.org, 2005).
Several studies that examined the gender effect on intention to pur-chase via the Internet explicitly considered product type. Girard,Korgaonkar, and Silverblatt (2003) investigated whether consumers’shopping orientations (such as price-consciousness, risk aversion,convenience-oriented, or variety-seeking inclination) and demo-graphic characteristics (including gender) affect online shopping pre-ferences. Postulating that these relationships differ by product type,they considered the categories of search, experience, and credenceproducts. Search products are those for which consumers can deter-mine full information about dominant attributes ‘‘before purchasing’’the product (e.g., books). Experience products are those for whichdominant attributes can be determined ‘‘after purchasing’’ the pro-duct (e.g., clothing). And credence products are those for which attri-butes cannot be determined until ‘‘after using’’ the product (e.g.,vitamins). Using a sample of consumers from the southeasternUnited States, they found that males preferred to shop online for
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search products (specifically cell phones and televisions) whilefemales preferred to shop online for experience products (specificallyclothing and perfume). No gender difference was found for credenceproducts.
In a similar study, Brown, Pope, and Voges (2003) investigatedwhether shopping orientation, product type, prior purchase on theInternet, and gender affected stated intention to purchase online inthe future. Using a national sample of U.S. consumers, theseresearchers used cluster analysis to identify seven online shoppingorientations: (1) personalizing, (2) recreational, (3) economic, (4)involved, (5) convenience-oriented recreational, (6) community-oriented, and (7) apathetic convenience-oriented. While shoppingorientation was not found to have a significant impact on consumers’intentions to purchase via the Internet, all three other variables (pro-duct type, prior purchase on the Internet, and gender) were signifi-cant in predicting intentions to purchase online. Six products thatrepresented varying degrees of search, experience, and credence cate-gories were used in the study (clothing, travel, automobile, insurance,sporting equipment, and entertainment tickets). An important find-ing was that the interaction between gender and product type wasalso significant.
The findings noted above lead us to state the second hypothesis tobe investigated in our study as
H2: Women and men differ in terms of the types of productsthey purchase online.
Another stream of research has focused on understanding gender-based differences in attitudes, beliefs, and perceptions held about theInternet and shopping online. Early studies yielded mixed results.Using a convenience sample in an offline retail environment,Kolsaker and Payne (2002) found a high level of concern about trustoverall when shopping online, but no significant difference betweenwomen and men. On the other hand, Rodgers and Harris (2003)found gender-based differences in issues of emotion, trust, and con-venience toward online shopping. Based on data from a regional sam-ple (Midwestern United States), they found that males reported amore emotionally satisfying online shopping experience and con-sidered online shopping more practical and convenient than females.Females were found not to trust e-Commerce to the same extent as
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males. They did note, however, that these differences in perceptionsmay be related to product type since men tend to shop online for pro-ducts that require less emotional involvement compared with women.Similarly, using regional data from the southwestern United States,Wolin and Korganonkar (2003) found that males considered Webadvertising to be more enjoyable, useful, and informative than adver-tising through other media. Women, on the other hand, believed thatWeb advertising was more annoying, offensive, and deceptivecompared with other types.
Recent studies provide additional evidence for how gender-baseddifferences in attitudes and perceptions affect online shopping.Using college students as subjects, Yang and Lester (2005) showedthat psychological factors such as anxiety about computers and atti-tudes about money affected females’ decisions to shop online, butwere not significant predictors for males. In a study of Europeanconsumers, Madleberger (2006) found that the antecedents of onlinepurchasing behavior are moderated by gender. Specifically, thebuying behaviors of women were significantly affected by their atti-tudes toward the online retailer. This was not found to be the casefor men.
The study most relevant to our work is that of Cyr and Bonanni(2005). A sample of undergraduate students participated in a researchtask that involved the hypothetical purchase of a digital camera onthe Canadian Sony Web site. Gender-based differences in perceptionsof seven dimensions (transaction security, information design, navi-gation design, visual design, Web site trust, Web site satisfaction,and e-loyalty) were examined. No significant differences wereobserved between men and women in attitudes toward transactionsecurity, trust, and e-loyalty. However, when analyzing individualitems rather than dimensions they found that, compared with men,women were significantly less trusting of the information presentedon the Web site. Furthermore, they found significant gender-based differences on the dimensions of Web site satisfaction (menfound the Web site more visually appealing and fulfilling comparedwith women) and on some elements of Web site design (males con-sidered the Web site better organized and easier to navigate comparedwith females). It is important to note that the sample of 76 under-graduates in their study had a male-to-female ratio of about 2:1.
Collectively, findings reported in the literature suggest that at leastsome differences exist between women and men in their attitudes and
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perceptions toward online shopping. Consequently, we propose asthe third hypothesis and focus of our study the following:
H3: Women and men differ in their perceptions about theimportance of various e-tailing quality dimensions.
METHODOLOGY
E-Tailing Quality Attributes
Our primary research objective is to explore gender-based differencesin perceptions about the importance of various e-tailing quality dimen-sions that affect the online shopping experience. As part of our ongoingresearch in this area, we developed a scale consisting of thirty-sevenitems representing attributes related to all the phases of online shopping.This scale, based on those cited in the quality and e-Commerce litera-ture, was finalized in pretests using a convenience sample of universityemployees and adult students who had reported making at least oneonline purchase in the last six months. The final list of attributes relateto the four phases of a consumer’s online shopping experience: (1)encountering the online retailer’s homepage, (2) selecting a product fromthe online catalog, (3) completing the order form, and (4) accessing cus-tomer service and support. Our attributes are expressed in concrete,measurable terms and have already been used to benchmark onlinetransactions for a sample of fifty-five e-tailers (Tamimi et al., 2003).
Twelve attributes relate to encountering an online retailer’s home-page and include (1) meta tags—Web site easily found by searchengines; (2) home page title—meaningful and easily recognizable;(3) domain name—unique and memorable; (4) speed of loading—timeit takes to download; (5) links—number of bad links; (6) contactinformation—visible and easily accessible from homepage; (7) timeli-ness of information—includes date of last update (8) privacy policies—explicit explanation on homepage; (9) search engines—present onhomepage for finding relevant information; (10) translation to multi-ple languages—ability to translate content of the Web site into multiplelanguages; (11) navigational bars or site maps—present on home pagefor ease of use; and (12) value added extra content—such as productreviews, free samples, contests, and=or online communities.
In the next phase, selecting a product from the online catalog,e-tailers must provide customers with sufficient realism in order to
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compensate for the inability to physically experience product offer-ings. With this in mind, we identify the following seven attributes:(1) presence of product search engine—allow searching for products bycategory, price range, or size; (2) price—adjacent to the product in thecatalog; (3) images—presence of clear color images of product offer-ings; (4) comprehensive product descriptions—include size, dimension,weight, etc.; (5) labeling of out-of-stock items—clear and easy to find;(6) brands and models—a wide variety offered; and (7) special offers—coupons and discounts offered in product catalog.
After selecting a product to buy, an online shopper encounters an orderform, typically integrated with an online shopping cart. Here security andtrust issues come to the forefront. We identify the following eight attri-butes associated with this phase: (1) breakdown of overall costs—includesshipping charges and sales tax; (2) multiple payment options—availabilityof various methods of payment; (3) shopping cart editing—ability to addand remove items from the cart; (4) security—presence of seals ofapproval logos or encryption technologies; (5) shipping options—availability of several options; (6) instructions—helpful in completingthe order form; (7) ease of transaction—minimum number of clicksrequired to complete; and (8) price calculation—correct and accurate.
Finally, the online shopping experience does not end with the com-pletion of an order form. Customer service and support are criticaldeterminants of satisfaction. We identify the following ten attributesrelated to customer service and support: (1) instant merchant notifi-cation—instant automated notification of order receipt; (2) ordertracking—issuance of order tracking number for products purchased;(3) on-time delivery—actual delivery matches promised delivery date;(4) honest product representation—product received matches onlinerepresentation; (5) explicit return policy—clear explanation of returnpolicy and restocking charges; (6) order cancellation—options forcanceling orders already submitted; (7) order changes—options forchanging the order already submitted; (8) product return—easy andhassle free; (9) customer help—available online help or toll free num-ber; and (10) accurate billing—actual bill is as expected and accurate.
Sample
Our national sample consisted of Internet shoppers definedas those who are engaged in buying products and services online.The sampling frame was obtained from Martin Worldwide
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(www.MartinWorldwide.net), a provider for direct mail and telemar-keting leads. The link to the Web survey was sent via e-mail to 6,666online consumers from their listing. Only one e-mail was sent to eachconsumer. In order to increase study participation, an incentive lot-tery was offered. Those who completed the survey had their namesentered into a lottery awarding a total of four cash prizes.
Survey Instrument
The first section of the questionnaire gathered information aboutbehaviors related to online shopping and purchase preferences (e.g.,frequency of browsing and purchasing, types of products and=or ser-vices purchased) as well as demographics (e.g., gender, age). Thesecond section consisted of thirty-seven statements that representall of the e-tailing quality attributes described above. The wordingof the statements was finalized after pilot testing with a conveniencesample of university employees and adult students who reported hav-ing shopped online. The statements were randomly ordered on thequestionnaire (not grouped according to phase). Respondents wereasked to rate how important each factor was in determining the qual-ity of an online retailer using a five-point scale (1¼ not important to5¼ very important). Finally, since several studies (e.g., Brown et al.,2003) have suggested that gender-based differences in perceptionsabout online shopping may be due, in part, to women and menpurchasing different types of products online, we included theopen-ended question ‘‘Which product or service are you most likelyto purchase online?’’
Data Analysis
Principal components factor analysis was used to determinewhether the observed correlations among the thirty-seven e-tailingquality attributes could be explained by the existence of a smallernumber of underlying e-tailing quality dimensions. Only factors thataccounted for a variance greater than one (eigenvalues> 1) wereextracted. Varimax rotation was used to improve interpretability(see Parasumaran et al., 2005). Gender-based differences in percep-tions about the derived e-tailing quality dimensions were examinedby analyzing the factor scores from the factor solution (see Cyr &Bonanni, 2005).
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RESULTS
Respondent Profile
A total of 422 respondents completed the online survey for aresponse rate of 6.3%. With regard to gender, 59% of the respondentsare female. The average age is 44, with the largest percentage (33%)falling in the 46 to 55 age category. Of those responding, the majorityare Caucasian (73%), employed full-time (62%), and married (57%).In terms of education level, the largest percentage (35%) indicatesthat they have completed some college. The majority (59%) have anannual household income of less than $50,000, with 21% in the high-est household income category (more than $75,000). For the com-plete demographic profile of respondents, including breakdown bygender, see Table 1.
In terms of online behaviors, 23% of our respondents indicate thatthey had made at least 10 purchases online during the last six months.The vast majority (77%) have been using the Internet for over fouryears, and 43% report browsing the Web daily (see Table 2). Respon-dents were also given a list of products=services and asked to indicatethose that they had purchased in the last six months. Those that hadbeen purchased online by at least 25% of our respondents includebooks and magazines (43%), apparel=clothing (37%), DVDs andvideos (32%), computer hardware or software (29%), music (25%),and travel (25%). See Table 3 for the complete frequency distribution.
E-Tailing Quality Dimensions
Factor analysis resulted in the extraction of seven factors account-ing for about 58% of the total variation in the observed importanceratings for the 37 e-tailing attributes. Figure 1 shows the itemsthat loaded strongly on each of the seven factors in descending orderof loading magnitude. In developing this factor solution, items withloadings less than .35 (after varimax rotation) were dropped. Eventhough varimax (orthogonal) rotation has been used by priorresearchers in this context (Parasumaran et al., 2005), we also rotatedthe factor solution allowing for correlations between dimensions (i.e.,promax rotation). The resulting seven-factor solution is strikinglysimilar with only three of the thirty-seven items loading on differentdimensions. Tables 4 and 5 provide detailed factor analysis results.
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TABLE 1. Respondent Demographic Profile
Demographic Variable % All % Female % Male
Age (in years) All (n¼414) Female (n¼ 240) Male (n¼170)
25 or under 6 7 4
26 to 35 18 21 12
36 to 45 31 33 25
46 to 55 33 26 42
56 or over 13 11 17
Household income ($) All (n¼407) Female (n¼ 239) Male (n¼165)
Less than $30,000 30 36 23
$30,000–$49,999 29 32 25
$50,000–$74,999 19 16 24
More than $75,000 21 16 29
Ethnic background All (n¼418) Female (n¼ 243) Male (n¼171)
African American 12 14 9
Hispanic 5 5 4
Asian 5 3 8
Caucasian 73 73 73
Other 6 5 6
Employment status All (n¼416) Female (n¼ 244) Male (n¼170)
Full-time 62 52 76
Part-time 13 16 9
Unemployed 25 33 15
Profession All (n¼315) Female (n¼ 175) Male (n¼140)
Executive Managerial=Administrative 24 24 24
Professional (e.g., doctor, lawyer) 17 13 22
Sales 12 11 12
Technical=Clerical 23 24 21
Other 25 28 21
Marital status All (n¼418) Female (n¼ 243) Male (n¼171)
Married 57 56 60
Single 22 21 23
Divorced 17 19 14
Widowed 3 3 4
Education level All (n¼419) Female (n¼ 243) Male (n¼172)
Some high school 2 2 2
High school 17 22 11
Some college 35 40 30
Associate degree 11 10 12
Bachelor’s degree 23 20 27
Advanced degree 12 7 20
Children at home All (n¼422) Female (n¼ 239) Male (n¼168)
No 52 45 63
Yes 48 55 37
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TABLE 2. Online Shopping Behaviors
Online Behaviors % All % Female % Male
Number of Purchases Last 6 Months All (n¼ 415) Female (n¼239) Male (n¼ 171)
None 10 9 12
1–3 31 31 32
4–6 25 28 21
7–9 11 8 15
10 or more 23 24 21
Browsing frequency All (n¼ 418) Female (n¼242) Male (n¼ 172)
Daily 43 46 38
Weekly 34 33 36
Biweekly 9 8 10
Monthly 9 10 8
Only a few times per year 5 4 8
Internet experience All (n¼ 419) Female (n¼243) Male (n¼ 171)
6 months to 1 year 1 1 1
1 year to 2 years 5 5 5
2 years to 4 years 17 20 14
Over 4 years 77 75 81
Type of Internet connection All (n¼ 370) Female (n¼211) Male (n¼ 159)
56k dial up 46 48 42
Broadband (DSL, cable, T1 line) 54 52 58
TABLE 3. Categories of Products Purchased Online In the Last Six Months
FIGURE 1. Factor Solutions: Items Loading On Each Factor
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The seven dimensions extracted are reliability, accessibility, order-ing services, convenience, product content, assurance, and credibility.As is typical, we labeled these dimensions based on the specific items
that loaded on each factor. In order to assess the internal consistencyof these derived factors, Cronbach’s alpha was computed as a mea-sure of reliability. Although the generally acceptable minimum alphais usually 0.70, Nunnally (1978) suggests a somewhat lower threshold,such as 0.60 or even 0.50, for exploratory work involving the use ofnewly developed scales. Reliability analysis results are included inFigure 1. Each factor has a Cronbach alpha greater than 0.65.
Gender and Other Demographics
Before exploring gender-based differences, it is important to under-stand how the females and males in our sample might differ withrespect to other demographic variables. Using the Chi-Square test ofindependence, we found that the women in our sample tend to beyounger (v2¼ 18.798; p¼ .001), are less likely to be employed (v2¼25.253; p< .001), are less likely to be college educated (v2¼ 26.624;p< .001), have lower household incomes (v2¼ 18.782; p< .001), andare more likely to have children at home (v2¼ 11.852; p¼ .001) com-pared with the men in our sample (see Table 1 for demographic dis-tributions for females versus males; please note that percentages maynot add to 100 due to rounding).
Gender and Online Shopping
In order to test our first set of hypotheses about gender-based dif-ferences in behaviors associated with online shopping, we again usedthe Chi-Square test for independence. Our results indicate that thenumber of online purchases made in the last six months (p¼ .124),Web browsing frequency (p¼ .281), Internet experience (p¼ .397),
TABLE 5. Principal Component Statistics
Factor Eigenvalue % of Variance Cumulative % of Variance
I 12.694 11.887 11.887
II 2.428 9.717 21.604
III 1.623 8.983 30.587
IV 1.271 8.541 39.128
V 1.146 6.713 45.842
VI 1.058 6.066 51.908
VII 1.014 5.482 57.390
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and type of Internet connection used (p¼ .236) are not related to gen-der. Consequently, our first hypothesis is supported.
The percentages of females and males purchasing various types ofproducts and services online are presented in Table 3. Again we usethe Chi-Square test, but this time for 2� 2 contingency tables, makingthe test equivalent to the z-test for the difference between proportionsfrom two independent samples. At the .05 level of significance, we foundthat females are more likely to have purchased apparel (p< .001), healthand beauty products (p< .001), toys and games (p¼ .004), and homeand garden products (p¼ .001), online than males. On the other hand,males are significantly more likely to have made online purchases forcomputer hardware or software (p< .001) and electronics (p¼ .012)than females. These results are consistent with those previously reportedby others, and support our second hypothesis that gender-based differ-ences exist with regard to the types of products purchased online.
Gender and E-Tailing Quality
In examining the potential gender effect on each of the sevene-tailing quality dimensions, we used the t test for independent sam-ples. Specifically, we tested whether the difference in mean factorscores between women and men was significant. Results are presentedin Table 6.
TABLE 6. T-test Results for Gender-Based Differences
E-Retailing Dimension Groups Mean Score F-stat. p-value
Reliability Male (n¼149) �.064 �.995 �.321
Female (n¼213) .042
Accessibility Male (n¼149) �.056 �.912 �.362
Female (n¼213) .042
Ordering Services Male (n¼149) �.061 �.967 �.334
Female (n¼213) .041
Convenience Male (n¼149) �.066 �1.193 �.234
Female (n¼213) .059
Product Content Male (n¼149) �.012 �.287 �.774
Female (n¼213) .019
Assurance Male (n¼149) �.236 �3.520 �.001���
Female (n¼213) .154
Credibility Male (n¼149) �.037 �.503 �.616
Female (n¼213) .016
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We find no significant difference in perceptions based on genderfor six of the seven e-tailing quality dimensions. That is, there is nodifference between women and men on how important they considerreliability, accessibility, ordering services, convenience, product content,and credibility to the quality of an online retailer. The one dimensionin which the two groups do differ significantly is assurance. Herewomen place more importance on assurance as an indicator of anonline retailer’s quality as compared with men. This dimension inclu-des items of privacy and security and is closely related to trust.
These results are consistent with those reported by Wolin andKorganonkar (2003) and Rodgers and Harris (2003). The latter study,in particular, concluded that females did not trust e-Commerce to thesame extent as males. However, they did note that this apparent genderdifference may, in part, be attributed to product type (since womentend to shop online for experience rather than search products).
In order to address this potential confounding of effects, we con-sider responses to the open-ended question ‘‘which product or serviceare you most likely to purchase online?’’ Analyzing responses to thisopen-ended question allowed us to group respondents according toproduct type (search, experience, or credence); this was not possibleusing responses to the question that asked respondents to indicatewhat products and=or services they had purchased in the last sixmonths. The open-ended question generated a wide range ofresponses, which we categorized into one of the three product types.We based our categorizations on the scheme provided by Girard,Silverblatt, and Korgaonkar (2002) except that we did not distinguishbetween experience-1 and experience-2 products.
Responses that could not be easily categorized were eliminated.A total of 294 respondents were categorized into one of the threeproduct type groups: 149 search, 95 experience, and 50 credence.Figure 2 shows the specific items included for each product type.
FIGURE 2. Responses in Each Product Category
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The Chi-Square test for independence revealed a significantrelationship between gender and product type (p< .001). As expected,a greater percentage of males (65%) compared with females (41%)report that they are most likely to purchase search products online;more females (41%) compared with males (20%) report that theyare most likely to purchase experience products online. About thesame percentage of men and women are likely to use the Internetto purchase credence products.
We reanalyzed the factor scores for all seven e-tailing qualitydimensions using ANOVA so we could include both gender and pro-duct type as factors. The gender effect on the e-tailing quality dimen-sion of assurance remains significant (p¼ .000), although producttype does not have a significant effect (p¼ .906). Moreover, the inter-action effect of gender and product type on perceptions about theimportance of assurance is not significant (p¼ .353). This suggeststhat the difference between women and men on this e-tailing qualitydimension is not due to differences in the types of products they aremost likely to purchase online.
DISCUSSION AND IMPLICATIONS
Our findings suggest that there is no difference between womenand men with respect to behaviors associated with online shopping,specifically the frequency with which they purchase online or browsethe Web. Our results are consistent with recent statistics (Horrigan,2008).
As expected, however, we did find that females and males shop fordifferent types of products online. Congruent with previous results,the females in our sample are more likely to purchase apparel, healthand beauty products, toys and games, and home and garden productsonline than males. Therefore, Web designers may perhaps customizetheir ad campaigns, in nonobtrusive ways, to make such productsmore conspicuous to target customers based on gender. For example,prominently displaying special offers, coupons, and discounts forproducts in categories such as apparel or health and beauty mayattract more women to a particular retailer’s Web site.
The primary objective of this article was to examine how gendermight affect the perceived quality of an online shopping experience.Other researchers who have developed similar scales (e.g., Wolfinbarger
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& Gilly, 2003; Parasumaran et al., 2005) did not explore gender dif-ferences. Yet some studies have suggested that gender-based differ-ences exist in the attitudes, beliefs, and perceptions about onlineshopping (Cyr & Bonanni, 2005). Our results suggest that femaleand male perceptions about the importance of various e-tailing qual-ity dimensions are quite similar with one significant exception:women place more importance on assurance compared with men.This dimension includes four items: (1) privacy policies on the homepage, (2) availability of online help or toll free number, (3) no bad links,and (4) security of orders. Two of these items (privacy and security)are commonly used to represent components of trust (e.g., Kolsaker& Payne; 2002). Some studies have suggested that females exhibit lessfavorable attitudes toward the Web and are less trusting ofe-Commerce than males (Wolin & Korgaonkar, 2003; Rodgers &Harris, 2003; Cyr & Bonanni, 2005). Our findings, therefore, are con-gruent with and lend additional support for this notion. Moreover,our study shows that this gender difference is not due to differencesin the types of products that men and women buy online, as suggestedby previous researchers. Furthermore, previous studies have useddata from regional and=or convenience samples. Our findings arebased on a national random sample of online consumers.
Another useful aspect of our study is that we examine online con-sumers’ perceptions about dimensions related to e-tailing quality thatare empirically derived from items that have already been used tobenchmark Web sites. Since each dimension is comprised of itemsthat can be measured objectively, they provide specific changes thate-tailers can implement for improving quality. For example, basedon our findings, e-tailers wishing to target women shoppers shouldfocus on assurance, the specifics of which include explicit privacypolicies on the home page and security logos and=or encryptiontechnologies on the order form.
Of course, our study has limitations. First, the response rate to oursurvey is low. Unfortunately, low response rates are common whenusing online surveys due to several factors such as increasing volumesof unsolicited e-mails, the threat of viruses, and churn (when userschange their Internet service providers and e-mail addresses). Eventhough we anticipated a low response rate and counteracted with alarge sample size, the issue of nonresponse bias remains. The demo-graphic characteristics for U.S. online shoppers reported in the latestsurvey by the Pew Internet & American Life Project (September 2007)
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show that 74% are white, 51% are female, 39% are college educated,44% have annual household incomes below $60,000, and 26% areunder 30 years of age (Horrigan, 2008). Our sample is comparablein terms of ethnicity (73% white) and education (35% have at leasta bachelor’s degree, 44% have at least an associate’s degree), buthas more females (59%). Although the age and income categorieson our survey are not the same as those used by Pew, we can say thatrespondents in our sample are somewhat older (24% are under 35years of age) and have lower annual household incomes (59% arebelow $50,000). Moreover, it is also encouraging that the onlineshopping behaviors and preferences of the women and men in oursample are consistent with previously reported results (Shop.org,2004).
Another limitation is that we are dealing with perceptions ratherthan outcomes. For example, even though our findings suggest thatassurance is more important to women than to men, an e-tailer thataddresses this issue is not guaranteed that more women shoppers willvisit and purchase online from them. Consequently, it is unclearwhether or not e-tailers should customize their sites (with respect toquality issues) based on the gender of their online consumers. Morework is needed to determine the importance of gender as a segmen-tation variable in the virtual marketplace and its impact on the devel-opment of effective strategies to target and retain online consumers.
Finally, future research in this area might be directed towardachieving some convergence with respect to e-tailing quality dimen-sions. A number of studies, both conceptual and empirical, have beenpublished that identify e-tailing quality dimensions. While many ofthe dimensions are common across studies, they are often given dif-ferent names and=or the same named dimensions encompass differentquality attributes. It seems that we cannot fully explore all issuesrelated to quality and e-Commerce=e-tailing without some commonlyagreed upon framework of dimensions.
REFERENCES
Berry, L. L. and Parasumaran, A. 1991. Marketing Services—Competing ThroughQuality. Free Press: New York.
Brown, M., Pope, N., and Voges, K. 2003. Buying or browsing? An exploration ofshopping orientations and online purchase intention. European Journal of Market-ing 37(11=12): 1666–1684.
Sebastianelli, Tamimi, and Rajan 467
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2014
Cho, N. and Park, S. 2002. Development of electronic commerce user-consumer sat-isfaction index (ECUSI) for Internet shopping. Industrial Management and DataSystems 101(8): 400–405.
Clickz.com. 2000. Demographics influence on spending. Retrieved on August 15,2004, from http://www.clickz.com
Cox, J. and Dale, B. G. 2001. Service quality and e-Commerce: An exploratoryanalysis. Managing Service Quality 11(2): 121–131.
Cronin, J. J. and Taylor, S. A. 1992. Measuring service quality: A reexamination andextension. Journal of Marketing 56: 55–68.
Cyr, D. and Bonanni, C. 2005. Gender and Web site design in e-business. Inter-national Journal of Electronic Business 3(6): 565–582.
DeLone, W. H. and McLean, E. R. 2003. The DeLone and McLean model of infor-mation systems success: A ten-year update. Journal of Management InformationSystems 19(4): 9–30.
Deming, W. E. 1986. Out of the Crisis. Cambridge, MA: MIT Center for AdvancedEngineering Study.
Forrester Research. 2006. Projected online retail sales: 2006 to 2011. U.S. CensusBureau. Retrieved October 15, 2007, from http://www.census.gov/compedia/statab/tables/08s1021.pdf
Garvin, D. A. 1984. What does product quality really mean? Sloan ManagementReview (Fall): 25–43.
Garvin, D. A. 1987. Competing on the eight dimensions of quality. Harvard BusinessReview 65: 101–109.
Girard, T., Korgaonkar, P., and Silverblatt, R. 2003. Relationship of type of pro-duct, shopping orientations, and demographics with preference for shopping onthe Internet. Journal of Business and Psychology 18(1): 101–120.
Horrigan, J. B. 2008. Online shopping. Washington, DC: Pew Internet & AmericanLife Project.
Kim, E. B. and Eom, S. B. 2002. Designing effective cyber store user interface. Indus-trial Management and Data Systems 102(5): 241–251.
Kolsaker, A. and Payne, C. 2002. Engendering trust in e-Commerce: A study ofgender-based concerns. Marketing Intelligence and Planning 20(4): 206–214.
Korgaonkar, P. and Wolin, L. D. 2002. Web usage, advertising, and shopping:Relationship patterns. Internet Research: Electronic Networking, Applicationsand Policy 12(2): 191–204.
Long, M. and McMellon, C. 2004. Exploring the determinants of retail quality onthe Internet. Journal of Services Marketing 18(1): 78–90.
Loiacono, E. T., Watson, R. T., and Goodhue, D. L. 2002. WEBQUAL: A measureof Web site quality. Marketing Educators’ Conference: Marketing Theory andApplications 13: 432–437.
Madlberger, M. 2006. Exogenous and endogenous antecedents of online shopping ina multichannel environment: Evidence from a catalog retailer in the German-speaking world. Journal of Electronic Commerce in Organizations 4(4): 29–51.
Madu, C. N. and Madu, A. A. 2002. Dimensions of e-quality. International Journalof Quality & Reliability Management 19(3): 246–258.
468 JOURNAL OF INTERNET COMMERCE
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by [
New
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rsity
] at
00:
28 2
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pril
2014
Nunnally, J. 1978. Psychometric theory. McGraw Hill: New York.Parasumaran, A., Zeithaml, V., and Berry, L. L. 1985. A conceptual model of service
quality and its implications for future research. Journal of Marketing 49: 41–50.Parasuraman, A., Zeithaml, V. A., and Malhotra, A. 2005. E-S-QUAL: A multiple-
item scale for assessing electronic service quality. Journal of Service Research 7(3):213–233.
Rodgers, S. and Harris, M. A. 2003. Gender and e-Commerce: An exploratorystudy. Journal of Advertising Research (September): 322–329.
Sexton, R. S., Johnson, R. A., and Hignite, M. A. 2002. Predicting Internet=e-Commerce use. Internet Research: Electronic Networking, Applications andPolicy 12(5): 402–410.
Shop.org. 2005. Statistics. Retrieved on May 15, 2007, from http://www.shop.org/learn/stats.html
Tamimi, N., Rajan, M., and Sebastianelli, R. 2003. The state of online retailing.Internet Research: Electronic Networking, Applications and Policy 13(3): 146–155.
Teo, T. S. H. 2001. Demographic and motivation variables associated with Internetusage activities. Internet Research Electronic Networking, Applications and Policy11(2): 125–137.
van Iwaarden, J., van der Wiele, T., Ball, L., and Millen, R. 2003. Perceptions aboutthe quality of Web sites: A survey amongst students at Northeastern universityand Erasmus university. Information & Management 41: 947–959.
Wolfinbarger, M. and Gilly, M. C. 2003. eTailQ: Dimensionalizing, measuring andpredicting e-tail quality. Journal of Retailing 79(3): 183–198.
Wolin, L. D. and Korgaonkar, P. 2003. Web advertising: Gender differences inbeliefs, attitudes, and behavior. Internet Research: Electronic Networking, Applica-tions and Policy 13(5): 375–385.
Yang, B. and Lester, D. 2005. Gender differences in e-Commerce. Applied Economics37(18): 2077–2098.
Yoo, B. and Donthu, N. 2001. Developing a scale to measure the perceived quality ofan Internet shopping site (SITEQUAL). Quarterly Journal of Electronic Commerce2(1): 31–46.
