Exploring effects of onlineshopping experiences on browser

satisfaction and e-tailperformance

Iryna Pentina and Aliaksandr AmialchukUniversity of Toledo,Toledo, Ohio, USA, and

David George TaylorJohn F. Welch College of Business, Sacred Heart University,

Fairfield, Connecticut, USA

Abstract

Purpose – The purpose of this paper is to empirically identify categories of online shoppingexperiences and web site functions facilitating these experiences, and to test the effect of thoseexperiences on browser satisfaction, conversion, and online store performance.

Design/methodology/approach – Two analytical methods (survey-based exploratory factoranalysis and secondary data-based regressions) were employed to test the mediating role ofbrowser satisfaction between online shopping experiences and e-tail performance for 115 top onlineretailers during 2006-2008.

Findings – In addition to supporting the existence of such parallel in-store and online experiences assensory, cognitive, pragmatic, and relational, a new type of online shopping experience(interactive/engagement) was identified. It comprises customer involvement with the online storeand with friends and other shoppers via the online store interface. The mediating role of browsersatisfaction in increasing sales and traffic to online stores was confirmed.

Research limitations/implications – Future research should account for potential multi-channeleffects of online shopping experiences.

Practical implications – Investing in web site features that facilitate such social experiences asproduct reviews and ratings sharing, and interacting with the site itself (site personalisation andmobile interface), and through the site with others (social networking, wish list, e-mail-a-friend, etc.),can positively influence site visitor satisfaction and lead to increased traffic and sales.

Originality/value – This paper is among the first to explore the nature and drivers of onlineshopping experiences. It uses multi-method approach to identify which online shopping experiencessignificantly affect browser satisfaction and, consequently, store performance.

Keywords Online shopping experiences, Browser satisfaction, Online sales, Shopping, Internet

Paper type Research paper

IntroductionAs the global economy emerges from recession, retailers are increasingly challengedby frugal and empowered customers, commoditised merchandise, fragmented markets,and intensified competition. In this environment, online retailing has a uniqueopportunity to take a leading role in the emerging digitised global marketplace byproviding location-free, customer-controlled, and information-rich retail services.Enhancing customer experience, online through the engagement and interactionafforded by dynamically evolving e-commerce technology appears to be a logical

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Received 14 June 2010Revised 23 March 2011Accepted 31 May 2011

International Journal of Retail &Distribution ManagementVol. 39 No. 10, 2011pp. 742-758q Emerald Group Publishing Limited0959-0552DOI 10.1108/09590551111162248

winning strategic decision (Doherty and Ellis-Chadwick, 2006). Although holisticin nature, customer experience with an online retailer is comprised of multiplefactors such as online retail atmosphere, social environment, and consumer levelof involvement (Verhoef et al., 2009). In order to achieve an experience-baseddifferentiation, it is important to determine which experiential dimensions are essentialfor improving an e-tailer’s bottom line, and what web site features and functionsshould be emphasised to enhance these experiences.

According to a recent report, in spite of tightening budgets, companies plan tocontinue investing in their web sites to better position themselves for the future and toset themselves apart from competitors (Shop.org/Forrester, 2009). Among the priorityweb site features slated for improvement are the shopping cart, checkout process,product image and detail presentation, and the site search function (Internet Retailer,2009a). In many cases, the decisions to invest in certain functions are based on anecdotalevidence of their effectiveness reported in press. However, to compete successfullyin an industry characterised by low entry barriers, high-technology transferability andlow customer-switching costs, it is important to emphasise those web site functions(and their combinations) that can truly deliver superior customer experiences and thehighest return on investment. While abundant marketing literature focuses on customerperspectives of satisfaction and service quality online at a functional level (Barnes andVidgen, 2002; Collier and Bienstock, 2006; Wolfinbarger and Gilly, 2003), no research hasanalysed the relative importance of various shopping experience components affectingsatisfaction and retailer performance.

The purpose of this paper is twofold: first, an exploratory investigation of onlinecustomer perceptions identifies categories of online shopping experiences and the website features and functions facilitating these experiences (exploratory phase). Second,it evaluates the role of these online shopping experiences in affecting browsersatisfaction, conversion, and online store performance using secondary data from theindustry (hypothesis testing phase). The remainder of the paper reviews the literature,presents both exploratory and hypothesis testing results, and offers discussion of thefindings, their managerial implications, and directions for future research.

Customer experience creation imperativeThe importance of creating positive experiences to strengthen relationshipswith customers, increase their hedonic value, and, as a result, improve company’sperformance has been recognised for decades (Holbrook and Hirschman, 1982). Morerecently, customer experience management has become a competitive differentiationimperative providing possibilities for unique brand creation through excitement,involvement, and emotional bonding with customers at all available touch points(Berry et al., 2002; Meyer and Schwager, 2007). Forrester’s Customer Experience Index(CxPi) that measures “enjoyability” of interaction, along with customer needssatisfaction and ease of working with the company, has consistently been highlycorrelated with customer loyalty, firm revenues, and stock performance (Manning et al.,2008). The importance of customer experience management strategy is underscored bythe 15-39 per cent intra-industry variation in CxPi, with firms using “experience-based”differentiation strategies demonstrating significantly better performance.

Previous research in retailing relies on the Mehrabian-Russell environmentalpsychology model of stimulus – organism – response (Mehrabian and Russell, 1974)

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to explain the role of in-store environment (music, colour, social cues, etc.) in influencingcustomers’ emotional states of pleasure and arousal and affecting shopping enjoyment,willingness to return, and the amount of money spent (Donovan et al., 1994; Hu andJasper, 2006). For example, classical music was shown to lead wine store customers tobuy more expensive wine than top 40 music (Areni and Kim, 1993); brighter lighting wasassociated with the perceptions of longer waiting time (Baker and Cameron, 1996); andpoorly designed stores were suggested to reduce shopping pleasure and worsencustomer mood (Spies et al., 1997). More recently, customer experience creation hasacquired greater prominence due to its potential to influence loyalty and generategrowth in the mature retail industry (Verhoef et al., 2009). The current definitions ofcustomer experience emphasise the multidimensionality of customers’ involvementwith their shopping process comprised of sensory (sight, hearing, touch, etc.), cognitive(creativity and problem solving), pragmatic (usability), emotional (moods and feelings),and relational (social) levels (Gentile et al., 2007). A retailer is believed to affect customerexperience through a combination of its store atmosphere, social environment, productassortment, price, promotion, channel, and customer service interface (Baker et al., 2002;Neslin et al., 2006).

In the online context, customer experience has predominantly been conceptualisedin a narrow sense of “flow” characterised by high levels of arousal, challenge, skill,control, and interactivity (Novak et al., 2000). Flow was shown to be positively relatedto fun and the amount of time spent online, and was suggested to mitigate consumerprice sensitivity (Novak et al., 2000). More recently, flow has been positively associatedwith purchase and revisit intentions (Hausman and Siekpe, 2009). However, the roles ofother types of online customer experiences, and the mechanism through which theymay affect online performance have not received sufficient attention in academicmarketing literature. This knowledge gap presents an important research opportunitysince increasing practitioner literature emphasises experience-based differentiation asa major online strategy for sustainable competitive advantage (Internet Retailer, 2009a;Shop.org/Forrester, 2009). Thus, identifying the types of online shopping experiences(and the contributing web site features) that could be emphasised to positively impactan e-tailer’s performance is crucial given the increasing commoditisation of e-tail storesin terms of product assortments, pricing, check-out procedures, and payment options.

Components of e-tail customer experience: exploratory phase andhypotheses formulationIn accordance with the definition of customer experience as a holistic response tointeracting with the company and its offerings, online customer experience may bedefined as the engagement of various customer capacities (e.g. sensory, cognitive,emotional, pragmatic, and relational) to participate in satisfying and value-creatinginteractions with the company, its offerings and other customers online (Gentile et al.,2007). It follows that the role of an online retailer is to create the proper environmentand “orchestrate the clues” (Verhoef et al., 2009, p. 32), artefacts, and contexts to assistconsumers in co-creating their own unique experiences (Caru and Cova, 2007) in theprocess of shopping. The existing conceptual literature proposes that customerexperiences can be represented by five dimensions:

(1) Sensory. Experiencing aesthetic pleasure and sense of beauty through theorgans of sight and hearing.

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(2) Cognitive.Engaging in creative problem solving and product/service co-creation.

(3) Emotional. Evoking moods, feelings and emotions in connection with theshopping process.

(4) Pragmatic. Exhibiting actions of using the interface to accomplish shoppinggoals.

(5) Relational. Developing fellowship with other shoppers, sense of belonging to asocial group, affirming particular values and lifestyles.

Since no classification of online shopping experiences exists in marketing literature,we conducted an exploratory investigation to identify potential experiential categories.We theorised that certain combinations of e-tail web site features and functions willenhance different customer online experience components. For example, using video onan e-commerce web site may help showcase products and build brand awareness byengaging consumers’ sensory capabilities. Similarly, product comparison andcustomisation tools may stimulate cognitive capacity and add an element ofco-creation to customer experience. Site personalisation, on the other hand, may evokeemotional reactions in customers who would like to express their individual tastes in thedesign of the online store interface. Engaging in social networking and being able to chatwith a company representative fills the need for socialising (relational experience) thathas been considered an exclusive advantage of traditional shopping. Finally, offeringsuch convenience features as store locator, coupons/rebates, alternative payments andcatalogue quick order facilitates ease of accomplishing the shopping task and boostself-efficacy of the consumer (enhancing pragmatic experience).

An online survey was administered to several cohorts of undergraduate businessstudents in two US universities. Students, representing Generation Y online shoppers,are an appropriate sample for this analysis, with 40 per cent of this 38 million-stronggeneration shopping online and spending $1.5 billion annually (Forrester Research,2008). At the beginning of the survey, the participants were requested to name up tothree online retailers that provided them with a great online shopping experience,defined as “an experience that leads to feelings of satisfaction, pleasure, excitement,enjoyment, and happiness.” The next page contained the list of 38 most common web sitefeatures and functions employed by online retailers according to the Internet RetailerAnnual Directory of Top 500 Online Stores (Internet Retailer, 2009b). The students wereasked to rate these features and functions on a scale from 1 to 5 depending on theirimportance in delivering great online shopping experiences. A total of 214 studentscompleted the survey. Their responses were examined using exploratory factor analysis(EFA) with varimax rotation.

In order to assess the appropriateness of the factor model, Bartlett’s test of sphericityand the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy were used. The x 2

statistic of the Bartlett’s test of 2,310.223 with 276 degrees of freedom was significant atthe 0.001 level, rejecting the null hypothesis that the population correlation matrix is anidentity matrix, and supporting the appropriateness of factor analysis. The value of theKMO statistic (0.871) was also large (.0.5), further confirming the appropriateness ofthe factor analytic technique (Hair et al., 2009). The principal components analysis wasemployed, as recommended when the primary goal is to determine the minimum numberof factors that would account for the maximum variance in the data. The number ofresulting factors was determined based on Eigenvalues greater than 1.0, the scree

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plot break point, and the percentage of variance extracted criteria. As a result, fiveseparate factors were extracted that cumulatively explained nearly 65 per cent of thevariance (Tables I and II). For the purpose of interpretation, each factor comprisedvariables that loaded 0.40 or higher on that factor and did not cross-load on other factors(Malhotra, 1995). The result of the EFA-based categorisation demonstrated similarityof shopping experiences in traditional and online channels, with goal achievement,problem-solving, sensory perceptions, and social engagement being important driversin both shopping contexts. The differences between physical retail experiences andonline shopping experiences are reflected in the additional facet of online experiences:

Rotated component matrix1 2 3 4 5

Social networking 0.788Blogs 0.740Wish list 0.675E-mail-a-friend feature 0.672Mobile interface 0.671Site personalisation 0.650Registry 0.616Colour swatching 0.827Dynamic imaging 0.750Zoom 0.723Enlarged product view 0.709Spin 0.653Rich media 0.581Guided navigation 0.794Interactive catalogue 0.735Product comparisons 0.688FAQ 0.594Product customisation 0.549Coupons rebates 0.770Daily seasonal specials 0.770Online gift certificates 0.732Outlet centre 0.718Customer reviews 0.815Product ratings 0.790

Notes: Extraction method: principal component analysis; rotation method: varimax with Kaisernormalisation

Table I.Results of EFA

Rotation sums of squared loadingsFactor Cronbach’s a Total % of variance Cumulative %

Interactive 0.871 4.283 17.847 17.847Sensory 0.842 3.462 14.424 32.271Cognitive 0.823 2.869 11.955 44.226Pragmatic 0.844 2.799 11.662 55.888Relational 0.764 2.079 8.662 64.550

Table II.Variance explainedand Cronbach’s a

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customer interaction with the web site, and interaction with other customers through theweb site. It is interesting that features that provide indirect reference and identificationexperiences (customer reviews and product ratings) load on a separate (relational) factorfrom those facilitating direct relationships with friends and other customers via blogs,social networks, etc. (interactive/engagement factor).

Previous research has considered the role of web site elements in affecting customerperceptions of e-tail quality (Francis, 2007; Wolfinbarger and Gilly, 2003), online apparelcustomer satisfaction (Kim and Stoel, 2004), and satisfaction with online service(Holloway and Beatty, 2008) and purchase intentions (Dholakia and Zhao, 2009).In online financial services, such system functions as product variety, service process,and service quality were found to affect the flow experience, which, in turn, influencedcustomer satisfaction with the service (Ding et al., 2010). Lim et al. (2009) found that website attributes promoting the feeling of participation (playfulness) increase ane-shopper’s intentions to continue buying from the web site. Additionally, Ballantine(2005) found that a web site’s interactivity increases visitors’ satisfaction. Based on thesefindings, it is logical to suggest that enhancing all dimensions of online shoppingexperiences will affect customer satisfaction with the online shopping process (browsersatisfaction). Browser satisfaction is “satisfaction of an online shopper who visited theweb site but did not necessarily complete a purchase during that visit” (Freed, 2006).This construct is more appropriate to e-tailing research than other existing measures ofe-tail satisfaction because it takes into account the attitudes and beliefs of all shoppersregardless of whether they completed a purchase during a particular web site visit.Our choice of browser satisfaction as an outcome variable was prompted by the low(3-5 per cent) industry-wide shopper-to-buyer conversion rates. Even though they maynot complete a purchase, these browsers may provide favourable word-of-mouth orre-visit the site if satisfaction is high. Conversely, if their satisfaction is low, they mayspread negative word-of-mouth or purchase from a competitor:

H1. There is a positive relationship between (a) sensory, (b) pragmatic, (c)cognitive, (d) relational, and (e) interactive online shopping experiences andbrowser satisfaction.

Given the lack of emphasis on objective performance measures in internet retailingresearch (Grewal et al., 2009), this paper addresses this gap in the literature.We hypothesise that online customer shopping experiences affect e-tail performancethrough the mechanism of browser satisfaction (Figure 1). The proposed mechanism isbased on the existing empirical evidence linking web site elements to online customersatisfaction (Francis, 2007; Kim and Stoel, 2004; Wolfinbarger and Gilly, 2003). The role ofvarious e-tail web site features and functions in affecting purchase intentions (Dholakiaand Zhao, 2009; Hausman and Siekpe, 2009) and e-tailer survival success (Weathers andMakienko, 2006) has been also established by previous empirical findings. Such“hygiene” web site factors as in-depth product/service information, order tracking, andclear categorisation have been associated with greater company value and marketperformance (Hausman and Siekpe, 2009). Availability of content and information onrelated products and services was found to strongly engage online shoppers and lead toweb site re-visits (Eisingerich and Kretschmer, 2008). The role of customer satisfaction inincreasing loyalty, purchase intentions, and word of mouth has been long recognised inboth online and off-line contexts (Anderson and Srinivasan, 2003; Oliver, 1980).

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Based on the above findings, we hypothesise positive relationships between varioustypes of online customer experiences and the online store performance measures ofunique and repeat visits, conversion rates, and sales. We further hypothesise that theserelationships are mediated by customer browser satisfaction:

H2. There is a positive relationship between (a) sensory, (b) pragmatic, (c)cognitive, (d) relational, and (e) interactive online shopping experiences ande-tailer performance measures.

H3. Browser satisfaction will mediate the relationships between online shoppingexperiences and online performance measures.

Hypotheses testing phaseData sourcesOur sample included online retailers listed in the internet retailer annual directory of500 largest (in annual online sales) retail web sites in the years 2006-2008. It containedmulti-channel, as well as online-only merchants representing diverse productcategories. The data on these retailers were collected from two sources: The InternetRetailer Top 500 Annual Directories for 2006, 2007, and 2008, and the ForeSeeResults browser satisfaction annual index for the same years (ForeSee Results, 2009).Descriptive statistics for the sample of 115 retailers for which complete data wereavailable are provided in Table III.

MeasuresPerformance. Online retailer performance was measured by five indicators: online sales(mln $), online sales growth rate (%), e-tail site monthly visits (mln), e-tail site uniquemonthly visits (mln), and conversion rate (%). Sales and sales growth rate have beenconsistently considered reliable, easily observable, and measurable indicators of a retailerperformance that are directly correlated with profitability and are comparable acrosschannels. The measures of monthly visits, monthly unique visitors, and conversionrates are specifically applicable to online retailers and are often called web analytics.They provide value to online retailers by estimating the variation in total number of site

Figure 1.Conceptual model of therole of customer shoppingexperiences in online retailperformance

Websitefeatures

andfunction

Interactivity

Relational

Onlinecustomershopping

experiences

Sensory

Cognitive

Browsersatisfaction

Online retailperformance

SalesSales growth rateMonthly visitsMonthly unique visitorsConversion rates

Pragmatic

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visitors, the proportion of repeat visitors to the site, as well as the percentage of visitorswho have become buyers. These variables have been recently named the top reasonsdriving e-tail site redesigns (Stambor, 2010), and as such represent essential e-commerceperformance measures. All the measures for the three years of interest were collectedfrom the Internet Retailer Directories and validated with available company reports.

Online shopping experiences. Five index variables (pragmatic, sensory, interactive,cognitive, and relational) reflecting the experience categories arrived at in exploratoryphase were created by summating the number of features in each category a retaileremployed every year. The features and functions available at each retailer’s web siteevery year were obtained from the Internet Retailer Directories for 2006-2008.

Browser satisfaction. The values for this variable were obtained from theannual publication of the Top Online Retail Satisfaction Index by the ForeSee Results(ForeSee Results, 2009). This measure represents an index metric computed usingthe American Customer Satisfaction Index methodology. It is collected by surveyingvisitors at the top online retail sites (by sales) and reflects satisfaction of browsers(shoppers who visited a web site but did not necessarily made a purchase) withthe retailer. It reports annual evaluations of e-tail web sites by a panel of 1.6 millionconsumer households on a 100-point scale. This measure was selected over othermeasures of online satisfaction because it is most representative of the attitude of sitevisitors who could potentially be converted to customers.

Empirical analyses and results. In order to test the hypothesised mechanism of onlineshopping experiences’ influence on online retailer performance, mediated by browsersatisfaction, we conducted ordinary least squares (OLS) regression and InstrumentalVariable (2SLS) regression analyses using the STATA (2007) statistical softwarepackage. We pooled observations from all companies and years, which allowed us tomaximize the amount of variation in this limited dataset of 115 online retailers and nomore than three years of data for each retailer. The 2SLS regression is the mostcommon method for estimating a model of an endogenous regressor mediating theeffect of several exogenous variables (instruments) on the outcome variable within asimultaneous equation framework (Greene, 2008; Wooldridge, 2002). In order to test themediation effect of browser satisfaction, the 2SLS technique analogous to testing fullmediation with the Barron and Kenny (1986) method simultaneously assessed the effectof web site experiences on browser satisfaction, and estimated the effect of browser

Variable n Mean SD Min. Max.

Sales (mln $) 322 791.085 1,711.852 50 19,170Growth rate (%) 309 0.225 0.792 20.451 13.7Monthly visits (mln) 310 13.298 28.432 0.389 275Monthly unique visitors (mln) 309 5.503 7.693 0.085 56.586Conversion rate (%) 298 0.052 0.069 0.002 0.96Pragmatic experiences scale 313 3.288 0.927 0 4Sensory experiences scale 312 2.324 1.256 0 5Interactive experiences scale 312 2.885 1.393 0 7Cognitive experiences scale 313 2.246 1.446 0 5Relational experiences scale 310 0.745 0.864 0 2Browser satisfaction index 274 74.062 3.904 62 86Online retailing experience (yrs) 322 9.649 2.946 0 20

Table III.Descriptive statistics

of the sample

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satisfaction on company performance. The key criteria used by the 2SLS technique arefor the instruments to:

. significantly affect the mediating variable; and

. have no direct effect on the outcome measure in the presence of the mediatingvariable (“the exclusion restriction”) (Wooldridge, 2002).

The results of pooled OLS regression with the browser satisfaction as the dependentvariable (Table IV) show positive effects of relational (b ¼ 0.747, p , 0.05), interactive(b ¼ 0.448, p , 0.05), and marginally significant sensory (b ¼ 0.42, p , 0.10),experiences on browser satisfaction, partially supporting H1. Neither pragmatic, norcognitive experiences appear to exert statistically significant influence on browsersatisfaction. The five pooled OLS regressions with the performance measures asdependent variables (Table V) show partial support for H2 with positive andstatistically significant effects of interactive and relational experiences on sales,monthly visits, and monthly unique visitors. Cognitive and sensory experiences do notappear to be associated with any performance variables, while pragmatic experiencesappear to make a positive impact on monthly visits and a small negative impact onconversion rates. Since only the interactive and relational experiences were found to besignificantly related to both browser satisfaction and company performance variablesat p , 0.05 level, we included only these two measures in the list of instruments for the2SLS regression (Basmann, 1960). Table VI contains the estimates from the first stageof 2SLS regression (the effect of two selected online shopping experiences on browsersatisfaction). It confirmed the positive and significant relationships between bothinteractive and relational experiences and browser satisfaction. The second stage of2SLS regression estimated the effect of browser satisfaction (instrumented withrelational and interactive experiences) on company performance measures (Table VII).A positive effect was observed on sales, monthly visits and monthly unique visitors.Table VII also reports the statistics to assess the validity of the structural relationship,i.e. full mediation of the interactive and relational experiences by browser satisfaction,partially supporting H3. F -statistics (above 10) and small associated p -values from thefirst-stage regression suggest that instruments are strong, i.e. interactive and relationalexperiences jointly significantly affect browser satisfaction. Our instruments also pass

Dependent variablesIndependent variables Browser satisfaction SE

Pragmatic experiences 0.320 0.272Sensory experiences 0.420 * 0.218Interactive experiences 0.448 * * 0.183Cognitive experiences 0.344 0.287Relational experiences 0.747 * * 0.326Constant 67.798 * * * 1.465Observations 272R 2 0.336

Notes: Statistical significance at: *p , 0.10, * *p , 0.05, * * *p , 0.01; all regressions includemerchant type and merchant category indicators, online experience, year indicators and “year launched”indicators as control variables

Table IV.OLS regressionsof browser satisfactionon online shoppingexperiences

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of company performanceon web site features

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the overidentifying restrictions test: as indicated by the p-value, we fail to reject thenull hypothesis that the instruments are valid instruments, which means they arecorrectly excluded from the second stage equation (Wooldridge, 2002).

DiscussionCustomer experience creation has been increasingly recognised as an importantcompetitive strategy capable of affecting customer loyalty and sales growth in themature retail industry (Verhoef et al., 2009). With the rapid expansion and popularity ofthe online retailing sector, the questions of how to create engaging online shoppingexperiences and whether and how these experiences may affect an e-tailer’s bottom lineand competitive advantage are becoming critical. Based on our analysis, it appears thatweb site features do not function by themselves to accomplish isolated tasks, but areperceived by consumers in combinations that contribute to creating various onlineshopping experiences. Our results show that online shopping experiences are similar tosuch previously conceptualised in store experiences as sensory (affecting vision andhearing), pragmatic (assisting with the purchasing process), cognitive (facilitatingproblem solving), and relational (connecting with other shoppers). Theinteractive/engagement experience that comprises customer interactions with the website (e.g. site personalisation and mobile interface) and with important referents throughthe web site (e.g. wish list, e-mail-a-friend feature, social networking, etc.) appears to bean additional type of experience characterising the online environment. The fact thatreading other customers’ reviews and checking product ratings constitutes a separateexperience from connecting to friends and directly interacting with social networkspoints to the existence of a whole spectrum of social experiences online. This, in turn,underscores the growing “socialness” of the web environment, including online retailsites, which intensifies the competition between online and in-store retailers byeliminating the social environment-based superiority of bricks-and-mortar retailing.

The analysis of three years (2006-2008) of archival data for the top 115 online retailerssuggests that not all online shopping experiences affect the satisfaction of onlineshoppers with the e-tail web site. For example, neither pragmatic experiences that assistconsumers with ordering products and locating stores, nor cognitive experiencesfacilitating products search and comparison, are effective in inducing satisfaction withshopping. Sensory experiences that help shoppers overcome the “touch and feel”deficiency of web site merchandise appear to be only a marginally significant ( p , 0.10)predictor of browser satisfaction. These experiences may be perceived by online

Dependent variablesIndependent variables Browser satisfaction SE

Interactive experiences 0.553 * * * 0.179Relational experiences 0.999 * * * 0.314Constant 69.329 * * * 1.207Observations 272R 2 0.317

Notes: Statistical significance at: *p , 0.1, * *p , 0.05, * * *p , 0.01; all regressions includemerchant type and merchant category indicators, online experience, year indicators and “yearlaunched” indicators as control variables

Table VI.OLS regressionsof browser satisfactionon web site features(first-stage regression)

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Table VII.Instrumental variable

(2SLS) regressionsof company performance

on web site features(second-stage regression)

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shoppers as “hygiene” factors responsible for delivering the expected outcomes, and notas “satisfiers” that can delight consumers (Hausman and Siekpe, 2009).

Both the relational (providing other customers’ opinions and ratings) and interactive(connecting shoppers to others in real time) experiences represent significant and strongpredictors of browser satisfaction. It can be concluded that browser satisfaction isdetermined by the ability of an e-tail web site to overcome the presumed drawbacks ofe-commerce compared to in-store shopping (e.g. lack of social interactions), and not bythe standard e-commerce features (e.g. product search and ordering) that are expectedfrom any online store. Our findings show that browser satisfaction fully mediates theeffects of relational and interactive experiences on such performance measures as onlinesales, monthly visits, and monthly unique visitors, thus emphasising the importance ofbrowser satisfaction for online retailers. The mechanism through which online shoppingexperiences appear to affect sales is somewhat unexpected: we believe that positiverelational and interactive/engagement experiences prompt web site shoppers torecommend the site to their friends and relatives (increasing the number of monthlyunique visitors) and to return to the web site (increasing the number of total monthlyvisits). However, the expected effect of shopping experiences and browser satisfactionon conversion rates (from shopper to buyer) is not present in our results, which meansthat sales increase not from higher conversions of those who are on the site, but as aresult of increased site traffic. This conclusion may mean that creating pleasurable andjoyful shopping experiences leading to browser satisfaction does not in itself induceimmediate purchases, but may be more influential in improving brand recognition andattitude, spreading viral messages, and affecting loyalty and repeat visits. Additionally,it is possible that pleasurable experiences and browser satisfaction may increase salesthrough larger average order size from loyal or repeat customers. However, thesesuppositions need to be tested in the future.

Conclusion and future research directionsThis paper is among the first to explore the nature and drivers of online shoppingexperiences, as well as their impact on browser satisfaction, and their role in online retailperformance. This study has employed two different analytical methods and data typesto arrive at a taxonomy of online shopping experiences, and to test the hypothesisedrelationships between these experiences and browser satisfaction, with the subsequenteffect on company performance. This research has contributed to the literature oncustomer experiences by empirically delineating five categories of online shoppingexperiences and confirming both their similarities to and differences from in-storeexperiences. In particular, in addition to supporting the existence of such parallelin-store and online experiences as sensory, cognitive, pragmatic, and relational, we havediscovered a new type of online shopping experience (interactive/engagement)comprising customer involvement with the online store (e.g. site personalisation) andwith friends and other shoppers via the online store interface (e.g. social networking).

This paper has applied the methodological triangulation approach to identify whichonline shopping experiences significantly affect browser satisfaction and,consequently, store performance. We have confirmed the important mediating roleof browser satisfaction in increasing sales and traffic to online stores. According to ourresults, enriching relational and interactive/engagement experiences on a site willincrease satisfaction of site visitors with their shopping, but will not necessarily induce

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immediate purchases. Instead, pleasurable and satisfying experiences may intensifypositive word of mouth and lead to repeat visits, thus increasing online sales byaffecting both unique and repeat traffic volume.

Future research should consider the role of other online customer experiencesresulting from antecedents other than web site features and functions (e.g. orderfulfilment, customer service, shipping and returns) in affecting online retailer’sperformance. It is possible that post-purchase experiences with the online retailer arealso influential in determining its performance. Additionally, comparing browsersatisfaction levels with those of buyer satisfaction may provide the missing link inantecedents to conversion. Drivers and consequences of browser dissatisfaction withonline retailers may be another interesting research issue. Finally, as online retailingexpands and the technology improves, new shopping experiences may becreated that need to be identified and evaluated in terms of their impact on e-tailerbottom line.

Implications and limitationsOur findings confirm that immersive online shopping experiences can provide asustainable competitive advantage for a company, and should be employed indesigning competitive strategies for online retailers. In particular, it appears thatinvesting in web site features that facilitate such social experiences as product reviewsand ratings sharing and interacting with the site itself (site personalisation andmobile interface), and through the site with others (social networking, wish list,e-mail-a-friend, etc.), can positively influence site visitor satisfaction and lead toincreased traffic and sales. Therefore, differentiating an online store by providingadditional (and unique) opportunities for networking, personalisation, content creationand consumption, as well as mobile interface capabilities can bring the companyhigher revenues and potentially market share (through higher numbers of uniquevisitors brought in by word-of-mouth). Additionally, further improving sensoryexperiences (e.g. by adding zoom and imaging capabilities) may increase browsersatisfaction, but not necessarily affect the bottom line. Further, such pragmatic andcognitive enriching experiences as improved site navigation, coupons, and online giftcertificates appear to be necessary “hygiene” factors that prevent shoppers from beingdissatisfied, but do not contribute to increased browser satisfaction or e-tailerperformance beyond increasing site traffic. Interestingly, pragmatic experiences mayeven be responsible for reducing conversions from shoppers to buyers, possibly bycreating a “channel shift” through promoting physical stores, or by promptingshoppers to wait for a better deal online. Based on our results, online shoppingexperiences do not appear to be major conversion drivers. Thus, other factors may beresponsible for affecting conversion rates, such as merchandise assortment, prices,order fulfilment, delivery, customer service, etc.

Our limited sample of 115 top internet retailers warrants caution in generalising theresults of this study. First, it did not allow us to test for differences among thecategory- and product-specific subsamples of retailers. Second, smaller online retailersmay not be able to fully benefit from these findings that are based on the largestretailers’ data. Additionally, the timeframe of this investigation may include therecessionary trend that may bias the results. Finally, this investigation did not accountfor potential multi-channel effects of online shopping experiences: we did not test

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whether enjoying visits to an online store of a multi-channel retailer had an impacton in-store visits or purchases. These limitations should be taken into account byexecutives when making decisions regarding online store re-design investments.

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About the authorsIryna Pentina is Assistant Professor of Marketing at the University of Toledo. Her researchinterests include internet retailing, applicability of marketing theory to online sales situations,virtual communities, and social-media and interactive marketing. She has published in theJournal of Retailing, European Journal of Marketing, Journal of Consumer Behaviour, Journal ofCustomer Behaviour, Journal of Electronic Commerce Research, and others. Iryna Pentina is thecorresponding author and can be contacted at: Iryna.Pentina@utoledo.edu

Aliaksandr Amialchuk is Assistant Professor of Economics at the University of Toledo. Hisresearch interests include applied econometrics and statistics, public health and populationeconomics. He has published in Southern Economics Journal, Applied Economics, Journal ofNutrition, and others.

David George Taylor is Assistant Professor of Marketing in the John F. Welch College ofBusiness at Sacred Heart University. His areas of expertise and interest include digitalmarketing, consumer/brand relationships and online word-of-mouth. His research has beenpublished in Journal of Business Research, Journal of Advertising Research, Electronic CommerceResearch, Journal of Consumer Behaviour, and a number of other journals. He holds a PhD fromthe University of North Texas.

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