Journal of Marketing Management

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Shopping motives as antecedents of e-satisfaction and e-loyaltyGeorge Christodoulides a & Nina Michaelidou aa University of Birmingham, UK

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To cite this article: George Christodoulides & Nina Michaelidou (2010): Shopping motives asantecedents of e-satisfaction and e-loyalty, Journal of Marketing Management, 27:1-2, 181-197

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Shopping motives as antecedents of e-satisfactionand e-loyalty

George Christodoulides, University of Birmingham, UKNina Michaelidou, University of Birmingham, UK

Abstract Customer loyalty is fundamental to the profitability and survival ofe-tailers. Yet research on antecedents of e-loyalty is relatively limited. This studycontributes to the literature by investigating the effect of motives for onlineshopping on e-satisfaction and e-loyalty. A structural equations model isdeveloped and tested through data from an online survey involving 797customers of two UK-based e-tailers focussing on hedonic products. The resultssuggest that convenience, variety seeking, and social interaction help predicte-satisfaction, and that social interaction is the only shopping motive examinedwith a direct relationship to e-loyalty. Data also show that e-satisfaction is a strongdeterminant of e-loyalty. These findings are discussed in the light of previousresearch and avenues of future research are proposed.

Keywords e-loyalty; e-satisfaction; shopping motives; e-tailing

Introduction

In the early days of the Internet, many dot.coms became obsessed with customeracquisition often at the expense of corporate profitability. Spending as much as $500(approximately £315) to acquire a single customer was and is not that unusual forfirms operating in cyberspace (Novak & Hoffman, 2000). The high cost of acquiringcustomers renders customer relationships unprofitable during early transactions. It isonly during later transactions when the cost of serving repeat customers falls thatrelationships start to generate profits (Reichheld & Schefter, 2000).

Researchers therefore recognise that customer loyalty is a key path to profitability(Srinivasan, Anderson, & Ponnavolu, 2002). As a rough rule of thumb, customeracquisition costs five times more than customer retention (Strauss, El-Ansary, &Frost, 2006). However, the benefits of loyalty for firms are not only in terms of costreduction, but also in terms of increasing revenue through inter alia increased buying(Harris & Goode, 2004; Sood & Kathuria, 2004/5), willingness to pay a premium(Reichheld & Sasser, 1990), and acquisition of new customers through positivereferrals (Dick & Basu, 1994).

ISSN 0267-257X print/ISSN 1472-1376 online

# 2010 Westburn Publishers Ltd.

DOI: 10.1080/0267257X.2010.489815

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Journal of Marketing ManagementVol. 27, Nos. 1–2, February 2011, 181–197

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Despite the compelling evidence in favour of customer retention, research showsthat online retailers still spend twice as much on acquisition than retention (ForresterResearch, 2008). This result relates to the additional difficulty involved in fosteringloyalty in a nearly perfect market (Economist, 2004). The pursuit of customer loyaltyin an online environment presents marketers with additional challenges, as defectingonline involves considerably less personal and time effort (Srinivasan et al., 2002).Using, for example a shopping comparison site, allows online customers to comparealternatives more easily than offline, particularly for functional products and services.The cost of switching between e-tailers is therefore extremely low, with competingoffers being just a few clicks away on the Internet (Kwon & Lennon, 2009).

Given the significance of customer loyalty for the profitability and survival ofe-stores, the extant marketing literature identifies a number of e-tailer-specificantecedents, including: trust (Gommans, Krishnan, & Scheffold, 2001; Harris &Goode, 2004; Reichheld & Schefter, 2000); customer service (Gommans et al.,2001; Srinivasan et al., 2002); website and technology (Gommans et al., 2001);customisation (Gommans et al., 2001; Srinivasan et al., 2002); perceived switchingbarriers (Balabanis, Reynolds, & Simintiras, 2006); e-satisfaction (Anderson &Srinivasan, 2003; Balabanis et al., 2006); and image (Kwon & Lennon, 2009).

This study is theoretically interesting because it enhances our understanding of theantecedents of e-satisfaction and e-loyalty that are not e-tailer specific. We examinegeneral shopping motives as antecedents of e-satisfaction and e-loyalty given theirunderlying presence across consumption phenomena (Babin, Darden, & Griffin,1994; Rohm & Swaminathan, 2004). We also shed further light on the relationshipbetween e-satisfaction and e-loyalty (Anderson & Srinivasan, 2003; Balabanis et al.,2006). Whilst there is agreement in the literature that e-satisfaction has a positiveeffect on e-loyalty, there is less agreement with regard to the strength of thisrelationship. In order to complement previous research, which has mostly looked ate-satisfaction and e-loyalty in the domain of more standardised functional products(Balabanis et al., 2006), our study focuses on hedonic products, namely fashion andfashion accessories. This research is also relevant from a managerial perspective, sincea positive relationship between shopping motives and e-satisfaction and e-loyaltyallows e-tailers to use shopping motives as a tool to manage customer satisfactionand loyalty (Arnold & Reynolds, 2003).

Background

Theoretical model and research hypotheses

The paper draws from previous theory to develop hypotheses with regard to theimpact of motives for online shopping on e-satisfaction and e-loyalty. We derive astructural equations model (Figure 1), which depicts the hypothesised relationshipsdiscussed in the subsequent sections.

The loyalty satisfaction relationship

Marketing researchers have defined and measured customer loyalty in many differentways (Jacoby & Chestnut, 1978). Marketing studies conceptualise loyalty as abehavioural response expressed over time, and gauge it through metrics such asproportion of purchase, purchase sequence, and purchase frequency (Brody &Cunningham, 1968; Cunningham, 1966; Kahn, Kalwani, & Morrison, 1986;

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Kumar & Shah, 2004; Olsen, 2002). However, treating loyalty exclusively asrepurchase behaviour is inherently problematic (Bodet, 2008; Day, 1969; Engel,Kollat, & Blackwell, 1982). Buying repeatedly or continuously from the samesupplier does not necessarily manifest psychological commitment towards the firm(Dick & Basu, 1994). For example, high levels of repeat purchasing behaviour mayreflect ‘spurious’ loyalty due to situational constraints such as lack of availability orinertia. On this basis, Dick and Basu (1994) define customer loyalty as the relationshipbetween relative attitude and repeat patronage. Consequently, several authorsemphasise the importance of considering both behavioural and attitudinal aspects ofloyalty (e.g. Hart, Smith, Sparks, & Tzokas, 1999; McCullan & Gilmore, 2008; Yi &La, 2004; Zeithaml, Berry, & Parasuraman, 1996). In line with Anderson andSrinivasan (2003), the authors define e-loyalty as the customer’s favourable attitudetowards an electronic business resulting in repeat buying behaviour.

Various antecedents of loyalty have emerged in traditional contexts (Odin, Odin, &Valette-Florence, 2001). However, research concerning the antecedents of e-loyaltyremains scarce (Balabanis et al., 2006). Gommans et al. (2001) develop a conceptualframework of e-loyalty comprising five antecedents: website and technology (e.g. fastpage downloads); customer service (e.g. fast response to customer inquiries); trustand security; brand building (e.g. community building); and value proposition(e.g. customised products). Similar to the work of Gommans et al. (2001),Srinivasan et al. (2002) focus on antecedents of e-loyalty that are specific to theindividual e-tailer. Their set of seven e-loyalty antecedents, which they empiricallytest in an online b2c context, include customisation, contact interactivity, care,community, cultivation, choice, and character. Consumers are thus more likely to beloyal to an e-tailer if they perceive the online storefront to provide high levels ofinteractivity, foster community, offer opportunities for customisation, and so forth.Balabanis et al. (2006) examine two antecedents of e-loyalty, e-satisfaction, andperceived switching barriers, including economic (e.g. ‘prices of other stores arehigher’), emotional (e.g. ‘if I change Internet store I am afraid that I will lose thebenefits I enjoy of being a loyal customer’), and speed (e.g. ‘delivery times of otherstores are longer’). The authors’ empirical data show that only ‘economic’ and‘familiarity’ switching barriers have a statistically significant effect on e-loyalty. The

Figure 1 Theoretical model and hypotheses.

H4b

H1E-satisfaction

InformationSeeking

VarietySeeking

E-loyalty

Convenience

H2a

H2b

H3aH3b

H4a

H5aH5b

SocialInteraction

Christodoulides and Michaelidou Shopping motives as antecedents of e-satisfaction and e-loyalty 183

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data also demonstrate a weak but statistically significant relationship betweene-satisfaction and e-loyalty.

E-satisfaction is, in this case, the contentment of the customer with respect to his orher prior purchasing experience with a given e-tailer (Anderson & Srinivasan, 2003).Whilst the marketing literature has scrutinised the satisfaction–loyalty dyad in thetraditional ‘bricks-and-mortar’ marketplace (e.g. Bloemer & de Ruyter, 1999;Bloemer & Lemmink, 1992; Chandrashekaran, Rotte, Tax, & Grewal, 2007;Helgesen, 2006; Oliver, 1999), this relationship, and particularly its strength,remains largely understudied in an e-tail context. Contrary to the findings ofBalabanis et al. (2006) showing a weak relationship between e-satisfaction ande-loyalty, the results of the ForeSee (2008) research suggest that e-satisfaction is themost influential predictor of e-loyalty. The latter research is based on a survey using thewell-established American Consumer Satisfaction Index methodology (Fornell,Johnson, Anderson, Cha, & Bryant, 1996) to measure customer satisfaction andassess the relationship between e-satisfaction and loyalty. Further support for therole of e-satisfaction as key determinant of e-loyalty comes from Evanschitzky, Iyer,Hesse, & Ahlert (2004) who examine antecedents of e-satisfaction for e-shopping.This leads to:

H1: E-satisfaction will positively affect e-loyalty.

Shopping motives as antecedents of e-satisfaction and e-loyalty

Previous research suggests that shopping motives represent a useful basis forunderstanding consumer outcomes such as satisfaction and loyalty (Childers, Carr,Peck, & Carson, 2001; Dawson, Bloch, & Ridgway, 1990; Eastlick & Feinberg,1999). Shopping motives emerge as forces guiding consumers’ behaviour in order tosatisfy internal needs (Westbrook & Black, 1985). In line with motivation theory, bothcognitive and affective motives help explain consumers’ motivation to shop (McGuire,1974). Consumers’ motives to shop have been examined across a range of retailcontexts including both store and non-store formats (e.g. Bellenger & Kargaonkar,1980; Gehrt & Shim, 1998; Noble, Griffith, & Adjei, 2006). A plethora of shoppingmotives have been identified, including utilitarian and experiential motives (Kaufman-Scarborough & Lindquist, 2002). However, specific motives emerge as having a keyrole in shopping, including convenience, information seeking (or price comparison),assortment or variety seeking, and social interaction (Noble et al., 2006).

Research suggests that shopping motives are linked to retail outcomes such assatisfaction and loyalty (Babin et al., 1994; Dawson et al., 1990; Westbrook &Black, 1985). Westbrook and Black (1985) suggest that satisfaction is an indicator ofconsumers’ motivational strength. Hence, motive strength is associated withpreference and satisfaction (Dawson et al., 1990). This study thus examines theimpact of four key motives to shop online (Rohm & Swaminathan, 2004) –convenience, information seeking, variety seeking, and social interaction – oncustomers’ e-satisfaction and e-loyalty.

Shopping convenience

Although there are various dimensions of convenience (Kaufman-Scarborough &Lindquist, 2002), previous research reports that consumers seeking conveniencemake choices on the basis of time and effort savings (Berry, Seiders, and Grewal,


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2002; Eastlick & Feinberg, 1999; Rohm & Swaminathan, 2004). Perceivedconvenience is thus determined by consumers’ energy expenditure or effort and timecosts to accomplish a task (Berry et al., 2002; Brown, 1990; Seiders, Berry, &Gresham, 2000). Previous research (Rohm & Swaminathan, 2004; Swaminathan,Lepkowska-White, & Rao, 1999) identified convenience as a key motive ofshopping both offline and online, while researchers agree that a convenienceorientation or motive has a major impact on consumers’ buying decisions andsatisfaction (Berry et al., 2002). In particular, research shows that customersatisfaction improves with increased time savings (e.g. lower waiting times) (Kumar,Kalawani, & Dada, 1997; Pruyn & Smidts, 1998). Thus consumers who are motivatedby convenience in terms of time and effort will be more satisfied if their shopping isperceived as time-saving and effortless. Further, in an online context, Anderson andSrinivasan (2003) advocate that convenience-oriented shoppers are less likely tosearch for new providers, thus tending to by more loyal. Additional theoreticalsupport from linking shopping convenience to e-loyalty derives from Srinivasanet al. (2002). On this basis, it is expected that convenience will have positive effectson e-satisfaction and e-loyalty.

H2a: Shopping convenience will positively affect e-satisfaction.

H2b: Shopping convenience will positively affect e-loyalty.

Information seeking

Information seeking refers to searching, comparing, and accessing information in ashopping context (Rohm & Swaminathan, 2004). In line with the informationaltheoretical framework, consumers with an information-seeking orientation havemore available and relevant information at their disposal. Therefore they are able tomake better evaluations (Olsen, 2002). Shankar, Smith, and Rangaswamy (2003)suggest that with more available information consumers will put more cognitiveeffort in their decision making in order to make a more informed choice, which mayresult in additional benefits (e.g. lower prices). This is particularly relevant for onlineshopping where ample product information is more widely available (Shankar et al.,2003). The Internet has a large capacity to disseminate information, allowingconsumers to engage in price comparisons (Hoffman & Novak, 1996; Li, Kuo, &Russell, 1999), make better decisions, and seek lower prices (Degeratu, Rangaswamy,& Wu, 2000). Therefore, more relevant information provided in an online contextwill allow consumers to make better decision with less effort and lead to greatere-satisfaction (Shankar et al., 2003; Szymanski & Hise, 2000).

However, consumers’ access of large amounts of information on the Internet andtheir ability to compare prices is likely to have a negative relationship with e-loyalty. Itis thus expected that information seeking will positively impact on e-satisfaction butwill have a negative impact on e-loyalty:

H3a: Information seeking will positively affect e-satisfaction.

H3b: Information seeking will negatively affect e-loyalty.

Variety seeking

Consumers exhibit variety seeking as a result of their need to maintain an optimal levelof stimulation (Hoyer & Ridgway, 1984; Van Trijp & Steenkamp, 1992). Repeat


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purchases of products reduce stimulation, leading to satiation and boredom(McAlister & Pessemier, 1982). Consumers often seek to increase stimulation viabrand switching and innovating (Price & Ridgway, 1982), mostly in purchases ofexperiential products (Inman, 2001). Variety seeking is an important motive in anonline context, given consumers’ enhanced ability to access and compare multipleofferings and providers on the Internet (Rohm & Swaminathan, 2004). Previousresearch suggests that consumers are more satisfied with e-tailers that providehigh-variety product assortments (Evanschitzky et al., 2004; Hoch, Bradlow, &Wansink, 1999). As such, it is suggested that high-variety seekers will be moresatisfied with those e-tailers that offer a wider variety of product choices (Shankaret al., 2003).

In contrast, Berne, Mugica, and Yague (2001) and Oliver (1999) suggest that varietyseeking has a negative effect on loyalty. Variety seekers get bored with products veryeasily and tend to switch to alternative offerings or try new ones (Trivedi & Morgan,2003; Van Trijp & Steenkamp, 1992). On this basis, the Internet may underline thenegative effect of variety seeking on loyalty. This is in line with Smith and Sivakumar(2004) who suggest that repeat purchases and site-loyal shopping are rare on theInternet. Therefore, variety seeking should have a positive effect on e-satisfactionand a negative effect on e-loyalty.

H4a: Variety seeking will positively affect e-satisfaction.

H4b: Variety seeking will negatively affect e-loyalty.

Social interaction

Social interaction is a primary motive for shopping that determines the choice of retailshopping format (Arnould, 2005; Darden & Dorsch, 1990). The key role of socialinteraction in shopping emphasises the social context of shopping (Evans,Christiansen, & Gill, 1996). The importance of social interaction in shopping stemsfrom the work of Tauber (1972) who argues that consumers shop for social motivesincluding communication and interaction with others. Rohm and Swaminathan(2004) suggest that consumers seeking social interaction in shopping are more likelyto choose to shop in a retail store rather than online. In fact, previous research suggestsa positive relationship between social interaction and retail-store patronage(e.g. Noble et al., 2006). However, in line with Moon (2000) and Wang, Baker,Wagner, and Wakefield (2007), websites can serve as social tools enabling consumersto perceive a human connection. Similarly, online communities enable consumers tosocialise and interact, further facilitating the exchange of information (Armstrong &Hagel, 1996). Thus consumers who value social interaction as part of their shoppingexperience will be more satisfied with an e-tailer who allows them to interact withvarious individuals through social networks (e.g. Facebook), blogs, and onlinecommunities to exchange product information and shopping experiences. Accordingto Srinivasan et al. (2002), consumers’ ability to exchange specific e-tailer informationand compare experiences via both random and online community social interactionsincreases e-satisfaction and e-loyalty. Social interaction is therefore expected to havepositive effects on e-satisfaction and e-loyalty.

H5a: Social interaction will positively affect e-satisfaction.

H5b: Social interaction will positively affect e-loyalty.


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Methodology

Sample

Following recommendations of previous research (Balabanis et al., 2006) for furtherstudies on e-loyalty, using non-student samples in new product categories, the presentstudy is conducted amongst customers of two e-tailers: a fashion e-tailer targetingmainly women (e-tailer A) and a fashion accessories e-tailer targeting mainly men(e-tailer B). A self-completed online survey with multiple items for the constructs ofinterest was developed and pilot tested. Customer databases are acceptable samplingframes in online research (e.g. Bradley, 1999) and, unlike unsolicited survey invitationsfrom unknown sources, are less inhibiting and have a better chance of response(Deutskens, de Ruyter, Wetzels, & Oosterveld, 2004). The questionnaire wasdesigned online using professional survey-design software. This carried the logos ofboth the companies and the University where the researchers come from. Respondentswere reassured that their responses would be confidential and that there would be noway for the company to trace a questionnaire to a specific customer. A personalisede-mail invitation with an embedded link (Ilieva, Baron, & Healey, 2002) was sent byeach e-tailer to all the customers in their database inviting them to participate in theresearch. To increase the response rate, respondents who fully completed the onlinequestionnaire were entered in a draw for two vouchers worth £50 each (per e-tailer).Only the researchers had access to the raw data set, and this was again explained in theintroduction of the questionnaire. To persuade the two e-tailers to participate in thisresearch, we promised a brief report on their customers’ satisfaction and loyalty, whichwe provided at the end of the project.

This e-mail campaign generated 797 fully usable questionnaires: 631 from e-tailerA and 166 from e-tailer B, a response rate of 16.6% and 12.2% respectively. Althoughsatisfaction and loyalty were in each case gauged for the specific e-tailer, respondents’previously bought products reflect the best-selling Internet articles.

Measures

The measures used in the questionnaire derive from previous research (Appendix 1).E-loyalty is measured using a scale of five items derived from Srinivasan et al. (2002).Items of e-loyalty tap both attitudinal and behavioural aspects of the construct in linewith previous research (Dick & Basu, 1994). E-satisfaction is measured by two items(‘very satisfied/very unsatisfied’ and ‘very pleased/very displeased’) derived fromSzymanski and Hise (2000). Measures of shopping motives are adapted from Rohmand Swaminathan (2004). All items are measured on a seven-point scale.

Analysis and findings

Table 1 shows the demographic characteristics of the sample obtained from eache-tailer, as well as for the combined sample.

Initial analysis involved independent-sample t-tests to establish any non-responsebias between early versus late respondents (Armstrong & Overtone, 1977). Tests showno significant differences between groups, indicating that the sample originates from asingle population.


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Structural equation modelling was used to test the hypothesised relationshipsbetween motives, e-satisfaction, and e-loyalty. AMOS 16.0 was used to analyse thedata with generalised least square method. Composite reliability analysis using AMOSoutput produced the following values: .89 for e-satisfaction, .64 for shoppingconvenience, .63 for information seeking, .67 for variety seeking, .72 for socialinteraction, and .84 for e-loyalty, which are within the acceptable levels in line withBagozzi and Yi (1988). Average variance extracted (AVE) was then calculated toestablish the convergent validity of the four motives (Fornell & Larcker, 1981). AVEestimates were .47 for social interaction, .53 for variety seeking, .46 for informationseeking, and .48 for convenience. All AVE estimates were higher than .45 (Netemeyer,Bearden, & Sharma, 2003). Discriminant validity was then assessed in line withFornell and Larcker (1981). The AVE for each variable within the model was greaterthan the square of the correlation between the two factors, which ranged between .01(social interaction and variety seeking) and .24 (information seeking and varietyseeking), suggesting that each motive is a distinct construct.

The chi-square generated for the model was 213.66 (p ¼ .000, df ¼ 40). This wasstatistically significant, which is not uncommon given chi-square is particularlysensitive to sample size (Hair, Anderson, Tatham, & Black, 2006). Then, wecarefully examined a number of fit indices to assess the fit of the model to the data.Indicatively, GFI was .95, AGFI was .91, CFI was .82, and RMSEA was .074. These fallwithin generally acceptable levels (Hair et al., 2006; Netemeyer et al., 2003), and thisthen allows us to examine path estimates to test our hypothesised relationshipsamongst the constructs. Table 2 shows the regression estimates and associated t- andp-values for each hypothesised path.

Findings provide full support for H1 and H5, whilst H2 and H4 are only partiallysupported. H3 is rejected in its entirety. Table 3 summarises the hypotheses tested.Figure 2 shows the modified model.

Differences between age groups

Further analysis involved examining differences between age groups in an attempt to testwhether the differences between our results and those of Balabanis et al. (2006) can beattributed to different ages of the sample. The sample was thus split into two age groups:under 35 (n ¼ 312) and over 35 (n ¼ 438), and structural equation modelling wasagain run on the two samples in order to test the model. Table 4 shows the standardisedregression coefficients of our model (Figure 1) for each of the two subsamples. It isevident that e-satisfaction is a strong predictor of e-loyalty across age groups and that

Table 1 Sample demographics.

Gender Age

Sample Overall Female Male 15–24 25–34 35–44 45–54 55–64 65+

E-tailer A 631 524 107 84 175 150 130 76 16

100% 83% 17% 13% 28% 24% 21% 12% 3%

E-tailer B 166 72 94 20 51 50 25 19 1

100% 43% 57% 12% 31% 30% 15% 11% 1%

Combined 797 596 201 104 226 200 156 95 17

100% 75% 25% 13% 28% 25% 20% 12% 2%


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Figure 2 Modified model.

E-satisfactionVarietySeeking

E-loyalty

Convenience

SocialInteraction

Table 2 SEM estimates.

PathStandardised beta

coefficient t-value p-value

H1: E-satisfaction! e-loyalty .835 8.480 .000

H2a: Convenience! e-satisfaction .536 7.245 .000

H2b: Convenience! e-loyalty �.153 �1.245 .213

H3a: Information seeking!e-satisfaction

.059 .987 .324

H3b: Information seeking! e-loyalty .170 1.462 .144

H4a: Variety seeking! e-satisfaction .121 2.191 .028

H4b: Variety seeking! e-loyalty .046 .180 .635

H5a: Social interaction! e-satisfaction .116 2.283 .022

H5b: Social interaction! e-loyalty .566 5.809 .000

Table 3 Summary of results.

Hypotheses Predicted effectSupported(Yes/No)

H1 E-satisfaction will positively affect e-loyalty Yes

H2a Convenience will positively affect e-satisfaction Yes

H2b Convenience will positively affect e-loyalty No

H3a Information seeking will positively affect e-satisfaction No

H3b Information seeking will negatively affect e-loyalty No

H4a Variety seeking will positively affect e-satisfaction Yes

H4b Variety seeking will negatively affect e-loyalty No

H5a Social interaction will positively affect e-satisfaction Yes

H5b Social interaction will positively affect e-loyalty Yes


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gives us more confidence to argue that the differences between our findings andBalabanis et al. (2006) are not due to the respondents’ age but the product categoriesinvolved. Whilst Balabanis et al. (2006) used more standardised functional products(i.e. CDs, DVDs, and books), our study used more hedonic products (i.e. fashion andfashion accessories).

Discussion

The findings of this study show that e-satisfaction is positively related with e-loyalty,and are consistent with previous research (Anderson & Srinivasan, 2003; Balabaniset al., 2006). However, in contrast to Balabanis et al. (2006), who report a weakrelationship between satisfaction and e-loyalty, our findings suggest that satisfactionwith the e-tailer is a strong predictor of e-loyalty (b ¼ .84).

This is in line with Evanschitzky et al. (2004) and ForeSee (2008) who contend thate-satisfaction is in fact the primary predictor of e-loyalty. The strength of therelationship between satisfaction and loyalty varies significantly under differentconditions Anderson and Srinivasan (2003). Previous research (Balabanis et al.,2006) has examined e-satisfaction and loyalty in the context of mostly standardisedfunctional products (e.g. books and CDs). Our research examines e-satisfaction andloyalty of customers in the domain of hedonic products (i.e. fashion accessories andfashion). We also explore the impact of motives for online shopping on e-satisfactionand loyalty towards a fashion accessories e-tailer and a fashion e-tailer.

Further, the study highlights that three out of four online shopping motivesexamined contribute to the prediction of e-satisfaction with an e-tailer(convenience, variety seeking, and social interaction) and also indirectly to e-loyalty,while social interaction is the only motive examined that is a direct antecedent toe-loyalty. In particular, we find that shopping convenience is the motive with thegreatest impact on e-satisfaction levels. This is in line with previous research(e.g. Burke, 2002; Evanschitzky et al., 2004; Szymanski & Hise, 2000), whichidentifies convenience as a key determinant of e-satisfaction in an online shoppingcontext. Consumers motivated by convenience are more likely to be satisfied withspecific e-tailers perceived to offer a convenient shopping experience. For instance,features that save time and effort such as ‘1-click buying’ on Amazon or Sainsbury’s‘Usuals list’ are considered to enhance convenience and satisfaction with a specific

Table 4 Standardised coefficients for samples split by age.

PathUnder 35n ¼ 312

Over 35n ¼ 438

E-satisfaction! e-loyalty .823 (.000) .874 (.000)

Convenience! e-satisfaction .287 (.130) .709 (.000)

Convenience! e-loyalty �.265 (.192) �.220 (.365)

Information seeking! e-satisfaction .089 (.324) �.103 (.223)

Information seeking! e-loyalty .113 (.429) .177 (.321)

Variety seeking! e-satisfaction .042 (.913) .022 (.761)

Variety seeking! e-loyalty .008 (.918) .070 (.641)

Social interaction! e-satisfaction �.024 (.731) .233 (.005)

Social interaction! e-loyalty .567 (.000) .564 (.000)


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e-tailer. It is notable that shoppers with a convenient orientation, even though they aremore likely to be satisfied with an e-tailer, will not necessarily be loyal.

Finding also show that variety seeking (b ¼ .12) positively affects e-satisfaction.Variety seekers are likely to consider a wider range of brands when shopping in orderto fulfil their need for stimulation (Givon, 1984). On this basis, variety seekers arelikely to be satisfied with e-tailers that offer a wider range of product alternatives anddynamic sites that change in response to specific browsing behaviour. The positiverelationship between variety seeking and e-satisfaction in this study may be explainedin that the Internet provides a context where consumers fulfil their need forstimulation via the diversity offered by e-tailers, which allows accessing variedproducts and unlimited information, product comparisons, and interaction (Menon& Kahn, 1995). Increased stimulation derived from a specific e-tail context maycompensate for the stimulation variety seekers seek in product choices (Menon &Kahn, 1995), leading to higher levels of e-satisfaction towards particular e-tailers.

The study further shows that social interaction predicts both e-loyalty (b ¼ .57)and e-satisfaction (b ¼ .12). This shows that social shoppers are more satisfied withand are loyal to e-tailers who offer an integrated social experience that comprisesshopping and non-shopping activities. Shopping is not always a rational process, ande-marketers will need to tap into the non-rational social side of online shopping.Traditionally, the Internet was viewed and utilised by e-tailers as a medium todisseminate information and facilitate convenient shopping. Changes such as fasterconnection speeds and the rise of social media enabled e-tailers to address not justutilitarian motives, but also experiential, social needs of consumers. Previous researchsuggests that consumers use specific websites as mediums to interact and socialise withothers (Wang et al., 2007). This socialisation enables consumers to exchange personalexperiences with specific e-tailers. Additionally, online communities enhanceshopping experience, and according to Srinivasan et al. (2002) and Kozinets (2002)have the potential to increase levels of e-satisfaction and e-loyalty.

Findings show that information seeking does not have a significant effect on eithere-satisfaction or e-loyalty. Previous research suggests that information search isimportant online (Rohm & Swaminathan, 2004). However, this study shows thatexperiential motives influence e-satisfaction and e-loyalty. In this case, varietyseeking and social interaction are more salient in explaining consumers’ satisfactionand loyalty to fashion accessories and fashion e-tailers. This contradicts previousresearch, which states that consumers use the Internet and other non-store shoppingchannels to accomplish a task (Eastlick & Feinberg, 1999; Schroder & Zaharia, 2008),indicating that, in the e-tail context examined, experiential motives outweighutilitarian motives. However, this finding may be linked to the specific context ofthe empirical study involving mostly experiential products (i.e. fashion items).

Finally yet importantly, findings from additional analysis on differences betweenage groups show that e-satisfaction is a strong predictor of e-loyalty across both agegroups. It is also interesting to note that for consumers over the age of 35, conveniencehas a significant influence on their satisfaction levels, whereas for under-35-year-olds,the path from convenience to e-satisfaction is statistically insignificant. Likewise,social interaction affects the satisfaction levels of respondents who are over 35 butnot those of younger respondents. This may be because younger respondents who arepredominantly users of social media consider social interactions facilitated by e-tailershygiene; presence of social interaction tools are unlikely to add to consumersatisfaction, whilst their absence may negatively affect it.


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Theoretical and managerial implications

This study contributes to knowledge by showing that shopping motives ande-satisfaction significantly impact loyalty to an e-tailer. The data of this empiricalstudy show that e-satisfaction is a key predictor of e-loyalty for consumers across agegroups. E-tailers should therefore focus on understanding the differences in drivers ofe-satisfaction for different age groups (and target segments). Retention strategiesshould focus on customers’ e-satisfaction with the online storefront. Regularmonitoring of e-satisfaction and its antecedents such as site design and financialsecurity (Szymanski & Hise, 2000) would allow e-tailers to diagnose and correctoperating deficiencies, enhancing customer satisfaction and ultimately loyalty.

Consumers’ shopping motives and their impact on e-satisfaction and e-loyalty alsohave implications for the development of marketing strategy. For example, varietyseeking and social interaction motives drive consumers shopping online forexperiential products (e.g. fashion accessories, fashion etc.), and impact theire-satisfaction and e-loyalty to specific e-tailers. Such consumers tend to consider abroader assortment of brands when shopping in order to fulfil their need for varietyand value social interaction when shopping. E-tailers may, therefore, focus onexpanding their brand portfolio and emphasise the wide range of brands whencommunicating with such individuals (e.g. ads, or in personalised communications).In addition, e-tailers need to facilitate opportunities for interaction on the websites notjust between consumers and the firm (e.g. through live text chat) but also amongstcustomers (e.g. through online communities), as these tools are likely to increase bothe-satisfaction and e-loyalty with the specific e-tailer (Srinivasan et al., 2002). Finally,findings imply that e-tailers should consider making online shopping as convenient aspossible, offering savings in terms of time and effort.

Limitations and further research

This study has some limitations that stem largely from the selection of our sample, andneed addressing in future research. First, our respondents, although comparable withthe profile of the average UK online shopper in terms of demographics and shoppinghabits, were all customers of two specialist e-tailers based in the UK focusing onfashion and fashion accessories. Confidence in the model would be enhanced if thestudy included more e-tailers covering an array of product categories, including bothsearch and experiential products. Second, both e-tailers used in this research wereniche players, and therefore relatively small compared to the top-100 e-tailers in theUK (IMRG, 2009). Although not tested in an online context, ‘double jeopardy’ theorywould imply that larger e-tailers are likely to enjoy higher levels of loyalty (Ehrenberg,Goodhardt, & Barwise, 1990). It is therefore important for further research toexamine whether the model put forward in this study may be applied to customersof larger and more generalist e-tailers. Third, the role of additional variables such asdifferent types of risks (e.g. Balabanis et al., 2006) and shopping motives is notassessed in this study. However, perceived risks may account for the differences inthe findings between our study and that of Balabanis et al. (2006) with regard to thestrength of the relationship between e-satisfaction and e-loyalty. Further researchcould also examine additional motives such as pleasure or enjoyment of theshopping experience, which is independent of product-specific or task-directed


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objectives (Bellenger & Kargaonkar, 1980; Rohm & Swaminathan, 2004). Lastly, asdifferences in the findings of this study were observed between younger and olderrespondents, we argue that researchers should refrain from relying solely on studentsamples to explain online shopping behaviour.

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Appendix 1. Measures

Convenience

The Internet is a convenient way of shoppingThe Internet is often frustratingR

I save a lot of time by shopping on the Internet

Information seeking

I like to have a great deal of information before I buyI always compare prices

Variety seeking

I carefully plan my purchasesR

I buy things I had not planned to purchase


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I enjoy exploring alternative stores/sitesInvestigating a new store/site is generally a waste of timeR

Social interaction

I like to shop where people know meWhile shopping on the Internet, I miss the experience of interacting with peopleI like browsing for the social experience

E-loyalty

I seldom consider switching to another websiteAs long as the present service continues, I doubt that I would switch websitesI try to use the website whenever I need to make a purchaseWhen I need to make a purchase of [product category], this website is my first choice.I believe that this is my favourite retail website.

E-satisfaction

Overall, how do you feel about your Internet-shopping experience?

1. Very dissatisfied (¼1) to very satisfied (¼7)2. Very displeased (¼1) to very pleased (¼7)

R: reverse-coding item

About the authors

George Christodoulides is a lecturer in marketing and Director of the Centre for Research inBrand Marketing at the University of Birmingham Business School. His research focuses onbranding and e-marketing, particularly the way the Internet and its related technologies affectbrands. George is a regular presenter at national and international conferences, and his researchhas appeared in journals such as the Journal of Advertising Research, Journal of MarketingManagement, Service Industries Journal, Marketing Theory, Journal of Product and BrandManagement, and Journal of Brand Management.

Corresponding author: George Christodoulides, Birmingham Business School, UniversityHouse, University of Birmingham, Edgbaston Park Road, Edgbaston, Birmingham, B15 2TT, UK

T þ44 121 414 8343

E [email protected]

Nina Michaelidou is a lecturer in marketing at Birmingham Business School, University ofBirmingham. She holds a PhD in consumer behaviour, and has published papers in Europeanand American journals on variety seeking, involvement, and social marketing. Dr Michaelidouteaches marketing communications and consumer behaviour on a range of degree programmes.

T þ44 121 414 8318

E [email protected]


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Journal of Marketing Management

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