Online consumer retention: contingent effects of online shopping habit and online shopping experience

Online consumer retention: contingent effects

of online shopping habit and online shopping

experience

Mohamed Khalifa1 andVanessa Liu2

1Department of Information Systems, City

University of Hong Kong, Kowloon Tong, Hong

Kong SAR, China; 2School of Management and

Department of Information Systems, New JerseyInstitute of Technology, University Heights,

Newark, NJ, U.S.A.

Correspondence: Mohamed Khalifa,Department of Information Systems, CityUniversity of Hong Kong, Tat Chee Avenue,Kowloon Tong, Hong Kong SAR, China.Tel: (852) 27887491;E-mail: [email protected]

Received: 28 January 2005Revised: 18 August 20052nd Revision: 12 June 20063rd Revision: 6 January 20074th Revision: 14 June 2007Accepted: 20 September 2007

AbstractIn this study, we further develop the information systems continuance model in

the context of online shopping, using a contingency theory that accounts for

the roles of online shopping habit and online shopping experience. Specifically,we argue and empirically demonstrate that although conceptually distinct,

online shopping habit and online shopping experience have similar effects on

repurchase intention. They both have positive mediated effects through

satisfaction and moderate the relationship between satisfaction and onlinerepurchase intention. The results of a survey study involving 122 online

customers provide strong support for our research model. We also identify

after-sale service, transaction efficiency, security, convenience, and cost savingsas important online shopping usefulness drivers. Theoretical and practical

implications include establishing a contingency theory to more fully explain

online customer retention as well as guidelines for development of customerrelationship management initiatives.

European Journal of Information Systems (2007) 16, 780–792.

doi:10.1057/palgrave.ejis.3000711

Keywords: online customer retention; online shopping habit; online shopping experience

IntroductionCustomer retention is considered by both scholars and practitioners to beone of the critical success factors for retail businesses with its implicationsfor cost savings and profitability (Doyle, 2003). The cost of acquiring newcustomers is five to seven times that of retaining existing ones.Furthermore, retained customers enhance profitability with their lowersensitivity to price changes and their higher likelihood of referring newcustomers (Doyle, 2003). Customer retention is an even more challengingissue in the context of online shopping, where severe competitors existand the switching costs for customers are minimal (Anderson & Srinivasan,2003). It is therefore important to identify the major determinants ofonline customer retention.

One such important factor identified in previous research is satisfaction.Its direct relationship with retention appears intuitive (Anderson &Srinivasan, 2003). Indeed, the marketing literature confirms that customersatisfaction is one of the main drivers of repurchase as verified in variousdifferent industrial and social contexts (e.g., see Rust & Zahorik, 1993; Rustet al., 1995; Hallowell, 1996). When a customer is satisfied with a particularinternet store, he or she is more likely to shop there again. In a study

European Journal of Information Systems (2007) 16, 780–792

& 2007 Operational Research Society Ltd. All rights reserved 0960-085X/07 $30.00

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(Jones & Suh, 2000), satisfaction is found to be a strongsingle predictor of repurchase behaviour, sometimesexplaining over 50%.

Most of the prior studies, however, were conducted inthe traditional shopping context. It is not clear whethertheir findings can be applied to the online shoppingsetting. Some information systems (IS) research (e.g.,Bhattacherjee, 2001a, b) adopted the marketing approachin explaining system usage continuance, suggestingperceived usefulness to be another factor affectingcontinuance in addition to satisfaction. A customer ismore likely to buy again from an internet store when heor she finds shopping there useful. Well established in thetraditional IS paradigm, perceived usefulness is a centralnotion that explains the usage or intended usage ofinformation technologies (Davis et al., 1989). Drawing onthe expectation disconfirmation theory, Bhattacherjee(2001a) modelled IS continuance intention as a directoutcome of both satisfaction and perceived usefulness.However, the adequacy of explaining repurchase inten-tion using satisfaction alone is questionable (Capraroet al., 2003). It has been reported that only 15–35% ofsatisfied customers did return (Reichheld, 1996). Thestrength of the effect of satisfaction on retention alsovaries, subject to factors like nature of industry (Jones &Sasser, 1995) and other social factors (Oliver, 1999). It istherefore important to examine the role of potentialmoderators in attaining a better understanding of therelationship between satisfaction and repurchase inten-tion in the online context (Oliver, 1999; Capraro et al.,2003; Nijssen et al., 2003).

Of particular interest to this study is the role of onlineshopping habit, which refers to the habit of shopping onthe internet in general. Habit is not a new phenomenonto the marketing literature and has been examined in thetraditional retailing context. Several empirical studies(e.g., Murray & Haubl, 2005) show that retention can beachieved indirectly when a habit exists. Habit is con-sidered as an important factor in explaining repeatedpurchases (Quinn & Wood, 2005). Nevertheless, mostprevious research on habit focus on the temporaldimension of the construct only (i.e., the frequency ofthe behaviour) with little consideration to the effect ofthe context in which a habit is practised (Khare & Inman,2005). The online channel represents an innovativeshopping context with a number of unique attributes,for example, interactivity, flexibility of navigation, etc.(Childers et al., 2001). The novelty of online shoppingimplies that some of the effects of the determinants ofonline repurchase may be contingent upon the develop-ment of the habit of using the online channel. Satisfac-tion, for example, may not necessarily lead to intentionto return to an internet store in the absence of an onlineshopping habit. An individual may not repurchase from acyber merchant when he or she has not formed a habitof online shopping despite his/her satisfaction withprevious transactions. An important implication of thiscontingency is that the retention theories developed and

validated in the physical shopping context need furtherenhancement to account for the effect of online shop-ping habit. Indeed, some researchers suggested that aweak habit may lower the sensitivity of consumerretention to satisfaction (Anderson & Srinivasan, 2003).The habit construct is also of appeal to practitioners.According to a recent report, online retailers consider thedevelopment of online shopping habit to have a majorimpact on internet sales (Cisco Systems, 2003). It istherefore important to examine the role of habit in theonline shopping context in general and its effect ononline repurchase intention in particular.

In addition, the research on habit often involves theuse of prior experience as its surrogate measure (e.g., Eastet al., 1994). Instead of examining the habit of doinggeneral shopping online, a number of studies looked intobroad experience with internet shopping. Prior experi-ence is indeed one of the key ingredients for habitformation (Triandis, 1971). Some scholars, however,argue that experience is conceptually distinct from habitand hence its validity as a proxy for habit becomesdoubtful (e.g., Ajzen, 1991). To throw light on thisambiguity, we study prior online shopping experiencein this research and compare its effects with those ofonline shopping habit.

In this study, we integrate the marketing and IStheories to develop a better model for explaining therepurchase intention of online customers. Specifically, weadopt a contingency approach including online shop-ping habit as the moderator of the relationship betweensatisfaction and repurchase intention. Furthermore, weclarify the distinction between habit and prior experienceby performing a comparative analysis. Our researchmodel therefore consists of two separate levels of researchvariables. Repurchase intention and its hypothesisedpredictors (satisfaction and perceived usefulness) areinternet retailer-specific while the moderators (onlineshopping habit and online shopping experience) arerelated to the online shopping context in general. Byrefining the existing online customer retention models,our paper also responds to Pateli & Giaglis (2004)’s call forthe development of more structured theoretical tools inthe electronic business literature.

In the next section, we introduce and present thetheoretical foundation of the research model. We thendescribe the research context and methodology, followedby a discussion of the empirical results. We conclude thepaper by discussing the practical implications of thefindings and suggesting directions for future research.

The research modelWith the growing maturity of the customer retentionliterature, this study extends early definitional researchand develops a research model with a focus on moredetailed ontological analyses of the relationships amongthe constructs underlying the customer retentionphenomenon (Pateli & Giaglis, 2004). The theoreticaldevelopment of our research model is grounded in the IS

Online consumer retention Mohamed Khalifa and Vanessa Liu 781

European Journal of Information Systems

continuance theory (Bhattacherjee, 2001a). In justifyingthe integration of the technology acceptance model(TAM) (Davis et al., 1989) with the disconfirmationtheory (Oliver & DeSarbo, 1988), Bhattacherjee (2001a)argued that IS continuance decisions are very similar toconsumers’ repurchase intention. He conducted his studyin the context of online banking systems. In his model,continuance intention is directly driven by satisfactionand perceived usefulness. Following the marketingapproach, Bhattacherjee (2001a) measured satisfactionas an evaluative outcome of a comparison processbetween expected and perceived performance known asconfirmation (Oliver & DeSarbo, 1988). Bhattacherjee’s(2001a) model is based on theories originally developedand validated in physical environments, with which thecustomers are quite familiar. We argue that the applica-tion of these theories to the online environment iscontingent upon the formation of online shopping habit.Our research model (see Figure 1) extends Bhattacherjee’s(2001a) study to capture the mediated and moderatingeffects of online shopping habit on the formation ofrepurchase intention.1

Repurchase intention, perceived usefulness, andsatisfactionRepurchase intention has been studied extensively bymarketing scholars. It is typically defined as the intentionto repeatedly purchase a particular product or serviceover time (see Copeland, 1923). Repurchase mirrors andconstitutes one important dimension of loyalty (Jacoby &Chestnut, 1978; Soderlund et al., 2001). As the issues ofconversion (converting customers to the online channel;Ho & Wu, 1999) and stickiness (continuation of using theonline channel; Khalifa et al., 2002) are of particularinterest to internet retailers, the definition of repurchaseintention originated from marketing may not be in thebest fit. In view of this, we define repurchase as there-usage of the online channel to buy from a particularretailer. Unlike other metrics such as click through ratios,repurchase can better capture the ‘process of retaining

prior customers with repeat business’ (Nemzow, 1999). Assuch, repurchase can be considered as continuancebehaviour (i.e., continue to shop from the same internetstore). The usage of a continuance model to explain thisbehaviour is therefore appropriate. Consistent with mostprior IS and marketing studies, our focus is continuanceintention rather than the behaviour itself. Intention isconsidered to be strongly correlated with volitionalbehaviour (i.e., a behaviour that ‘a person can decide atwill to perform or not perform’, Ajzen, 1991). Volitionalbehaviours typically involve a number of alternativesamong which a choice can be made at discretion.Intention can accurately predict behaviours that aregenerally under one’s control (see Ajzen, 1988). Thestrong association between intention and volitionalbehaviour is validated empirically (Davis et al., 1989;Taylor & Todd, 1995). Repurchase is a form of volitionalbehaviour, as customers generally enjoy more than oneoption of which store to buy again. Additional theoreticaljustification for this association comes from the cognitivedissonance theory (Festinger, 1957), which stipulates thatindividuals attempt to relieve the psychological tensiondue to discrepancies between intention and behaviour bybeing consistent with their intention. There is also apractical consideration for focusing on intention. In thecontext of continuance, it is difficult to tell whether anindividual who has not repurchased for a certain periodof time has indeed discontinued.

According to the TAM, perceived usefulness is one ofthe major determinants of intention formation. Thenotion was originally developed in the organisationalsetting, denoting the extent to which an individual’s jobperformance is enhanced with the use of a specifictechnology (Davis et al., 1989). In the context of onlineshopping, it refers to the salient beliefs of customersregarding the instrumentality of repurchase (Davis et al.,1989). For example, online shopping may be perceived asmore useful when it confers convenience and costsavings. The original operationalisation of perceivedusefulness (Davis et al., 1989) involved scales that aregeneralisable across both applications and contexts. Theinitial usefulness measures were developed with respectto the use of several office applications, namely, an e-mailsystem, a graphics system, and a file editor application.The measures include ‘quality of work’, ‘control overwork’, ‘work more quickly’, ‘critical to my job’, ‘increaseproductivity’, ‘job performance’, ‘accomplish morework’, ‘effectiveness’, ‘makes job easier’, and ‘useful’(Davis et al., 1989, p. 329, Table 6). Ajzen, 1991subsequently argues that researchers should considersalient beliefs that are specific to the context, as it isonly at the level of specific beliefs that we can learn aboutthe unique factors that induce one person to engage inthe behaviour of interest. He further contends that thesesalient beliefs must be elicited from the respondentsthemselves, or in pilot work from a sample of respon-dents that is representative of the research population.Some examples of usefulness factors identified in the

PerceivedUsefulness

Online ShoppingSatisfaction

OnlineShopping Habit /

Experience

OnlineRepurchase

Intention+

+

+

+

+

Figure 1 The research model.

1We exclude the confirmation construct as its effects are fullymediated through satisfaction (Oliver & DeSarbo, 1988). Itsinclusion would not add to the explanation of the focal variablein this study, i.e., online repurchase intention.

Online consumer retention Mohamed Khalifa and Vanessa Liu782


e-commerce literature include faster search, improvedsearch and buying, increased shopping productivity,enhanced shopping performance (e.g., see Gefen &Straub, 2000; Koufaris, 2002). Accordingly, we haveconducted a belief elicitation process (described later inthe Methodology section) involving a sample of ourrespondents (i.e., online shoppers) to identify salientusefulness beliefs specific to the online shopping contextand the internet technology, resulting in five usefulnessfactors (i.e. after-sales service, transaction efficiency,security, convenience, and cost savings). The effect ofperceived usefulness on behavioural intention (such asrepurchase) has been consistently reported in mostempirical studies in various contexts, including informa-tion technology and electronic commerce (see a sum-mary in Gefen & Straub, 2000). In this accord,Bhattacherjee (2001a) also suggested and empiricallydemonstrated that an individual is more likely to intendfor continued usage when such usage is perceived to beuseful. Applying TAM in the context of online shopping,individuals will be more likely to intend to repurchaseonline when they believe such behaviour to be able toconfer positive values (Davis et al., 1989). For example,one may be more willing to shop again (i.e., repurchaseintention) from an online store if one perceives it to becheaper than other physical or online stores or providingbetter after-sales services. Consistent with prior studies,we hypothesise that:

H1: Perceived Usefulness has a significant positive effect on

Online Repurchase Intention.

Satisfaction is another major determinant of repurch-ase intention. It has been widely defined as a post-evaluative judgement over a particular purchase (Oliver,1979, 1980; Churchill & Suprenant, 1982; Bearden &Teel, 1983; Oliver & DeSarbo, 1988). It is an attitudinalvariable, and as such is an important determinant ofintention as per TAM (Davis et al., 1989). We rely on thetransaction cycle to characterise the customer’s satisfac-tion with his/her online shopping experience. Consistentwith Feinberg et al. (2002), Khalifa et al. (2002), andLu (2003), our research model identifies three typesof satisfaction (i.e., pre-purchase, at-purchase, andpost-purchase satisfaction). The pre-purchase satisfactionis related to the customer’s experience with onlineactivities performed prior to placing an order, forexample, consumer education, product search, andproduct comparison. The purchase satisfaction is formedbased on the customer’s experience with the orderplacement and payment processes. The post-purchasesatisfaction, on the other hand, is associated with thecustomer’s experience with after-sale services, for exam-ple, online customer support, helpdesks handlingreturns/refunds, and online installation manuals, etc.

Judgement of satisfaction is entirely subjective andindividual (Oliver, 1997). Perceived usefulness representssalient beliefs that are developed based on realistic

consideration of actual situational factors (Davis et al.,1989). Previous TAM studies found that perceived useful-ness significantly, consistently, and substantively drivesthe formation of user acceptance (e.g., Davis et al., 1989;Mathieson, 1991; Taylor & Todd, 1995; Karahanna et al.,1999) and attitudes in general, such as satisfaction.Similarly, the disconfirmation theory posits that satisfac-tion is an evaluative outcome of the gap betweenperformance and expectations (Oliver & DeSarbo,1988), with higher performance levels associated withmore positive disconfirmation and increased satisfaction.Perceived usefulness refers to beliefs regarding perfor-mance (e.g., time savings, cost savings). An individual ismore likely to be satisfied with an offering or experiencewhen he or she perceives better performance thereforegreater usefulness. We accordingly hypothesise that:

H2: Perceived Usefulness has a significant positive effect onOnline Shopping Satisfaction.

The direct relationship between satisfaction and con-tinuance intention is at the core of the IS continuancemodel and is validated empirically (Bhattacherjee,2001a). In the online shopping context, a customer ismore likely to intend to return to a particular store if heor she is satisfied with the previous purchases from thatstore. Unpleasant buying experience easily leads todissatisfaction, discouraging the customer from comingback (Oliver, 1999). There is ample empirical evidencefrom the marketing literature that customer satisfactionis positively linked to repurchase intention (e.g., see Rustet al., 1995; Hallowell, 1996; Anderson & Srinivasan,2003). We therefore hypothesise that:

H3: Online Shopping Satisfaction has a significant positiveeffect on Online Repurchase Intention.

Online shopping habitThe effects of online shopping habit on online repurch-ase intention are two-fold: (1) mediated through satisfac-tion and (2) moderating the relationship betweensatisfaction and repurchase intention. Habit refers to‘situation-behaviour sequences that are or have becomeautomaticy the individual is usually not conscious ofthese sequences’ (Triandis, 1980, p. 204). It is a beha-vioural tendency resulting from prior experience (Gefen,2003). It represents behavioural disposition to repeatprevious action, developed through frequent perfor-mance in a stable context (Ouellete & Wood, 1998). Itcan be viewed as an automatic behavioural responsetriggered by a situational stimulus without beingpreceded by a cognitive analysis process (Aarts et al.,1998). The individual is not necessarily aware of thebehaviour nor is required to devote thought or rationalevaluations prior to engaging in such behaviour (Ouellete& Wood, 1998). In other words, habitual behaviours



require minimal conscious thoughts (Aarts & Dijkster-huis, 2000), enabling individuals to conserve theirlimited mental resources (Bargh & Ferguson, 2000). Suchautomaticity allows habitual behaviours to be performedeasily and quickly (Quinn & Wood, 2005). In the contextof online shopping, individuals with online shoppinghabits would turn to the online channel (i.e., thebehavioural response) instead of a physical outlet auto-matically without further consideration when they feelshopping needs (i.e., situational stimulus). Past researchreported that habit is a major driver of affect (Limayem &Hirt, 2003). Affect is defined as ‘feelings of joy, elation, orpleasure, or depression, disgust, displeasure, or hateassociated by an individual with a particular act’(Triandis, 1971, p. 211) and an ‘emotional response tothe thought of the behaviour’ (Limayem & Hirt, 2003, p.69). Consistent with Bhattacherjee (2001a), we argue thatsatisfaction is also an affect. By giving rise to a favourablefeeling towards a behaviour (Triandis, 1971), habit affectssatisfaction directly (Limayem & Hirt, 2003). In otherwords, a customer is likely to be more satisfied with aninternet store if he or she has acquired the habit ofshopping online. We therefore hypothesise that:

H4: Online Shopping Habit has a significant positive effecton Online Shopping Satisfaction.

In addition to its mediated effect, habit is also supposedto positively moderate the relationship between satisfac-tion and repurchase intention. According to the theory ofattitude and attitude change, behavioural intention isdetermined by the interaction of habit and affect amongother constructs (i.e., social norms and attitude) (Trian-dis, 1971). With the same level of affect (i.e., satisfaction),an individual who has acquired a habit of a behaviour ismore likely to intend to repeat the behaviour in thefuture than others without such habit. In other words,given particular level of satisfaction with an online store,a customer who has acquired a stronger online shoppinghabit is more likely to intend to repurchase from thatstore. A customer who rarely shops online may not returndespite that he or she is satisfied. Accordingly, wehypothesise that:

H5: Online Shopping Habit positively moderates therelationship between Online Shopping Satisfaction andOnline Repurchase Intention.

Online shopping experienceSeveral previous studies have used experience as asurrogate measure for habit (e.g., Mittal, 1988; Montano& Taplin, 1991; East et al., 1994; Thompson et al., 1994;Verplanken et al., 1994; Bergeron et al., 1995; Verplankenet al., 1996; Verplanken & Svenson, 1997; Murray &Haubl, 2005). Accordingly, habit is often operationalisedbased on self-reported frequency of past behaviour (Aarts,

1996). Other researchers, however, have argued that suchoperationalisation of habit is not optimal (e.g., Ajzen,1991; Eagly & Chaiken, 1993). Although experience is aprecursor of habit (Triandis, 1971; Chaudhuri, 1999), thetwo constructs are conceptually different. Unlike experi-ence, the formation of a habit is partly subject to theability of the individual to convert/absorb the behaviourinto cognitive schemata (Limayem et al., 2001). Thedevelopment of experience, on the other hand, solelyrequires actual usage behaviour. A prolonged usageexperience does not necessarily imply that an individualhas acquired the usage habit. We therefore develop twoseparate measurement scales for habit and experience,hypothesising the conceptual discrimination betweenthe two constructs.

Although conceptually different, experience and habitseem to have similar effects. This may explain the usageof experience as surrogate of habit by some researchers. Inthe context of our study, experience is expected to havepositive effects on satisfaction and moderating effects onthe relationship between satisfaction and repurchaseintention. Prior studies showed that repeated behaviour(i.e., experience) enhances the affective component ofone’s attitude (e.g., satisfaction) towards such behaviour(Verplanken et al., 1998). Accordingly, we hypothesisethat:

H6: Online Shopping Experience has a significant positiveeffect on Online Shopping Satisfaction.

Also, experience is shown to strengthen the accessi-bility of the affective component of attitude, for example,satisfaction (Verplanken et al., 1998). Accessibility refersto the speed of retrieving affect from memory. Enhancedaccessibility, in turn, improves affect–behaviour consis-tency (Fazio et al., 1982). This is consistent with studiesreporting moderating effects of experience on therelationship between attitude and behavioural intention(Zanna et al., 1980; Fazio & Zanna, 1981). Therefore, wealso argue that for the same level of satisfaction, higherexperience leads to better accessibility of the satisfactionin memory, strengthening in this way the effect ofsatisfaction on intention. Accordingly, we hypothesisethat:

H7: Online Shopping Experience positively moderates therelationship between Online Shopping Satisfaction andOnline Repurchase Intention.

Methodology

Research procedureThe research model was tested by conducting a surveystudy, which presents the advantages of enabling replic-ability, strengthening statistical power and serving as afoundation for building generalisability (Teo et al., 2003).The invitation to participate in the survey was distributed



by email through an internet service provider thatidentified a pool of online shoppers. The survey instru-ment was then electronically administered to respondents.The response rate could not be estimated as we were notgranted permission to access the master mailing list.

ParticipantsRespondents of the survey composed of 122 onlinecustomers who had previously shopped from variousinternet stores. Participation was entirely voluntary.Table 1 presents the demographics of the respondents.

MeasurementIn line with our definition of repurchase intention, therespondents were asked to think of a specific online storefrom which they had previously shopped in answering allquestions in the survey. We measured online shoppinghabit, online shopping experience, and online repurch-ase intention using reflective items that were adaptedfrom the literature (e.g., see Limayem & Hirt (2003) forhabit, and Limayem et al. (2000) for online repurchaseintention) and validated through the card sortingprocedure (Moore & Benbasat, 1991). To identify specificusefulness factors for all phases of online shopping, werelied on belief elicitation to develop a formativemeasurement model for the construct of perceivedusefulness. We wanted to distinguish between thedifferent phases of online shopping (i.e. pre-purchase,purchase, and post-purchase experiences). Therefore, wealso operationalised satisfaction as a formative, emergentconstruct formed with items based on these phases.Although the items of both perceived usefulness andsatisfaction measure the perceptions towards similarofferings, for example, after-sale service, the majordistinction lies in the contextual difference. Perceivedusefulness refers to the evaluation of a specific offering,while satisfaction measures the general attitudes towardsa stage in the purchase cycle as a whole. We developed aninitial pool of formative items from a review of theliterature. Twenty online shoppers were invited toparticipate in focused group discussions where they wereasked to identify important usefulness factors, wherebyassessing the face validity of the factors. Appendix Apresents the formative items developed based on thebelief elicitation results as well as all other measures inthe survey.

As respondents with more online shopping experienceare likely to be more satisfied, hence introducingpotential response bias, we examined the responsedistribution for satisfaction. An even distribution of theresponses indicated that the possibility of having such abias was remote.

Although we rely on the same respondents for thedependent and independent variables, the structure ofthe research model (i.e. moderating effects) minimisespotential common method bias. The moderating effects,if found to be significant, will provide a strong indicationof the lack of common method bias (Evans, 1985). While

the respondents may anticipate the linear relationshipsand answer accordingly, they are not likely to predict themoderating relationships. Existence of a common meth-od bias increases statistically the covariance among theindependent variables, impairing the likelihood of de-tecting a significant moderating effect. Therefore, withthe verification of moderating effects, the presence of acommon method bias becomes highly unlikely (Brockneret al., 1997).

To check for possible non-response bias, we comparedearly respondents with late respondents (Armstrong &Overton, 1977). The last 25% to submit their responsewere considered to be late responses and were deemedto be representative of online shoppers who ultimatelydid not respond to the survey (Li & Calantone, 1998).We then compared the means of all items for the twogroups and could not detect any significant differences.To assess the representativeness of our sample, wecompared the demographics with those of global internetusers (TNS-Infratest, 2005) and detected no significantdifferences.

In formulating and testing the moderating effect ofonline shopping habit (experience) on the relationshipbetween online shopping satisfaction and online re-purchase intention, we followed a hierarchical processsimilar to multiple regressions where we compared theresults of two models. That is, one with and one without

Table 1 Demographics of respondents

Demographic Category Percentage %

Gender Male 62

Female 38

Age Less than 20 8

20–35 74

36–50 16

Over 50 2

Marital Status Single 79

Married 21

Education High school 20

Undergraduate degree 38

Postgraduate degree 42

Experience with internet

usage

Less than 1 year 4

1–2 years 5

3–4 years 33

Over 4 years 58

Online shopping frequency Less than once a month 70

A few times a month 19

A few times a week 7

About once a day 4

Residing countries United states 100



the interaction construct: online shopping habit (experi-ence)�online shopping satisfaction. The interactionconstruct is developed by multiplying the factor scorefor the satisfaction construct (with formative indicators)with each of the standardised items of the habit(experience) construct (with reflective indicators). Themoderating effects are tested and interpreted accordingto the guidelines recommended by Carte & Russell(2003). More specifically, we assessed the significance ofthe change in the R2, using the following formula (Carte& Russell, 2003):

DR2=ðdfmul � dfaddÞð1 � R2

mulÞ=ðN � dfmul � 1Þ

Measurement scaleThe survey instrument consisted of both reflective andformative indicators measured with a semantic differen-tial scale in the form of a slider that was used to recordthe respondents’ answers (see Appendix A). The slider is agraphical scale with two anchors only appearing at bothends (‘strongly disagree’ and ‘strongly agree’). The neutralpoint is labelled as ‘Indifferent’. With a resolutionranging from 1 to 100, the slider provides 100-scale steps.According to numerous psychometric studies, the relia-bility of individual rating scales is a monotonicallyincreasing function of the number of steps (Nunnally,1978). Graphical scales are reported to be superior tonumeric scales as people usually think of quantities asrepresented by degrees of physical extensions (e.g., theyardstick). Graphical scales can also help to convey theidea of a rating continuum and lessen clerical errors inmaking ratings (Nunnally, 1978). To mitigate potentialthreats to the reliability of the scale measurementresulting from different screen sizes and operativenessof input devices of the respondents, the exact ratingsselected were indicated next to the slider. In this way,respondents were also able to re-adjust the slider whenthey mishandled the mouse devices.

Data analysisSeparate analyses were performed with research modelsusing online shopping habit and online shoppingexperience respectively as the moderator. This allows usto compare the moderating and mediated effects of theseconstructs. From a methodological perspective, simulta-neous testing of test effects may also be problematic.Although experience and habit may plausibly haveindependent effects on online repurchase intention,these two constructs may potentially be highly correlatedas experience constitutes one of the pre-requisites for theformation of habit.

The analysis of the data was done in a holistic mannerwith the partial least squares (PLS) procedure using PLSgraph (Chin, 1998). The PLS procedure (Wold, 1989)allows the researcher to both specify the relationshipsamong the conceptual factors of interest and the

measures underlying each construct. Specifically, reflec-tive indicators are invoked to account for the observedvariances and covariance while formative indicators areweighted based on their relative importance in theformation of the construct (Chin, 1998; Law et al.,1998). The result of such a procedure is a simultaneousanalysis of (1) how well the measures relate to eachconstruct and (2) whether the hypothesised relationshipsat the theoretical level are empirically confirmed.Furthermore, due to non-normality of the data, structuralequation modelling was not appropriate (Chin & Gopal,1995; Bhattacherjee, 2004). PLS, on the other hand, isconsidered a better fit as it adopts a component-basedapproach and does not require multivariate normaldistributions or a large sample size (Fornell & Bookstein,1982). Compared to structural equation modelling, PLS ismore focused on predictability by maximising thevariance explained in constructs rather than overallfitness of the model (Barclay et al., 1995). With theprediction-oriented nature of the research questions, PLSis therefore considered preferable for this study. Tests ofsignificance for all paths were conducted using thebootstrap re-sampling procedure (Cotterman & Senn,1992). For reflective measures, we adopt the standardapproach for evaluation, where all path loadings fromconstruct to measures are expected to be strong (i.e., 0.70or higher). The internal consistency of the reflectiveitems was estimated using the composite reliabilityformula (Chin, 1998). We did not rely on the Cronbach’salpha, which represents a lower bound estimate ofinternal consistency due to its assumption of equalweightings of items. In the case of formative measures,we assess the contribution of each item to the pertainingconstruct based on their weights (Chin, 1998).

Results and discussion

Measurement model validationTables 2, 3 and 4 present the results of the test of themeasurement model. As shown in Table 2, the loadings ofall reflective items are high (above 0.8) with significanceat 1% level, demonstrating convergent validity. Compo-site reliability scores and average variance extracted (AVE)were computed to further assess convergent validity aswell as internal consistency. Results (see Table 3) showedthat for all constructs the composite reliability scores arehigher than the recommended benchmark of 0.80(Nunnally, 1978) whereas the AVEs exceeded the thresh-old of 0.50 (Hair et al., 1998), verifying internalconsistency.

As for discriminant validity, which denotes the extentto which the measures for each construct are distinctfrom each other, we performed the assessment byverifying whether the correlations between a referentconstruct with others are substantially different from thesquare roots of the AVE scores of that construct(Anderson, 1987). Such a comparison provided supportfor discriminant validity (correlation between repurchase



and habit¼ 0.50; correlation between repurchase andexperience¼0.61; see Table 3).

We performed two tests to verify the conceptualdistinction between online shopping habit and onlineshopping experience. First, we relied on the card sortingprocedure. Habit and experience were classified into twoseparate constructs by the panel judges involved in theprocedure, indicating face validity and hence discrimi-nant validity. Furthermore, the square root of the AVE ofthe experience construct (0.84) is higher than thecorrelation of experience with habit (0.74) (see Table 3),therefore demonstrating discriminant validity. Based onthese results, we conclude that online shopping habitand online shopping experience are conceptually distinctconstructs. The higher correlation value, however,

suggests potential multicollinearity problem if bothconstructs are included in the same model.

The weights and the significance of all formativemeasures indicate that the items contribute significantlyto the formation of the intended construct (Table 4).After-sale service and transaction efficiency emerged asthe two most important drivers of perceived usefulnesswith identical weights of 0.42, both significant at the 1%level. The effects of security, convenience and costsavings are comparatively small, but are neverthelesssignificant.

To check for the potential effects of demographics, weincluded age and gender in the model, but their pathcoefficients (links to online repurchase intention) werenot significant. We therefore concluded that age and

Table 2 Reflective measurements

Construct items Loadings Std. error t-Values

Online repurchase intention

Anticipate to repurchase in the near future 0.92 0.02 61.23

Likely to repurchase in the near future 0.93 0.02 57.78

Expect to repurchase in the near future 0.95 0.01 91.03

Online shopping habit

Natural behaviour 0.87 0.03 27.94

First thing that comes to mind 0.90 0.02 47.15

Spontaneous 0.84 0.05 17.66

Online shopping experience

Used extensively 0.91 0.02 43.07

Used for a long time 0.86 0.03 29.61

Used frequently 0.82 0.04 18.92

Table 3 Assessment of internal consistency, convergent validity, and discriminant validity

Constructs No. of

items

Composite

reliability (r)

AVE

(sq. root)

Correlations with online

repurchase intention

Correlations with online

shopping habit

Online repurchase intention 3 0.95 0.87 (0.93) 1.00

Online shopping habit 3 0.91 0.76 (0.87) 0.50

Online shopping experience 3 0.91 0.71 (0.84) 0.61 0.74

Table 4 Formative measurements

Constructs Items Weights Std. error t-Values

Online shopping satisfaction Post-purchase experience 0.59** 0.10 6.02

Pre-purchase experience 0.34** 0.13 2.81

Purchase experience 0.26** 0.11 2.35

Perceived usefulness After-sale service 0.42** 0.16 2.62

Transaction efficiency 0.42** 0.10 4.08

Security 0.24** 0.12 2.01

Convenience 0.23** 0.09 2.46

Cost savings 0.20** 0.10 2.02

**Significance at 1% level.



gender differences did not provide additional explana-tion for intention formation.

Results of PLS analysisFigure 2 provides the results of the PLS analysis of fullmodel, including the habit construct. The estimated patheffects are given along with their significance. All pathcoefficients are significant providing strong support forall the hypothesised relationships. Both perceived useful-ness (b¼0.31; P¼0.01) and online shopping satisfaction(b¼0.44; P¼0.01) have significant positive effects ononline repurchase intention, confirming H1 and H3. Ashypothesised (H2), perceived usefulness also has asignificant positive effect on satisfaction (b¼0.65;P¼ 0.01). These results confirm the applicability of theIS continuance model (Bhattacherjee, 2001a) to theonline shopping context.

The reduced model (without habit) explains about 66%of the variance in repurchase intention. Interestingly, thisR2 is identical to the one reported by Bhattacherjee(2001a). The full model (including habit), on the otherhand, explains about 70% of the variance. Our resultsshow strong support for both the mediated and moderat-ing effects of online shopping habit. As hypothesised inH4, habit has a significant effect on satisfaction (b¼0.18;P¼ 0.01). The moderating effect of habit is also verified,as indicated by a significant change in R2 (F¼15.4;P¼ 0.001)2, confirming H5. In other words, as onlineshopping becomes more habitual, the effect of satisfac-tion on repurchase intention becomes stronger.

We replicated the analysis using online shoppingexperience to compare its effects with those of onlineshopping habit (see Figure 3). The empirical results arequite similar. The full model with online shoppingexperience explains a comparable variance of onlinerepurchase intention (R2¼ 71%) to that of the full modelwith habit (R2¼70%). Similar to habit, experience alsoexhibits mediated and moderating effects on onlinerepurchase intention. As hypothesised in H6, experiencehas a significant effect on satisfaction (b¼0.28; P¼0.01).Confirmed by the significant change in R2 (F¼19.3;P¼ 0.001),3 experience also positively moderates therelationship between satisfaction and repurchase inten-tion, verifying H7. These findings imply that experiencedonline customers are more likely to intend to repurchasewhen satisfied. The significance of H7 also qualifiesonline shopping experience as a valid segmentationcriterion for customer retention. Managers may opt tocollect data on customers’ online shopping experience inplace of habit, which is relatively more difficult tomeasure.

DiscussionIn this study, we develop, operationalise, and empiricallytest a model that explains online consumer retention asmeasured by repurchase intention. Earlier studies in themarketing literature modelled repurchase as a directeffect of satisfaction. Recent IS research suggested con-tinuance intention to be an outcome of satisfaction andperceived usefulness. Our findings demonstrated thatsuch models may not be fully sufficient for explainingonline shopping and that a contingency theory wasneeded (i.e. moderating factors of the link betweensatisfaction and repurchase intention should be in-cluded).

Theoretical implicationsThe empirical evidence provides strong support for ourmodel, as demonstrated by the significant effects of habitand experience and the improved explanatory power (R2)of the model. These results confirm our argument for thedevelopment of a contingency theory to account for thenovelty of the online channel. Our findings also verifythe conceptual distinction of the habit and experienceconstructs, reinforcing the argument of previous studiesthat experience is necessary but not sufficient for theformation of habit. Although conceptually distinct,experience and habit seem to have comparable effects.

PerceivedUsefulness

OnlineShopping

Satisfaction

OnlineShopping

Habit

OnlineRepurchase

Intention0.65**

0.31**

0.44**

R2: 0.56

R2: 0.70

0.18**

** p < 0.01

∆ R2 (F = 15.4**)

Figure 2 Results of PLS analysis – full model with habit.

Perceived

Usefulness

Online

Shopping

Satisfaction

Online

Shopping

Experience

Online

Repurchase

Intention0.59**

0.30**

0.40**R2: 0.60

R2: 0.71

0.28**

∆ R2 (F = 19.3**)

** p < 0.01

Figure 3 Results of PLS analysis – full model with experience.

2F¼ [R2/(dfmul�dfadd)]/[(1�R2mul)/(N�dfmul�1)]¼ [(0.70�0.66)/

(5�4)]/[(1�0.7)/(122�5�1)]¼15.4.3F¼ [R2/(dfmul�dfadd)]/[(1�R2

mul)/(N�dfmul�1)]¼ [(0.71�0.66)/(5�4)]/[(1�0.71)/(122�5�1)]¼19.3.



The similarity of their effects may be due to the fact thatexperience is a pre-requisite for habit formation. Whenwe tried to include both constructs in the model, theirhigh correlation created multicollinearity effects andonly one of them (i.e., experience) remained significant.As to which construct should be considered, we suspectthat for cases where online shopping habit is strong, itsinclusion may provide a better explanation of onlinerepurchase intention. On the other hand, when con-sumers have not acquired an online shopping habityet, the inclusion of online shopping experience mayprovide a better explanatory power. The interplay ofexperience and habit should be investigated further infuture research to shed more light on their relativeimportance in explaining online repurchase intention.

The significant moderating effects of online shoppingexperience and habit provide important implications.The formation of online shopping habit may notnecessarily involve a prolonged usage experience. Ourresults show that the effect of satisfaction on repurchaseintention is strengthened with both habitual and experi-enced online shoppers. In other words, customers withonline shopping habits are as likely to return as thoseexperienced others when satisfied. Often times, customerretention programs focus extensively on experiencedonline shoppers. Our study demonstrates that habitualcustomers are worth similar attention despite they maynot have a long history of online shopping.

Managerial implicationsOn the practical side, our study offers guidelines oncustomer profiling and prioritisation of customer reten-tion programs for online retailers. Many companiesgrouped customers based on their satisfaction level. Asour results show that experienced and habitual custo-mers, when satisfied, are more likely to be retained,another useful market segmentation criterion may be thelevel of prior online shopping experience and onlineshopping habit. Customers may be segmented into fourmajor profiles: (1) satisfied shoppers with online shop-ping experience or habit; (2) satisfied shoppers with lessor without online shopping experience or habit; and (3)dissatisfied shoppers with online shopping experience orhabit; and (4) dissatisfied shoppers without onlineshopping experience or habit. Each of these profilesshould be assigned different marketing objectives andpriority of retention efforts. For both the first and secondprofiles where customers are satisfied, the primarymarketing objective should be the continuous enhance-ment of customer satisfaction in order to increase thelikelihood of their repeat purchases. But for the secondcategory, customer retention requires extra efforts onreinforcing the development of online shopping habit/experience, converting the customers to more habitualonline shoppers and hence increasing their life timeprofitability to the company. Managerial influence in thiscase is, however, limited. Internet merchants may exertlittle direct influence on such general habit/experience as

the formation of which is subject to a number ofindividual factors, such as personality traits and generalattitudes towards online shopping. Alternatively, man-agers may enhance the development of specific habit/experience with buying from their particular stores. Theonline shopping channel may be useful in serving thispurpose. For example, multimedia design may be in-corporated in the website to improve the virtual shop-ping experience. Online retailers may also provide freeonline previews or trials of products to encourage theacquisition of usage experience. Furthermore, abolish-ment of traditional channels may be effective in enhan-cing the development of online shopping habit as in thecase of banking services. Closures of bank branches havesignificantly contributed to the habit formation of usinge-banking services. Product improvement may be the toppriority for marketing managers serving the remainingtwo profiles in order to restore satisfaction from thedissatisfied customers. As our results also show the effectsof habit and experience to be similar, practitioners mayrely on prior experience in profiling customers who havenot acquired the online shopping habit yet.

Future research directionsThe role of online shopping habit may vary acrossindustry contexts. For products or services where themajority of customers may already be accustomed tobuying online (e.g., books and airline tickets), the role ofhabit in enhancing repurchase intention may not be assalient as it does in contexts where online shoppingremains in its infancy stage (e.g., grocery shopping).Future research is needed to inform these variations byreplicating our study in industry contexts varying interms of the innovativeness of the online channel willshed more light on the relative importance of themoderating effects of habit and experience.

Another limitation of this research is that its scope islimited to the online factors. Several empirical studies(e.g., Parasuraman et al., 1985) reported that the qualityof how a product and service is actually delivered playeda key role in satisfaction formation. Such studies suggestthat the success of the online channel remains dependenton the effectiveness and efficiency of the physicalchannel, for example, delivery. Future research shouldconsider online and contingent offline factors simulta-neously. As our research model incorporates variablesmainly from the TAM, future researchers should alsoexpand our study by investigating the relative impor-tance of other predictors of intention. For example,earlier empirical evidence demonstrates that onlinecustomers’ personality traits have a significant impacton the effectiveness of interaction with online retailers(Jahng et al., 2002). These variables have not beencontrolled for in the present study. Other potentiallyrelevant factors include perceived usability and systemquality (Juan et al., 2006). As well, testing the applic-ability of our research model to online customercommunities may be of interest to internet retailers



given the recent proliferation of such e-commerceinitiatives. Such examination may require additionalvariables like trust (e.g., see van der Heijden et al.,2003), which are important factors to consider wheninterpersonal relationships among customers are in-volved (Lankton & McKnight, 2006).

Another contextual direction for future research is toextend and re-test our conceptual model in the context ofactual purchase behaviour to assess its adequacy and theneed for additional explanatory variables. From a meth-odological perspective, future research may also adopt alongitudinal design, investigating further the interplay of

experience and habit and their relative importance inexplaining continuance behaviour.

ConclusionIn conclusion, our study highlights the importance of theroles of online shopping habit and online shoppingexperience in the achievement of online customerretention. As verified by our data, online repurchaseintention is not only an outcome of a rational analysis ofsatisfaction with and the perceived usefulness of previoustransactions. Both habit and experience are also signifi-cant driving forces of online shopping behaviour.

About the authors

Professor Mohamed Khalifa received the degrees of M.A.in Decision sciences and Ph.D. in Information Systemsfrom the Wharton Business School of the Universityof Pennsylvania. He is currently Professor at theDepartment of Information Systems, City University ofHong Kong. His current research interests includeelectronic business and knowledge management. Hiswork appeared in journals such as Communications ofthe ACM, OMEGA, Journal of the Association of InformationSystems, IEEE Transactions on Engineering Management,IEEE Transactions on Systems Man and Cybernetics, DecisionSupport Systems, Data Base and Information andManagement.

Vanessa Liu is currently Assistant Professor at the Schoolof Management with joint appointment with the Depart-ment of Information systems at the New Jersey Instituteof Technology. She received the degrees of M.Phil. andPh.D. in information systems at the City University ofHong Kong. Her work has been published in some ISacademic journals including Journal of the Association forInformation Systems, OMEGA the International Journal ofManagement Science and IEEE Transactions on ProfessionalCommunication. She was one of the authors of the paperthat was awarded the ‘Best Paper in Conference Theme’ atICIS 2005. Her research interests include electroniccommerce and knowledge management.

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Appendix A

Online survey instrument

mid-point of the slider

Click to display the pointer

Indifferent

Strongly Agree

Strongly Disagree

Items measuring Online Repurchase Intention (Limayemet al., 2000)

I anticipate to repurchase from this internet store inthe near futureIt is likely that I will repurchase from this internetstore in the near futureI expect to repurchase from this internet store in thenear future

Items measuring Online Shopping Habit (Limayem & Hirt,2003)

Shopping online has become a natural act for meWhenever I think of shopping, the internet comes tomy mindOnline shopping has become spontaneous for me

Items measuring Online Shopping Experience (Limayem &Hirt, 2003)

I have shopped online extensivelyI have used the internet to shop for a long timeI shop online frequently

Items measuring Online Shopping Satisfaction (Bhattacher-jee, 2001a)

I am satisfied with the pre-purchase experience ofthis internet store (e.g., consumer education, productsearch, quality of information about products,product comparison)I am satisfied with the purchase experience of thisinternet store (e.g., ordering, payment procedure)I am satisfied with the post-purchase experience ofthis internet store (e.g., customer support andafter sale support, handling of returns/refunds,delivery care)

Items measuring Perceived Usefulness (Bhattacherjee, 2001a)I get good after-sale service from this internet storeTransaction processing in this internet store isefficient (e.g., fast retrieval of information, ordering,payment processing and scheduling delivery)I am not too concerned about the security of thisinternet storeShopping from this internet store is convenientPurchasing from this internet store allows me tosave money



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Online consumer retention: contingent effects of online shopping habit and online shopping experience