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Consumer responses to online retailer’s service recovery after a service failure A perspective of justice theory Hsin-Hui Lin Department of Distribution Management, National Taichung Institute of Technology, Taichung City, Taiwan Yi-Shun Wang Department of Information Management, National Changhua University of Education, Changhua, Taiwan, and Li-Kuan Chang Graduate School of Business Administration, National Taichung Institute of Technology, Taichung City, Taiwan Abstract Purpose – The purpose of this paper is to investigate consumer responses to online retailer service recovery remedies following a service failure and explores whether the phenomenon of the service recovery paradox exists within the context of online retailing. Design/methodology/approach – This paper reports on the results of two studies. Study I explores the main and interaction effects of the various dimensions of service recovery justice (i.e. distributive justice, procedural justice, and interactional justice) on customer satisfaction, negative word-of-mouth (WOM), and repurchase intention based on the justice theory. Study II investigates whether the phenomenon of the service recovery paradox exists (i.e. whether customers have higher satisfaction, higher repurchase intention, and lower negative word-of-mouth after experiencing an effectively remedied service failure as compared to if the service failure had not occurred). A laboratory experimental design is used to test the research hypotheses. Findings – The results show that distributive justice, procedural justice, and interactional justice have a significant positive influence on customer satisfaction. Among the three dimensions of service recovery justice, only distributive justice has a significant positive influence on repurchase intention, and only interactional justice has a significant negative influence on negative WOM. Additionally, both the interaction between distributive justice and procedural justice and the interaction between distributive justice and interactional justice are found to significantly influence customer satisfaction, negative WOM, and repurchase intention. The results also indicate that the service recovery paradox does not appear to exist in the online retailing context. Practical implications – The findings will allow online retailers to develop more effective strategies for preventing service failure and improving customer satisfaction, negative WOM, and repurchase intention. Originality/value – This study contributes to the understanding of consumer responses to online retailer’s service recovery after a service failure. Keywords Service failures, Service recovery, Justice theory, Service recovery paradox, Online retailing, Electronic commerce Paper type Research paper The current issue and full text archive of this journal is available at www.emeraldinsight.com/0960-4529.htm Online retailer’s service recovery 511 Managing Service Quality Vol. 21 No. 5, 2011 pp. 511-534 q Emerald Group Publishing Limited 0960-4529 DOI 10.1108/09604521111159807

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Page 1: Consumer responses to online retailer's service recovery after a service failure

Consumer responses to onlineretailer’s service recovery after

a service failureA perspective of justice theory

Hsin-Hui LinDepartment of Distribution Management,

National Taichung Institute of Technology, Taichung City, Taiwan

Yi-Shun WangDepartment of Information Management,

National Changhua University of Education, Changhua, Taiwan, and

Li-Kuan ChangGraduate School of Business Administration,

National Taichung Institute of Technology, Taichung City, Taiwan

Abstract

Purpose – The purpose of this paper is to investigate consumer responses to online retailer servicerecovery remedies following a service failure and explores whether the phenomenon of the servicerecovery paradox exists within the context of online retailing.

Design/methodology/approach – This paper reports on the results of two studies. Study Iexplores the main and interaction effects of the various dimensions of service recovery justice (i.e.distributive justice, procedural justice, and interactional justice) on customer satisfaction, negativeword-of-mouth (WOM), and repurchase intention based on the justice theory. Study II investigateswhether the phenomenon of the service recovery paradox exists (i.e. whether customers have highersatisfaction, higher repurchase intention, and lower negative word-of-mouth after experiencing aneffectively remedied service failure as compared to if the service failure had not occurred). A laboratoryexperimental design is used to test the research hypotheses.

Findings – The results show that distributive justice, procedural justice, and interactional justicehave a significant positive influence on customer satisfaction. Among the three dimensions of servicerecovery justice, only distributive justice has a significant positive influence on repurchase intention,and only interactional justice has a significant negative influence on negative WOM. Additionally,both the interaction between distributive justice and procedural justice and the interaction betweendistributive justice and interactional justice are found to significantly influence customer satisfaction,negative WOM, and repurchase intention. The results also indicate that the service recovery paradoxdoes not appear to exist in the online retailing context.

Practical implications – The findings will allow online retailers to develop more effectivestrategies for preventing service failure and improving customer satisfaction, negative WOM, andrepurchase intention.

Originality/value – This study contributes to the understanding of consumer responses to onlineretailer’s service recovery after a service failure.

Keywords Service failures, Service recovery, Justice theory, Service recovery paradox, Online retailing,Electronic commerce

Paper type Research paper

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/0960-4529.htm

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511

Managing Service QualityVol. 21 No. 5, 2011

pp. 511-534q Emerald Group Publishing Limited

0960-4529DOI 10.1108/09604521111159807

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1. IntroductionAmid the rise of service orientation and consumer awareness, how companies maintaingood relationships with customers is an important issue for academics andpractitioners. According to Tax and Brown (1998), the cost of attracting a newcustomer is five times that of retaining an old one. As such, companies that are unableto retain existing customers face massive hurdles associated with the constant attemptto develop new customers. For most companies, service failure is the predominantreason for the loss of existing customers. If a service failure is not properly remedied,customers may experience reduced satisfaction and even engage in negativeword-of-mouth (WOM) communications (Barlow and Moller, 1996; Ranaweera andPrabhu, 2003). Empirical evidence has shown that dissatisfied customers will sharetheir experience to eight to ten persons on average; however, 20 percent of agitatedcustomers will express their anger to approximately 20 persons (TARP, 1980). Thatsaid, when a service failure occurs, effective handling of the customer complaint andservice recovery process can often turn angry and disappointed customers into loyalones (Boshoff, 1997; Boshoff and Leong, 1998; Michel, 2001). In contrast, ineffectiveservice recovery may further erode customer satisfaction, trust, and support for thefirm, and even result in customers switching to competitors (Keaveney, 1995; Michel,2001; Smith and Bolton, 1998; Tax et al., 1998).

In recent years, the development of e-commerce has seen the internet become a newshopping channel for consumers, such that many companies engage in online retailing.Compared with brick-and-mortar retailers, online retailers generally provide moreconvenient and faster services, allowing consumers to save time and costs. However,online retailers and brick-and-mortar retailers adopt different shopping procedures.For instance, as compared to brick-and-mortar retailing, there is no face-to-face marketexchange in online retailing. Further, consumers do not have any physical contact withtheir desired products, and they are exposed to perceived insecurities related toshopping on the internet (Warrington et al., 1993). Thus, service failures – gapsbetween perceived and expected service levels – are more likely to occur and causedissatisfaction in the context of online retailing than in the context of brick-and-mortarretailing. Given that service failures influence customer repurchase behaviors, servicefailure and recovery management within online retailing has become an importanttopic for academics and practitioners (Holloway and Beatty, 2003).

Previous studies on customer attitudes and behaviors associated with the Internetcontext have focused mainly on the effect of service quality and/or satisfaction onbehavioral intention (e.g., Wang, 2008; Sanchez-Franco and Rondan-Cataluna, 2010;Udo et al., 2010). However, little research has explored customer attitudes or behaviorsin online retailing from the perspective of service failure and recovery. Furthermore,while some studies have investigated the issues regarding service failure and recoveryin the traditional service settings (e.g., Blodgett et al., 1997; Ha and Jang, 2009; Ecclesand Durand, 1998; Boshoff, 2005; Bamford and Xystouri, 2005; Shapiro andNieman-Gonder, 2006; Thwaites and Williams, 2006), few studies have been conductedon the effect of service recovery on post-recovery attitudes and behaviors from theperspective of the justice theory within the context of online retailing (Wang et al.,2011). Thus, there is a need for research to explore the effect of service recovery justiceon customer attitudes and behaviors in an online retailing setting. Further, the “servicerecovery paradox”, which states that given a highly effective service recovery for a

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service failure, consumers may have higher ratings of loyalty and satisfaction towardthe service than if the failure had never happened (Magnini et al., 2007; Matos et al.,2007; McCollough and Bharadwaj, 1992) has been empirically tested inbrick-and-mortar retailing settings (e.g., Bolton and Drew, 1992; Boshoff, 1997;Hocutt et al., 1997, 2006; Smith and Bolton, 1998; McCollough, 2000; Maxham andNetemeyer, 2002a, b; Magnini et al., 2007; Berry et al., 1990). However, few studies haveinvestigated whether this paradox also exists in online retailing environments;moreover, the previous results on the service recovery paradox have been mixed (e.g.,Bolton and Drew, 1992; Berry et al., 1990; Maxham, 2001; Andreassen, 2001). Therefore,there is a need for research to explore the phenomenon of the service recovery paradoxin an online retailing context.

The main purposes of this study are to:. investigate the main and interaction effect of the various dimensions of service

recovery justice (i.e. distributive justice, procedural justice, and interactionaljustice) on customer satisfaction, negative WOM, and repurchase intention; and

. test whether customers have higher satisfaction, lower negative WOM, andhigher repurchase intention after experiencing a remedied service failure ascompared to if the service failure has not occurred (i.e. whether the servicerecovery paradox exists in an online retail setting).

The findings of this empirical study are useful to researchers in terms of developingand testing theories related to online retailing service failure and recovery, as well as topractitioners in understanding the strategies for the management of service failure andrecovery in online retailing.

The remainder of this paper is organized as follows. The next section reviews theconceptualization of service failure, service recovery justice, and service recoveryparadox, followed by an outline of the proposed research model and hypotheses basedon previous theories and literature. Section 4 describes the research design andmethods used in this study, while section 5 presents the results of the data analysis andhypotheses tests. Finally, practical implications and directions for future research arediscussed.

2. Literature review2.1 Service failure and recoveryA service failure occurs when customer perceptions of a service delivered are lowerthan their expectation or zone of tolerance (Zeithaml et al., 1993). Palmer et al. (2000)defined service failure as when customers think the service is flawed or irresponsible.Bitner et al. (1990) proposed that a service failure occurs when service is not fulfilled, isdelayed, or fails to reach the expected standard. For online retailers, service failuresmay also occur within the service process when any customer requirement is not met,such that the customer expectation for the service is higher than perception of theservice delivered. Holloway and Beatty (2003) identified several types of servicefailures in online retailing:

. delivery problems (e.g., purchase arrived later than promised);

. web site design problems (e.g., navigational problems at site);

. customer service problems (e.g., poor customer service support);

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. payment problems (e.g., credit card overcharged);

. security problems (e.g., credit card fraud); and

. miscellaneous/others (e.g., retailer charged some customers more than others).

Of these, delivery problems represent the most commonly reported failure type ofonline retailing. On the other hand, Kuo et al. (2009) proposed three types of onlineauction service failures:

(1) service delivery system failures;

(2) buyer needs and requests; and

(3) unprompted and unsolicited seller actions.

Although service providers cannot pre-empt all service failures, they can learn how torespond to the various types of service failure. The response, known as servicerecovery, is defined as the process by which the service providers attempt to correct afailed service (Kelley et al., 1993). Gronroos (1988) suggested that service recoveryrefers to the actions a service provider takes in response to customer complaints causedby a service failure, while Hart et al. (1990) mentioned that service recovery is aremedial behavior for a service failure that can reinforce the connection betweencustomers and the business. Several other researchers have also stated that servicerecovery consists of the actions taken by the service provider to turn customerdissatisfaction into satisfaction and retain customer loyalty through a timely andgenuine response to a customer complaint (Hart et al., 1990; Maxham, 2001;Andreassen, 2000).

2.2 The effect of service recovery justice on customer attitude and behaviorPrior studies have discussed the effect of service recovery on post-recovery attitudesand behavior from the perspective of the justice theory in the physical businessenvironment. Based on the justice framework, service recovery justice can be definedas the customer’s assessment of the fairness of the way in which service failures arehandled from three different perspectives: distributive justice, procedural justice, andinteractional justice (Blodgett et al., 1997; McColl-Kennedy and Sparks, 2003; Ha andJang, 2009; Smith et al., 1999). All three dimensions of service recovery justice affectcustomers’ post-recovery attitudes and behavior.

Based on the justice theory, Blodgett et al. (1997) investigated the effect of perceivedjustice of recovery on post-complaint behavior and found that when consumersperceive unfairness, they become agitated, reduce intention for repatronage, and evenengage in negative WOM communications. Bitner et al. (1994) pointed out that 42.9percent of consumers become dissatisfied when they encounter improper handling ofservice failures by frontline service employees. Other researchers have also suggestedthat customer satisfaction and future behavioral intention (e.g., repurchase intention)are influenced by perceived justice in terms of service recovery (McCollough et al.,2000; Smith et al., 1999; Tax et al., 1998; McColl-Kennedy and Sparks, 2003; Ha andJang, 2009; Kim et al., 2009). Service recovery is intended not only to enhance customersatisfaction and build customer loyalty, but also to minimize the potential for negativeWOM (Hart et al., 1990). Based on the above, customer satisfaction, repurchaseintention, and negative WOM are chosen as the dependent variables in this study.

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As aforementioned, based on the justice theory, this study explores the effect ofservice recovery justice on customer satisfaction, negative WOM, and repurchaseintention within an online retailing context. Based on Oliver (1992), customersatisfaction in this study refers to a consumer’s post-purchase evaluation and affectiveresponse to the overall product or service experience. Further, WOM can be dividedinto positive and negative WOM: when a consumer has a pleasant service experience,he/she will likely recommend the service provider through positive WOMcommunication, and vice versa (Barlow and Moller, 1996; Ranaweera and Prabhu,2003). Previous research has indicated that consumers are more likely to engage innegative WOM than positive WOM (e.g., Arndt, 1967). Therefore, this study adoptednegative WOM as a dependent variable. Moreover, Jones and Sasser (1995) stated thatrepurchase intention refers to a customer’s willingness to repeat a particularconsumption pattern. Based on Srinivasan et al. (2002) and Lin and Wang (2006),repurchase intention in this study is defined as a customer’s favorable attitude towardan online retailer that results in repeat buying behavior.

2.3 The phenomenon of service recovery paradoxPrevious studies have suggested that following a service failure, a highly effectiveservice recovery provides a chance for the firm to achieve higher satisfaction andloyalty ratings from customers than if the failure had never happened (Magnini et al.,2007; Matos et al., 2007; McCollough and Bharadwaj, 1992). Goodwin and Ross (1992)also noted that while customers are satisfied when there is no service failure, they aremore satisfied when a complaint is effectively handled. To date, much of the literatureexploring the service recovery paradox within the physical business environment hasgenerated mixed results. For instance, Boshoff (1997) conducted a scenario-basedexperiment in the airline industry, and found that the service recovery paradox existsin situations where the supervisor immediately offers a customer a full refund and anadditional free airline ticket. On the other hand, Berry et al. (1990) conducted a surveyof customers in various industries, and found that “no service problem” is better than a“service problem resolved satisfactorily”, which does not support the service recoveryparadox. Zeithaml et al. (1996) also found that no problem is better as compared to agood recovery, which in turn is preferred to a bad recovery; as such, they posited thatthe service recovery paradox does not exist. Furthermore, Hocutt et al. (2006) did afactorial design experiment within a hotel context to examine the difference incustomer satisfaction and negative WOM between post-service recovery and noservice failure, and found that the paradox existed only for the best recovery scenarioas compared to the no failure scenario. Their findings only partially support the servicerecovery paradox. These mixed results regarding the service recovery paradox providethe impetus to further investigate this phenomenon within the online retail context.

3. Research hypothesisThe justice theory has received much attention from academia as a theoreticalframework for service recovery research (Ha and Jang, 2009; Smith et al., 1999; Sparksand McColl-Kennedy, 1998; Tax et al., 1998). Based on the justice theory, this sectionposits a set of research hypotheses regarding the main and interaction effects of thevarious types of service recovery justice (i.e. distributive justice, procedural justice, andinteractional justice) on customer satisfaction, negative WOM, and repurchase

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intention, as well as the phenomenon of the service recovery paradox within thecontext of online retailing.

3.1 Distributive justiceDistributive justice refers to the customer perception that the outcome of a servicerecovery is deserved, necessary, and fair (Tax et al., 1998); it is based on what customersreceive as an outcome of a recovery effort (Ha and Jang, 2009) and within the onlineretailing context it may include discounts, coupons, or a product replacement. Greenberg(1996) contended that when customers perceive an injustice, they might expressdissatisfaction, spread negative WOM, and reduce their repatronage intentions.Rıo-Lanza et al. (2009) also found that distributive justice during service recovery has asignificant influence on overall satisfaction with the service recovery. Furthermore,previous research has suggested that higher levels of distributive justice result in morefavorable repatronage intentions and a decreased likelihood of negative WOM (e.g.,Blodgett et al., 1993, 1997; Clemmer, 1993). Based on the aforementioned literature, thisstudy contends that in the context of online retailing, consumers will feel grateful andchange their attitude if they are given tangible restitution for a service failure: theirperception of distributive justice in terms of the service recovery affects their relationshipwith the service provider. Thus, this study proposes the following hypotheses:

H1-1. Distributive justice in service recovery has a positive influence on customersatisfaction in the context of online retailing.

H1-2. Distributive justice in service recovery has a negative influence on negativeWOM in the context of online retailing.

H1-3. Distributive justice in service recovery has a positive influence onrepurchase intention in the context of online retailing.

3.2 Procedural justiceProcedural justice refers to the perceived fairness of procedures and criteria used inarriving at the outcome of a service recovery (Tax et al., 1998; Blodgett et al., 1997).This form of justice may include formal policies and structural considerations relatedto service recovery, such as the length of time required to receive a refund, as well asthe responsiveness and flexibility displayed during the recovery (McColl-Kennedy andSparks, 2003; Chebat and Slusarczyk, 2005; Ha and Jang, 2009). Hart et al. (1990)mentioned that empowering frontline employees is critical, because they are often thefirst to identify problems and can make initial judgment calls as to how to satisfycustomers. If sufficiently authorized, they can make immediate decisions to addresscustomer complaints, resulting in a higher level of procedural justice in servicerecovery. Goodwin and Ross (1992) used the perspective of procedural justice toinvestigate customer reactions to service failures. Their findings indicate thatcustomer-perceived procedural justice affects customer satisfaction. Rıo-Lanza et al.(2009) also found that procedural justice during service recovery has a significantinfluence on satisfaction with the service recovery, while Ha and Jang (2009) noted thatperceived justice brought about by service recovery efforts has a positive influence oncustomer WOM and revisit intention. In addition, other studies have suggested thathigher levels of procedural justice will lead to a more favorable repatronage intentionand a decreased likelihood of negative WOM (e.g., Blodgett et al., 1993, 1997; Clemmer,

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1993). Based on the aforementioned literature, this study infers that if an online retailercan address service failures immediately (i.e. its service recovery measures areinstantly put into action), customers will perceive a higher level of procedural justiceassociated with the service recovery, and their relationship with the online retailer willbe positively affected, resulting in increased satisfaction, decreased negative WOM,and increased repurchase intention. Thus, this study proposes the followinghypotheses:

H2-1. Procedural justice in service recovery has a positive influence on customersatisfaction in the context of online retailing.

H2-2. Procedural justice in service recovery has a negative influence on negativeWOM in the context of online retailing.

H2-3. Procedural justice in service recovery has a positive influence on repurchaseintention in the context of online retailing.

3.3 Interactional justiceInteractional justice refers to the manner in which service failures are handled byservice providers in terms of the communication between the service provider and thecustomer (Blodgett et al., 1993; McColl-Kennedy and Sparks, 2003). Similarly, Tax et al.(1998) suggested that interactional justice refers to the perceived fairness of theinterpersonal treatment received from employees during a service recovery. Thus,interactional justice may include interpersonal sensitivity, treating people with dignityand respect, and providing appropriate explanations for the service failure in thecontext of service recovery (Ha and Jang, 2009). Further, interactions betweenemployees and consumers during a service recovery directly affect consumer attitudesand behavior. Goodwin and Ross (1992) investigated service recovery from the view ofinteractional justice, and found that employee apologies are particularly related tocomplaint resolutions. McColl-Kennedy and Sparks (2003) proposed that consumersfeel more negative emotions when they perceive an absence of care or empathy on thepart of the service provider during a service recovery. Rıo-Lanza et al. (2009) also foundthat interactional justice during service recovery has a significant influence on overallsatisfaction with the service recovery, while other researchers noted that higher levelsof interactional justice will lead to more favorable repatronage intentions and adecreased likelihood of negative WOM (e.g., Blodgett et al., 1993, 1997; Clemmer, 1993).Based on the aforementioned literature, the current study suggests that following aservice failure, online retailers that maintain positive interactions with consumersthrough apologies or expressions of genuine concern regarding the problem willincrease consumer perceptions of interactional justice, and thereby improverelationships with the consumers. Thus, the following hypotheses are proposed:

H3-1. Interactional justice in service recovery has a positive influence on customersatisfaction in the context of online retailing.

H3-2. Interactional justice in service recovery has a negative influence on negativeWOM in the context of online retailing.

H3-3. Interactional justice in service recovery has a positive influence onrepurchase intention in the context of online retailing.

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3.4 Interaction effect of perceived justicesThe interaction effect of distributive justice and procedural justice on customerreactions has been affirmed in previous research (Brockner and Wiesenfeld, 1996).Sparks and McColl-Kennedy (2001) pointed out that the interaction betweendistributive justice and procedural justice influences customer’s perceived loyalty.Wirtz and Mattila (2004) also stated that the three dimensions of justice have a jointeffect on post-recovery customer satisfaction. Based on the above, this studyhypothesizes that each pair of the three dimensions of service recovery justice has ajoint effect on post-recovery satisfaction, negative WOM, and repurchase intention.Thus, the following hypotheses are proposed:

H4-1. There is an interaction effect of distributive justice and procedural justiceon customer satisfaction.

H4-2. There is an interaction effect of distributive justice and procedural justiceon negative WOM.

H4-3. There is an interaction effect of distributive justice and procedural justiceon repurchase intention.

H5-1. There is an interaction effect of distributive justice and interactional justiceon customer satisfaction.

H5-2. There is an interaction effect of distributive justice and interactional justiceon negative WOM.

H5-3. There is an interaction effect of distributive justice and interactional justiceon repurchase intention.

H6-1. There is an interaction effect of procedural justice and interactional justiceon customer satisfaction.

H6-2. There is an interaction effect of procedural justice and interactional justiceon negative WOM.

H6-3. There is an interaction effect of procedural justice and interactional justiceon repurchase intention.

3.5 Service recovery paradoxAs note earlier, prior studies have noted mixed results regarding the service recoveryparadox (e.g., Bolton and Drew, 1992; Boshoff, 1997; Berry et al., 1990; Maxham andNetemeyer, 2002a, b; Zeithaml et al., 1996; Maxham, 2001; Andreassen, 2001). Kelleyet al. (1993) suggested that effective service recovery might lead to a situation wherebycustomers exhibit higher satisfaction after a problem has been corrected as comparedto an experience that was problem-free. Smith and Bolton (1998) also found thatcumulative satisfaction and repatronage intentions both increase following a verysatisfactory service recovery. Additional studies have provided partial support for thephenomenon of the service recovery paradox (Hocutt et al., 1997, 2006; McCollough,2000; Magnini et al., 2007). Based on the viewpoint of the service recovery paradox, thisstudy suggests that in the context of online retailing, service failures may notnecessarily dissatisfy customers, and that an effective recovery can even improve

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customers’ attitudes and behavior. Thus, this study proposes the followinghypotheses:

H7-1. Consumers exhibit increased satisfaction after a service failure has beenproperly remedied as compared to if the service failure had not occurred.

H7-2. Consumers exhibit decreased negative WOM after a service failure has beenproperly remedied as compared to if the service failure had not occurred.

H7-3. Consumers exhibit increased repurchase intention after a service failure hasbeen properly remedied as compared to if the service failure had notoccurred.

4. Research methodology4.1 Research designIn this study, the scenario simulation method was adopted to avoid issues associatedwith field experiments (significant amounts of time and money generally required) andto reduce the memory bias of the subjects (Smith et al., 1999). Further, by controllingthe exogenous variables, this method enhanced the internal validity (Cook andCampbell, 1979; Churchill, 1995). This study designed several online retailer scenariosassociated with delay delivery problems, since these types of problems represent acommon dissatisfier for e-commerce website (Ramanathan, 2010) and are the mostcommonly reported failure type within online retailing (Holloway and Beatty, 2003).The respondents answered questionnaire items associated with the designed scenarios.

The current research designed Study I and Study II to test the proposed hypotheses.For Study I, a 2 £ 2 £ 2 factorial experiment was designed to investigate the main andinteraction effects of the three dimensions of service recovery justice (i.e. distributive,procedural, and interactional) on post-recovery satisfaction, negative WOM, andrepurchase intention. Thus, three independent variables were manipulated:distributive justice, procedural justice, and interactional justice; two levels weredesigned for each variable.

Study II was designed to investigate the differences in customer satisfaction,negative WOM, and repurchase intention between scenarios with a remedied servicefailure and a scenario that lacked a service failure (i.e. to verify the existence of theservice recovery paradox in online retailing). Scenarios with a remedied service failurerefer to the eight scenarios developed for Study I. In Study II, these eight scenarioswere respectively compared to the scenario that lacked a service failure in terms ofcustomer satisfaction, negative WOM, and repurchase intention.

The experimental subjects in this study were college and university students inTaiwan. As college and university students in Taiwan generally have a high level ofinvolvement in 3C products and online games, this study employed two relatedexperimental products – one physical and one virtual. The former was an MP3 playersold in an online store, while the latter was a virtual treasure sold in an online gamee-mall.

4.2 Determination of independent variable levelsAccording to Tax et al. (1998), distributive justice comes in the forms ofreimbursement/refund, replacement, repair, and credit, among others; thedimensions of procedural justice include assuming responsibility, timing/speed,

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convenience, process control, flexibility, and knowledge of process; while thedimensions of procedural justice include politeness, empathy, effort,explanation/information, honesty, and attitude. In order to simplify the manipulationof independent variables in the factorial experiment, this study used discount, time toredeliver the ordered product, and politeness and empathy to represent distributivejustice, procedural justice, and interactional justice, respectively, based on the researchresults of Tax et al. (1998).

This study selected 35 college students in Taiwan via convenience sampling todetermine the high and low levels of distributive justice and procedural justice in thefactorial experiment. These students were given a pretest to determine the levels ofhigh distributive justice, low distributive justice, high procedural justice, and lowprocedural justice used in the subsequent experiment. During the pretest, a briefexplanation of the scenarios involving service failures and recoveries was given. Therespondents were required to evaluate a reasonable and satisfactory “discount ratio”for a delivery failure that the retailer should make up when redelivering the orderedproduct. The mode for the discount ratio was 10 percent. Thus, this study adopted a 10percent discount ratio as the “high distributive justice level”, while no discount wasdefined as the “low distributive justice level” in the factorial experiment. Further, therespondents were required to indicate in an open-ended question the “range ofredelivery time” they would tolerate (including the anticipated quickest redelivery timeand the longest tolerable time) if the retailer decided to redeliver the ordered product.The means for the anticipated quickest redelivery time for physical products andvirtual products were two days and 30 minutes, respectively, which were used as the“high procedural justice level” in this factorial experiment. In addition, the means forthe longest tolerable redelivery time for physical products and virtual products wereseven days and two days, respectively, which were used as the “low procedural justicelevel” in this experiment. Finally, this study adopted “replying to customers’ problemswith politeness and empathy” as the “high interactional justice level” and “replying tocustomers’ problems without politeness or empathy” was defined as the “lowinteractional justice level”.

4.3 Manipulation and check of independent variablesAs noted earlier, the experimental products included one physical product (MP3player) and one virtual product (virtual treasure in an online game). In order to simplifythe experimental design, the service failures were limited to “delay delivery problems”.There were three dimensions of service recovery justice in the factorial experiment,including distributive justice, procedural justice, and interactional justice, and twolevels were defined for each. In the manipulation of independent variables, this studydefined redelivery plus discount as high distributive justice and redelivery with nodiscount as low distributive justice; delivery within two days for physical products/30minutes for virtual products as high procedural justice and delivery within seven daysfor physical products/two days for virtual products as low procedural justice; and highlevel of politeness and empathy in retailer response as high interactional justice andlow level of politeness and empathy in retailer response as low interactional justice.

A total of eight scenarios with different levels of service recovery justice wereemployed in the experiment, which is a paper-based process. That is, each participantwas randomly assigned to read a document describing one experimental scenario and

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then to fill in a questionnaire according to their perceptions towards the scenario. Thefollowing paragraph illustrates the scenario involving “a delivery failure of thephysical product” remedied by a service recovery with high distributive justice, highprocedural justice, and high interactional justice: describes the product exchangescenario – the process where consumers purchased the MP3 player in an online storeand the promised delivery time; describes the service failure scenario – delivery failureoccurred when the delivery of the MP3 player was delayed; describes the servicerecovery scenario – the retailer treated the customer with a high level of politeness andempathy when responding to their problem (high interactional justice); the retailerdelivered the ordered product to the consumer in a speedy manner (high proceduraljustice) and offered a discount (high distributive justice) to the customer:

(1) Assume that you plan to buy an MP3 player. After searching for related information onthe Web, you see a product that complies with your needs in XYZ online store and decide topurchase that product. After the order and online payment with credit card are made, you arenotified that the product will be delivered within five days. (2) After five days, you still havenot received the ordered MP3 player. Thus, you write an e-mail to the customer service centerof this online store to report the delay. (3) Upon receipt of your report, the online store repliesby saying “We sincerely apologize for the problem you have reported. We will contact thedepartment in charge and demand that they address the problem as soon as possible. We wouldlike to express our apology again for this inconvenience.” After the delay problem is reported,you receive the ordered product two days later and are notified that you will receive a 10percent discount as compensation for this service failure.

In order to confirm that the manipulation of independent variables led to a significantdifference in the respective means of the groups with low and high levels of thevariables, this study also measured respondent perceptions of distributive justice,procedural justice, and interactional justice as part of the factorial experiment. Basedon Blodgett et al. (1997) and Smith et al. (1999), three items were used to measuredistributive justice:

(1) taking everything into consideration, the outcome I received was fair;

(2) in solving the problem, the online retailer gave me what I wanted; and

(3) the outcome I received was right.

Two items were employed to measure procedural justice:

(1) the online retailer was quick in dealing with my problem; and

(2) the online retailer showed adequate flexibility in dealing with my problem.

Finally, three items were used to measure interactional justice:

(1) the online retailer was appropriately concerned about my problem;

(2) the online retailer put an appropriate amount of effort into solving my problem;and

(3) the online retailer treated me with the courtesy I deserved.

4.4 Measurement of dependent variablesAs to the measures of dependent variables, three items for measuring customersatisfaction were adapted from Goodwin and Ross (1992), including:

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(1) Overall, I feel that the service of the online retailer is good.

(2) Overall, I am satisfied with the way the online retailer delivers service.

(3) Overall, I am satisfied with the service of the online retailer.

For negative WOM, two items were developed based on Blodgett et al. (1997),including:

(1) I will complain to my friends and relatives about the online retailer.

(2) I will make sure to tell my friends and relatives not to shop at the online retailer.

For repurchase intention, two items were developed based on Blodgett et al. (1997),including:

(1) I will shop at the online retailer again.

(2) I will still shop at the online retailer in the future.

All the measurement items were designed to be evaluated on a seven-point Likert scaleranging from “strongly disagree” to “strongly agree”. The measures for customersatisfaction, negative WOM, and repurchase intention were used for both the scenariosassociated with service failures and recovery and in the scenario that did not include aservice failure.

5. Results5.1 SamplesIn order to decide the sample size of the 2 £ 2 £ 2 MANOVA factorial design, this studyused a priori power analysis to calculate the required sample size given a errorprobability ¼ 0:05, power (1 2 b error probabilityÞ ¼ 0:95, and effect size f 2 ¼ 0:15(i.e. medium effect size suggested by Cohen (1988)). Using G *Power 3.1.2 (Faul et al.,2007, 2009), we obtained a required total sample size of 56 (i.e. cell size ¼ 7). Inaddition, Hair et al. (1998) suggested that sample size in MANOVA must exceedspecific thresholds in each cell (group) of analysis and recommended a minimum cellsize of 20 observations. Thus, data used to test the research hypotheses were gatheredfrom a willing sample of 225 volunteer participants, who were randomly assigned toone of the nine experimental groups/scenarios (eight scenarios involving servicefailures and recoveries and one scenario that lacked a service failure). The respondentswere: mostly female (72.6 percent), between 21 and 25 years of age (50.8 percent),possessed a college degree (or in the process of completing one) (97.5 percent), and hadat least eight years of the internet experience (41.6 percent). Each participant wasrequired to fill out two questionnaires – one for the physical product and one for thevirtual product. As such, a total of 450 responses were obtained, of which 394 werevalid. The number of valid responses for each group ranged between 38 and 46, whichwas larger than the recommended cell size mentioned above.

5.2 Reliability and validity analysisThe reliability of the measures was evaluated using Cronbach’s a. The Cronbach’s avalues for the measures of distributive justice, procedural justice, interactional justice,customer satisfaction, negative WOM, and repurchase intention were 0.703, 0.709,0.853, 0.823, 0.781, and 0.748, respectively. These results indicate that all the measures

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had good reliability. Further, all the measures employed in this study were drawn frompreviously validated instruments and adapted to the current study, thus ensuring thecontent validity of the measures. This study also tested the convergent validity anddiscriminant validity of the measures using the factor analysis approach. The resultsindicate that all the measurement items loaded significantly (i.e. loading . 0:5) on thesingle factor to which they belonged, while no cross-loading items were found; theseresults demonstrate the convergent validity and discriminant validity of the measures.

5.3 Check of experimental manipulationsThe independent variables manipulated in this study were distributive justice,procedural justice, and interactional justice. Samples for each variable were dividedinto two groups, and t-tests were used to determine significant differences between themeans of the two groups. The results show that there were significant differences interms of the means of the high and low distributive justice groups (p ¼ 0:02), the highand low procedural justice groups (p ¼ 0:005), and the high and low interactionaljustice groups (p ¼ 0:005), implying that the manipulations of the independentvariables in this study were valid. The details are presented in Table I.

5.4 The effects of justice perceptions on dependent variablesWhile MANOVA and structural equation modeling (SEM) are considered asalternative approaches to the detection of multivariate mean differences in betweengroups designs, MANOVA remains the most commonly implemented multivariate testof between groups mean differences (Cole et al., 1993). Previous studies also found thatSEM and MANOVA yield similar results (e.g., Green and Thompson, 2006; Cole et al.,1993). However, Kline (1998) suggest that adding a mean structure to the SEM modeland the subsequent programming may be quite complicated, especially if the model is

Independent variables Means compared p-value

Distributive justice 4.9778/4.5717 0.02I think the online retailer’s restitution for the servicefailure is reasonableI think the online retailer’s restitution for the servicefailure satisfies my needsI think that I deserve the online retailer’s restitutionfor the service failureProcedural justice 4.3267/3.5000 0.005I think the online retailer handles customercomplaints in a speedy mannerI think the online retailer has sufficient flexibility incomplaint handling to satisfy my needsInteractional justice 4.5517/3.7303 0.005I think the online retailer has adequately assisted mewith the problemI think the online retailer has made efforts to resolvemy problemI think the online retailer has a friendly attitudetoward complainants

Table I.Manipulation checks

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analyzed across multiple groups. Thus, we chose MANOVA as the data analysisapproach of this study.

This study first tested the assumptions of using MANOVA. The Study I sampledata was tested using the Bartlett’s test, and a significance level of p , 0:05 wasobtained, implying that there was correlation among customer satisfaction, negativeWOM, and repurchase intention. The p-value for Box’s M test was 0.106 (p . 0:05),meaning that data pertaining to customer satisfaction, negative WOM, and repurchaseintention complied with the criterion of homoscedasticity (i.e. the equivalence ofvariance-covariance matrices across the groups). In addition, the normal probabilityplot, which compared the cumulative distribution of actual data values with thecumulative distribution of a normal distribution, was used to test the normality of eachvariable. The results indicated that all variables met the requirement of normality.According to the aforementioned results, MANOVA was deemed to be a suitable toolfor analyzing the sample data.

Main effect. First, the effects of distributive justice, procedural justice, andinteractional justice on customer satisfaction, negative WOM, and repurchase intentionwere tested using MANOVA. As shown in Tables II and III, the p-values for the effectsof distributive justice on customer satisfaction, negative WOM, and repurchaseintention were 0.005, 0.30, and 0.09, respectively, and the high distributive justicegroup showed higher satisfaction and repurchase intention than the low distributive

Mean sq. F-stat. p-value

SatisfactionDistributive 35.079 20.308 0.005 * *

Procedural 8.025 4.646 0.03 *

Interactional 16.971 9.825 0.005 * *

Distributive £ procedural 7.349 4.255 0.04 *

Distributive £ interactional 21.011 12.164 0.005 * *

Procedural £ interactional 1.829 1.059 0.30Distributive £ procedural £ interactional 19.579 11.335 0.005 * *

Negative WOMDistributive 1.451 1.039 0.30Procedural 0.100 0.072 0.78Interactional 5.014 3.591 0.05 *

Distributive £ procedural 12.607 9.030 0.005 * *

Distributive £ interactional 6.999 5.013 0.02 *

Procedural £ interactional 2.667 1.911 0.16Distributive £ procedural £ interactional 26.988 19.331 0.005 * *

Repurchase intentionDistributive 4.600 2.772 0.09 * * *

Procedural 0.336 0.203 0.65Interactional 0.059 0.036 0.85Distributive £ procedural 21.289 12.830 0.005 * *

Distributive £ interactional 6.868 4.139 0.04 *

Procedural £ interactional 0.607 0.366 0.54Distributive £ procedural £ interactional 28.019 16.885 0.005 * *

Note: * p , 0:05; * *p , 0:01; * * *p , 0:1

Table II.MANOVA withsatisfaction, negativeWOM, and repurchaseintention as thedependent variables

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justice group. Therefore, H1-1 and H1-3 are supported, while H1-2 is not supported. Inaddition, the p-values for the effects of procedural justice on customer satisfaction,negative WOM, and repurchase intention were 0.03, 0.78, and 0.65, respectively, andthe high procedural justice group showed higher satisfaction than the low proceduraljustice group. Therefore, H2-1 is supported, while H2-2 and H2-3 are not supported.Finally, the p-values for the effects of interactional justice on customer satisfaction,negative WOM, and repurchase intention were 0.005, 0.05, and 0.85, respectively, suchthat the high interactional justice group showed higher satisfaction and lower negativeWOM than the low interactional justice group. Therefore, H3-1 and H3-2 aresupported, while H3-3 is not supported.

Interaction effect. As shown in Table II, the p-values for the interaction effects ofdistributive justice and procedural justice on customer satisfaction, negative WOM, andrepurchase intention were 0.04, 0.005, and 0.005, respectively. All the p-values are lessthan thesignificance level of0.05, offering support forH4-1,H4-2, andH4-3.Thep-valuesfor the interaction effects of distributive justice and interactional justice on customersatisfaction, negative WOM, and repurchase intention were 0.005, 0.02, and 0.04,respectively. None of the p-values exceed 0.05, supportingH5-1,H5-2, andH5-3. Finally,the p-values for the interaction effects of procedural justice and interactional justice oncustomer satisfaction, negative WOM, and repurchase intention were all greater than0.05, indicating that H6-1, H6-2, and H6-3 are not supported. The two-way interactioneffects of distributive justice £ procedural justice and distributive justice £ interactionaljustice on the three dependent variables are plotted in Figures 1 and 2, respectively.

5.5 Test of service recovery paradoxStudy II was intended to test whether the phenomenon of the service recovery paradoxexists in online retailing. Differences in customer satisfaction, negative WOM, and

Variables Satisfaction Negative WOM Repurchase intention

Distributive (high/low) 4.119/3.486 * * 4.500/4.654 3.941/3.694 * * *

Procedural (high/low) 3.945/3.651 * 4.542/4.608 3.801/3.840Interactional (high/low) 4.022/3.582 * * 4.456/4.691 * 3.804/3.837

Note: * p , 0:05; * *p , 0:01; * * *p , 0:1

Table III.Comparison of main

effect means

Figure 1.The interaction effect ofdistributive justice and

procedural justice on thethree dependent variables

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repurchase intention between the scenarios that included service failures andrecoveries and the scenario that lacked a service failure were tested using t-tests todetermine whether consumers have increased customer satisfaction, decreasednegative WOM, and increased repurchase intention following a service failure that wasproperly remedied as compared to when a service failure did not occur. The resultsshow that there are significant differences between the scenarios with service failuresand recoveries and the scenario that lacked a service failure. Specifically, customers inany of the eight service failure and recovery scenarios had decreased customersatisfaction, increased negative WOM, and decreased repurchase intention ascompared to those in the scenario that did not include a service failure. Thus, H7-1,H7-2, and H7-3 are not supported. According to these results presented in Table IV,the phenomenon of the service recovery paradox does not exist in the context of onlineretailing.

6. DiscussionThis study explores consumer responses to online retailer service recoveries followinga service failure using an experiment design. The results show that distributive justice,procedural justice, and interactional justice have a significant positive influence oncustomer satisfaction in the context of online retailing. However, among the threedimensions of service recovery justice, only distributive justice has a significantpositive influence on repurchase intention, and only interactional justice has asignificant negative influence on negative WOM. In addition, both the interactionbetween distributive justice and procedural justice and the interaction betweendistributive justice and interactional justice are found to significantly influencecustomer satisfaction, negative WOM, and repurchase intention. The results alsoindicate that the service recovery paradox does not appear to exist in the onlineretailing context. The aforementioned findings suggest several important implicationsfor customer relationship management strategies associated with online retailing.

6.1 Strategies for enhancing customer satisfactionSince distributive justice, procedural justice, and interactional justice have a positiveinfluence on post-recovery customer satisfaction in the context of online retailing,online retailers can effectively improve customer satisfaction through these threeaspects of service recovery justice. When delay delivery problems occur within thecontext of online retailing, customers will be more satisfied when they are provided

Figure 2.The interaction effect ofdistributive justice andinteractional justice on thethree dependent variables

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with high monetary compensation. Online retailers must offer a sufficient number ofeffective reimbursement mechanisms to customers when their product delivery isdelayed. Further, customer satisfaction will also be higher if the problem is resolved ina speedy manner. Hence, online retailers need to redeliver the goods to customers assoon as possible to enhance post-recovery customer satisfaction. After the occurrenceof a delay delivery problem, customers will exhibit higher satisfaction with onlineretailers if interactions between customers and retailers are friendly and empathetic.Thus, when a delivery delay occurs, online retailers should apologize and expressconcern regarding the issue, treat customers with dignity and respect, and provideappropriate explanations and recovery measures for the service failure, so as toenhance customers’ post-recovery satisfaction.

Satisfaction Negative WOMRepurchase

intentionScenarios Mean p-value Mean p-value Mean p-value

High distributiveHigh procedural 5.04 0.06 * * 3.85 0.005 * 4.52 0.005 *

High interactional

Low distributiveHigh procedural 3.16 0.005 * 5.19 0.005 * 2.95 0.005 *

High interactional

High distributiveLow procedural 4.12 0.005 * 4.64 0.005 * 3.61 0.005 *

High interactional

Low distributiveLow procedural 3.76 0.005 * 4.12 0.005 * 4.16 0.005 *

High interactional

High distributiveHigh procedural 3.79 0.005 * 4.75 0.005 * 3.79 0.005 *

Low interactional

Low distributiveHigh procedural 3.83 0.005 * 4.42 0.005 * 3.91 0.005 *

Low interactional

High distributiveLow procedural 3.52 0.005 * 4.78 0.005 * 3.84 0.005 *

Low interactional

Low distributiveLow procedural 3.20 0.005 * 4.80 0.005 * 3.82 0.005 *

Low interactional

Note: *p , 0:01; * *p , 0:1; the means of customer satisfaction, negative WOM, and repurchaseintention for the scenario with no service failure are 5.43, 2.27, and 5.30, respectively

Table IV.Test of difference

between the scenarioswith service failure and

recovery and the scenariowithout service failure

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The empirical evidence in the current study suggests that distributive justice andprocedural justice have an interaction effect on customer satisfaction. According to theresults listed in Figure 1, when procedural justice increases, the positive effect ofdistributive justice on customer loyalty also increases. In the case of a delivery delay,high distributive justice plus high procedural justice can result in a higher level ofcustomer satisfaction as compared to high distributive justice plus low proceduraljustice. Therefore, for online retailers, redelivering the goods to customers as soon aspossible and providing high monetary compensation (in the form of a discount) is thebest policy for enhancing post-recovery customer satisfaction.

In addition, distributive justice and interactional justice have an interaction effect oncustomer satisfaction. Based on the results in Figure 2, when interactional justiceincreases, the positive effect of distributive justice on customer loyalty also increases.When a delivery delay occurs in online retailing, high distributive justice plus highinteractional justice will result in higher customer satisfaction as compared to highdistributive justice plus low interactional justice. Therefore, online retailers shouldensure to apologize for the service failure in a respectful manner and provide highmonetary compensation (in the form of a discount for the current purchase) so as toenhance post-recovery customer satisfaction.

6.2 Strategies for reducing negative WOMThe results indicate that among the three dimensions of service recovery justice, onlyinteractional justice has a significant negative effect on negative WOM in the contextof online retailing. That is, online retailers can effectively decrease negative WOM onlythrough employing interactional justice during service recovery. Thus, when adelivery delay occurs, online retailers must treat their customers in a friendly andrespectful manner so as to prevent the spread of negative WOM.

The results also indicate that there is an interaction effect of distributive justice andprocedural justice on negative WOM. According to Figure 1, when procedural justice islow, there is a positive effect of distributive justice on negative WOM; when proceduraljustice is high, there is a negative effect of distributive justice on negative WOM. Thesefindings suggest that when a delay delivery problem occurs in online retailing, onlineretailers should pay attention to the consistency between procedural justice anddistributive justice in their service recovery activities. In order for online retailers toensure lower negative WOM, they should redeliver the ordered product to theirconsumers in a speedy manner (i.e. high procedural justice), and also offer a discountfor the current purchase (i.e. high distributive justice).

Similarly, distributive justice and interactional justice are also observed to have aninteraction effect on negative WOM. The results listed in Figure 2 suggest that wheninteractional justice is low, there is a positive influence of distributive justice onnegative WOM; when interactional justice is high, there is a negative influence ofdistributive justice on negative WOM. These findings suggest that when a delaydelivery problem occurs within the online retailing context, online retailers shouldensure consonance between interactional justice and distributive justice in their servicerecovery measures. For example, in order for online retailers to achieve lower negativeWOM, they should treat their customers with a high level of politeness and empathywhen responding to problem (i.e. high interactional justice), and also them a discount(i.e. high distributive justice).

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6.3 Strategies for enhancing repurchase intentionThe current results indicate that among the three dimensions of service recoveryjustice, only distributive justice has a significant positive influence on repurchaseintention within the online retail context. More specifically, online retailers caneffectively enhance repurchase intentions only by employing distributive justiceduring service recovery. Thus, when a delivery delay occurs, online retailers must offerhigh monetary compensation to their customers in order to improve post-recoveryrepurchase intentions.

The results also show that there is an interaction effect of distributive justice andprocedural justice on repurchase intention. According to Figure 1, when proceduraljustice is low, distributive justice has a negative effect on repurchase intention; whenprocedural justice is high, distributive justice has a positive effect on repurchaseintention. These findings suggest that when a delay delivery problem occurs in onlineretailing, online retailers must consistently employ both procedural justice anddistributive justice in their service recovery measures. As such, these retailers have toredeliver the ordered product to their consumers in a speedy manner (i.e. highprocedural justice), and at the same time offer them a discount (i.e. high distributivejustice) so as to obtain a higher repurchase intention.

Similarly, distributive justice and interactional justice are also observed to have aninteraction effect on repurchase intention. According to Figure 2, when interactionaljustice is low, distributive justice exhibits a negative influence on repurchase intention;when interactional justice is high, distributive justice exhibits a positive influence onrepurchase intention. This finding implies that high interactional justice plus highdistributive justice can result in the highest repurchase intention among all thecompositions of interactional justice and distributive justice. Thus, in order for onlineretailers to obtain a higher repurchase intention, they must treat their customers withhigh level of politeness and empathy when responding to their issues (i.e. highinteractional justice), and also offer high compensation to them (i.e. high distributivejustice).

6.4 Strategies for preventing service failureThe current results indicate that the phenomenon of the service recovery paradox doesnot exist in the context of online retailing in terms of customer satisfaction, negativeWOM, andrepurchase intention.Thisfinding implies thatwhenaservice failure occurs inonline retailing, any type of service recovery measure will still lead to a worse result withrespect to customer satisfaction, negative WOM, and repurchase intention than if thefailure had not occurred. Thus, online retailers should stress prevention as opposed tocures in terms of the management of online retailing service failure and recovery.Specifically, online retailers should improve their storefront systems quality and servicepersonnel training to decrease the probability of an online retailing service failure.

Further, given that online retailing service failures are inevitable, online retailersshould encourage customers who experience service failures to file complaints throughconvenient customer support mechanisms. Customer reactions to an online retailer’sservice failure may include filing complaints with the online retailer, filing nocomplaint but switching to another online retailer, and spreading news of theirdissatisfaction to others. As such, online retailers who do not receive a record ofcustomer complaints will experience greater difficulty in recovering from their service

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failure and in fixing the customer relationship. Specifically, if an online retailer is notinformed of customer complaints, it is unlikely to adopt any service recovery measures;in turn, it will lose the opportunity to: satisfy the customer, prevent negative WOM,and increase repurchase intention. This will certainly result in the loss of customers.Thus, online retailers need to provide multiple convenient channels for customers tofile complaints and should resolve every complaint effectively in order to avoidnegative WOM and retain customers. Careful handling of customer complaints canhelp online retailers to achieve the ultimate goal of customer relationship management– retaining and maintaining existing customers.

7. Limitations and conclusionsWhile this study was conducted with methodological rigor, there are some limitationsthat may be addressed in the future. First, the discussed findings and their implicationswere obtained from the current study that targeted a specific customer group in Taiwan.Thus, caution must be taken when generalizing our findings and discussion to othercustomer groups. Second, the most common service failure type “delivery failure”proposed by Holloway and Beatty (2003) was adopted in this study to design servicefailure and recovery scenarios. However, the results may not be generalizable to othertypes of service failure scenarios. Finally, this study selected MP3 players as therepresentative for physical products and an online game virtual treasure as therepresentative for virtual products when designing the experimental scenarios. It ispossible that different results would be obtained if different products were selected.Therefore, future research is needed togeneralize the findings of this study and extend thediscussion to other customer groups, service failure scenarios, and product categories.

This study contributes to a more thorough understanding of the main andinteraction effects of the various dimensions of service recovery justice on customersatisfaction, negative WOM, and repurchase intention and of the phenomenon of theservice recovery paradox in online retailing. The findings of this study provide severalimportant theoretical and practical implications in terms of online retailing servicefailure and recovery.

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Corresponding authorHsin-Hui Lin can be contacted at: [email protected]

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