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2006 Blackwell Science, LtdOxford, UKIJCInternational Journal of Consumer Studies1470-6423Blackwell Publishing Ltd, 2005 302137149Original ArticleConsumer satisfaction in e-servicesH.-Y. Ha

doi: 10.1111/j.1470-6431.2005.00458.x

An integrative model of consumer satisfaction in the context of e-services

Hong-Youl Ha

Department of Marketing, Manchester Business School, Manchester, UK

Abstract

This research examines how customer satisfaction affects its antecedent and outcome variables such as expectation, performance, disconfirmation, word-of-mouth, brand loyalty, attribution and repurchase. This research also takes the important integrative step of understanding the consumer behavioural constructs of consumer satisfaction. Although researchers have focused on the antecedents of consumer satisfaction, our integrative model has extended the outcomes of consumer behaviour on consumer satisfaction. Furthermore, this model strongly suggests a positive view of the inter-relationships between the antecedent variables and outcome variables of satisfaction. In particular, our model is not consistent with Oliver's attribution models in which satisfaction is a consequence of attribution processing. All hypothesized variables were supported by our empirical study. The findings present a variety of guides to formulating marketing strategies for both practitioners and academics. Keywords Expectation, performance, disconfirmation, attribution, e-service.

Introduction

Customer satisfaction is conceptualized as a cumulative construct that is affected by service expectations and performance perceptions in any given period and is affected by past satisfaction from period to period.1 It plays an important role in such a competitive industry as e-commerce because it closely affects customer loyalty.2­5 Not surprisingly, many practical and theoretical models of customer retention have explored satisfaction as a key determinant in customers' decisions to keep or

Correspondence Hong-Youl Ha, Department of Marketing, Manchester Business School, PO Box 88, Manchester, M60 1QD, UK. E-mail: [email protected]

drop, that is, continue or discontinue, a given product or service relationship.6­8 Researchers have argued for a need to focus more on the invariance of the relationship between antecedent and consequence variables.9­12 Although existing models may be applied to explain current e-consumer satisfaction, most previous research based on the Oliver study13 has just investigated individual elements of customer satisfaction, namely, expectation,1,14­17 performance,18­20 disconfirmation,21,22 attribution,23 word-of-mouth,24,25 brand loyalty3,26 and repurchase intention.27­29 These studies provided significant insights into consumer studies literature, but it is necessary to build an integrative model for a systematic understanding of esatisfaction because the perceptions of customer satisfaction are changing over time, and because contemporary customers are becoming more sophisticated. In particular, theoretical approaches to studying Internet shoppers are to date limited in the academic literature even though theoretical models are beneficial to conceptualizing relationships among variables and deepening understanding of process.30 Apart from Bitner's model,31 based on perceived service quality or satisfaction, to our knowledge, there is little empirical or systematic research as to why an integrative model of customer satisfaction is important on the web. Shankar et al.'s study2 has revealed that although the levels of customer satisfaction for a service chosen online is the same as when it is chosen offline, loyalty to the service provider is higher when the service is chosen online than offline. Accordingly, it is important to study the outcomes of customer satisfaction behaviours as well as the antecedents on the basis of the expectation­disconfirmation theory. Despite the overwhelming quantity of literature surrounding the concept of satisfaction, Anderson and Fornell32 (p. 244) note that certain `key issues have either gone unresolved or have recently been brought into question'. One such issue is the question, `what is

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the actual satisfaction model on the web?' Therefore, the purpose of this study is threefold. First, we will examine the previous literature on both antecedent variables and outcome variables of customer satisfaction through empirical study; second, we will build an integrative model of the research; and third, we will present an integrative guide to customer satisfaction on the web for marketing practitioners and academics.

Theoretical foundation, hypotheses and integrative model Antecedent variables for customer satisfaction

Although satisfaction is recognized as an important facet of marketing, there is no general agreement of how the concept should be defined.33 This lack of a concise definition further validates the supposition that satisfaction does not mean the same thing to everyone.13 In this study, we utilize a recent perspective to define esatisfaction as the degree of customer contentment with regard to his/her prior purchase experience with a given electronic commerce firm.34 Expectation One of the most widely studied antecedents of satisfaction is prepurchase expectation.13 Expectations have become the central construct of consumer satisfaction research. Expectations for service performance represent an a priori standard that consumers bring to a consumption experience. Within the marketing literature, expectations appear most widely in definitions of service quality and consumer satisfaction, but here they can range from being subjective desires to more objective predictions.17,21,35,36 The disconfirmation paradigm defines expectation as `estimative expectation' that is assumed to be a direct path influencing customer satisfaction or an indirect preceding factor to disconfirmation. In other words, while rising expectation increases the perception of service performance, disconfirmation increases as well. Specifically, an individual's expectations are: (a) positively disconfirmed when performance exceeds experiences; (b) negatively disconfirmed when performance is less than expectations; and (c) confirmed when performance is approximately equal to experiences.23,37

This model suggests that the effects of expectations are primarily through disconfirmation, but, they also have an effect through perceived performance as many studied have found a direct effect of perceived performance on satisfaction. Thus, increasing consumer expectations might affect a direction of confirmation/ disconfirmation associated with customer satisfaction on the web.38 It has been well established that expectations have a positive effect on perceptions of performance in that perceptions are assimilated toward expectations.18,21,39 An example that clearly shows customers' expectancyperformance results is that of Hanaro, a web-based travel agency in Korea, which promises to offer free airline tickets to dissatisfied customers. Surveys indicate that the vast majority of Hanaro customers who invoke the guarantee say they would again use Hanaro travel packages, and nearly one in three of the customers who do not express dissatisfaction say the guarantee makes them more likely to use Hanaro services.6 Moreover, Oliver and Bearden16 show that expectation has a significant effect on satisfaction, whereas Hirschman40 illustrate the opposite effect. Holloway and Beatty41 point out that after experiencing problems with lesserknown companies, consumers no longer visit `fly-bynight e-tailors.' In contrast, if a consumer experiences good feelings with lesser-known companies, the consumer will be willing to revisit these websites. This is consistent with previous research showing that customer expectations for higher satisfaction adjust based on experience over time.42 For the plant product, expectations, performance and disconfirmation had significant effects on satisfaction, whereas for the VDP product only performance had a significant effect. Despite these mixed results regarding expectation­ satisfaction on product performance, we consider positive effects because consumer perceptions on the web may differ from that of the traditional marketplace. On the one hand, one of the findings of Fornell et al.43 is that the perception of quality can be created without any actual experience with the product or service. It is very similar with the expectation theory. We logically assume that some component of expectation is closely related to the performance or e-satisfaction that a particular website can deliver as promised. Thus, our study presents the following hypotheses:

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H1a: The higher the expectation, the higher the consumer satisfaction. H1b: The higher the expectation, the higher the perceived product performance. H1c: The higher the expectation, the lower the customer expectation­disconfirmation paradigm. Perceived performance Expectations are compared to perceived performance in order to arrive at an evaluation. Performance here refers to the customers' perceived level of service quality relative to the price they pay (i.e. quality per dollar or value). Perceived performance is affected by characteristics of the service and circumstances surrounding its acquisition. In addition, it indirectly affects satisfaction through disconfirmation22,44 or influences it directly without parameters.45,46 Furthermore, Kumar47 find that satisfaction with a service provider can be simply explained by how well the product performed. This implies that performance might become an independent determinant of satisfaction. It is consistent with some studies that support a direct performance effect.12,48 More recently, O'Cass and Fenech49 find that a positive experience/performance regarding service information on the web influences customer satisfaction. Hence, the following hypotheses are constructed: H2a: Perceived product or service performance affects the expectation­disconfirmation. H2b: Perceived product or service performance affects the customer satisfaction. Disconfirmation Disconfirmation of expectations usually means that service performance falls short of (or exceeds) what a consumer expected when making a purchase decision with negative (or positive) implications for the experience.50 Therefore, it seems plausible that a service performance exceeding expectations can cause pleasure and a shortfall in performance can cause displeasure. Some studies have shown that customer satisfaction is directly affected by expectation17,29,51 without the mediating role of disconfirmation. However, Wirtz and Bateson21 find that confirmation/disconfirmation has a direct and positive effect on pleasure, and both, in turn, have direct and positive effects on satisfaction. That is, consumers experience pleasure during the consumption

process when they perceive positive disconfirmation and that they experience displeasure when they perceive negative disconfirmation. These results clearly show that satisfaction literature still has a gap of relationship between satisfaction and disconfirmation. Accordingly, the inconsistency of consumers' previous expectations can cause a gap between their satisfaction and disconfirmation. Ultimately, this gap might tend to be resolved by discounting the evidence rather than forming a higher expectation. In the theoretical perspective, we assume that the gap will decrease customer satisfaction, and, in turn, will influence the customers' switching behaviours. Thus, positive or negative disconfirmation results when perceived performance is compared with expectations and the evaluation is outside the zone and differs enough from the norm to be recognized as such. The corresponding hypothesis tested is as follows: H3: The higher the levels of positive expectation­ disconfirmation, the greater the customer satisfaction.

Outcome variables for consumer satisfaction

Word-of-mouth `Willingness to recommend' and `recommendations to others' measures are widely used in practice to assess the impact of customers' overall levels of satisfaction. Many researchers who have studied customer satisfaction as a result of consumption experience suggest that customer satisfaction/dissatisfaction plays a crucial role in facilitating word-of-mouth communication. Ha38 shows that when consumers' perceptions of service satisfaction on the web are high, consumers are willing to recommend the company to others. Furthermore, Boulding et al.15 find that service quality relates positively to saying positive things regarding the company to others. Moreover, as word-of-mouth communication spreads much more quickly on the web than in the offline world, Iglesias et al.52 point out that satisfied users recommend it to others. More specifically, Anderson53 reports a positive relationship between customer satisfaction and word-of-mouth. As a result, we assume that customer satisfaction is an antecedent of word-of-mouth intentions on the web. Consequently, the following is hypothesized:

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H4: Satisfied customers (dissatisfied customers) positively (negatively) take part in word-of-mouth communication on the web. Brand (website) loyalty All companies are trying to cultivate brand loyalty along with customer loyalty. Brand-loyal consumers may be willing to pay more for a brand because they perceive some unique value in the brand that no alternative can provide.54,55 However, it is not easy to build such loyalty on the web. Beyond positive associations with a brand satisfaction, brand loyalty arises from a favourable brand relationship. With respect to brand relationship, few suggest that fostering communities on the web influences brand loyalty.56 To understand brand loyalty associated with customer satisfaction, we consider both its behavioural and cognitive aspects. By behavioural aspect, brand loyalty is understood to describe the characteristics of those consumers who have a strong commitment to a brand because they view that brand as being more satisfactory than the alternatives, and this evaluation is reinforced through repeat use.57 On the other hand, the cognitive school proposes that only measures of a consumer's mental processes and beliefs can make the distinction between actual brand loyalty and spurious behaviour.58 Bridging the gap between behavioural and cognitive perspectives as measure constructs can help us understand user behaviour better on the web. It is contended that a highly satisfied customer is closely related to a loyal customer on the web34 and that satisfaction is an antecedent of loyalty.31,59 Kandampully and Suhartanto60 also indicate that customer satisfaction is positively correlated to customer loyalty. Thus, it can be concluded that there is a positive relationship between customer satisfaction and customer loyalty. Thus, the hypothesis stating this is: H5: Higher levels of satisfaction have an important influence upon building brand loyalty. Attribution When actual performance of a service does not come up to consumer expectations ­ that is, when disconfirmation occurs ­ the consumer feels uncomfortable psychologically, and such psychological discord makes

consumers experience the attribution process.61 In other words, if the consumer experiences satisfaction or dissatisfaction about the brand, he or she consciously or unconsciously carries out causal inference about satisfaction or cause of dissatisfaction and responsibility. More specifically, if the perceived cause is expected to remain the same (stable) then success is followed by an increase of success expectancy and motivation, while failure is detrimental to both expectancy and motivation. On the other hand, if the cause is not expected to re-occur (unstable), then success leads to lower expectancies and motivation, but failure need not necessarily decrease the expectation of future success and motivation to engage in similar behaviours. Apart from the proposition that attribution influence motivation via expectancies, Weiner62 also maintain that attributions have a direct effect on motivated behaviour. In terms of customer satisfaction, Oliver23 emphasizes that satisfaction is a consequence of attribution processing. However, based on our knowledge, we cannot fully accept this argument because satisfaction is closely related to attribution63 and, in turn, the attribution process, based on satisfaction, affects repurchase intentions. The nature of customer attributions has important implications for customer evaluations but has received limited research in marketing.63 In particular, we expect that consumer attribution processes on the web will positively affect the repurchase intention through an ongoing relationship between consumers and the particular website, because a positive attribution process is in increasing customer satisfaction that results from an impression in a product or service being offered. Thus, the following hypotheses arise: H6a: Satisfaction affects a customer's positive attribution process. H6b: Customers who have experienced the positive attribution process are positively affected by repurchase intention. Repeat-purchase intention On e-commerce, repeat-purchase behaviour of satisfied consumers might be closely related to consumer loyalty. Uncles et al.64 argue that consumer loyalty can be improved by considering consumer habits and

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reinforcement in a variety of situations. To support their argument, they proposed that the benefits of a loyalty program may simply reward habitual purchasers or reinforce existing behaviours. In particular, they argued that ongoing propensities are not apparently altered much by the introduction of loyalty programs. Accordingly, they suggested that habit and reinforcement dominate the effect as outlined above. Meanwhile, Shankar et al.2 find that service encounter satisfaction for a service chosen online is higher when information content at the website is deeper and when web users enjoy a good online experience. In the customer satisfaction perspective, customers' repurchase intent is reference dependent in that it incorporates their evaluation of the focal supplier's performance relative to that of a reference competing supplier with whom they have had a concurrent or past experience.47 The related hypothesis to this is as follows: H7: Customer satisfaction leads to a strong repurchase intention.

Research methodology

A survey-based procedure was used to collect data for a number of websites. Our final instrument was administered as an e-mail: web fill-out form, posted from January to February 2003 in Korea. The survey was designed to include a number of different websites based on consumer experience, which included auctions, bookstore and travels. Such sites are now very popular worldwide, and most university students and other individuals have the opportunity and experience of purchasing from and enjoying a website; respondents with the appropriate background to be surveyed were not hard to find. Furthermore, these three categories are useful for measuring customer satisfaction because they are broadly accessed by many users and are now globally competing to keep loyal customers on the web. Before completing the questionnaire, we asked subjects to evaluate their cumulative experiences associated with past purchase performances. This research did not evaluate the transaction-specific satisfaction for one-time consumption but evaluated the cumulative future-oriented behaviours regarding customer satisfaction. Accordingly, a customer's repetitive purchase

behaviour was critical and we represented several websites in order to evaluate their web purchase behaviour: (1) travel (Interpark.co.kr); (2) bookstore (Yes24.com); and (3) auction (eAuction.co.kr). Each subject evaluated a particular website related to their repetitive experiences. A total of 680 subjects were randomly sent e-mails of which 229 (33.6%) returned as valid. Therefore, the total sample size of the study was 229. To raise efficiency and reliability of the response, a pretest was carried out to detect any necessary changes in the wording of items and the range to be used for evaluation. Similarly, with the pretest of Sundaram and Webster65 on the undergraduate students, pretest interviews were conducted with a convenience sample of 17 postgraduate students having various e-purchase experiences in Manchester city, in a manner similar to that intended for the final survey. Comments and feedback were then integrated in the final questionnaire. As a result of this process, a total of 18 items were obtained. All the variables considered were measured on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree; or 1 = very unlikely to 5 = very likely) (see Appendix). For example, consumer satisfaction was measured with three 5-point scales anchored as `Very satisfied/Very dissatisfied', `Very pleased/Very displeased', and `Delighted/Terrible'.19

Analysis of results

The reliability analysis of these scales yielded favourable results. The constructs exhibited a high degree of reliability in terms of coefficient alpha. All values exceeded the recommended value of Cronbach's alphas 0.7 (see Appendix).66 Factor analysis was used to explain groups among ratio scales. Table 1 shows the result of factor analysis for varimax rotation. In all cases, we maintained a high degree of individual items reliability by retaining only items with factor loadings greater than 0.50. We assessed the validity of the measures using confirmatory factor analysis (CFA).67 In the CFA, we standardized our model by constraining one indicator for each construct to 1.0. The results of the CFA suggest that our measurement model provides a good fit to the data (X2 = 1261.85, d.f. = 613, P = 0.01; Goodness-of-Fit

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Table 1 Differences of impact on determinants of customer satisfaction among the three populations

Auction respondents n = 48 Mean (Standard deviation) Book respondents n = 134 Mean (Standard deviation) Travel respondents n = 47 Mean (Standard deviation)

Measurement item Exogenous constructs Expectation Purchase expectation Site interesting Performance Good quality Delivery time Disconfirmation Positive disconfirmation Good price Endogenous constructs Satisfaction Overall satisfaction Good choice Unhappiness Word-of-mouth Recommendation Familiarity Brand loyalty High preference Switching barriers Trust regardless of price Attribution Quality evaluation Providing information Evaluation helps by providing information Repurchase Repurchase intention Frequency visits

Factor loading

3.38 (0.49) 4.42 (0.92) 2.67 (0.48) 2.04 (0.20) 2.38 (0.49) 3.30 (0.54)

3.78 (0.65) 3.34 (0.71) 3.75 (0.96) 3.60 (0.74) 3.42 (0.72) 3.60 (0.85)

4.21 (0.83) 4.06 (0.96) 4.38 (0.71) 4.12 (0.90) 4.21 (0.83) 4.31 (0.86)

0.52 0.61 0.83 0.72 0.88 0.78

4.13 (0.52) 4.63 (0.61) 4.34 (0.62) 4.23 (0.58) 4.20 (0.71) 3.79 (0.88) 3.46 (1.03) 2.93 (0.82) 3.96 (0.20) 3.96 (0.20) 3.38 (0.49)

4.22 (0.82) 4.42 (0.70) 4.01 (0.76) 4.16 (0.64) 4.18 (0.80) 3.84 (0.86) 3.33 (1.03) 2.81 (0.79) 3.39 (0.98) 3.48 (0.70) 3.55 (0.49)

4.01 (0.82) 4.31 (0.65) 4.66 (0.82) 4.66 (0.48) 4.49 (0.72) 4.45 (0.78) 3.93 (1.30) 2.83 (0.79) 2.66 (0.76) 3.87 (0.61) 4.21 (0.72)

0.76 0.84 0.86 0.67 0.79 0.64 0.67 0.79 0.73 0.77 0.81

3.71 (0.46) 4.04 (0.80)

3.82 (0.67) 3.58 (1.13)

4.58 (0.58) 4.45 (0.65)

0.57 0.72

Index [GFI] = 0.94, Adjusted Goodness-of-Fit Index [AGFI] = 0.88, Normed Fit Index [NFI] = 0.96, Comparative Fit Index [CFI] = 0.98, root mean square error of approximation [RMSEA] = 0.06). As evidence of convergent validity, the CFI results indicate that all items are significantly (P < 0.01) related to their hypothesized factors without high cross loadings. The average variance extracted (AVE) could be calculated for the picture outcome constructs (because they have multiple measures), and all exceeded the min-

imum of 0.50, as suggested by Fornell and Larcker.68 The factor inter-correlations from this model and AVE for the constructs with multiple indicators are presented in Table 2. The data from this study was analyzed in two stages. The measurement model was assessed to confirm that the scales were undimensional and reliable. When the reliability of the measures had been established, the structural model was tested using AMOS 4.01 causal modeling program.69 This testing determined the

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Table 2 Measurement model factor inter-correlations

AVE 1. 2. 3. 4. 5. 6. 7. 8. Expectation Disconfirmation Performance Satisfaction Word-of-mouth Loyalty Repurchase Attribution 0.57 0.79 0.85 0.81 0.75 0.71 0.68 0.76 1 2 3 4 5 6 7

0.68 0.63 0.70 0.44 0.49 0.50 0.27

0.88 0.72 0.69 0.56 0.59 0.26

0.77 0.75 0.55 0.57 0.29

0.61 0.69 0.71 0.19

0.36 0.18 0.56

0.84 0.44

0.37

strength of individual relationships, the model's goodness of fit and the various hypothesized paths. The twostep procedure followed here reduces the number of interpretational confound. Structural equations methodology was used to test the hypothesized model. In specifying this model, each of the constructs with multiple measures was represented by a single scale score. This was done because this is the way most of these widely used constructs are represented empirically in research literature. In addition, because the dichotomous turnover measure was not distributed normally, it was treated as censored variable ­ censored at zero from below.70 Finally, the measurement error terms for each construct were fixed at zero for the initial estimation and revision of the proposed model and then fixed at (1-a) times the variance of each scale in the final model to determine the extent to which measurement error affected the observed pattern of relationships. As mentioned earlier, path analysis (AMOS 4.01) was used for testing the model and hypotheses, as shown in Fig. 1. In this path analysis, the multiple indicators were summed together for each construct and the resulting summated score was used to represent that construct in the simultaneous equation model. The overall fit of the structural model was determined initially by examining the X2 statistics for the study, which was significant. A significant X2 statistic could indicate an inadequate fit, but this statistic is sensitive to sample size and model complexity; therefore, rejection of a model based on this evidence alone is inappropriate.71 As such, other measures of fit compensating for sample size were also applied: Bentler and Bonett's72 normed fit index (D),

Expectation

Word-of-mouth

H1b

H1c

Disconfirmation

H1a H3 H2b

Attribution Satisfaction

H4 H5 H7

Repurchase Loyalty

H6a H2a

Performance

H6b

Figure 1 An integrative model on determinants of customer satisfaction and outcomes.

Tucker and Lewis's non-normed fit index (r) and Bentler and Chou's67 comparative fit index (CFI). Each of these indices showed an adequate fit (good fit): D was 0.974; r was 0.962; CFI was 0.983, respectively (see Table 3).

Discussion

The purpose of this study was to: first, examine both antecedent variables and outcome variables of customer satisfaction through empirical study; second, to build an integrative model of the satisfaction research; and finally, to present implications for marketing practitioners and academics. The current study provides valuable insights into consumer behaviour literature to better understand the e-satisfaction process as an integrative model on customer satisfaction under the eservice environment. Consequently, we extended existing models of customer satisfaction as the key contribution to this study. In particular, unlike existing satisfaction models, our model's real contribution is

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Table 3 Model measures for the hypotheses

Description ExpectationÆSatisfaction (H1a) ExpectationÆPerformance (H1b) ExpectationÆDisconfirmation (H1c) PerformanceÆDisconfirmation (H2a) PerformanceÆSatisfaction (H2b) DisconfirmationÆSatisfaction (H3) SatisfactionÆWord-of-mouth (H4) SatisfactionÆBrand Loyalty (H5) SatisfactionÆAttribution (H6.1) AttributionÆRepurchase (H6.2) SatisfactionÆRepurchase (H7) Parameter 0.209 0.682 0.438 0.129 0.114 0.167 0.425 0.245 0.278 0.239 0.152 t-Value 2.87 11.08 8.21 2.23 1.51 2.80 5.32 3.96 4.47 3.75 2.53 Prob. 0.004 0.000 0.000 0.003 0.001 0.009 0.000 0.000 0.000 0.000 0.012

Goodness-of-Fit Statistics: X2 = 1675.995; Chi-square (d.f) = 875; X2/d.f. = 1.915; Normed fit index (NFI) = 0.974, CFI = 0.983; RFI = 0.936; RMSEA = 0.047. All parameters were significant at below the P < 0.05 level. The model clearly shows that our integrative model on customer satisfaction is very significant on the web as well.

the hypothesized satisfaction Æ attribution Æ repurchase path. In terms of expectancy disconfirmation, our findings are slightly differentiated from several models.13,17,21,23 For example, Oliver and Bearden16 show that scores of this construction are significantly correlated with a summary measure of overall disconfirmation, which, in turn, is correlated with satisfaction. Although our findings are partially consistent with their results, they limited the width of the construct within customer satisfaction. The satisfaction literature has found subjective disconfirmation (how one perceives the difference between expectations and observations) to be more explanatory of the satisfaction judgement process than `objective' disconfirmation measures.13,16,73 One of the major contributions is a counter-argument on Bitner's31 service encounter model as an integrated model of customer satisfaction. Based on expectancy­ disconfirmation theory, she has built an integrated model on evaluation of the service encounter. While Bitner's study has mainly focused on perceived service quality in the physical environment, our study has emphasized e-consumer satisfaction in the online marketplace. In contrast, our findings particularly show that customer satisfaction directly affects the outcome vari-

ables without the mediating role of perceived service quality. More specifically, service quality is not a requirement, but a sufficient condition. We also point out that service quality might not be an antecedent of customer satisfaction because service quality is more abstract than customer satisfaction and it is likely to be influenced by variables such as advertising, other forms of communication and the experience of other consumers. Sureshchandar et al.74 find that service quality and customer satisfaction do exhibit independence and are, indeed, different constructs from the customer's point of view. Johnson et al.1 argue that the disconfirmation model may not be appropriate for explaining aggregate market satisfaction in that market satisfaction is conceptualized as a cumulative rather than a transaction-specific evaluation. However, their argument did not fully consider the dynamics of the disconfirmation theory. Further and first, we argue that disconfirmation can be also accumulated by repeat experiences, and, in turn, influence the judgements of customer satisfaction. These processes are not simply a transaction-specific evaluation. We logically assume that disconfirmation might be closely related to the affective construct and the processes cannot be explained by just the transaction-specific evaluation. Finally, in marketing literature, attribution processes have been understood as an inter-relationship or antecedent of customer satisfaction,23,75 whereas our findings that the processes exert a significant influence on the outcome of customer satisfaction are very interesting issues. As consumer researchers have typically applied Weiner's paradigm to explain post-purchase issues such as customer satisfaction and repurchase (see Folkes),75 attribution processes also influence repurchase intentions as a cognitive construct, similar to disconfirmation processes. However, Burton et al.76 show that customer attributions do not appear to influence performance judgement but are significantly associated with satisfaction levels. Therefore, our research provides some remarkable answers to the question: `What is the relationship between attribution processes and outcomes of customer satisfaction?' Our study clearly shows that attributions are likely to have two sides of both cognitive and affective construct. Weiner63 argues that attribution process addresses how thinking and emotion

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together influence consumer behaviour. Based on these findings, attributions significantly affect e-loyalty and we suggest that attributions may be a type of emotional construct because the construct is strongly mediated by satisfaction ­ one of emotional scale measures. Additionally, the suggestion is consistent with Zeelenberg et al.'s77 study that extreme affective reactions are associated with more internal attributions. Research in this field shows that affective reactions leading to success and failure are largely determined by attributions.78 Thus, it is clear that attribution is a consequence of customer satisfaction. More importantly, our model criticizes Oliver's attribution process models.23 In his models, the process allows for an examination of the outcome Æ disconfirmation Æ attribution Æ affect sequence as it plays into final satisfaction judgement. However, his models have mainly focused on the transaction-specific satisfaction model. (In this study, the basic premise was that customer experience was being accumulated over time, and in turn, their satisfaction was also re-evaluated.) In addition, the concept of emotion in the marketing field is not just temporary, but an accumulated and longterm construct; that is, emotional components are not totally influenced by satisfaction because satisfaction is also an emotional component.79 More recently, Martin80 suggests that emotions influence cognitive capacity. Because mental states are not directly observable, they can only be inferred from observable features of the consumer such as behaviour and the situational context that the consumer is in. In terms of motivated behaviour, attributions consist of both goal- and affectiveoriented behaviours. Specifically, the affective-oriented behaviour is not simply a cognitive process but a reactive behaviour after judgement of customer satisfaction. It can be explained by our model that attribution plays an important mediating role in bridging relationships between satisfaction and the outcomes of satisfaction. Oliver81 notes that the cognitive and affective responses in post-purchase judgements may be seen as distinct components in response to environmental events and each would appear to be introducing its own influence on the consumption. However, our results point out that both constructs might coexist in the minds of consumers. In addition, the dominating effect of dis-

confirmation over affective response was also found in Westbrook and Reilly's study.82 In this study, attribution processes play an important mediating role in bridging relationships of both cognitive and emotional constructs. Thus, these findings suggest that the judgements of consumer satisfaction do not influence only cognitive or emotional component. In other words, consumers evaluate that their satisfaction is influenced by relationships of both constructs. Given the extreme interest all companies should have in creating and maintaining a customer's positive experience, it would seem imperative to understand both the antecedents and outcomes of customer satisfaction. The indication from this research is that customer satisfaction should be newly understood by the mediating role of both disconfirmation perceptions and attribution processes. Thus, marketers need to know how to influence and manipulate the two constructs to achieve greater satisfaction.

Limitations and future research

While this research offers an insight into preceding variables versus outcome variables of customer satisfaction, it is not without its limitations. Customer satisfaction as discussed in this study has been defined by the disconfirmation paradigm. However, many researchers have emphasized that customer satisfaction arises from multiple standards of comparison.83 First, our study did not consider relationships as they pertain toward the preceding variables that influence customer satisfaction. For example, the solid relationship between a company and a customer can mould the customer's optimal expectations and significantly affect satisfaction. Future research should consider impacts generated by both customer­company relationship and interaction at the same time. Second, the items used as variable measures were insufficient. Some measurements toward a variable were not used because of the difficulty of manipulating multiple variables. The research associated with eservice is still in its infancy. It requires organic and indepth research on each individual variable. For instance, the measurement for brand loyalty on the web is not still clear. In addition to traditional measurements, if future research considers newly introduced technologi-

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cal ingredients, it will provide more reliable insights into consumer behaviour on the Internet. Finally, `objective' disconfirmation is just the algebraic difference of the expectation level and the evaluation of product or service performance or other variable.84 As a result, we logically assume that objective disconfirmation is a cognitive process. The cognitive process excludes an individual's emotional state such as satisfaction. Accordingly, cognitive processes can affect other cognitive constructs, and, in turn, if disconfirmation directly influences consumers' attribution processes, satisfaction literature should be required further work to extend existing models.

References

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Appendix

Expectation (adopted from Writz and Bateson, 1999) With respect to the purchases, the website will offer good services to me I expect the website will offer an interesting event/entertainment to me Performance (adopted from Shaffer and Sherrell, 1997) How did you think the product or service actually performed? Consistently meeting delivery rates on time as promised? Disconfirmation (adopted from Oliver, 1980) Quality of product or service is much more higher than my expectation I am happy that prices offered by the website were lower than my expectation Word-of-Mouth (adopted from Iglesias et al. 2001; Srinivasan et al. 2002) I say positive things about the web store to other people I have told more people about the web store's familiar customer services than I have told about most alternatives Brand Loyalty (adopted from Jacoby and Chestnut, 1978) I seldom consider switching to another website I can be willing to pay more at this website relative to the competition for the same benefit? I prefer using this website to another website Attribution (adopted Oliver and DeSarbo, 1988) I am satisfied with my decision to get good product or service Information regarding product or service was useful for my choice Repurchase (adopted from Uncles et al. 1998) Even if this web store would be more difficult to reach, I would still keep repurchasing there I often visit the website in order to buy another product or service Satisfaction (adapted from Auh et al. 1998; Anderson and Sullivan, 1993) I overall satisfy a specific experience with the website I am unhappy about my decision to purchase from this website. I am satisfied with my decision to purchase from this website X = 0.84

X = 0.88

X = 0.74

X = 0.74

X = 0.70

X = 0.75

X = 0.86

X = 0.82

All measures used a 5-point Likert type (from `strongly agree' to `strongly disagree'; from `very pleased' to `very displeased'; from `very likely' to `not at all likely').

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