A model of Online Customer Behavior

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Marketing Theory

DOI: 10.1177/1470593105049601 2005; 5; 51 Marketing Theory

Chatura Ranaweera, Gordon McDougall and Harvir Bansal effects of customer characteristics

A model of online customer behavior during the initial transaction: Moderating

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A model of online customer behavior duringthe initial transaction: Moderating effects ofcustomer characteristics

Chatura RanaweeraWilfrid Laurier University, Canada

Gordon McDougallWilfrid Laurier University, Canada

Harvir BansalWilfrid Laurier University, Canada

Abstract. Recent research shows that observations based on overall online consumerbehavior can lead to erroneous conclusions since behavior can be substantially differ-ent among groups of individuals. This article proposes a theoretical model, which captures the main characteristics of the website and explains how the user reaction to the website, determined by a set of user characteristics, could moderate consumerperceptions of websites as well as subsequent behaviors especially in a B2C context. Acomprehensive understanding of these user characteristics will enable researchers tofurther the understanding of online consumer behavior during the crucial initialtransaction, which often raises the biggest challenge for service providers. An under-standing of user characteristics will assist service providers in designing customizedwebsites for competitive advantage. Key Words • customer characteristics • loyalty

• moderating effects • website satisfaction • website quality

Introduction

The increasing prevalence of the Internet coupled with the efficiency and con-venience of online transactions is likely to significantly change customer behaviorand future business practices (Rush, 2004; Wolfinbarger and Gilly, 2003).

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HP Mini

Urgent

Although the commercial potential of this medium has encouraged researchers tofocus on studying online customer behavior, the field is still in an exploratorystage. There is increasing recognition and agreement that website satisfaction isfundamental to establishing long-term relationships with customers and henceensuring the long-term profitability of online operations (McKinney et al., 2002;Straub et al., 2002). The first step in establishing a long-term relationship is likelyto occur during the initial transaction. Yet, attracting customers is considerablymore difficult and expensive than retaining them (Reichheld and Schefter, 2000).Since the primary medium available to convince online customers during this initial transaction is the website, its quality is critical for a successful transaction.

Here, the initial transaction is defined as the initial experience with a new webbased service provider to make a commercial transaction rather than the adoptionof the Internet medium for a first time transaction. The rationale for specificallyfocusing on the initial transaction is twofold. First, the initial transaction, par-ticularly with a relatively little known service provider, is likely to be determinedprimarily by the nature of the consumers’ interaction with the website, whereassubsequent transactions will be influenced by the actual delivery and consump-tion of the product/service relating to the initial transaction. Indeed, in theabsence of adequate delivery or order fulfillment, customers are unlikely to returnto a website irrespective of their perceptions of the website. Second, since acquisi-tion costs are typically significantly higher than retention costs (Reichheld andSchefter, 2000), the most difficult challenge for the online marketer is likely to beto convert first-time visitors into actual buyers. Therefore differentiating the initial transaction from subsequent transactions is crucial, as is developing models that adequately explain the initial transaction.

Research has identified some of the main antecedents of website satisfaction,such as ease of use, website content, and security (Bansal et al., forthcoming;Loiacono et al., 2002), as well as consequences of website satisfaction, such asintention to return and willingness to recommend to friends (Forsythe and Shi,2003; Yen and Gwinner, 2003). While the study of linear relationships betweenantecedents and consequences of website satisfaction has been a dominant focusof extant research, little attention has been paid to possible moderating effects ofcustomer characteristics such as demographics, trust disposition, risk perceptions,technology readiness, flow, and involvement. Furthermore, extant research focusing on the crucial initial transaction with a particular website is also scant.To our knowledge, a comprehensive model that incorporates the antecedents andconsequences of website satisfaction, together with the moderating effects on thechain of linear relationships, and that captures the dynamics during this initialtransaction, has not yet been developed. The article addresses this gap in the literature.

The proposed model captures the main characteristics of the website, whichreflect website quality, and explains how the user reaction to the website, deter-mined by a set of user characteristics, moderates consequent perceptions, atti-tudes and behaviors. Specifically, the model details the drivers and outcomes ofwebsite satisfaction, as well as the moderating effects of user characteristics on the

marketing theory 5(1)articles

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relationship between website quality and website satisfaction, and between web-site satisfaction and customer behavioral and attitudinal outcomes (also referredto in the literature, and in the following discussion, as ‘customer loyalty’).

Based on the proposed model, we establish a set of testable propositions forfuture research. The article hopes to make the following contributions to the fieldof online customer behavior. First, the study of a set of moderating effects, manyof which have previously been untested in an online context, on the linear chainof effects between website quality, website satisfaction, and website loyalty,extends the extant literature. Second, the set of customer characteristics proposedas possible moderators will contribute towards developing alternate bases for segmenting online customers. Third, categorization of the moderating effects intotwo levels in the chain of linear relationships, specifically those acting on the quality-satisfaction link, and on the satisfaction-loyalty link, takes the model to ahigher level of specificity, thereby contributing to a greater understanding of someof the intricacies of online customer behavior. Finally, by developing a model thatcaptures the dynamics of the initial transaction, we draw attention to the funda-mental importance of the initial transaction to e-business.

Online customer behaviorChatura Ranaweera et al.

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Dimensions ofwebsite qualityEase of use Web contentSecurity/privacyInteractivity

• Technology readiness• Flow• Demographics• Involvement

• Technology readiness• Flow• Demographics• Trust disposition• Risk perceptions

Websitesatisfaction

WOM

Purchase

Commitment

Drivers Focal construct Behavioral and attitudinal outcomes

Figure 1

The proposed model of online customer behavior during the initial transactionwith moderating effects

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Conceptual foundation

A significant body of online customer behavior research has focused on both theantecedents of website satisfaction (Bansal et al., forthcoming; Francis and White,2002; Wolfinbarger and Gilly, 2003) and consequences of website satisfaction (e.g.Ba, 2001; Shankar et al., 2003; Srinivasan et al., 2002). However, little research hasexamined possible moderating effects on these direct linear relationships.Traditionally, research has focused on the study of customer characteristics asmoderators of buying behavior (e.g. Bryant and Cha, 1996; Mittal and Kamakura,2001). This approach is primarily based on the premise that groups of customersdisplaying unique characteristics react differently to the same context. Suchunique reactions have consistently been found in the way customers respond toinnovations (Rogers, 1995).

The Internet too is an innovative medium for commercial transactions. Itscharacteristics include the anonymity associated with the medium, inability toobserve the other party physically, requirement to divulge personal informationto a non-physical firm, and concerns about security and privacy (Hoffman et al.,1999). The impact of such characteristics could be accentuated by the customers’lack of familiarity with the medium as well as low technology readiness(Parasuraman, 2000). Therefore, customer characteristics could have a significantimpact on the adoption and use of the Internet as a medium for commercialtransactions by household customers. Literature on the adoption of innovativeinformation services in general (Atkin et al., 1998), and Internet adoption in par-ticular (Atkin et al., 1998) supports such a proposition. Recent research on onlinetransactions found that due to cross-sectional heterogeneity among browsers,simple website metrics computed at the aggregate level diverge substantially fromindividual results (Bucklin and Sismeiro, 2003). If there are substantial differencesin behavior among individuals, or at least among groups of individuals, such differences could occur at least partly from consumers’ different reactions to themedium.

To gain a more comprehensive understanding of online consumer behavior, itis necessary to incorporate a set of moderating effects that are based on consumercharacteristics. Consumer characteristics such as demographics have been studiedin multiple contexts to ascertain their moderating effects on buying behavior(Bryant and Cha, 1996; Mittal and Kamakura, 2001). Other characteristics suchas: trust disposition, a person-specific trait (Moore et al., 1987); perceptions ofrisk (Cox, 1967); purchase involvement (Laurent and Kapferer, 1985); technologyreadiness of the customer (Parasuraman, 2000); and flow (Novak et al., 2000)have been found to have direct effects on purchase behavior. However, as far aswe are aware, while their moderating effects have been implied, these variableshave not been formally presented as having possible moderating effects on thechain of effects leading to online purchase and loyalty.

Based on a literature review, we propose a set of variables: demographics; trustdisposition; risk perceptions; purchase involvement; technology readiness; andflow as having moderating effects on the relationships between consumer per-


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ceptions of website quality and website satisfaction, and between website satisfac-tion and subsequent outcomes (Figure 1). These moderators reflect the effects ofcustomer differences and lead to a better understanding of online customerbehavior at the segment level, as opposed to the less optimal aggregate level. Thismodel should serve as a guide not only for managers in developing improved websites and website metrics, but also for researchers who wish to further theunderstanding of this phenomenon.

Literature review

The key construct – website satisfaction

Customer satisfaction is fundamental to the marketing concept, which holds thatsatisfying customer needs is the key to generating customer loyalty. Satisfactionhas been studied extensively in offline environments, often as the single mostimportant construct that determines subsequent customer behavior (Oliver,1997). Recent research in the online environment has also emphasized satisfactionas fundamental to establishing customer loyalty. In an empirical study using bothonline and offline contexts, Shankar et al. (2003) found that overall satisfactionenhanced loyalty and that the positive relationship between satisfaction and loyalty is in fact stronger online than offline.

While customer satisfaction is known to be a fundamental determinant of sub-sequent behavior, researchers have also used trust as the focal construct in onlinebehavior, sometimes in lieu of satisfaction (Belanger et al., 2002; McKnight et al.,2002), and in other instances, as a conceptual antecedent to satisfaction (Shankaret al., 2002). Given the lack of face-to-face interactions, the role of trust would bemore important in online transactions than in an offline environment. Trust hasalso been proposed as a situational construct in online behavior, determined byboth system dependent and transaction specific uncertainty (Grabner-Krauter andKaluscha, 2003). In our model, we propose satisfaction, as opposed to trust, as thefocal construct for the following reasons. First, the antecedents and outcomes ofsatisfaction (in the chain of links; website quality → website satisfaction → web-site loyalty) are supported by a solid body of research, whereas Shankar et al.(2002) emphasized the lack of clear distinctions between the underlying dimen-sions and the antecedents of online trust. Second, our model captures the effect ofuncertainty associated with online consumer behavior and the consumer’s uniquereaction to this uncertainty through a set of user characteristics (the moderators).

Drivers of website satisfaction

Understanding the specific drivers of satisfaction is particularly useful for man-agers in effectively allocating resources to enhance customer loyalty (Zeithaml,2000). In an online environment characterized by non-personal service encoun-ters (vis-à-vis offline encounters where the potential for relationship building


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through personal contact is greater), satisfying and then retaining customers islikely to be relatively more difficult. Therefore, identifying drivers of website satisfaction is especially crucial for online businesses.

A growing body of research has examined the relationship between websitequality and website satisfaction, and website satisfaction as a predictor of loyalty(see Wolfinbarger and Gilly, 2003 and Zeithaml et al., 2002 for reviews). It isknown that in the offline context, the service provider–customer interaction during the service encounter is the major driver of customer satisfaction (Bitneret al., 1990). In the online context, the service encounter is a non-personal inter-action (Szymanski and Hise, 2000). Therefore, satisfaction with the transaction islikely to be driven by website quality. The model (Figure 1) proposes that consumer perceptions of website quality captured by four key dimensions driveswebsite satisfaction. These dimensions were selected based on previous research.

Based on a comprehensive literature review, Wolfinbarger and Gilly (2003)identified four higher order dimensions that reflect e-tail (electronic retail) quality. Two of these four dimensions – customer service and order fulfillment –related to a domain outside the website interface, and as such is beyond the scopeof our website quality construct. In this article, we use the remaining two higherorder dimensions – website design and security/privacy – to reflect website quali-ty, but, based on previous research, expand website design into three categories:ease of use, web content, and interactivity.

A number of recent studies have examined dimensions of website quality (e.g.Francis and White, 2002; Wolfinbarger and Gilly, 2003; Zeithaml et al., 2000), andsome have offered a set of website dimensions that are most likely to have a significant impact on website satisfaction. Among this set, ease of use and websitecontent have been consistently identified as playing key roles (Wolfinbarger andGilly, 2003). Ease of use reflects the ease with which an individual can navigate thewebsite. Website content reflects the depth and range of information on productor service offerings. The underlying rationale for the significant impact of thesetwo dimensions on satisfaction is that if a website is difficult to navigate or lacksrelevant content, the user can easily move to another website (Reichheld andSchefter, 2000).

Interactivity is also receiving increasing attention as another website dimensionthat impacts website satisfaction (see McMillan and Hwang [2002] for a review).While interactivity has been conceptualized as a process as well as a function, adominant view is to look at interactivity as a perception (Day, 1998; Kiousis,1999). Here, we follow Kiousis (1999), who defines interactivity as the ability ofusers to perceive the experience to be a simulation of interpersonal communi-cation and increase their awareness of telepresence. Kiousis (1999) focuses on theinterpersonal communication aspect of interactivity, whereas other definitionseither focus on the person rather than the medium, or tend to be broad, and thus,encapsulate other dimensions of websites such as ease of use.

Finally, a majority of studies suggests another dimension, customer security/privacy, which reflects the website’s information regarding its security and privacypolicies, is also likely to influence website satisfaction (Belanger et al., 2002;


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Wolfinbarger and Gilly, 2003). Often cited as one of the major reasons why con-sumers are unlikely to complete an online purchase is the concern regarding theconsequences of providing personal information including credit card data toonline marketers (Hoffman et al., 1999; Lee and Turban, 2001).

Since the above cited website dimensions are known to have a positive impacton website satisfaction, we expect to see that overall website quality consisting ofthe combined effects of these dimensions will enhance website satisfaction.

Proposition 1: Overall perceptions of website quality (captured by ease of use, web content,security/ privacy and interactivity) will have a significant, positive relationship with website satisfaction.

Outcomes of website satisfaction

Research on the relationship between website satisfaction and subsequent behav-ioural and attitudinal outcomes (or loyalty) has found that overall satisfactionwith the online service encounters led to customer loyalty towards the serviceprovider (Shankar et al., 2003). Yen and Gwinner (2003) found that overall satis-faction with Internet self-service technologies had a positive impact on loyaltyintentions; specifically on the willingness to continue purchasing from the serviceprovider. Therefore, we expect to find that website satisfaction will lead to loyaltytowards the website.

An extensive body of literature is available on loyalty in offline environments(see Zeithaml [2000] for a review). While customer loyalty in an online settingmay be formed differently from an offline setting, its definition should still bevalid irrespective of the setting. Customer loyalty has often been defined in termsof its multiple behavioral dimensions of purchase/repeat purchase intentions oractual purchase/ repeat purchase behavior; propensity to give positive word ofmouth (WOM); price tolerance, and propensity to make additional purchasesfrom the same source (Boulding et al., 1993; Zeithaml et al., 1996). Others haveexplicitly focused on an attitudinal form of loyalty (e.g. Shankar et al., 2003),which is similar to the affective loyalty proposed by Oliver (1999). According toShankar et al. (2003), this represents a higher order commitment to the firm.

Therefore, online customer behavior models need to: incorporate both behavioral and attitudinal dimensions of loyalty; to include purchase intentions(Boulding et al., 1993); word of mouth (Gremler et al., 2001); and commitment(Moorman et al., 1992) as outcome variables. Purchase intentions, often definedas the propensity to purchase a product or service in the future, are perhaps the most important dimension from a financial viewpoint. However, making distinctions among the different dimensions is also vital. Customers can makepurchases without attitudinal loyalty and thus true commitment for numerousreasons, including but not limited to switching barriers, or indifference(Ranaweera and Neely, 2003; Shankar et al., 2003). In the absence of true commitment, they are unlikely to give positive WOM.

Commitment has received considerable attention in the marketing literature.While studies have covered such areas as product marketing, business-to-business


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marketing, and services marketing, in many the focus was on long-term relation-ship development through building commitment (Dwyer et al., 1987; Morganand Hunt, 1994). Research on commitment in the online sector has been limited.Customer commitment refers to the strength of relational ties, and the desire tomaintain a relationship (Bansal et al., 2004). Bendapudi and Berry (1997) arguedthat customers maintain relationships either because of constraints (they ‘have tostay’ in the relationship) or because of dedication (‘want to stay’). According to this typology, commitment reflects the latter, a true desire to maintain the rela-tionship. Current understanding of how website satisfaction affects commitmentin a context characterized by low contact and a transaction orientation is limitedand deserves further investigation.

While drivers of purchase or repeat purchase intentions can be numerous, bothcommitment and WOM reflect a true, attitudinal form of loyalty. Word of mouthcommunication has traditionally been defined as oral, person-to-person com-munication between a receiver and a communicator, whom the receiver perceivesas non-commercial, regarding a brand, a product, or a service (Arndt, 1967). Inview of the alternative means of communication made possible by the advent ofthe Internet, it is plausible to broaden this definition to include mediums otherthan oral communication, such as email, and consequently, to include multiplerecipients of the communication.

Extant literature clearly illustrates the importance of WOM (or referral by loyalcustomers) to a company’s profits. WOM is particularly significant since it is freeof charge to the company (Reichheld and Sasser, 1990), and unlike advertising, ismore credible since it is customer generated (Gremler et al., 2001). In an onlinecontext, where customer–staff contact is sometimes non-existent, the importanceof WOM is enhanced. Furthermore, online WOM is also likely to have greater andfaster reach. The importance of WOM in the digital age is also widely espoused by industry. According to Amazon, word of ‘mouse’ is so effective (referring torecommendations posted by consumers) that they have decided to eliminate itsentire budget for television and general-purpose print advertising. Thus, study ofWOM, as part of the loyalty outcome construct in an online context, is relevant.

Proposition 2: Website satisfaction will have a significant, positive relationship with loyalty tothe website reflected by purchase intentions, commitment, and positive WOM.

Moderating effects of customer characteristics

While direct effects are fundamental to the understanding of online consumerbehavior, these effects may mask the underlying core difference in online trans-actions, vis-à-vis traditional environments, arising from the nature of the inter-face and the consumers’ reaction to this interface. It has also been argued that hypothesizing direct effects may be somewhat obvious and that it is moremeaningful to investigate moderating effects of external factors such as consumerand situational characteristics (e.g. Ajzen, 1991; Baron and Kenny, 1986).

Bucklin and Sismeiro (2003) found that due to cross-sectional heterogeneityamong browsers, arguably arising from their unique reaction to the interface,


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simple website metrics computed at the aggregate level diverge substantially fromindividual results. As a result, they argued that reliance on observing overallonline consumer behavior can lead to erroneous conclusions since individualresponses to websites can be substantially different. This suggests that a set ofcharacteristics that are unique to individuals, referred to here as customer charac-teristics, can moderate both customers’ level of satisfaction, as well as loyalty. Theimpact of these moderating effects is likely to be more significant during the initial transaction than subsequent transactions.

A number of moderating effects on the relationship between attitudes (such asquality perceptions or satisfaction) and behaviors (such as purchase, repurchase,or switching) has been identified and tested in offline environments. These moderating effects, such as consumer demographics and involvement, are likelyto hold in the online environment because they relate to basic consumer decisionmaking. However, it is unlikely that they will adequately capture interface-specific moderators that result from customer differences associated with the useof a relatively new technology, characterized by lack of face-to-face interactions.While these moderating effects are fundamental in the quest for a comprehensiveunderstanding of online consumer behaviors, to date, they have received limitedattention. Here, we propose a further set of variables, specifically, technologyreadiness, flow, risk perceptions, and trust disposition, that we argue will havemoderating effects on the website quality perceptions → website satisfaction →website loyalty links. The six moderating variables were chosen based on existingliterature. What follows is a brief overview of the rationale for the selection ofthese six moderators.

Overview of interface specific moderators

The emergence of new technologies and their significant impact on the day-to-dayactivities of consumers requires an in-depth examination of customers’ ability andwillingness to adopt these technologies. This attitude towards technology affectsboth adoption and usage (Meuter et al., 2003; Parasuraman, 2000). It is also critical to assess the relative comfort level displayed by customers in using thesetechnologies (Novak et al., 2000). The technology readiness construct capturesconsumers’ attitude towards accepting new technology. Overall, technology readiness, relative to similar constructs discussed below, is broad enough to capture the online buying context, yet has been relatively understudied, indicatingclear potential for further study as a moderator. The flow construct captures therelative comfort level displayed by consumers when using the new medium. Flow has been studied extensively in the context of online environments, yet notnecessarily as a moderator of behavior.

A further reason for feeling hesitancy towards the Internet as a medium forcommerce is that the purchase context is characterized by a lack of face-to-faceinteractions, and often with an unknown service provider (Grabner-Krauter andKaluscha, 2003). Based on extant literature, the model incorporates two con-structs, risk perceptions and trust disposition, to capture this effect.


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Moderating effects of technology readiness

The significant impact of new technologies on society makes it imperative toexplore the ability and willingness of customers to use these new technologies.Considerable literature is available on customer’s propensity to adopt technologyand innovations (Rogers, 1995). Following pioneering work by Rogers (1962),these models have been applied to different contexts. Cited below is some of thekey literature on the adoption and usage of new technologies, which helps in formulating the propositions relating to the moderating effects of technologyreadiness. While much of the literature relates to self-service technologies, thesearguments can be extended to the Internet since the results have clear implicationson adopting and using an innovative medium such as the Internet for commerce.

The concept of technology readiness developed by Parasuraman (2000) focuseson predicting consumers’ predisposition towards using new technology. It can beviewed as an overall state of mind resulting from a gestalt of mental enablers and inhibitors that collectively determine a person’s disposition to use new technologies (Parasuraman, 2000). The technology readiness construct is concept-ualized as multi-dimensional, consisting of: optimism; innovativeness; discomfort;and insecurity (Parasuraman, 2000). Insecurity here refers to a personality trait thatreflects attitude towards technology in general, and is distinct from the level ofsecurity associated with a particular website.

Dabholkar and Bagozzi (2002) studied the moderating effect of inherent novelty seeking, one aspect of innovativeness also seen among technology readysegments, within a core attitudinal model for a self-service technology. Noveltyseeking has been defined as the desire to seek out new stimuli, and those havingthis trait have been found to look more favorably on the use of technology thanothers (Hirschman, 1980). Dabholkar and Bagozzi (2002) found that the relationship between ease of use (a key dimension of quality) of the technology,and perceptions towards the technology (similar to satisfaction with the tech-nology in a general context, and website satisfaction in the current context) wasstrengthened for consumers displaying inherent novelty seeking traits. The moderating effect of inherent novelty seeking in the context of their study suggestsa parallel moderating effect of technology readiness on the website quality → web-site satisfaction link in the current context.

Meuter et al. (2003) studied technology anxiety, a related construct, whichreflects users’ negative attitude towards technology in adopting and using self-service technologies. The conceptualization of technology anxiety as ‘willingnessto use technology’ has many similarities with the conceptualization of technologyreadiness as ‘propensity to embrace technology’. Results showed that those withlow technology anxiety were more satisfied, more likely to reuse the technology,and more likely to spread positive word of mouth (WOM) than others.

Overall, based on the above findings it can be expected that for those displayingtechnology readiness, the relationship between website quality and website satisfaction would be strengthened. Specifically, respondents with ‘satisfyingexperiences’ in a self-service technology setting are likely to be similar to those


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perceiving the website to be a positive experience, thus, perceiving high websitequality. If, in such a context, low levels of technology anxiety (similar to high technology readiness) leads to enhanced levels of satisfaction, it can be inferredthat high levels of technology readiness will also enhance the relationship betweenwebsite quality and website satisfaction.

Proposition 3: Higher levels of technology readiness, or greater readiness to embrace and use the Internet medium will have a positive reinforcing effect on the relationship between perceptions of website quality and website satisfaction.

Meuter et al. (2003) also found that, for a given level of satisfaction, those dis-playing low technology anxiety (similar to high technology readiness) were morelikely to use the self-service technology again, and more likely to spread positiveWOM than respondents with ‘high technology anxiety’. Thus, for a given level ofsatisfaction, technology readiness is likely to enhance propensity to repurchase,displaying commitment towards the service provider, and enhance the propensityfor WOM. By extension, we can infer that for a given level of satisfaction, thosewho are more technology ready are likely to have a greater propensity to purchasein the first place, as well as display greater commitment.

The moderating effect of technology readiness on WOM in particular can befurther supported by literature from offline contexts. Bansal and Voyer (2000)found that those possessing expertise of a particular product or service have moreimpact and influence on receivers of WOM and that their views are more oftensought after. If so, it is plausible that experts in turn will be more likely to giveWOM, as they are likely to perceive that it is expected of them. We argue thatthose who are technology ready are also likely to possess relatively higher levels ofexpertise in the use of new technologies or innovations, and therefore, it can beinferred that for a given level of satisfaction, higher levels of technology readinesswill enhance likelihood of WOM.

Furthermore, there can also be intuitive support for at least the moderatingeffect on the satisfaction → purchase link. Those who are technology ready arelikely to be less hesitant to buy, provided they are satisfied with the website. Lesstechnology ready consumers may be satisfied with the website yet may hesitate tomove to the purchase stage. If so, a high level of technology readiness is likely tostrengthen the effect of satisfaction on purchase.

Proposition 4: Higher levels of technology readiness, or greater readiness to embrace and con-tinuously use the Internet medium will have a positive reinforcing effect on the relationshipbetween website satisfaction and (a) purchase, (b) commitment to the website, and (c) propen-sity for WOM.

Moderating effects of flow

Flow was first presented by Csikszentmihalyi (1977, 1990) in the context of study-ing what makes people truly happy, satisfied and fulfilled. He suggested that people enter into a state of flow when they are fully absorbed in an activity duringwhich they lose their sense of time and have feelings of great satisfaction. He


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further described flow as being completely involved in an activity for its own sake(Csikszentmihalyi, 1990). This description indicates similarities between flow andinvolvement. This is noteworthy since we present involvement in this model as adistinct moderator. However, as argued in Hoffman and Novak (1996), Privetteand Bundrick’s (1987) distinction between flow, peak performance, and peakexperience is helpful in establishing how flow differs from these related constructsas well as from involvement. They suggested that involvement is a common quality of flow, peak performance and peak experience, but that involvement doesnot differentiate between the three.

Koufaris (2002) described flow as the level of intrinsic enjoyment of an activity.Hoffman and Novak (1996) described consumers who achieve flow on the web asthose who focus exclusively on the interaction and find web browsing extremelygratifying. They argued that for flow to be present, consumers needed to possesstwo traits: ability to focus, and to see in themselves adequate skills to meet thechallenges of the interaction. Based on that model of flow, Novak et al. (2000)conceptualized flow specifically in relation to online transactions as a conditionexperienced during web navigation. Their description of flow reflects a segment ofbrowsers who possess a set of characteristics that enable them to reach a height-ened level of navigation experience. Consequently, such customers are also likelyto have distinct responses to the context.

Specifically, Csikszentmihalyi (1977, 1990) described how the state of flow isassociated with feelings of great satisfaction. This implies that flow has a reinforc-ing effect on satisfaction. Some customers will find a high quality website to be an enabler in reaching the state of flow. The resultant state of flow in turn will further strengthen their level of satisfaction. Conversely, those seeking a state offlow and finding the website does not meet expectations, and thus does not act asan enabler, will have a reinforced negative impact on satisfaction, which wouldlikely lead to the consumer immediately leaving the website.

Proposition 5: Higher levels of flow during the use of the Internet medium will have a positivereinforcing effect on the relationship between perceptions of website quality and website satisfaction.

The moderating effect of flow is unlikely to be limited to the quality–satisfactionrelationship. Privette and Bundrick’s (1987) explanation of flow, as that whichoccurs in structured activities where action follows action, indicates that state offlow increases the possibility of further action, or in this context moving to thenext ‘action’ of purchase. Therefore, flow can have a reinforcing effect on the relationship between satisfaction and purchase. If flow enables complete fulfil-ment (Csikszentmihalyi, 1977, 1990), it is also likely to reinforce a certain level ofcommitment. If so, flow will also reinforce the effect of website satisfaction oncommitment to the website. In turn such commitment is likely to be manifestedsubsequently by a desire to revisit the same website to experience the state of flowagain.

Finally, as noted by Hoffman and Novak (1996), the Internet has enabled a paradigm shift in communication models. They also argue that flow has a signifi-


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cant impact on this new communication model, specifically in terms of increasedexploratory and participatory behavior. These arguments in combination withpropensity for increased action (Privette and Bundrick, 1987), suggest that flowwill enhance the effect of satisfaction on WOM communication. Given the greaterpropensity of those in a state of flow to continue experiencing the web, and notleave the medium, such WOM could also be communicated through the samemedium, and thus more ‘word of mouse’ than ‘word of mouth’.

Proposition 6: Higher levels of flow during use of the Internet medium will have a positive reinforcing effect on the relationship between website satisfaction and (a) purchase behaviortowards website, (b) commitment to the website and (c) propensity for WOM about the website.

Moderating effects of demographics

There is general agreement that demographic characteristics play an importantrole in marketing since they provide the opportunity to customize products, services, as well as communications to better meet consumer needs. Based on ananalysis of data from the American Customer Satisfaction Index, Bryant and Cha(1996) found that there were many systematic differences between differentdemographic groups in their level of satisfaction. Dabholkar (1996), based on astudy of a hair salon context, found moderating effects of gender on factors driving hair-cutting experience satisfaction as well as reasons for switching serviceproviders. This suggests that demographic characteristics can have a moderatingimpact on both satisfaction as well as certain outcomes of satisfaction such asswitching, reflecting lack of commitment. Mittal and Kamakura (2001) found thatconsumers having different demographic characteristics have different thresholdsto switching. Results showed the presence of significant moderating effects of gender, age, family status, level of education, and area of residence on the satisfaction-retention link.

Demographic characteristics have often been used as a means of explaining customers’ reactions to innovations. Bateson (1985), Zeithaml and Gilly (1987),and Dabholkar (1992) used demographic characteristics as the basis for adoptingand using self-service technologies. A cross-cultural comparison of online buyingbehavior found significant differences between American and Korean consumers(Park and Jun, 2003). Garbarino and Strahilevitz (2004) found that there were significant differences between males and females in their online consumer behavior arising from differing risk perceptions. The significant gender differ-ences were found in relation to both online buying and effects of website recom-mendations (or WOM).

However, demographics have not been able to explain technology adoption andusage on a consistent basis. A meta-analysis investigating age and adoption ofinnovations found that about half of the 228 studies established no relationships(Meuter et al., 2003). Furthermore, even studies that did find significant relation-ships often gave conflicting results. For instance, studies have found both youngeras well as older respondents to be more likely adopters of innovations (Rogers,


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1995). Meuter et al. (2003) found that while demographic variables in general hadsome impact on usage of self-service technologies, their impact was not definitive.Overall, findings on the impact of demographics on using new technologies andinnovations have not been consistent.

Based on the above, we argue that the effects of customer demographics arelikely to be context dependent, and thus development of a general theory thatspecifies a priori how each demographic characteristic would affect the relation-ship between website quality and website satisfaction and between website satisfaction and subsequent outcomes is rather ambitious. Our approach is to follow Meuter et al. (2003) in their study of demographics in a self-service technology context. While acknowledging some effects, they used demographiccharacteristics more for comparison purposes; particularly as a means of com-paring the impact of demographics relative to alternative modes of segmentingcustomers based on personality traits. Thus, we do not develop a priori proposi-tions for each characteristic. However, based on the above discussion we expectdemographic characteristics to have a moderating impact on both links (quality→ satisfaction and satisfaction → outcomes), though not as definitive as those ofother moderators.

Proposition 7: Demographic characteristics (gender, age, family status, level of education, andarea of residence, household income) in general, will have some moderating effects on the relationship between (a) website quality and website satisfaction and (b) between website satisfaction and website loyalty.

Moderating effects of trust disposition

Trust disposition can be divided into two components, global and specific. Theglobal component is an attitudinal or affective component that reflects an overallview of the world, whereas the specific component is more situational and cogni-tive (Driscoll, 1978). In a pioneering article on interpersonal trust, Rotter (1967)suggested that the global component corresponds to the personality based view oftrust disposition that endows individuals with a general tendency to trust others.Here, trust disposition is considered to endure across contexts and to be particu-larly relevant in ambiguous, unstructured situations (Johnson-George and Swap,1982). Further, this global view of trust does not reflect an individuals’ trust in another under particular circumstances and Holmes (1991) offered empiricalevidence to support the view that global and specific views of trust disposition arenot strongly correlated. It can be argued, then, that models of consumer decision-making in various contexts, in particular online behavior, should explicitlyaccount for these two facets of trust.

Recently, considerable effort has been made to understand the role of the specific component of trust, labelled online trust (Shankar et al., 2002), in business-to-business as well as business-to-consumer e-business activities(Shankar et al., 2002; Smith et al., 2000; Yoon, 2002). While this has added to ourknowledge of the specific trust component in e-business environments, littleattention has been paid to the role of the global component of trust in such


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settings (for an exception, see Murphy, 2003). Lee and Turban suggest that ‘. . . aconsideration of consumer trust in Internet shopping that did not examine trustpropensity, a personality trait of the concerned consumers, would be quite inadequate’ (2001: 77). A personality trait, this global component of trust disposition, is our construct of interest.

Evidence suggests that propensity to trust has a significant impact on consumerattitudes and behaviors in an offline (Doney and Cannon, 1997), as well as in anonline setting (Urban et al., 2000). Further, it has been argued that the effects ofpropensity to trust are likely to be very strong in situations where consumers donot have prior experience with a website, in other words, when it is an initialtransaction (Murphy, 2003). Given its importance and the paucity of research,researchers have called for increasing attention to exploring the role of disposi-tional trust in online research, including its role as a moderator of attitudes andbehaviors (Grabner-Krauter and Kaluscha, 2003).

As discussed above, dispositional trust should have a significant impact on consumer attitudes and behaviors. Arguably, consumers with a high trust dis-position would be more inclined to have positive attitudes and behaviors towardsa particular website. While this may be true, it needs to be recognized that trustdoes not imply blind faith (Ring and Van de Ven, 1994). Consumers will not havepositive attitudes and exhibit positive behaviors towards a particular websitebecause of their trust disposition alone. Consumers will seek other sources ofinformation to safeguard themselves against potential harm resulting from atransaction with a particular website, rather than acting solely out of trust. In anonline context, the primary source of information about a website comes from theconsumer’s interaction with the website. Consumers who have a higher trust dis-position are more likely to engage in positive behaviors and have positive attitudestowards a website, provided they are satisfied with the website. Consumers havinga lower trust disposition, although satisfied with the website, may be hesitant toengage in positive behaviors and have positive attitudes towards that website.Hence, we suggest that a consumer’s trust disposition will act as a moderator of the relationship between website satisfaction and consumer attitudes andbehaviors. Therefore:

Proposition 8: Higher levels of trust disposition will have a positive reinforcing effect on therelationship between website satisfaction and (a) purchase behavior toward the website, (b) commitment to the website, and (c) propensity for WOM about the website.

Moderating effects of perceived risk

Perceived risk has a major influence on consumer behavior. Consumers areapprehensive when they cannot be sure that purchases will allow them to achievetheir buying goals. Cox and Rich (1964) conceptualized perceived risk, as thenature and amount of risk perceived by a consumer in contemplating a particularpurchase decision. Perceived risk can thus be considered a function of the uncertainty associated with outcomes and the possible unpleasantness of thoseoutcomes (Forsythe and Shi, 2003).


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Several types of perceived risks have been identified in the literature (Jacobyand Kaplan, 1972; Peter and Tarpey, 1975). While much of the literature on riskhas been based in the offline context, recent research has highlighted the impactof various types of risks on loyalty in an online context (Forsythe and Shi, 2003).Risk perceptions on the part of consumers have been deemed ‘to be a primaryobstacle to the growth of online commerce’ (Miyazaki and Fernandez, 2001: 27).While these risk perceptions emanate from various sources, the focus of thisresearch is not on exploring each risk source individually. We limit our argumentsto exploring the impact of an overall perception of risk on website loyalty.

In an online context, perceived risk refers to beliefs about negative con-sequences that may result from using the web, which has a significant impact onconsumer attitudes and behaviors towards a particular website (Grazioli andJarvenpaa, 2000). It has been argued that perceived risk has a moderating effect onthe relationship between consumer perceptions and attitudes and behaviorsdirected at that organization in an online context (Ruyter et al., 2001). While theseorganizational perceptions have been identified as organizational reputation(Ruyter et al., 2001), it is recognized that organizational reputation develops overtime and with repeated interactions. In the absence of repeated interactions, consumers will use other information that helps differentiate between good andbad websites (Boulding and Kirmani, 1993). Website satisfaction will serve as akey information source for evaluation in the current context.

Furthermore, Featherman and Fuller’s (2002) study on e-payments demon-strated that perceived risk tempers the effect of consumer evaluations of the website on behavioral intentions. Although they use ease of use (EOU) as the basisfor website evaluation and not website satisfaction per se, they acknowledge that in assessing EOU, consumers not only evaluate the usability of the websiteinterface, but also the likely service performance of the entire system. Their resultssuggest that the influence of website evaluation on behavioral intentions isreduced as risk perceptions increase. Hence, we posit different effects for differentlevels of perceived risk. When consumers perceive the risks to be high, we do notexpect significant differences in the relationship between varying levels of websitesatisfaction and website loyalty. When consumers perceive risks to be low, weexpect to see a significant difference in the relationship between varying levels ofwebsite satisfaction and website loyalty. Thus:

Proposition 9: Lower levels of risk perceptions will have a positive reinforcing effect on the relationship between website satisfaction and (a) purchase behavior toward the website, (b) commitment to the website, and (c) and propensity for WOM about the website.

Moderating effect of involvement

Involvement is a motivational variable reflecting the extent of personal relevanceof the decision to the individual in terms of basic goals, values, and self-concept(Engel and Blackwell, 1986). An individual difference variable, involvement hasbeen closely related to the information processing perspective on behavior withsignificant implications for consumer attitudes and behaviors (Mittal, 1989;


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Zaichowsky, 1985). Past studies of consumer involvement have concentrated on:establishing the personal relevance of a purchase for the individual consumer; the degree or amount of involvement generated by a particular product; and theconsumers’ level of attention and information processing activity (Gabbott andHogg, 1999).

For an online purchase, previous conceptual definitions of the ‘purchase decision involvement’ construct by Mittal (1989) that included such terms as‘care’, ‘important’ and ‘concerned’, as well as the ‘perceived differences betweenalternatives’ suggested in the original conceptualization of the involvement con-struct by Laurent and Kapferer (1985) are relevant. While these might form thebasis for the level of involvement a consumer experiences, the focus here is toexamine the influence of involvement in an online purchase context.

In an online context, a higher degree of involvement results in increased atten-tion being paid to the relevant website (Hoffman and Novak, 1996). This suggestsinvolvement levels of consumers are likely to impact consumer evaluations ofwebsites. While highly involved consumers would pay more attention to keyaspects of the website, less involved consumers would pay attention to moreperipheral aspects related to the website (Balabanis and Reynolds, 2001). Thesekey and peripheral aspects of the website design have been identified as high taskrelevant and low task relevant cues respectively (Eroglu et al., 2001). Whereas hightask relevant cues (e.g. customer service policies) ‘facilitate and enable the con-sumer’s shopping goal attainment’, low task relevant cues (e.g. music and sounds)are ‘relatively inconsequential to the completion of the shopping task’ (Eroglu etal., 2001: 180). Further, these cues represent an appropriate typology for definingthe characteristics of an online store, similar to the concept of offline retail storeatmospherics proposed by Bitner (1992). In the current context, elements of website quality identified by us refer to the cues that define the atmospherics(characteristics) of that website.

Based on the stimulus-organism-response (S → O → R) framework from environmental psychology, it has been suggested that atmospheric cues could be categorized as the ‘stimuli’, consumers’ affective reactions as ‘organism’, and consumers approach/avoidance behaviors as ‘response’ (Donovan and Rossiter,1982). In the current context, website quality dimensions (ease of use, web content, security/privacy, and interactivity) would define the atmospherics for aparticular website, website satisfaction would be the subsequent affective reaction,and approach/avoidance responses would be captured by the resulting behavioraland attitudinal outcomes (purchase, commitment, and word of mouth). In addition, it has been suggested that certain consumer characteristics such asinvolvement would moderate the relationship between atmospherics and affectivereactions on the part of the consumer in an online context (Eroglu et al., 2001,2003). If elements of website quality are cues that define atmospherics of that website, and satisfaction with the relevant website is seen as an affective reactionto that evaluation, then involvement would act as a moderator of the relationship.As Eroglu et al. suggest:


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For shoppers who value the environmental quality of a shopping encounter [high involve-ment], it is more likely that the store atmosphere would lead to affective states it is designed toproduce. On the other hand, for those who do not have a heightened sensitivity to their surroundings [low involvement], the effect of the site design on their affective reactions wouldnot be significant. (2003: 144)

Proposition 10: Higher levels of involvement will have a positive reinforcing effect on the relationship between perceived website quality and website satisfaction.

Conclusions

Understanding customers is fundamental to successful business strategies. In anonline context, it is even more important given that despite its prevalence, thisinterface as a means for commercial transactions is still in its innovative stage. In such a context, users’ reaction to the interface will be as significant as their reaction to the product or service offering. Furthermore, this medium is charac-terized by lack of face-to-face interactions. During the initial transaction, which isthe first step towards establishing long-term customer relationships, the website isthe only medium available to the service provider to convince apprehensive con-sumers. A major challenge for online businesses is therefore to understand thedynamics that impact on the relationships between website quality and websitesatisfaction and between website satisfaction and subsequent positive outcomesthat these businesses seek. Consequently, models of online buying behavior thatalso account for moderating effects of customer characteristics should lead to abetter understanding of online customer behavior.

Such strategies could revolve around a level of customization that is currentlylacking in many websites. In an increasingly competitive e-business environment,with many alternatives available at the click of a mouse, customization of websitesis likely to give firms a clear competitive advantage. Innovative technologyincreasingly enables this customization. However, to make use of this technologywe need a better understanding of customer characteristics and their impact onwebsite perceptions and subsequent outcomes. The proposed model suggests theexamination of significant moderating effects of customer characteristics thatwould enable a set of customer profiles to be developed. Future research couldthen examine the impact of customized websites on the different customer segments identified.

We believe this article has contributed to the extant literature by providing a theoretically grounded understanding of how customer characteristics affect therelationship between website satisfaction and loyalty. Hopefully, this would lead toa more comprehensive understanding of online customer behavior. Throughempirical research, the significance of these moderating variables can be validated.In addition, the proposed model also presents the opportunity of addressing anumber of strategic and tactical issues related to managing online customer behav-ior. Ultimately, the knowledge gained will lead to improved business performancethrough a better understanding of the needs of different consumer segments.


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Chatura Ranaweera is an Assistant Professor of Marketing at Wilfrid LaurierUniversity, Waterloo, Canada. He holds a PhD from Cambridge University. Hisresearch interests include service marketing, and marketing high technology products &innovations. His recent research has appeared in International Journal of ServiceIndustry Management, International Journal of Operations and Production Managementand Journal of Targeting Measurement and Analysis for Marketing. Address: WilfridLaurier University, School of Business, 75 University Avenue West, Waterloo, ON N2L3C5, Canada. [email: [email protected]]


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Gordon H. G. McDougall is Professor of Marketing at Wilfrid Laurier University. Heholds a PhD from the University of Western Ontario. His research interests includeservice marketing and customer satisfaction and retention. He has published in a widerange of journals including: Journal of Marketing, Journal of Retailing, Journal ofConsumer Research, Journal of Services Marketing and Journal of Service Research.Address: Wilfrid Laurier University, School of Business, 75 University Avenue West,Waterloo, ON N2L 3C5, Canada. [email: [email protected]]

Harvir S. Bansal is an Associate Professor of Marketing at Wilfrid Laurier University.He holds a PhD from Queen’s University. His research interests include service marketing, customer switching behavior and word-of-mouth processes. He has published in journals including Journal of the Academy of Marketing Science, Journal ofService Research, Journal of Quality Management, and Psychology and Marketing.Address: Wilfrid Laurier University, School of Business, 75 University Avenue West,Waterloo, ON N2L 3C5, Canada. [email: [email protected]]


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A model of Online Customer Behavior

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