International Journal of Information Management The ...

International Journal of Information Management 31 (2011) 350–359

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International Journal of Information Management journal homepage: www.elsevier.com/locate/ijinfomgt

The relationship of service failure severity, service recovery justice and perceived switching costs with customer loyalty in the context of e-tailing Yi-Shun Wang a,∗ , Shun-Cheng Wu b , Hsin-Hui Lin c , Yu-Yin Wang d a

Department of Information Management, National Changhua University of Education, No. 2, Shi-da Road, Changhua 500, Taiwan Department of International Business, Vanung University, No.1, Van-Nung Road, Chung-Li, Tao-Yuan 320, Taiwan Department of Logistics, National Taichung Institute of Technology, 129 Sec. 3, Sanmin Road, Taichung City 404, Taiwan d Department of Information Management, National Sun Yat-sen University, No. 70, Lian-hai Road, Kaohsiung 804, Taiwan b c

a r t i c l e

i n f o

Article history: Available online 14 October 2010 Keywords: E-tailing Service failure severity Service recovery justice Perceived switching costs Customer loyalty

a b s t r a c t Given that e-tailing service failure is inevitable, a better understanding of how service failure and recovery affect customer loyalty represents an important topic for academics and practitioners. This study explores the relationship of service failure severity, service recovery justice (i.e., interactional justice, procedural justice, and distributive justice), and perceived switching costs with customer loyalty; as well, the moderating relationship of service recovery justice and perceived switching costs on the link between service failure severity and customer loyalty in the context of e-tailing are investigated. Data collected from 221 useful respondents are tested against the research model using the partial least squares (PLS) approach. The results indicate that service failure severity, interactional justice, procedural justice and perceived switching costs have a significant relationship with customer loyalty, and that interactional justice can mitigate the negative relationship between service failure severity and customer loyalty. These findings provide several important theoretical and practical implications in terms of e-tailing service failure and recovery. © 2010 Elsevier Ltd. All rights reserved.

1. Introduction The widespread utilization of e-commerce has gradually altered the styles and patterns of retailing. The proliferation of business to consumer (B2C) e-commerce has resulted in more and more people purchasing commodities via electronic shopping platforms rather than from physical shores. The potential profits have attracted numerous firms to this industry; however, competition is high, as there are thousands of these types of firms on the Internet. As such, many B2C website owners have realized the importance of maintaining strong relationships with customers in order to enhance their loyalty. Even so, service failures may occur that affect customer repurchase behaviors in the context of e-tailing; therefore, e-tailing service failure and recovery management have become important topics for academics and practitioners (Holloway & Beatty, 2003). While several studies have investigated the determinants of loyalty in the context of electronic/mobile commerce (e.g., Deng, Lu, Wei, & Zhang, 2010; Doong, Wang, & Foxall, 2010; Liao, Palvia, & Lin, 2006; Lin & Wang, 2006; Udo et al., 2010; Varnali & Toker, 2010; Wang, 2008), most have focused predominantly on the relation-

∗ Corresponding author. E-mail address: [email protected] (Y.-S. Wang). 0268-4012/$ – see front matter © 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijinfomgt.2010.09.001

ships between perceived quality, perceived value, satisfaction and loyalty. Holloway and Beatty (2003) note that while offline service recovery research has recently received increasing attention in the literature, research has yet to examine the role of service recovery management in the context of online retailing, and that we are only beginning to understanding the issues regarding online service failure and recovery. Considering that the occasional service failures during service provision and delivery are inevitable (Webster & Sundaram, 1998) and that these may be harmful to online retailing customer retention (Holloway & Beatty, 2003), there is a need for research to investigate the relationships between service failure, service recovery, and customer loyalty, and to identify the factors that can weaken the potential negative relationship of service failure to customer loyalty within an e-tailing environment. As such, the main purpose of this study is to explore the relationship of service failure severity, service recovery justice (i.e., interactional justice, procedural justice, and distributive justice), and perceived switching costs to customer loyalty, and the moderating relationship of service recovery justice and perceived switching costs on the link between service failure severity and customer loyalty in the context of e-tailing. The findings of this empirical study are useful to researchers in terms of developing and testing theories related to e-tailing service failure and recovery, and to practitioners in terms of under-

Y.-S. Wang et al. / International Journal of Information Management 31 (2011) 350–359

2.2. Service failure severity

Service Recovery Justice - Distributive Justice - Procedural Justice - Interactional Justice H2a.H2b.H2c H3a.H3b.H3c Service Failure Severity

351

Customer Loyalty

H1 H5 H4 Perceived Switching Costs Fig. 1. Research model.

standing the strategies that underlie service failure and recovery management. The remainder of this paper is organized as follows. The next section reviews the literature on customer loyalty, service recovery severity, service recovery justice, and switching costs. The research model and hypotheses are then proposed based on previous literature. This is followed by descriptions of the construct measures and data collection methods used in this study. Next, the results of the data analysis and hypotheses tests are presented. Finally, practical implications and directions for future research are discussed. 2. Theoretical background and hypothesis development This study attempts to develop a better understating of the relationship of service failure severity, service recovery justice, and perceived switching costs to customer loyalty in the context of etailing. Based on the previous literature, this section conceptualizes the constructs and derives the hypotheses for the research model shown in Fig. 1. This model suggests that service failure severity, perceived switching costs, and service recovery justice all have a relationship with customer loyalty, and that perceived switching costs and service recovery justice both moderate the relationship between service failure severity and customer loyalty. 2.1. Customer loyalty The development of the Internet economy has increased the importance of retaining customer loyalty. As such, this study explores the factors that have a relationship with customer loyalty in an e-tailing context where the dependent variable is customer loyalty. Oliver (1999) defined brand loyalty as “a deeply held commitment to re-buy or re-patronize a preferred product/service consistently in the future, thereby causing repetitive same-brand or same brand-set purchasing, despite situational influences and marketing efforts having the potential to cause switching behavior.” Grant and Schlesinger (1995) contended that maintaining good customer loyalty via good and stable customer relationships can directly increase company profits. In the context of electronic/mobile commerce, customer loyalty is usually conceptualized as conative (behavioral intention) loyalty. For example, Srinivasan, Anderson, and Ponnavolu (2002) and Lin and Wang (2006) defined customer loyalty as a customer’s favorable attitude toward the electronic/mobile vendor that results in repeat buying behavior. Thus, customer loyalty in this study also refers to conative loyalty, which is defined as the behavioral intention to repurchase from a specific e-tailer.

Due to the characteristics of intangibility, inseparability, and variability of service, service failures are inevitable (Goodwin & Ross, 1992; Levesque & McDougall, 2000). Bitner, Booms, and Tetreault (1990) stated that service failure occurs when service is not fulfilled, is delayed, or fails to reach the expected standard. Palmer, Beggs, and Keown-McMullan (2000) defined service failure as a situation where customers find the service to be flawed and irresponsible. Lewis and Spyrakopoulos (2001) conceptualized service failure as customers’ dissatisfaction towards the service or service provider, regardless of the causes. Further, Maxham and Netemeyer (2003) defined service failure as any service related misshape, whether real or perceived, that occurs while the customers has contact with the company in question. Prior studies have suggested that service failure severity should be taken into account when discussing service failure and recovery to ensure the integrity of the study results (Hart, Heskett, & Sasser, 1990; Kelley, Hoffman, & Davis, 1993; Webster & Sundaram, 1998). Service failure severity is defined as a customer’s perceived intensity of a service problem: the more intense or severe the service failure, the greater the customer’s perceived loss (Weun, Beatty, & Jones, 2004). Holloway and Beatty (2003) identified several types of service failures in online retailing: (1) delivery problems, (2) website design problems, (3) customer service problems, (4) payment problems, (5) security problems, and (6) miscellaneous/others. It is worth noting that service failure severity is conceptually and operationally different from customer satisfaction, since the former can only be assessed following a service failure, while the latter can be measured either with or without a service failure occurrence. Furthermore, satisfaction is conceptualized as an affective variable (Oliver, 1996), while service failure severity is conceptualized as a cognitive construct. Weun et al. (2004) also found that service failure severity has a negative relationship with satisfaction in terms of the service recovery. Thus, service failure severity and satisfaction are related but distinct constructs. Zeithaml, Berry, and Parasuraman (1993) argued that service failure involves activities that occur as a result of customer perceptions regarding initial service delivery behavior falling below their expectations. Such failures can lead to lost customers (Bitner, Brown, & Meuter, 2000). Some studies also suggest that service failure acts as one significant motivator in customer switching behavior (McCollough, Berry, & Yadav, 2000; Roos, 1999). Further, as customer loyalty towards a firm depends at least in part on perceived service quality during the transaction experience (Cronin, Brady, & Hult, 2000; Wang, 2008), this sense of loyalty is likely to deteriorate subsequent to a service failure. Previous studies (Bejou & Palmer, 1998; Buttle & Burton, 2002; Weun et al., 2004) have warned about the negative link between service failure severity and future customer relationships with that service provider. Bolton (1998) also found that consumer perceptions of losses experienced during transactions will reduce the customer relationship duration. Thus, service failure severity is expected to have a negative relationship with customer loyalty within the e-tailing context, and the following hypothesis is proposed: H1. Service failure severity has a negative relationship with customer loyalty. 2.3. Service recovery justice Given that service failures are inevitable, understanding how to control the frequency of service failure occurrence and provide appropriate service recovery is important for the establishment or maintenance of sustainable customer relationships. Fornell and Wernerfelt (1988) contended that a common approach to deal-

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ing with consumer dissatisfaction can be described as “defensive marketing”, or protection of the existing customer base. Although service providers cannot avoid all service failures, they can learn how to respond to service failures. This response, known as service recovery, is defined as the process by which service providers attempt to correct a service failure (Kelley et al., 1993). Previous researchers have suggested that service recovery concerns any action taken to retain customer loyalty through a timely and appreciated response to a customer complaint (Hart et al., 1990; Johnston & Hewa, 1997; Maxham, 2001). Service recovery methods include any action that assists customers who have experienced service failure to return to a state of satisfaction (Buttle & Burton, 2002). Andreassen (2000) also contended that service recovery includes all the actions taken by the service provider to turn customers’ dissatisfaction into satisfaction and thereby retain them. Justice theory has received a great deal of attention within academia as a theoretical framework for service recovery (Ha & Jang, 2009; Smith, Bolton, & Wagner, 1999; Sparks & McCollKennedy, 1998; Tax, Brown, & Chandrashekaran, 1998). Previous research suggested that customer satisfaction and future behavioral intention (e.g., repurchase intention) are affected by customer-perceived justice in service recovery (Ha & Jang, 2009; Kim, Kim, & Kim, 2009; McColl-Kennedy & Sparks, 2003). Based on the justice framework, service recovery justice can be defined as the customer’s assessment of the fairness of the way in which service failures are handled from three different perspectives: distributive justice, procedural justice, and interactional justice (Blodgett, Hilll, & Tax, 1997; Ha & Jang, 2009; McColl-Kennedy & Sparks, 2003). In the context of service failure and recovery, distributive justice refers to the fairness of resource distribution as well as transaction outcomes (Deutsch, 1975); more specifically, it is what customers receive as an outcome of recovery efforts (Ha & Jang, 2009). Potential options within the e-tailing context include discounts, coupons and replacements. Procedural justice concerns the procedures used to reach the outcomes of an exchange (Lind & Tyler, 1988; Thibaut & Walker, 1975). It refers to the perceived fairness of the procedures and criteria used to arrive at the recovery outcomes (Blodgett et al., 1997). This form of justice may include formal policies and structural considerations related to service recovery such as refund policies, time to get the refund, and responsiveness and flexibility during the recovery process (Chebat & Slusarczyk, 2005; Clemmer, 1993; Ha & Jang, 2009; McColl-Kennedy & Sparks, 2003). Finally, interactional justice refers to the manner in which service failures are handled by service providers, as well as the interactions between service providers and their customers (Blodgett, Granbois, & Walters, 1993; McColl-Kennedy & Sparks, 2003). This may include interpersonal sensitivity, treating people with dignity and respect, and providing appropriate explanations for the service failure in the context of service recovery (Ha & Jang, 2009). Although these three types of justice refer to the different concerns, prior studies suggest that they are not mutually exclusive but correlated (Folger, 1984; Greenberg & McCarty, 1990). Effective service recovery measures can strengthen customer satisfaction with the quality of purchased products or services, and thus increase customer loyalty. Previous research has suggested that recovery efforts to resolve service failures are crucial in order to maintain relationships with existing customers (Ha & Jang, 2009). Blodgett et al. (1997) also contended that perceived justice in service recovery affects customer behavioral intentions. Ha and Jang (2009) found that perceived justice brought about through service recovery efforts has a positive impact on customers’ revisit intention. As such, this study suggests that service recovery justice has a positive relationship with customer loyalty in the context of etailing service failure and recovery. The following hypotheses are proposed:

H2a. Distributive justice has a positive relationship with customer loyalty. H2b. Procedural justice has a positive relationship with customer loyalty. H2c. Interactional justice has a positive relationship with customer loyalty. In addition, although a service failure decreases customer confidence pertaining to the future transaction relationship, immediate and fair recoveries for the failure can mitigate the damage to customer loyalty. Empirical research also found that perceived justice can negatively affect customers’ negative emotions and positively affect customers’ positive emotions or satisfaction in certain service recovery situations (Chebat & Slusarczyk, 2005; Río-Lanza, Vázquez-Casielles, & Díaz-Martín, 2009). This implies that the three dimensions of service recovery justice can weaken the negative relationship between service failure severity and customer loyalty in the context of e-tailing. Based on the above reasoning, the following hypotheses are proposed: H3a. When distributive justice is higher, the negative relationship between service failure severity and customer loyalty is weaker. H3b. When procedural justice is higher, the negative relationship between service failure severity and customer loyalty is weaker. H3c. When interactional justice is higher, the negative relationship between service failure severity and customer loyalty is weaker. 2.4. Perceived switching costs Switching costs refer to the one-time costs incurred when a customer changes from one supplier or marketplace to another (Burnham, Frels, & Mahajan, 2003; Porter, 1980). Switching costs arise from a variety of factors, including the general nature of the product, the characteristics of customers that firms attract, or deliberate strategies and investments by product and service providers (Chen & Hitt, 2002). More specifically, these costs include economic costs (Morgan & Hunt, 1994) and subjective costs in terms of both psychology and emotion (Sharma & Patterson, 2000). Previous studies suggested that switching costs are based on consumer perceptions of the time, money, and effort associated with switching service providers (Chang & Chen, 2008; Dick & Basu, 1994; Jones, Mothersbaugh, & Beatty, 2000; Ping, 1993), which affect customer loyalty by deterring customers from changing service providers (Chang & Chen, 2008; Fornell, 1992). Based on previous studies, perceived switching costs in this study are defined as consumer perceptions of the time, money, and effort associated with changing from one e-tailer to another. Although online markets appear to have low switching costs, since a competing firm is “just a click away”, recent research has pointed out that there is significant evidence of customer loyalty within electronics markets (Chang & Chen, 2008). Reinchheld and Schefter (2000) argued that the ability to create switching costs and build customer loyalty is a major driver of success in e-commerce businesses. Previous studies have also suggested that switching costs are crucial to maintaining customer loyalty (Lam, Shankar, Erramilli, & Murthy, 2004). Colgate and Lang (2001) examined the relationship between switching costs and customer loyalty, and found that when customers feel the costs associated with changing from the original supplier are higher than those associated with creating a relationship with another supplier, they will tend to remain loyal to the original supplier. Other empirical studies also supported the positive relationship between switching costs and customer loyalty (Chang & Chen, 2008; Deng et al., 2010; Liu, 2008).


Based on the aforementioned arguments, this study proposes the following hypothesis: H4. Perceived switching costs have a positive relationship with customer loyalty. Further, switching costs can potentially deter customers from leaving an existing service provider when negative experiences such as service failure or dissatisfaction occur. As suggested by Porter (1980), customers who perceive the switching cost to be high are unlikely to consider changing their supplier even though they are not satisfied with the service. Lam et al. (2004) also noted that customers will stay with a service provider under high switching costs regardless of their satisfaction level; in contrast, dissatisfied customers under low switching costs often switch to other service providers at will. Empirical studies support that switching costs/barriers can decrease the link between customer satisfaction and customer loyalty/retention (Jones et al., 2000; Lee, Lee, & Feick, 2001; Ranaweera & Prabhu, 2003). This implies that perceived switching costs may alleviate the negative relationship between service failure severity and customer loyalty. Based on the above reasoning, this study proposes the following hypothesis: H5. When perceived switching costs are higher, the negative relationship between service failure severity and customer loyalty is weaker. 3. Methods 3.1. Measures of the constructs Selected measurement items must represent the concept about which generalizations are to be made to ensure the content validity of the measurement (Bohmstedt, 1970). Therefore, to ensure content validity, measurement items in this study were mainly adapted from prior studies. Specifically, the scale for service failure severity was adapted from Weun et al. (2004), and the scales for distributive justice, procedural justice, and interactional justice were modified from Río-Lanza et al. (2009). The scale for perceived switching costs was adapted from Jones et al. (2000) and Lam et al. (2004). Finally, the customer loyalty measures were adapted from Parasuraman, Zeithaml, and Malhotra (2005). Likert scales (ranging from 1 to 7), with anchors ranging from “strongly disagree” to “strongly agree” were used for all construct items. The survey items were pre-tested by a small number of e-commerce experts and were modified to fit the e-tailing service failure and recovery context studied. The survey items are listed in Appendix A. 3.2. Data collection Since this study aimed to explore the relationship between service failure severity and customer loyalty in the context of e-tailing, subjects included those who had experience with e-tailing service failure. Data used to test the research model was gathered from an online convenience sample in Taiwan from March 2009 to May 2009. The online survey questionnaire was uploaded to a survey portal (i.e., http://survey.youthwant.com.tw/) in Taiwan that every Internet surfer could connect to. Actually, there are several different survey questionnaires listed on the survey portal, and Internet surfers can click and participate in every survey in which they are interested if they are qualified to the survey. Volunteers who clicked and showed interest in the survey of this study were first asked whether they had ever experienced e-tailing service failures; if they replied in the affirmative, they were asked to participate in the survey. The questionnaire asked the respondents to think back to the last time they had experienced an e-tailer service failure and to answer the remaining questions in terms

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Table 1 Respondent characteristics. Characteristic

Number

Percentage

Gender Female Male

174 47

78.7 21.3

Age 16–20 21–25 26–30 31–35 36–40 >41

33 116 62 7 2 1

14.9 52.5 28.0 3.2 0.9 0.5

Education High school Junior college Bachelor’s degree Master’s degree Doctorate degree

7 8 149 56 1

3.2 3.6 67.4 25.3 0.5

Industry Manufacturing Service Student Government agencies Education and research Other

12 24 138 6 8 33

5.4 10.9 62.5 2.7 3.6 14.9

42 14 74 81 10

19.0 6.3 33.5 36.7 4.5

Service failure experienced Security/privacy problems Customer service problems Delivery problems Payment/product quality problems Website design problems

of that e-tailer’s service failure and service recovery. Specifically, respondents were asked to write down the name of the e-tailer associated with the service failure and the type of service failure that they had experienced. The respondents were then instructed to answer the questions by assessing that e-tailer’s service failure severity and service recovery. For each question, respondents were asked to choose the response that best described their degree of agreement. A total of 221 usable responses were obtained from a variety of respondents with different demographic backgrounds. The characteristics of the respondents are shown in Table 1. 4. Results The empirical data was analyzed using the partial least squares (PLS) approach, because it does not require the data to have a multivariate normal distribution and is less demanding in terms of sample size. SmartPLS software was used during the data analysis stage, which consisted of two steps. In the first step, all measurement models were examined for their psychometric properties, while the second step focused on testing the research model and hypotheses. The PLS provides a convenient approach for simultaneous analysis of the measurement model, the structural model, and interaction relationships. In order to increase the interpretability of interactions between the variables, this study centered the predictor variables according to previous researcher recommendations (e.g. Aiken & West, 1991; Judd & McClelland, 1989). 4.1. Measurement model Assessment of the measurement model involved evaluations of reliability, convergent validity, and discriminant validity of the construct measures. Reliability was examined using Cronbach’s ˛ and composite reliability. As shown in Tables 2 and 3, reliability exceeded 0.8 for each construct. Convergent validity of the construct measures was examined using factor loadings and average

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Table 2 Cronbach’s ˛ and factor loadings. Construct

Item

Mean

S.D.

Factor loading

Cronbach’s ˛

Service failure severity

SFS1 SFS2 SFS3

0.870 0.933 0.856

0.029 0.019 0.036

0.871 0.935 0.856

0.865

Customer loyalty

CL1 CL2 CL3 CL4 CL5

0.895 0.931 0.956 0.934 0.905

0.020 0.013 0.007 0.012 0.017

0.895 0.932 0.956 0.935 0.906

0.958

Distributive justice

DJ1 DJ2 DJ3 DJ4 DJ5

0.902 0.948 0.957 0.953 0.958

0.020 0.011 0.009 0.008 0.006

0.901 0.947 0.957 0.953 0.957

0.969

Procedural justice

PJ1 PJ2 PJ3 PJ4 PJ5

0.922 0.924 0.932 0.946 0.930

0.013 0.016 0.011 0.009 0.012

0.923 0.923 0.931 0.946 0.930

0.961

Interactional justice

IJ1 IJ2 IJ3 IJ4 IJ5 IJ6 IJ7

0.916 0.940 0.950 0.883 0.897 0.925 0.931

0.013 0.008 0.008 0.021 0.018 0.013 0.012

0.914 0.939 0.949 0.882 0.896 0.924 0.929

0.969

Perceived switching cost

PSC1 PSC2 PSC3 PSC4

0.927 0.925 0.928 0.710

0.018 0.024 0.017 0.061

0.929 0.925 0.928 0.716

0.898

variance extracted (AVE). Following Hair, Anderson, Tatham, and Black’s (1992) recommendation, factor loadings greater than 0.50 were considered to be significant. All of the factor loadings of the items in the research model were greater than 0.70 (see Table 2). As shown in Table 3, the AVE for each construct exceeded the recommended level of 0.50, which means that more than one-half of the variances observed in the items were accounted for by their hypothesized constructs. To examine discriminant validity, this study compared the shared variances between factors with the AVE of the individual factors (Fornell & Larcker, 1981). This analysis indicated that the shared variances between factors were lower than the AVE of the individual factors, confirming discriminant validity (see Table 3). Thus, the measurement model demonstrated adequate reliability, convergent validity, and discriminant validity. 4.2. Structural model This study proceeded to test the path significances using a bootstrapping resampling technique. Statistical results of the structural model, including path coefficients, t-values, p-values, and R2 are shown in Table 4. As expected, service failure severity had a significant negative relationship with customer loyalty (ˇ = −0.194). Thus, H1 was supported. Procedural justice was found to have a

significant positive relationship with customer loyalty (ˇ = 0.173); as such, H2b was supported. Likewise, interactional justice had a significant positive association with customer loyalty (ˇ = 0.219), meaning that H2c was supported. However, the relationship of distributive justice with customer loyalty was not significant (ˇ = 0.056), so H2a was not supported. Yet perceived switching costs did exhibit a significant positive relationship with customer loyalty (ˇ = 0.114), supporting H4. As to the moderating relationships, interactional justice was observed to moderate the relationship between service failure severity and customer loyalty, with higher interactional justice leading to a lower negative relationship between service failure severity and customer loyalty (ˇ = 0.207). Therefore, H3c was supported. Fig. 2 shows how interactional justice moderated the relationship between service failure severity and customer loyalty. However, distributive justice was unexpectedly found not to moderate the relationship between service failure severity and customer loyalty (ˇ = −0.052). Thus, H3a was not supported. Similarly, procedural justice had an insignificant moderating relationship on the link between service failure severity and customer loyalty (ˇ = −0.004). Therefore, H3b was also not supported. Finally, perceived switching costs did not moderate the relationship between service failure severity and customer loyalty (ˇ = −0.038). Thus,

Table 3 Composite reliability, average variance extracted and discriminant validity. Construct

CR

SFS

CL

DJ

PJ

IJ

PSC

SFS CL DJ PJ IJ PSC

0.918 0.967 0.976 0.970 0.974 0.931

0.789 0.160 0.230 0.206 0.182 0.024

0.856 0.298 0.331 0.320 0.114

0.889 0.841 0.701 0.132

0.865 0.803 0.171

0.845 0.157

0.773

Notes: 1. CR: composite reliability. 2. SFS: service failure severity; CL: customer loyalty; DJ: distributive justice; PJ: procedural justice; IJ: interactional justice; PSC: perceived switching costs. 3. Diagonal elements are the average variance extracted; off-diagonal elements are the shared variance.


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Table 4 Statistical results of the structural model. Dependent variable Customer loyalty

+ *

Independent variable

Path coefficient

(H1) Service Failure Severity (H2a) Distributive Justice (H2b) Procedural Justice (H2c) Interactional Justice (H4) Perceived Switching Costs (H3a) Service Failure Severity × Distributive Justice (H3b) Service Failure Severity × Procedural Justice (H3c) Service Failure Severity × Interactional Justice (H5) Service Failure Severity × Perceived Switching Costs

−0.194 0.056 0.173 0.219 0.114 −0.052 −0.004 0.207 −0.038

t value *

3.332 0.607 1.311+ 1.812* 1.744* 0.498 0.033 1.987* 0.653

R2 0.404

p < 0.1. p < 0.05.

H5 was not supported. Altogether, about 40.4% of the variance in customer loyalty was accounted for by the research model, with interactional justice having the strongest relationship with customer loyalty among the explanatory variables. 5. Discussion Considering that e-tailing service failures are inevitable and likely to affect customer retention, this study investigates the relationship of service failure severity, service recovery justice (i.e., interactional justice, procedural justice, and distributive justice), and perceived switching costs with customer loyalty in the context of e-tailing. The findings of this study provide several important implications in terms of e-tailing service failure and recovery. The results indicate that service failure severity has a significant negative relationship with customer loyalty in the context of etailing. As such, customers who experience a high severity service failure are more likely to exhibit low customer loyalty as compared to those who experience a service failure that they perceive to be low severity. This finding is similar to that of Weun et al. (2004), who found that the perceived severity of a service failure has a negative impact on customer commitment in the traditional service industries. While previous research has focused mainly on the impact of perceived value, perceived quality, and satisfaction on loyalty (e.g., Cronin et al., 2000; Lin & Wang, 2006; Parasuraman & Grewal, 2000; Patterson & Spreng, 1997; Wang, 2008; Zeithaml, 1988), the current study confirms that service failure severity plays a critical role in understanding customer loyalty behaviors.

Fig. 2. The moderating relationship of interactional justice on the link between service failure severity and customer loyalty.

As expected, interactional justice in service recovery was not only found to have a positive relationship with customer loyalty, but also to moderate the relationship between service failure severity and customer loyalty, with higher interactional justice leading to a lower negative relationship between service failure severity and customer loyalty. This result implies that in the context of e-tailing service failure, customers who perceive high interactional justice in service recovery are more likely to exhibit high post-failure customer loyalty and to mitigate their feelings concerning the negative relationship between service failure severity and customer loyalty as compared to those who perceive low interactional justice. The finding concerning the relationship between interactional justice and loyalty is consistent with that of Chebat and Slusarczyk (2005), who also found that interactional justice has a positive relationship with exit-loyalty behavior. However, the significant moderating relationship of interactional justice on the association between e-tailing service failure severity and customer loyalty represents a new finding of this study––interactional justice has a significant positive relationship with customer loyalty and also weakens the negative relationship between service failure severity and customer loyalty. Specifically, methods used to remedy and recover from service failures, in conjunction with customer perceptions regarding interactional justice during the service recovery process have a strong relationship with customer loyalty, even to the point of offsetting the negative relationship between service failure severity and customer loyalty. As shown in Fig. 2, when interactional justice is higher, the negative relationship between service failure severity and customer loyalty is weaker. Nonetheless, this result should not imply the existence of the “service recovery paradox” phenomenon within the e-tailing context, which contends that a highly effective service recovery following a service failure provides a retailer with the chance to achieve even higher satisfaction and loyalty ratings from customers than if the failure had never happened (Magnini, Ford, Markowski, & Honeycutt, 2007; Matos, Henrique, & Rossi, 2007; McCollough & Bharadwaj, 1992). Interestingly, procedural justice in service recovery was found to have a positive relationship with customer loyalty, but not to moderate the relationship between service failure severity and customer loyalty. This finding suggests that in the context of e-tailing service failure, customers who perceive high procedural justice in service recovery will exhibit higher post-failure customer loyalty, but will not alleviate their perceptions with respect to the negative relationship of service failure severity with customer loyalty, as compared to those who perceive low procedural justice. This finding concerning the relationship between procedural justice and loyalty is similar to that of Río-Lanza et al. (2009), who found that procedural justice has a positive relationship with service recovery satisfaction, which may, in turn, positively affect customer loyalty. The insignificant moderating relationship of procedural justice on the link between service failure severity and customer loyalty

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implies that the fair arrangement of process or policy in providing e-tailing service recovery (e.g., speedy response) will not weaken the negative relationship between service failure severity and customer loyalty. This may be because online customers deem that procedural justice and speedy responses are necessary elements for a successful e-tailing service recovery, thereby leading to this insignificant result concerning procedural justice. Importantly, distributive justice in service recovery was observed to have an insignificant relationship with customer loyalty and an insignificant moderating relationship on the association between service failure severity and customer loyalty. This finding demonstrates that online customers who perceive high distributive justice in service recovery do not exhibit higher post-failure customer loyalty, nor do they weaken their perceptions regarding the negative relationship between service failure severity and customer loyalty as compared to those who perceive low distributive justice. This finding is inconsistent with Chebat and Slusarczyk (2005), who found that distributive justice has an indirect positive impact on exit-loyalty behavior. The insignificant results concerning distributive justice imply that the fair allocation of costs and benefits in providing e-tailing service recovery has no relationship with customer loyalty and does not weaken the negative relationship between service failure severity and customer loyalty. This result may be due to the fact that most e-tailers respond to service failures by providing customers with basic distributive recovery, such as free-of-charge commodity replacements, as opposed to advanced distributive recovery, such as monetary compensation, coupons for future consumption, clear explanations, and sincere apologies. In turn, this causes indifference in terms of the perceived distributive justice associated with e-tailer service recovery, and leads to the insignificant results concerning distributive justice. Switching costs are commonly regarded as an important determinant of customer loyalty (Jones et al., 2000; Lee & Cunningham, 2001). Perceived switching costs had a significant positive relationship with customer loyalty in the context of e-tailing within the present study. This finding is consistent with previous empirical research, which found that switching costs serve as an essential element in maintaining customer loyalty (Chang & Chen, 2008; Deng et al., 2010; Lam et al., 2004; Liu, 2008). However, perceived switching costs were unexpectedly found not to moderate the relationship between service failure severity and customer loyalty. This finding suggests that while perceived switching costs have a positive relationship with customer loyalty, they do not mitigate the negative relationship between service failure severity and customer loyalty. This phenomenon may be due to the fact that e-tailer failures often result in decreased levels of customer trust and commitment for that e-tailer, meaning that perceived switching costs or cognitive lock-in are most likely unable to weaken the negative relationship between service failure severity and customer loyalty.

6. Implications The results of this study provide several critical implications for the practice of e-tailing service failure and recovery. The significant negative relationship between service failure severity and customer loyalty suggests that in order to retain customers, etailers should do their best to avoid service failures throughout the entire customer service process, including customer entrance, product information search, ordering, payment, product delivery, and any applicable after-sales service. In addition, the results of this study show that interactional justice has a positive relationship with customer loyalty and also weakens the negative relationship between service failure severity and customer loyalty. This means that in e-tailing service failure situations, e-tailers can enhance customer loyalty and

alleviate the negative relationship of service failure severity to customer loyalty by providing dissatisfied customers with appropriate interactional justice-based service recovery––communicating with customers and treating them fairly. In the context of service recovery, interactional justice is measured by the degree to which customers evaluate employees’ politeness, sympathy, and willingness to recover from the failure (Homburg & Fürst, 2005; Karatepe, 2006). Clemmer and Schneider (1996) also suggested that the level of politeness and sincerity shown by employees is positively correlated with customer satisfaction in terms of the service failure recovery procedure. Thus, e-tailers should realize that good-mannered employees and storefront systems can directly contribute to the success of service failure recovery. Training on standard operating procedures and occasional case studies that deal with service failures should be a required for every employee. While procedural justice was found not to moderate the link between service failure severity and customer loyalty, it was found to have a salient positive relationship with customer loyalty. Procedural justice is concerned with the fair arrangement of process or policies when providing remedies and recovery. A variety of factors related to procedural justice have been taken into account in marketing literatures. According to Tax et al. (1998), the concept of procedural justice can be simply categorized into five segments, including process control (Goodwin & Ross, 1992), decision control (Heide & John, 1992), accessibility (Bowen & Lawler, 1995), timing/speed (Taylor, 1994), and flexibility (Narver & Slater, 1990). Smith et al. (1999) evaluated procedural justice based on the response speed (the time span of the entire recovery procedure). Sparks and McColl-Kennedy (2001) found that the customers’ rights of speech and the neutrality of service providers would affect the performance of procedural justice in the context of service recovery. E-tailers should take into account the above-mentioned aspects of procedural justice in conducting their service recovery. The insignificant results concerning distributive justice imply that among the three perceived service recovery justices, distributive justice is least important for e-tailing customers. However, this does not mean that distributive justice is unimportant for e-tailing service recovery. As noted earlier, the insignificant results may have resulted from e-tailer indifference towards distributive justice during the service recovery process. As such, this may provide a good opportunity for e-tailers to establish a service differentiation competitive advantage. When facing service failures, customers might still choose to assess the fairness of the way in which they are dealt with from the perspective of distributive justice. Therefore, in order for e-tailers to improve the quality of their service recovery and establish a service differentiation advantage, they still need to conduct service recovery from the viewpoint of distributive justice. Since distributive justice is concerned with the fair allocation of costs and benefits among individuals, e-tailers should note that some customers may desire both a substantial remedy (e.g. monetary compensation, free-of-charge commodity replacement, or coupons for future consumption) as well as psychological recovery (e.g. clear explanation and a sincere apology) following a service failure. If customers perceive high distributive justice during service recovery, they may in turn become more satisfied and choose to repurchase in the future. Perceived switching costs were found to have a significant positive relationship with customer loyalty. As mentioned earlier, perceived switching costs can be regarded as consumers’ cost evaluation of replacing their current service provider. Thus, e-tailers should allocate their resources to create a high customerperceived switching cost, which can serve as a barrier for switching to alternative e-tailers. For example, an e-tailer may choose to take advantage of data mining approaches to analyze customer preferences and recommend products/services to them. Once customers become used to this product recommendation function,


they may be inclined to repurchase from the e-tailer simply due to the fact that switching to an alternative e-tailer will require that they “reteach” the new e-tailer about their preferences––a high perceived switching cost. On the other hand, this study found that perceived switching costs do not moderate the relationship between service failure severity and customer loyalty. This means that e-tailers cannot merely depend on the establishment of switching costs to weaken the negative relationship between service failure and customer loyalty. Consequently, minimizing service failure occurrences and providing justice-based service recovery seems to be the best strategy for e-tailers to retain their customers, based on the findings of this study.

7. Limitations and conclusions While this study was conducted with methodological rigor, there are some limitations to address in the future. First, the discussed findings and their implications are based on a specific customer group in Taiwan. Future research is needed to generalize the findings of this study and extend the discussion to other national or cultural groups. Second, this study does not incorporate all potential determinants of customer loyalty into the model. Hence, there may be a need to search for additional factors that can improve predictions regarding customer loyalty in the e-tailing service recovery environment. Other possible determinants of customer loyalty may include customer satisfaction, perceived value, and trust (Lin & Wang, 2006; Yang & Peterson, 2004). Future research can examine how these factors interact with service failure and recovery variables to affect customer loyalty. Finally, this study employs a snapshot research approach. Additional research efforts are needed to evaluate the validity of the investigated model and our findings. Longitudinal evidence might enhance the current understanding of the relationships among service failure severity, service recovery justice, perceived switching costs, and customer loyalty. This study contributes to a more thorough understanding of the relationships between service failure severity, service recovery justice, perceived switching costs, and customer loyalty in the context of e-tailing. The contributions of this study to research on e-tailing service failure and recovery are fivefold. First, in comparison to previous research on e-tailing loyalty that focuses mainly on the relationships of perceived value, perceived quality, and satisfaction with loyalty, the current study primarily explores the relationships of service failure severity, service recovery justice, and perceived switching costs with customer loyalty. As such, this study represents a new direction for e-tailing loyalty research. Second, this study supports that service failure severity has a negative relationship with customer loyalty, which represents a new finding in the context of e-tailing. Third, this study provides empirical support to show that interactional justice not only has a positive relationship with customer loyalty, but also weakens the negative relationship between service failure severity and customer loyalty. This is also a new finding, since the main and moderating relationships between service failure severity, interactional justice, and customer loyalty have rarely been explored in previous research on e-tailing loyalty. Fourth, both procedural justice and perceived switching costs are shown to have a significant positive relationship with customer loyalty, but to have an insignificant moderating relationship on the link between service failure severity and customer loyalty. Finally, differing from previous research findings in the context of brick-and-mortar retailing, distributive justice is found to have an insignificant relationship with customer loyalty as well as an insignificant moderating relationship on the association between service failure severity and

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customer loyalty. This result also confirms that the customer loyalty model in the context of brick-and-mortar retailing is somehow different from that in the context of e-tailing. Therefore, additional empirical studies are needed to address the remaining issues pertaining to e-tailing service failure and recovery in the future. Appendix A. Measuring items used in this study Service failure severity SFS1: The above-mentioned service failure caused by the e-tailer that happened to me was severe. SFS2: The above-mentioned service failure caused by the e-tailer that happened to me made me feel angry. SFS3: The above-mentioned service failure caused by the e-tailer that happened to me was unpleasant. Distributive justice DJ1: Considering the trouble caused and the time lost, the compensation I received from the e-tailer was acceptable. DJ2: The e-tailer took good compensation measures to solve the problem. DJ3: The e-tailer’s efforts were sufficient to offer a satisfactory compensation. DJ4: I think the e-tailer was quite fair when compensating me for the problem that occurred. DJ5: In general, the e-tailer was able to compensate me adequately to solve the problems it had in the delivery of the product/service. Procedural justice PJ1: I think my problem was resolved in the right way. PJ2: I think the e-tailer has good policies and practices for dealing with problems. PJ3: Despite the trouble caused by the problem, the e-tailer was able to respond adequately. PJ4: The e-tailer proved flexible in solving the problem. PJ5: The e-tailer tried to solve the problem as quickly as possible. Interactional justice IJ1: The e-tailer showed interest in my problem. IJ2: The e-tailer did everything possible to solve my problem. IJ3: The e-tailer was honest when dealing with my problem. IJ4: The e-tailer proved able and sufficiently competent to solve the problem. IJ5: The e-tailer dealt with me courteously when solving the problem. IJ6: The e-tailer showed interest in being fair when solving the problem. IJ7: The treatment and communication with the e-tailer to solve the problem were acceptable. Perceived switching costs PSC1: It would cost me a lot of money to switch from my current e-tailer to another one. PSC2: It would take me a lot of effort to switch from my current e-tailer to another one. PSC3: It would take me a lot of time to switch from my current e-tailer to another one. PSC4: I would feel uncertain if I had to choose a new e-tailer.

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