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Exploring effects of online shopping experiences on browser satisfaction and e-tail performance Iryna Pentina and Aliaksandr Amialchuk
Received 14 June 2010 Revised 23 March 2011 Accepted 31 May 2011
University of Toledo,Toledo, Ohio, USA, and
David George Taylor John 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 shopping experiences and web site functions facilitating these experiences, and to test the effect of those experiences on browser satisfaction, conversion, and online store performance. Design/methodology/approach – Two analytical methods (survey-based exploratory factor analysis and secondary data-based regressions) were employed to test the mediating role of browser satisfaction between online shopping experiences and e-tail performance for 115 top online retailers during 2006-2008. Findings – In addition to supporting the existence of such parallel in-store and online experiences as sensory, cognitive, pragmatic, and relational, a new type of online shopping experience (interactive/engagement) was identified. It comprises customer involvement with the online store and with friends and other shoppers via the online store interface. The mediating role of browser satisfaction in increasing sales and traffic to online stores was confirmed. Research limitations/implications – Future research should account for potential multi-channel effects of online shopping experiences. Practical implications – Investing in web site features that facilitate such social experiences as product reviews and ratings sharing, and interacting with the site itself (site personalisation and mobile 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 online shopping experiences. It uses multi-method approach to identify which online shopping experiences significantly affect browser satisfaction and, consequently, store performance. Keywords Online shopping experiences, Browser satisfaction, Online sales, Shopping, Internet Paper type Research paper
International Journal of Retail & Distribution Management Vol. 39 No. 10, 2011 pp. 742-758 q Emerald Group Publishing Limited 0959-0552 DOI 10.1108/09590551111162248
Introduction As the global economy emerges from recession, retailers are increasingly challenged by frugal and empowered customers, commoditised merchandise, fragmented markets, and intensified competition. In this environment, online retailing has a unique opportunity to take a leading role in the emerging digitised global marketplace by providing location-free, customer-controlled, and information-rich retail services. Enhancing customer experience, online through the engagement and interaction afforded by dynamically evolving e-commerce technology appears to be a logical
winning strategic decision (Doherty and Ellis-Chadwick, 2006). Although holistic in nature, customer experience with an online retailer is comprised of multiple factors such as online retail atmosphere, social environment, and consumer level of involvement (Verhoef et al., 2009). In order to achieve an experience-based differentiation, it is important to determine which experiential dimensions are essential for improving an e-tailer’s bottom line, and what web site features and functions should be emphasised to enhance these experiences. According to a recent report, in spite of tightening budgets, companies plan to continue investing in their web sites to better position themselves for the future and to set themselves apart from competitors (Shop.org/Forrester, 2009). Among the priority web 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 anecdotal evidence of their effectiveness reported in press. However, to compete successfully in an industry characterised by low entry barriers, high-technology transferability and low customer-switching costs, it is important to emphasise those web site functions (and their combinations) that can truly deliver superior customer experiences and the highest return on investment. While abundant marketing literature focuses on customer perspectives of satisfaction and service quality online at a functional level (Barnes and Vidgen, 2002; Collier and Bienstock, 2006; Wolfinbarger and Gilly, 2003), no research has analysed the relative importance of various shopping experience components affecting satisfaction and retailer performance. The purpose of this paper is twofold: first, an exploratory investigation of online customer perceptions identifies categories of online shopping experiences and the web site features and functions facilitating these experiences (exploratory phase). Second, it evaluates the role of these online shopping experiences in affecting browser satisfaction, conversion, and online store performance using secondary data from the industry (hypothesis testing phase). The remainder of the paper reviews the literature, presents both exploratory and hypothesis testing results, and offers discussion of the findings, their managerial implications, and directions for future research. Customer experience creation imperative The importance of creating positive experiences to strengthen relationships with customers, increase their hedonic value, and, as a result, improve company’s performance has been recognised for decades (Holbrook and Hirschman, 1982). More recently, customer experience management has become a competitive differentiation imperative 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 needs satisfaction and ease of working with the company, has consistently been highly correlated with customer loyalty, firm revenues, and stock performance (Manning et al., 2008). The importance of customer experience management strategy is underscored by the 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 environmental psychology 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 influencing customers’ emotional states of pleasure and arousal and affecting shopping enjoyment, willingness to return, and the amount of money spent (Donovan et al., 1994; Hu and Jasper, 2006). For example, classical music was shown to lead wine store customers to buy more expensive wine than top 40 music (Areni and Kim, 1993); brighter lighting was associated with the perceptions of longer waiting time (Baker and Cameron, 1996); and poorly designed stores were suggested to reduce shopping pleasure and worsen customer mood (Spies et al., 1997). More recently, customer experience creation has acquired greater prominence due to its potential to influence loyalty and generate growth in the mature retail industry (Verhoef et al., 2009). The current definitions of customer experience emphasise the multidimensionality of customers’ involvement with 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 customer experience through a combination of its store atmosphere, social environment, product assortment, price, promotion, channel, and customer service interface (Baker et al., 2002; Neslin et al., 2006). In the online context, customer experience has predominantly been conceptualised in 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 related to fun and the amount of time spent online, and was suggested to mitigate consumer price sensitivity (Novak et al., 2000). More recently, flow has been positively associated with purchase and revisit intentions (Hausman and Siekpe, 2009). However, the roles of other types of online customer experiences, and the mechanism through which they may affect online performance have not received sufficient attention in academic marketing literature. This knowledge gap presents an important research opportunity since increasing practitioner literature emphasises experience-based differentiation as a 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 impact an e-tailer’s performance is crucial given the increasing commoditisation of e-tail stores in terms of product assortments, pricing, check-out procedures, and payment options. Components of e-tail customer experience: exploratory phase and hypotheses formulation In accordance with the definition of customer experience as a holistic response to interacting with the company and its offerings, online customer experience may be defined as the engagement of various customer capacities (e.g. sensory, cognitive, emotional, pragmatic, and relational) to participate in satisfying and value-creating interactions 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 environment and “orchestrate the clues” (Verhoef et al., 2009, p. 32), artefacts, and contexts to assist consumers in co-creating their own unique experiences (Caru and Cova, 2007) in the process of shopping. The existing conceptual literature proposes that customer experiences can be represented by five dimensions: (1) Sensory. Experiencing aesthetic pleasure and sense of beauty through the organs of sight and hearing.
(2) Cognitive. Engaging in creative problem solving and product/service co-creation. (3) Emotional. Evoking moods, feelings and emotions in connection with the shopping process. (4) Pragmatic. Exhibiting actions of using the interface to accomplish shopping goals. (5) Relational. Developing fellowship with other shoppers, sense of belonging to a social 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 will enhance different customer online experience components. For example, using video on an e-commerce web site may help showcase products and build brand awareness by engaging consumers’ sensory capabilities. Similarly, product comparison and customisation tools may stimulate cognitive capacity and add an element of co-creation to customer experience. Site personalisation, on the other hand, may evoke emotional reactions in customers who would like to express their individual tastes in the design of the online store interface. Engaging in social networking and being able to chat with a company representative fills the need for socialising (relational experience) that has been considered an exclusive advantage of traditional shopping. Finally, offering such convenience features as store locator, coupons/rebates, alternative payments and catalogue quick order facilitates ease of accomplishing the shopping task and boost self-efficacy of the consumer (enhancing pragmatic experience). An online survey was administered to several cohorts of undergraduate business students 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-strong generation shopping online and spending $1.5 billion annually (Forrester Research, 2008). At the beginning of the survey, the participants were requested to name up to three 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 site features and functions employed by online retailers according to the Internet Retailer Annual Directory of Top 500 Online Stores (Internet Retailer, 2009b). The students were asked to rate these features and functions on a scale from 1 to 5 depending on their importance in delivering great online shopping experiences. A total of 214 students completed 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 sphericity and 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 at the 0.001 level, rejecting the null hypothesis that the population correlation matrix is an identity matrix, and supporting the appropriateness of factor analysis. The value of the KMO statistic (0.871) was also large (. 0.5), further confirming the appropriateness of the factor analytic technique (Hair et al., 2009). The principal components analysis was employed, as recommended when the primary goal is to determine the minimum number of factors that would account for the maximum variance in the data. The number of resulting 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, five separate factors were extracted that cumulatively explained nearly 65 per cent of the variance (Tables I and II). For the purpose of interpretation, each factor comprised variables 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 similarity of shopping experiences in traditional and online channels, with goal achievement, problem-solving, sensory perceptions, and social engagement being important drivers in both shopping contexts. The differences between physical retail experiences and online shopping experiences are reflected in the additional facet of online experiences:
1 Social networking Blogs Wish list E-mail-a-friend feature Mobile interface Site personalisation Registry Colour swatching Dynamic imaging Zoom Enlarged product view Spin Rich media Guided navigation Interactive catalogue Product comparisons FAQ Product customisation Coupons rebates Daily seasonal specials Online gift certificates Outlet centre Customer reviews Product ratings Table I. Results of EFA
3
4
5
0.788 0.740 0.675 0.672 0.671 0.650 0.616 0.827 0.750 0.723 0.709 0.653 0.581 0.794 0.735 0.688 0.594 0.549 0.770 0.770 0.732 0.718 0.815 0.790
Notes: Extraction method: principal component analysis; rotation method: varimax with Kaiser normalisation
Factor
Table II. Variance explained and Cronbach’s a
Rotated component matrix 2
Interactive Sensory Cognitive Pragmatic Relational
Cronbach’s a
Total
0.871 0.842 0.823 0.844 0.764
4.283 3.462 2.869 2.799 2.079
Rotation sums of squared loadings % of variance Cumulative % 17.847 14.424 11.955 11.662 8.662
17.847 32.271 44.226 55.888 64.550
customer interaction with the web site, and interaction with other customers through the web site. It is interesting that features that provide indirect reference and identification experiences (customer reviews and product ratings) load on a separate (relational) factor from 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 customer perceptions of e-tail quality (Francis, 2007; Wolfinbarger and Gilly, 2003), online apparel customer 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, influenced customer satisfaction with the service (Ding et al., 2010). Lim et al. (2009) found that web site attributes promoting the feeling of participation (playfulness) increase an e-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 these findings, it is logical to suggest that enhancing all dimensions of online shopping experiences will affect customer satisfaction with the online shopping process (browser satisfaction). Browser satisfaction is “satisfaction of an online shopper who visited the web 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 of e-tail satisfaction because it takes into account the attitudes and beliefs of all shoppers regardless 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 may not complete a purchase, these browsers may provide favourable word-of-mouth or re-visit the site if satisfaction is high. Conversely, if their satisfaction is low, they may spread 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 and browser satisfaction. Given the lack of emphasis on objective performance measures in internet retailing research (Grewal et al., 2009), this paper addresses this gap in the literature. We hypothesise that online customer shopping experiences affect e-tail performance through the mechanism of browser satisfaction (Figure 1). The proposed mechanism is based on the existing empirical evidence linking web site elements to online customer satisfaction (Francis, 2007; Kim and Stoel, 2004; Wolfinbarger and Gilly, 2003). The role of various e-tail web site features and functions in affecting purchase intentions (Dholakia and Zhao, 2009; Hausman and Siekpe, 2009) and e-tailer survival success (Weathers and Makienko, 2006) has been also established by previous empirical findings. Such “hygiene” web site factors as in-depth product/service information, order tracking, and clear categorisation have been associated with greater company value and market performance (Hausman and Siekpe, 2009). Availability of content and information on related products and services was found to strongly engage online shoppers and lead to web site re-visits (Eisingerich and Kretschmer, 2008). The role of customer satisfaction in increasing loyalty, purchase intentions, and word of mouth has been long recognised in both online and off-line contexts (Anderson and Srinivasan, 2003; Oliver, 1980).
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Figure 1. Conceptual model of the role of customer shopping experiences in online retail performance
Website features and function
Online customer shopping experiences
Online retail performance Sensory
Browser satisfaction
Sales Sales growth rate Monthly visits Monthly unique visitors Conversion rates
Cognitive Pragmatic
Based on the above findings, we hypothesise positive relationships between various types of online customer experiences and the online store performance measures of unique and repeat visits, conversion rates, and sales. We further hypothesise that these relationships 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 and e-tailer performance measures. H3. Browser satisfaction will mediate the relationships between online shopping experiences and online performance measures. Hypotheses testing phase Data sources Our sample included online retailers listed in the internet retailer annual directory of 500 largest (in annual online sales) retail web sites in the years 2006-2008. It contained multi-channel, as well as online-only merchants representing diverse product categories. The data on these retailers were collected from two sources: The Internet Retailer Top 500 Annual Directories for 2006, 2007, and 2008, and the ForeSee Results browser satisfaction annual index for the same years (ForeSee Results, 2009). Descriptive statistics for the sample of 115 retailers for which complete data were available are provided in Table III. Measures Performance. Online retailer performance was measured by five indicators: online sales (mln $), online sales growth rate (%), e-tail site monthly visits (mln), e-tail site unique monthly visits (mln), and conversion rate (%). Sales and sales growth rate have been consistently considered reliable, easily observable, and measurable indicators of a retailer performance that are directly correlated with profitability and are comparable across channels. The measures of monthly visits, monthly unique visitors, and conversion rates 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
Variable Sales (mln $) Growth rate (%) Monthly visits (mln) Monthly unique visitors (mln) Conversion rate (%) Pragmatic experiences scale Sensory experiences scale Interactive experiences scale Cognitive experiences scale Relational experiences scale Browser satisfaction index Online retailing experience (yrs)
n
Mean
SD
Min.
Max.
322 309 310 309 298 313 312 312 313 310 274 322
791.085 0.225 13.298 5.503 0.052 3.288 2.324 2.885 2.246 0.745 74.062 9.649
1,711.852 0.792 28.432 7.693 0.069 0.927 1.256 1.393 1.446 0.864 3.904 2.946
50 20.451 0.389 0.085 0.002 0 0 0 0 0 62 0
19,170 13.7 275 56.586 0.96 4 5 7 5 2 86 20
visitors, the proportion of repeat visitors to the site, as well as the percentage of visitors who have become buyers. These variables have been recently named the top reasons driving e-tail site redesigns (Stambor, 2010), and as such represent essential e-commerce performance measures. All the measures for the three years of interest were collected from 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 exploratory phase were created by summating the number of features in each category a retailer employed every year. The features and functions available at each retailer’s web site every year were obtained from the Internet Retailer Directories for 2006-2008. Browser satisfaction. The values for this variable were obtained from the annual publication of the Top Online Retail Satisfaction Index by the ForeSee Results (ForeSee Results, 2009). This measure represents an index metric computed using the American Customer Satisfaction Index methodology. It is collected by surveying visitors 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) with the retailer. It reports annual evaluations of e-tail web sites by a panel of 1.6 million consumer households on a 100-point scale. This measure was selected over other measures of online satisfaction because it is most representative of the attitude of site visitors who could potentially be converted to customers. Empirical analyses and results. In order to test the hypothesised mechanism of online shopping experiences’ influence on online retailer performance, mediated by browser satisfaction, we conducted ordinary least squares (OLS) regression and Instrumental Variable (2SLS) regression analyses using the STATA (2007) statistical software package. We pooled observations from all companies and years, which allowed us to maximize the amount of variation in this limited dataset of 115 online retailers and no more than three years of data for each retailer. The 2SLS regression is the most common method for estimating a model of an endogenous regressor mediating the effect of several exogenous variables (instruments) on the outcome variable within a simultaneous equation framework (Greene, 2008; Wooldridge, 2002). In order to test the mediation effect of browser satisfaction, the 2SLS technique analogous to testing full mediation with the Barron and Kenny (1986) method simultaneously assessed the effect of web site experiences on browser satisfaction, and estimated the effect of browser
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Table III. Descriptive statistics of the sample
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satisfaction on company performance. The key criteria used by the 2SLS technique are for the instruments to: . significantly affect the mediating variable; and . have no direct effect on the outcome measure in the presence of the mediating variable (“the exclusion restriction”) (Wooldridge, 2002).
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The results of pooled OLS regression with the browser satisfaction as the dependent variable (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, nor cognitive experiences appear to exert statistically significant influence on browser satisfaction. The five pooled OLS regressions with the performance measures as dependent variables (Table V) show partial support for H2 with positive and statistically significant effects of interactive and relational experiences on sales, monthly visits, and monthly unique visitors. Cognitive and sensory experiences do not appear to be associated with any performance variables, while pragmatic experiences appear to make a positive impact on monthly visits and a small negative impact on conversion rates. Since only the interactive and relational experiences were found to be significantly related to both browser satisfaction and company performance variables at p , 0.05 level, we included only these two measures in the list of instruments for the 2SLS regression (Basmann, 1960). Table VI contains the estimates from the first stage of 2SLS regression (the effect of two selected online shopping experiences on browser satisfaction). It confirmed the positive and significant relationships between both interactive and relational experiences and browser satisfaction. The second stage of 2SLS regression estimated the effect of browser satisfaction (instrumented with relational 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 the first-stage regression suggest that instruments are strong, i.e. interactive and relational experiences jointly significantly affect browser satisfaction. Our instruments also pass
Independent variables
Table IV. OLS regressions of browser satisfaction on online shopping experiences
Pragmatic experiences Sensory experiences Interactive experiences Cognitive experiences Relational experiences Constant Observations R2
Dependent variables Browser satisfaction 0.320 0.420 * 0.448 * * 0.344 0.747 * * 67.798 * * * 272 0.336
SE 0.272 0.218 0.183 0.287 0.326 1.465
Notes: Statistical significance at: *p , 0.10, * *p , 0.05, * * *p , 0.01; all regressions include merchant type and merchant category indicators, online experience, year indicators and “year launched” indicators as control variables
SE 0.002 2 0.063 2 0.034 0.070 0.134 * 0.425 306 0.053
Growth rate 0.058 0.050 0.041 0.065 0.073 0.303
SE 0.027 0.162 * * * 0.042 0.282 * * * 0.690 * * 307 0.407
0.133 * * 0.066 0.057 0.046 0.073 0.082 0.342 0.000 0.124 * * * 0.014 0.355 * * * 20.158 306 0.388
0.128 *
Dependent variables Ln Ln (monthly visits) SE (monthly unique visitors) 0.068 0.058 0.047 0.074 0.084 0.348
SE
0.004 0.004 0.003 0.005 0.006 0.023
20.014 * * * 0.004 20.002 0.005 20.002 0.060 * * * 295 0.296
SE
Conversion rate
Notes: Statistical significance at: *p , 0.1, * *p , 0.05, * * *p , 0.01; all regressions include merchant type and merchant category indicators, online experience, year indicators and “year launched” indicators as control variables
Pragmatic experiences 20.017 0.054 Sensory experiences 0.064 0.046 Interactive experiences 0.167 * * * 0.037 Cognitive experiences 0.094 0.058 Relational experiences 0.202 * * * 0.067 Constant 4.743 * * * 0.276 Observations 310 R2 0.543
Independent variables
Ln (sales)
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Table V. OLS regressions of company performance on web site features
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the overidentifying restrictions test: as indicated by the p-value, we fail to reject the null hypothesis that the instruments are valid instruments, which means they are correctly excluded from the second stage equation (Wooldridge, 2002). Discussion Customer experience creation has been increasingly recognised as an important competitive strategy capable of affecting customer loyalty and sales growth in the mature retail industry (Verhoef et al., 2009). With the rapid expansion and popularity of the online retailing sector, the questions of how to create engaging online shopping experiences and whether and how these experiences may affect an e-tailer’s bottom line and competitive advantage are becoming critical. Based on our analysis, it appears that web site features do not function by themselves to accomplish isolated tasks, but are perceived by consumers in combinations that contribute to creating various online shopping experiences. Our results show that online shopping experiences are similar to such previously conceptualised in store experiences as sensory (affecting vision and hearing), pragmatic (assisting with the purchasing process), cognitive (facilitating problem solving), and relational (connecting with other shoppers). The interactive/engagement experience that comprises customer interactions with the web site (e.g. site personalisation and mobile interface) and with important referents through the web site (e.g. wish list, e-mail-a-friend feature, social networking, etc.) appears to be an additional type of experience characterising the online environment. The fact that reading other customers’ reviews and checking product ratings constitutes a separate experience from connecting to friends and directly interacting with social networks points to the existence of a whole spectrum of social experiences online. This, in turn, underscores the growing “socialness” of the web environment, including online retail sites, which intensifies the competition between online and in-store retailers by eliminating 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 retailers suggests that not all online shopping experiences affect the satisfaction of online shoppers with the e-tail web site. For example, neither pragmatic experiences that assist consumers with ordering products and locating stores, nor cognitive experiences facilitating products search and comparison, are effective in inducing satisfaction with shopping. 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
Independent variables
Table VI. OLS regressions of browser satisfaction on web site features (first-stage regression)
Interactive experiences Relational experiences Constant Observations R2
Dependent variables Browser satisfaction 0.553 * * * 0.999 * * * 69.329 * * * 272 0.317
SE 0.179 0.314 1.207
Notes: Statistical significance at: *p , 0.1, * *p , 0.05, * * *p , 0.01; all regressions include merchant type and merchant category indicators, online experience, year indicators and “year launched” indicators as control variables
0.048 3.494
272 0.323 0.619 14.030 0.000
SE
0.260 * * * 212.991 * * * 0.032 2 1.965 272 0.023 0.072 14.030 0.000
Growth rate 0.046 3.366
SE
272 0.170 0.716 14.030 0.000
0.328 * * * 2 21.527 * * * 0.059 4.271 271 0.136 0.480 14.384 0.000
0.314 * * * 221.486 * * *
Dependent variables Ln Ln (monthly visits) SE (monthly unique visitors) 0.058 4.234
SE
SE 0.004 0.306
Conversion rate 2 0.005 0.365 261 0.228 0.586 10.974 0.000
Notes: Statistical significance at: *p , 0.1, * *p , 0.05, * * *p , 0.01; all regressions include merchant type and merchant category indicators, online experience, year indicators and “year launched” indicators as control variables
Browser satisfaction Constant Observations R2 Overid test ( p-value) F-statistic (first stage) IV F-test p-value
Independent variables
Ln (sales)
Online shopping experiences
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Table VII. Instrumental variable (2SLS) regressions of company performance on web site features (second-stage regression)
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shoppers as “hygiene” factors responsible for delivering the expected outcomes, and not as “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 strong predictors of browser satisfaction. It can be concluded that browser satisfaction is determined by the ability of an e-tail web site to overcome the presumed drawbacks of e-commerce compared to in-store shopping (e.g. lack of social interactions), and not by the standard e-commerce features (e.g. product search and ordering) that are expected from any online store. Our findings show that browser satisfaction fully mediates the effects of relational and interactive experiences on such performance measures as online sales, monthly visits, and monthly unique visitors, thus emphasising the importance of browser satisfaction for online retailers. The mechanism through which online shopping experiences appear to affect sales is somewhat unexpected: we believe that positive relational and interactive/engagement experiences prompt web site shoppers to recommend the site to their friends and relatives (increasing the number of monthly unique visitors) and to return to the web site (increasing the number of total monthly visits). However, the expected effect of shopping experiences and browser satisfaction on conversion rates (from shopper to buyer) is not present in our results, which means that sales increase not from higher conversions of those who are on the site, but as a result of increased site traffic. This conclusion may mean that creating pleasurable and joyful shopping experiences leading to browser satisfaction does not in itself induce immediate purchases, but may be more influential in improving brand recognition and attitude, spreading viral messages, and affecting loyalty and repeat visits. Additionally, it is possible that pleasurable experiences and browser satisfaction may increase sales through larger average order size from loyal or repeat customers. However, these suppositions need to be tested in the future. Conclusion and future research directions This paper is among the first to explore the nature and drivers of online shopping experiences, as well as their impact on browser satisfaction, and their role in online retail performance. This study has employed two different analytical methods and data types to arrive at a taxonomy of online shopping experiences, and to test the hypothesised relationships between these experiences and browser satisfaction, with the subsequent effect on company performance. This research has contributed to the literature on customer experiences by empirically delineating five categories of online shopping experiences and confirming both their similarities to and differences from in-store experiences. In particular, in addition to supporting the existence of such parallel in-store and online experiences as sensory, cognitive, pragmatic, and relational, we have discovered a new type of online shopping experience (interactive/engagement) comprising customer involvement with the online store (e.g. site personalisation) and with friends and other shoppers via the online store interface (e.g. social networking). This paper has applied the methodological triangulation approach to identify which online shopping experiences significantly affect browser satisfaction and, consequently, store performance. We have confirmed the important mediating role of browser satisfaction in increasing sales and traffic to online stores. According to our results, enriching relational and interactive/engagement experiences on a site will increase satisfaction of site visitors with their shopping, but will not necessarily induce
immediate purchases. Instead, pleasurable and satisfying experiences may intensify positive word of mouth and lead to repeat visits, thus increasing online sales by affecting both unique and repeat traffic volume. Future research should consider the role of other online customer experiences resulting from antecedents other than web site features and functions (e.g. order fulfilment, customer service, shipping and returns) in affecting online retailer’s performance. It is possible that post-purchase experiences with the online retailer are also influential in determining its performance. Additionally, comparing browser satisfaction levels with those of buyer satisfaction may provide the missing link in antecedents to conversion. Drivers and consequences of browser dissatisfaction with online retailers may be another interesting research issue. Finally, as online retailing expands and the technology improves, new shopping experiences may be created that need to be identified and evaluated in terms of their impact on e-tailer bottom line. Implications and limitations Our findings confirm that immersive online shopping experiences can provide a sustainable competitive advantage for a company, and should be employed in designing competitive strategies for online retailers. In particular, it appears that investing in web site features that facilitate such social experiences as product reviews and ratings sharing and interacting with the site itself (site personalisation and mobile 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. Therefore, differentiating an online store by providing additional (and unique) opportunities for networking, personalisation, content creation and consumption, as well as mobile interface capabilities can bring the company higher revenues and potentially market share (through higher numbers of unique visitors brought in by word-of-mouth). Additionally, further improving sensory experiences (e.g. by adding zoom and imaging capabilities) may increase browser satisfaction, but not necessarily affect the bottom line. Further, such pragmatic and cognitive enriching experiences as improved site navigation, coupons, and online gift certificates appear to be necessary “hygiene” factors that prevent shoppers from being dissatisfied, but do not contribute to increased browser satisfaction or e-tailer performance beyond increasing site traffic. Interestingly, pragmatic experiences may even be responsible for reducing conversions from shoppers to buyers, possibly by creating a “channel shift” through promoting physical stores, or by prompting shoppers to wait for a better deal online. Based on our results, online shopping experiences do not appear to be major conversion drivers. Thus, other factors may be responsible 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 the results of this study. First, it did not allow us to test for differences among the category- and product-specific subsamples of retailers. Second, smaller online retailers may not be able to fully benefit from these findings that are based on the largest retailers’ data. Additionally, the timeframe of this investigation may include the recessionary trend that may bias the results. Finally, this investigation did not account for potential multi-channel effects of online shopping experiences: we did not test
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[email protected] Aliaksandr Amialchuk is Assistant Professor of Economics at the University of Toledo. His research interests include applied econometrics and statistics, public health and population economics. He has published in Southern Economics Journal, Applied Economics, Journal of Nutrition, and others. David George Taylor is Assistant Professor of Marketing in the John F. Welch College of Business at Sacred Heart University. His areas of expertise and interest include digital marketing, consumer/brand relationships and online word-of-mouth. His research has been published in Journal of Business Research, Journal of Advertising Research, Electronic Commerce Research, Journal of Consumer Behaviour, and a number of other journals. He holds a PhD from the University of North Texas.
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