APPLYING RFM MODEL TO EVALUATE THE E-LOYALTY: THE MODERATE ROLE OF SWITCHING COST

APPLYING RFM MODEL TO EVALUATE THE E-LOYALTY: THE MODERATE ROLE OF SWITCHING COST Yi-Wen Liao Chai Nan University of Pharmacy and Science pinkwen923@mail2000.com.tw Abstract Nowadays companies increasingly derive revenue from the creation and sustenance of long-term relationships with their customers. In such an environment, marketing serves the purpose of maximizing customer lifetime value (CLV) and customer equity, which is the sum of the lifetime values of the company s customers. That is, increasing customer purchasing behavior is an important issue in the e-tailing context. In this paper, we used recency, frequency and monetary (RFM) measures approach to determine customers purchase behavior. This study explores the relationship of customer satisfaction, customer loyalty, and perceived switching costs with customer purchase behavior; as well, the moderating relationship of switching costs on the link between customer loyalty and purchase behavior in the context of e-tailing are investigated. Data collected from 266 useful respondents are tested against the research model using the partial least squares (PLS) approach. The results indicate that customer satisfaction has a significant relationship with customer loyalty, and that switching costs can mitigate the negative relationship between customer loyalty and purchase behavior. These findings provide several important theoretical and practical implications in terms of e-tailing service. Keywords: E-tailing, RFM model, Switching costs, Customer loyalty, Customer satisfaction 1. INTRODUCTION The phenomenal growth in numbers of Internet users and the enormous potential of electronic commerce (e-commerce) have pushed merchants to conduct business online (Wang and Emurian, 2005a). 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 one of the intangible assets hard to transfer and imitate, the customer relationship is playing an increasingly important role in the success of a company in the competitive market. As such, many B2C website owners have realized the importance of maintaining strong relationships with customers in order to enhance their loyalty. Acquiring and retaining customers, especially the attractive customers, hence becomes an essential task of customer relationship management (CRM). For such a task, one need understand the value of customers, identifying which relationships will create the most value for him and which will be the least. There is no standard definition of CLV so far. However, a popular viewpoint takes CLV as the net present value of all future contributions to profit and overhead expected from an individual customer (Jackson, 1994; Roberts and Berger, 1989; Courtheoux, 1995; Pearson, 1996). Generally, methods for measuring CLV include regency, frequency and monetary value (RFM). A number of authors (Hwang et al., 2004; Verhwf and B 212

Donkers, 2001; Colombo and Jiang, 1999; Shih and Lu, 2003; Jackson, 1985) suggest that the RFM method would avoid focusing on less profitable customers and allow resources to be diverted to more profitable ones. Some researchers used more traditional variables such as recency, frequency and monetary (RFM) to derive CLV ranking to assist market practitioners in performing more effective market segmentation or market programs [SI, (Saarenvirta, 1998). In fact, RFM has been used in direct marketing to predict customer behavior for more than 50 years. It is one of the most powerful techniques available to database marketing. It does not require any additional data. From the behavioral perspective, the RFM method is an important one for assessing the relationship between enterprise and customers. Furthermore, in determining the development of loyalty, satisfaction has traditionally been identified as the main inputs for customer loyalty (Dick and Basu, 1994; Fornell, 1992), and likewise, e-satisfaction is believed to drive e-loyalty (Anderson and Srinivasan, 2003; Balabanis, Reynolds, and Simintiras, 2006). However, customer satisfaction is a necessary, but insufficient precursor of customer loyalty (Oliver, 1999). Purchases may not always repeat despite a fair level of satisfaction and loyalty. Therefore, a core proposition is that the effects of satisfaction and loyalty on repeat purchase intention depend on the magnitude of switching costs in the online shopping context. Satisfied customers may not be loyal customers because of low switching costs (Jones, Mothersbaugh, and Beatty, 2000), whereas dissatisfied customers may remain loyal because of high switching costs. For example, moving to a new service provider requires an investment of effort, time and money, which acts as significant barrier to consumers taking action when dissatisfied with their current service provider (Colgate and Lang, 2001). Hence, most previous studies (Balabanis et al., 2006; Lee, Lee, and Feick, 2001) have treated switching costs as a moderator of the relationship between satisfaction and loyalty. More specifically, the contribution to the development of satisfaction, e-loyalty, and customer lifetime value has not been examined for customers with different levels of switching costs. In an attempt to fill these gaps, this study proposed a conceptual framework to synthesize the existing literature on creating e-loyalty. The aims of this study include (1) investigating whether perceptions of customer satisfaction and customer loyalty significantly impact customer lifetime value (RFM model) in e-commerce; (2) examining whether switching costs moderates the relationships among customer satisfaction and e-loyalty and customer lifetime value (RFM model). The remainder of this paper is organized as follows. The next section reviews the literature on customer satisfaction, customer loyalty, customer lifetime value, switching costs, involvement, and product type. 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. THEORECTIAL BACKGROUND AND HYPOTHESIS DEVELOPMENT This study attempts to develop a better understating of the relationship of customer satisfaction, switching cost and customer loyalty to customer lifetime value in the context of Internet. In addition, we will compare the moderator effects of different switching costs. 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 customer satisfaction justice all have a relationship with customer B 213

loyalty and customer lifetime value (RFM model), and that switching costs justice both moderate the relationship between customer loyalty and customer lifetime value (RFM model). 2.1. Customer satisfaction Customer satisfaction is a growing concern for marketing scholars, is an important factor affecting purchase intentions, and requires further empirical research (Homburg & Rudolph, 2001). Satisfaction is an affective response to purchase situations (Babin & Griffin, 1998; Bagozzi, Gopinath, & Nyer, 1999; Chang & Chen, 2008). In the evaluation model, customers assess the overall performance of products or services. Oliver (1997) defines satisfaction as the summary psychological state resulting when the emotions surrounding disconfirmed expectations are coupled with the prior feeling of consumers about the consumer experience. In general, satisfied customers develop re-purchase (visit) intentions. However, unsatisfied customers seek out other products or services (Bearden & Teel, 1983; Cronin & Taylor, 1992; Oliver, 1980; Spreng, Harrel, & Mackoy, 1995; Park & Lee, 2011). For example, customers who receive auto repairs at an auto service outlet and are satisfied usually revisit that outlet rather than seeking repairs elsewhere (Bearden & Teel, 1983). According to Besterfield (1994); Barsky (1995) and Kanji and Moura (2002), customer satisfaction is a complex construct as it has been approached differently. In Levesque and McDougall (1996), satisfaction is conceptualized as an overall customer attitude towards a service provider (Boohene and Agyapong, 2011). In comparison, cumulative customer satisfaction is an overall evaluation based on the overall experience with the goods and services of a particular firm over time (Oliver, 1980). Anderson and Srinivasan (2003) investigate the impact of customer satisfaction on customer loyalty in the context of e-commerce, and defined customer satisfaction as the contentment of customer regarding their prior purchasing experience with a given e-commerce firm. This study treats customer satisfaction as cumulative and follows Anderson and Srinivasan (2003) in defining customer satisfaction as the level of customer contentment regarding prior purchasing experience with a specific website (Chang & Chen, 2008). Customer satisfaction evidently has a direct influence on a customer s behavioral intentions or loyalty (Fornell, 1992; Chang & Chen, 2008). H 2 Recency H 5 Customer Satisfaction H 1 Customer Loyalty H 6 H 3 Frequency H 8 H 9 H 10 H 7 H 4 Monetary Switching Cost Figure 1. Research Model B 214

2.2. Customer loyalty The development of the Internet economy has increased the importance of retaining customer loyalty. As mentioned before, customer loyalty is an important goal in the consumer marketing community as it is a key component for a company s long-term viability (Krishnamurthi and Raj, 1991). 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. Customer loyalty can be defined and assessed by both attitudinal and behavioral measures. The attitudinal measure of customer loyalty refers to a specific desire to continue a relationship with a service provider while the behavioral perspective refers to the concept of repeat patronage (Chen andtsai, 2008). Oliver (1999) defines customer loyalty as a deeply held commitment to re-buy or re-patronize a preferred product or service consistently in the future, despite situational influences and marketing efforts having the potential to cause switching behavior (Chang & Chen, 2008). 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 cognitive (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. The concept of e-loyalty extends the traditional concept of loyalty to online consumer behavior. Anderson and Srinivasan (2003) investigate customer loyalty in the context of e-commerce and define customer loyalty as a customer s favorable attitude toward the e-retailer that leads to repeat buying behavior. Although the underlying theoretical foundations of traditional loyalty and the newly defined phenomena of e-loyalty are generally similar, they have unique aspects related to Internet based marketing and buyer behavior (Chang & Chen, 2008). As a proxy definition of e-loyalty, this study defines e-loyalty as a favorable customer attitude toward the e-store that predisposes the customer to repeat buying behavior. E-loyalty is thus considered a cognitive and action construct in the conceptual framework, which is defined as the behavioral intention to repurchase from a specific e-tailer. 2.3. RFM evaluation To identify customer behavior, the well known method called recency, frequency and monetary (RFM) model is used to represent customer behavior characteristics (Chan, 2005; Hsieh, 2004). The first dimension is recency, which indicates the length of time since the start of a transaction. Meanwhile, the second dimension is Frequency, which indicates how frequently a customer purchases products during a particular period. Finally, monetary value measures the amount of money that customer spending during a period (Jonker et al., 2004; Chan, 2008). The basic assumption of using the RFM model is that future patterns of consumer trading resemble past and current patterns. The calculated RFM values are summarized to clarify customer behavior patterns. This study proposes using the following RFM variables (Chan, 2005; Chan, 2008): Recency (R): the latest purchase amount. Frequency (F): the total number of purchases during a specific period. Monetary (M): monetary value spent during one specific period. B 215

2.4. Perceived switching costs The concept of switching costs is theoretically backed by both social psychological exchange theory (Blau, 1964) and institutional economics (Williamson, 1975). Both approaches focus mainly on investments made by the parties involved in an exchange relationship (Chang and Chen, 2008). Switching costs refer to the one-time costs incurred when a customer changes from one supplier or marketplace to another (Burnham, Frels, and Mahajan, 2003; Porter, 1980). Other studies have suggested that switching costs result from consumer perceptions of the time, money, and effort associated with switching service providers (Dick and Basu, 1994; Jones et al., 2000; Ping, 1993) which influence customer retention by deterring customers from changing service providers (Fornell, 1992). 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 and Hitt, 2002). More specifically, these costs include economic costs (Morgan and Hunt, 1994) and subjective costs in terms of both psychology and emotion (Sharma and 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 and Chen, 2008; Dick and Basu, 1994; Jones, Mothersbaugh, and Beatty, 2000; Ping, 1993), which affect customer loyalty by deterring customers from changing service providers (Chang and 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 and Chen, 2008). Reichheld 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, and 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 and Chen, 2008; Deng et al., 2010; Liu, 2008). 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, and Feick, 2001; Ranaweera and Prabhu, 2003; Chang and Chen, 2008). B 216

2.5. The relationship among switching costs, customer satisfaction and Customer Loyalty Whereas some researchers established a link between satisfaction and loyalty, others did not. For instance, Fornell (1992) was of the view that high customer satisfaction will result in increased loyalty for the firm and that customers will be less prone to overtures from competition. Similarly, Jones and Sasser (1995) found that an increase in customer satisfaction produces a stronger effect on loyalty among customers who are at the high end of the satisfaction scale. Additionally, the relationship, between satisfaction and loyalty is neither simple nor linear and satisfied customers may defect (Jones and Sasser, 1995). As a result, there are no simple solutions for turning loyalty into profits. If it were easy, however, everyone would already be doing it (Keiningham et al., 2007; Vázquez-Casielles, 2009). Despite the lack of consensus, however, they agreed there exist some relationship between customer satisfaction and customer loyalty. Three hypothesis can be inferred from the above discussion. H1: High level of customer satisfaction will result in high level of customer loyalty. H2: High level of customer loyalty will result in high level of Recency. H3: High level of customer loyalty will result in high level of Frequency. H4: High level of customer loyalty will result in high level of Monetary. Oliver (1999) found that satisfaction leads to loyalty, but that loyalty can only be achieved when other factors are present. In e-commerce, it appears difficult to build customer loyalty because of the low switching costs, since competing firms are just a click away (Chang and Chen, 2008). Consequently, it would be commercially advantageous to incorporate high switching costs into online markets. Fornell (1992) was one of the first authors to consider the impact of switching costs on the relationship between customer loyalty and customer purchase behavior. Hauser et al. (1994) also note that consumer sensitivity to satisfaction level reduces with increasing switching costs. Switching costs are important moderators of the relationship between customer loyalty and customer purchase behavior (Lee et al., 2001; Wangenheim, 2003). Similarly, Jones et al. (2000) and Caruana (2004) both find evidence of moderates the relationship between customer loyalty and recency. Consistent with these studies, hypothesizes that: H5: High level of switching cost will result in high level of recency. H6: High level of switching cost will result in high level of frequency. H7: High level of switching cost will result in high level of monetary. H8: Switching costs moderates the relationship between customer loyalty and recency. H9: Switching costs moderates the relationship between customer loyalty and frequency. H10: Switching costs moderates the relationship between customer loyalty and monetary. 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. The scale for switching costs was adapted from Jones et al. B 217

(2000), Lam et al. (2004), and Chang and Chen (2008). The measurement of RFM model was adapted from Chan (2008). Finally, the customer satisfaction and loyalty measures were adapted from Parasuraman et al. (2005) and Chang and Chen (2008). 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 context studied. 3.2. Data collection Since this study aimed to explore the relationship between customer satisfaction, loyalty and purchase behavior in the context of e-tailing, subjects included those who had experience with e-tailing service. Data used to test the research model was gathered from an online convenience sample in Taiwan from June 2011 to September 2011. 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; 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 and to answer the remaining questions in terms of that e-tailer s purchase experience. Specifically, respondents were asked to write down the name of the e-tailer associated with the experience they had experienced. The respondents were then instructed to answer the questions by assessing that purchase experience. For each question, respondents were asked to choose the response that best described their degree of agreement. A total of 266 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 andwest, 1991; Judd and 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 variance extracted (AVE). Following Hair, Anderson, Tatham, and Black s (1992) recommendation, factor B 218

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). Characteristic Gender Table 1. Respondent characteristics Number Percentage Characteristic Number Percentage Income Male 114 42.86% < 20,000 154 57.89% Female 152 57.14% 20,000 ~ 40,000 Age 40,000 ~ < 20 127 47.74% 60,000 60,000 ~ 80,000 86 32.33% 17 6.39% 6 2.26% 20-29 100 37.59% > 80,000 3 1.13% 30-39 31 11.65% Internet Experience 40-49 7 2.63% few 35 13.16% > 50 1 0.38% ordinary 47 17.67% Education frequently 53 19.92% High school 31 11.65% often 131 49.25% junior college 12 4.51% Online shopping experience college 209 78.57% few 104 39.10% Graduate 14 5.26% ordinary 85 31.95% Industry frequently 36 13.53% Industry/ manufacturing Education and research/government agencies Finance/Insurance/ negotiable securities 17 6.39% often 41 15.41% 10 3.76% 21 7.89% Information 23 8.65% Service 68 25.56% Student 127 47.74% B 219

Table 2. Results of AVE Construct Mean Variance SD Cronbach s Composite AVE AVE alpha Reliability Customer Satisfaction 5.0573 0.987 0.993 0.9434 0.959342 0.855069 0.924699 Customer Loyalty 4.9489 0.968 0.984 0.8988 0.924596 0.676538 0.822519 Switching Cost 4.5414 0.921 0.960 0.8387 0.891032 0.672026 0.819772 Recency 5.6466 1.663 1.290 1 1 1 1 Frequency 4.5000 0.591 0.768 1 1 1 1 Monetary 4.3835 0.547 0.739 1 1 1 1 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 and 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. Sat 0.924699 Table 3. Correlation between constructs Sat Loy SC R F M Loy 0.827892 0.822519 SC 0.413357 0.517028 0.819772 R 0.211623 0.205457-0.053547 1 F 0.236623 0.241465 0.105786 0.323631 1 M 0.062328 0.095205 0.111915 0.28113 0.391814 1 SAT: Customer Satisfaction; Loy: Customer Loyalty;SC: Switching Cost; R: Recency; F: Frequency; M: Monetary Diagonal elements are the average variance extracted (AVE). Off-diagonal elements are the shared variance. 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, customer satisfaction had a significant negative relationship with customer loyalty (R2 = 0.194). Thus, H1 B 220

was supported. Customer satisfaction was found to have a significant positive relationship with customer loyalty (R2= 0.685). Likewise, Loyalty had a significant positive association with monetary (R2 =0.054), meaning that H4 was supported. However, the relationship of loyalty with recency and frequency was not significant (R2 = 0.091 and R2 = 0.047) so H2 and H3 was not supported. Table 4. Statistical results of the structural model. Dependent Independent variable Path t value R 2 variable coefficient Loyalty Customer Satisfaction 0.828*** 42.182 0.685 Recency Loyalty -0.070 0.288 0.091 Frequency Loyalty -0.127 0.392 0.066 Montary Loyalty -0.615* 1.888 0.047 Recency Switching Cost -0.640 2.078 Frequency Switching Cost -0.502 1.298 Montary Switching Cost -0.793 1.900 Recency Loyalty x Switching Cost 0.716 1.415 Frequency Loyalty x Switching Cost 0.753 1.213 Montary Loyalty x Switching Cost 1.362* 1.983 Switching cost had a significant positive association with monetary, meaning that H7 was supported. However, the relationship of switching cost with recency was not significant. Thus, H5 was not supported. Similarly, the relationship of switching cost with monetary was not significant. Therefore, H6 was also not supported. As to the moderating relationships, switching cost was observed to moderate the relationship between loyalty and monetary, with higher switching cost leading to a higher positive relationship between loyalty and monetary. Therefore, H10 were supported. However, switching cost was unexpectedly found not to moderate the relationship between loyalty and recency and between loyalty and frequency. Thus, H8 and H9 was not supported. Figure 2 shows how switching cost moderated the relationship between loyalty and recency, frequency and monetary. Figure 3, 4 and 5 show the effectiveness of switching cost moderated the relationship between loyalty and recency, frequency and monetary. B 221

-0.070 Recency (R 2 =9.1%) Customer Satisfaction 0.828*** Loyalty (R 2 =68.5%) -0.127 Frequency (R 2 =6.6%) 0.716 0.753 1.362* -0.640 Switching Cost -0.615* -0.502-0.793* Monetary (R 2 =4.7%) *p<0.05, **p<0.01, ***p<0.001 Figure 2. Hypotheses testing results. Fig. 3. The moderating relationship of switching cost on the link between customer loyalty and recency. B 222

Fig. 4. The moderating relationship of switching cost on the link between customer loyalty and frequency. Fig. 5. The moderating relationship of switching cost on the link between customer loyalty and monetary. 5. DICUSSION This work has presented an e-loyalty evaluating procedure for information-based websites. By expanding the definition of e-loyalty, the performance of all kinds of websites can now be measured by implementing our approach. We also defined a browsing transaction by the proposed browsing RFM model. As a result, switching cost negatively moderated the influence of loyalty on monetary of purchase behavior. The importance of loyalty reduced as a predictor of purchase behavior when perceived switching cost increased. However, switching cost did not significantly and negatively moderate the influence of loyalty on recency and frequency of purchase behavior. A possible explanation is that switching cost could not influence recency and frequency of purchase behavior. But the real purchasing amount will be affected by switching cost. In terms of theory building, this study develops a parsimonious model to examine the e-loyalty effects on customer purchase behavior construct. From a descriptive standpoint, psychological contract violation represents an B 223

additional key element of buyer-seller relationships in online shopping that has been ignored in the literature. A major finding of the study is the moderating role of switching cost in the relationship between loyalty and repeat purchase intention. Our results suggest that the impact of loyalty on repeat purchase intention alters under contingency conditions. A buyer will tend to repeat purchases despite less than ideal loyalty if they perceive that the economic and psychological costs of switching to a new online seller are too high. It is important to search for moderating variables that turn simple main effects into more insightful conditional relationships (Featherman and Fuller, 2003). Evidence presented suggests that a deeper understanding of satisfaction, loyalty, and purchase behavior is possible when interactions are taken into consideration. 6. LIMITATIONS AND FUTURE RESEARCH As with any research, care should be taken when generalizing the results of this study. First, the survey was conducted using Web-based forms and employed a non-random convenience sample. Gathering a larger sample using an alternate survey modality and random sampling methods would be costly. The online survey method was appropriate for collecting data from participants with Internet experience and who were free of geographical constraints. However, most of the samples were collected from university students. Although researchers indicate that adopting students as a survey sample is considered applicable to evaluate online consumer behavior, the generalizability could be enhanced if future research systematically sampled from a more dispersed sample. Second, examine the relative importance of the different product types in affecting repeat purchase intention. Switching cost plays a significant moderating role in the relationship between loyalty and purchase behavior and a significant mediating role in the relationship between loyalty and purchase behavior. Chitturi et al. (2008) examined the relationship between product design benefits (hedonic versus utilitarian) and the post-consumption feelings of customer delight and satisfaction across three studies with cell phones, laptop computers, and automobiles. They found that hedonic benefits significantly affected delight through promotion emotions, while utilitarian benefits significantly affected satisfaction through prevention emotions. Delight and satisfaction had significant effects on customer loyalty and purchase behavior. Future research could verify whether such relationships are supported in the online shopping context. There are different types (sub-dimensions) of switching cost. Therefore, an interesting area for future research is to examine the relative importance of the different product types of switching cost in affecting repeat purchase intention. In addition, another interesting area for future research is to explore the sources of switching cost. Third, this study considers e-commerce in general and uses respondent self selection of a familiar website from among a limited selection. However, website characteristics may influence the e-loyalty creation. Future research should attempt to further examine the role of website characteristics in an e-loyalty model. Finally, this study adopted switching as a moderating variable based on the suggestion of previous researchers. However, other variables, such as Internet experience, Internet self efficacy, and shopper style, may also be important moderators in understanding consumers online shopping behavior and possibly should be considered in future research dealing with this topic. B 224

7. CONCLUSIONS AND IMPLICATIONS The results of this study shed light on some important issues related to the e-loyalty construct that have not been addressed by previous studies. First, this study confirms that customer satisfaction is a critical influence on the establishment of e-loyalty. In an e-commerce context, building e-loyalty is a difficult challenge that may require consideration by online firms wishing to differentiate themselves from competitors. Currently, online firms are eager to launch e-loyalty programs in which customers obtain substantial benefits by doing most of their online shopping through a single website (positive lock-in). This finding is particularly important for managers of online firms as they decide how to allocate resources in designing their website interface. For example, online firms could invest substantially in digital imaging and multimedia technology to ensure that all images of products on their website are presented using high quality graphics and multimedia, which will arouse customers positive emotions such as enjoyment, excitement, and satisfaction, and in turn, enhance e-loyalty. Second, this study finds that customer switching costs negatively influences customer value and positively moderates the relationship between customer loyalty and customer value, especially the construct of monetary. From the results of this study, we found customers will spend more money when their perceived switching cost is lower. In addition, customers loyalty is high; they will spend more money when their perceived switching cost is high. On the contrary, customers loyalty is low; they will spend less money when their perceived switching cost is low. In an e-commerce context, customers may consider the perceived benefit of continuing a business relationship with their current vendor versus the perceived costs of switching to another online seller. Thus, to remain competitive, online firms must continuously work at enhancing perceived benefit for customers to discourage their switching to competitors. Finally, this study suggests that strategies for retaining high Internet experience customers should be based on attempts to increase perceptions of switching costs in order for customers to perceive these benefits. Therefore, online sellers must pay extra attention to these relationships: providing customers fair transactions and good service can increase the perceptions of switching cost. If customers are satisfied with the present seller they will not think about switching because they will face considerable risk and uncertainty in choosing an alternative seller. REFERENCE Aiken, L. S., & West, S. G. 1991. Multiple regression: Testing and interpreting interactions. Newbury Park, CA: Sage Publications. Anderson, R. E., & Srinivasan, S. S. 2003. E-satisfaction and e-loyalty: A contingency framework, Psychology and Marketing, 20(2), 123 128. Babin, B. J., & Griffin, M. 1998. The nature of satisfaction: An updated examination and analysis. Journal of Business Research, 41(2), 127-136. Bagozzi, R. P., Gopinath, M., & Nyer, P. U. 1999. The role of emotions in marketing. Journal of the Academy of Marketing Science, 27(2)1999, 184-206. Balabanis, G., Reynolds, N., & Simintiras, A. 2006. Bases of e-store loyalty: Perceived switching barriers and satisfaction. Journal of Business Research, 59(2), 214-224. Blau, P. M. 1964. Exchange in power of social life. NY: John Wiley and Sons Inc. Bohmstedt, G. W. 1970. Reliability and validity assessment in attitude measurement. In G. F. Summers (Ed.), Attitude measurement. Chicago: Rand-McNally. B 225

Boohene, R., & Agyapong, G.K.Q. 2011. Analysis of the Antecedents of Customer Loyalty of Telecommunication Industry in Ghana: The Case of Vodafone (Ghana), International Business Research, 4(1), 229-240. Borenstein, S. MacKie-Mason, J., & Netz, J. 2000. Exercising market power in proprietary aftermarkets. J. Econ. Manage. Strategy, 9(2), 157-188. Burnham, T. A., Frels, J. K., & Mahajan, V. 2003. Consumer switching costs: A typology, antecedents, and consequences. Journal of the Academy of Marketing Science, 31(2), 109-126. Caruana, A. 2004. The impact of switching costs on customer loyalty: A study among corporate customers of mobile telephony. Journal of Targeting Measurement and Analysis for Marketing, 2(3), 256-68. Chan, C.C.H. 2005. Online auction customer segmentation using a neural network model. International Journal of Applied Science and Engineering, 3(2), 101-109. Chan, C.C.H. 2008. Intelligent value-based customer segmentation method for campaign management: A case study of automobile retailer. Expert Systems with Applications, 34, 2754-2762 Chang, H.H., and Chen, S.W. 2008. The impact of customer interface quality, satisfaction and switching costs on e-loyalty: Internet experience as a moderator. Computers in Human Behavior, 24, 2927-2944. Chen, R.Y. 2008. RFM-based eco-efficiency analysis using Takagi Sugeno fuzzy and AHP approach. Environmental Impact Assessment Review, 2, 157 164. Chan Henry Chu-Chai. 2005. Online auction customer segmentation using a neural network model. Int J Appl Sci Eng, 3(2), 101-109. Chen, C.F., Tsai, M.H., 2008. Perceived value, satisfaction, and loyalty of TV travel product shopping: Involvement as a moderator, Tourism Management, 29, 1166-1171 Chen, P.-Y., and Hitt, L. M. 2002. Measuring switching costs and the determinants of customers retention in Internet-enabled businesses: A study of the online brokerage industry. Information Systems Research, 13(3), 255-274. Chiang, W.Y., 2011. To mine association rules of customer values via a data mining procedure with improved model: An empirical case study. Expert Systems with Applications, 38, 1716-1722. Chiu, C.M., Hsu, M.H., Fang, Y.H., & Yen, C.H. 2010. Exploring Online Repeat Purchase Intentions: The Moderating Role of Switching Cost, International Conference on Business and Information, Kitakyushu, Japan. Chiu, C.M., Hsu, M.H., Fang, Y.H., & Yen, C.H. 2010. Exploring Online Repeat Purchase Intentions: The Moderating Role of Switching Cost. BAI 2010 International Conference on Business and Information. Churchhill, G. A., & Surprenant, C. 1982. An investigation into the determinants of customer satisfaction. Journal of Marketing Research, 19, 491-504. Clarke, R. 2004. Alternative Decision Models in Consumer Internet Commerce, IFIP Int'l Conf. on Decision Support Systems (DSS2004), Florence (Prato). Colgate, M., & Lang, B. 2001. Switching barriers in consumer markets: An investigation of the financial services industry. Journal of Consumer Marketing, 18(4), 323-347. Colquitt, J.A., 2001. On the dimensionality of organizational justice: A construct validation of a measure. Journal of Applied Psychology, 86(3), 386-400. Colombo, R., & Jiang, W. 1999. A stochastic FSM model. Journal of Interactive Marketing, 13(3), 2-12. B 226

Courtheoux, R. 1995. Customer retention: how much to invest. Research and the Customer Lifecycle. New York, NY: DMA. Deng, Z, Lu, Y., Wei, K. K., & Zhang, J. 2010. Understanding customer satisfaction and loyalty: An empirical study of mobile instant messages in China. International Journal of Information Management, 30(4), 289-300. Dick, A. S., & Basu, K. 1994. Customer loyalty: Toward an integrated conceptual framework. Journal of the Academy of Marketing Science, 22(2), 99113. Fornell, C., & Larcker, D. F. 1981. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. Fornell, C. 1992. A national customer satisfaction barometer: The Swedish experience. Journal of Marketing, 56(1), 6-12. Grant, A. W. H., & Schlesinger, L. A. 1995. Realize your customers full profit potential. Harvard Business Review, 73(5), 59-72. Hauser, J. R., Simester, D. I., & Wernerfelt, B. 1994. Customer satisfaction incentives. Marketing Science, 13(4), 327-350. Hair, J. T., Anderson, R. E., Tatham, R. L., & Black, W. C. 1992. Multivariate data analysis with readings (3rd ed.). New York: Macmillan. Hsieh, N.C. 2004. An integrated data mining and behavioral scoring model for analyzing bank customers. Expert Systems with Applications, 27, 623-633. Hwang, H., Lung, T. & Suh, E. 2004. An LTV model and customer segmentation based on customer value: a case study on the wireless telecommunication industry. Expert systems with applications, 26, 181-188. Jackson, D. R. 1994. Strategic application of customer lifetime value in the direct marketing environment. Journal of Targeting Measurement and Analysis for Marketing, 3(1), 9-17. Jackson, B. B. 1985. Winning and keeping industrial customers: The dynamics of customer relationships. Lexington Books, Lexington, MA. Jones, M. A., Mothersbaugh, D. L., & Beatty, S. E. 2000. Switching barriers and repurchase intentions in services. Journal of Retailing, 76(2), 259-274. Jones, T. and Sasser, W. 1995. Why satisfied customers defect. Harvard Business Review. 73(6), 88-99 Jonker, J.J., Piersma, N., & Poel, D.V.N. 2004. Joint optimization of customer segmentation and marketing policy to maximize long-term profitability. Expert Systems with Applications, 27, 159-168. Judd, C. M., & McClelland, G. H. 1989. Data analysis: A model-comparison approach, San Diego, CA: Harcourt Brace Jovanovich. Keiningham, T.L., Cooil, B., Andreassen, T.W. & Aksoy, L. 2007. A longitudinal examination of net promoter on firm revenue growth. Journal of Marketing. Knox, S., & Walker, D. 2003. Empirical developments in the measurement of involvement, brand loyalty and their relationship in grocery markets. Journal of Strategic Marketing, 11, 271-286. Krishnamurthi, L., & Raj, S. P. 1991. An empirical analysis of the relationship between brand loyalty and customer piece elasticity. Marketing Science, 10(2), 1991, 172-183. Lam, S. Y., Shankar, V., Erramilli, M. K., & Murthy, B. 2004. Customer value, satisfaction, loyalty, and switching costs: An illustration from a business-tobusiness service context. Journal of the Academy of Marketing Science, 32(3), 293-311. Lee, J., Lee, J., & Feick, L. 2001. The impact of switching costs on the customer satisfaction loyalty link. The Journal of Services Marketing, 15(1), 35-52. B 227

Lin, H.-H., and Wang, Y.-S. 2006. Anexamination of the determinants of customer loyalty in mobile commerce contexts. Information and Management, 43(3), 271-282. Liu, C.-T. 2008. The impact of service quality and switching cost on customer loyalty in information asymmetric services. International Journal of Internet and Enterprise Management, 5(3), 237-251. Lee, J, Lee, J., & Feick, L. 2001. The impact of switching costs on the customer loyalty link: Mobile phone service in France. Journal of Service Marketing 15(1), 35-48. Lee, J., Lee, J., & Feick, L. 2001. The impact of switching costs on the customer satisfaction loyalty link. The Journal of Services Marketing, 15(1), 35-52. Morgan, R. M., & Hunt, S. D. 1994. The commitment trust theory of relationship marketing. Journal of Marketing, 58(3), 20-38. Oliver, R. L. 1999. Whence consumer loyalty? Journal of Marketing, 63(4), 33-44. Oliver, R. L. 1997. Satisfaction: A behavioral perspective on the consumer. McGraw-Hill. Parasuraman, A., Zeithaml, V. A., & Malhotra, A. 2005. E-S-QUAL: A multipleitem scale for assessing electronic service quality. Journal Service Research, 7(3), 213-233. Pearson, S. 1996. Building brands directly: creating business value and customer relationships. London: MacMillan Business. Petty, R. E., & Cacioppo, J. T. 1981. Issue involvement as a moderator of the effects on attitude of advertising content and context. Advances in Consumer Research, 8, 20-24. Petty, R. E., and Cacioppo, J. T. 1983. Central and peripheral routes to persuasion: application to advertising. In L. Percy and A. Woodside (eds.). Advertising and Consumer Psychology, Lexington Books, Lexington, MA, 3-23. Ping, R. A. 1993. The effects of satisfaction and structural constraints on retailer exiting, voice, loyalty, opportunism, and neglect. Journal of Retailing, 69(3), 320-352. Porter, M. E. 1980. Competitive strategy. New York: Free Press Porter, M. E. 1980. Competitive strategy: Techniques for analyzing industries and competitors. New York: Free Press. Ranaweera, C., & Prabhu, J. 2003. The influence of satisfaction, trust and switching barriers on customer retention in a continuous purchasing setting. International Journal of Service Industry Management,14(3/4), 374-395. Reichheld, F. F., and Schefter, P. 2000. E-loyalty: Your secret weapon on the Web. Harvard Business Review, 78(4), 105-113. Roberts, M. L., & Berger, P. D. 1989. Direct marketing management, PrenticeHall Inc., Englewood Cliffs, NJ. Shih, Y.-Y., & Lu, C.-Y.L, 2003. A method for customer lifetime value ranking-combining the analytic hierarchy process and clustering analysis. Database Marketing and Customer Strategy Management, 11(2), 159-172. Saarenvirta, G. 1998. Data mining to improve profitability, CMA Magazine, 8-12. Shang, R.-A., Chen, Y.-Ch., & Liao, H.-J. 2006. The value of participation in virtual consumer communities on brand loyalty. Internet Research, 16(4), 398-418. Sharma, N., & Patterson, P. 2000. Switching costs, alternative attractiveness and experience as moderators of relationship commitment in professional consumer service. International Journal of Service Industry Management, 11(5), 470-490. Srinivasan, S. S., Anderson, R., & Ponnavolu, K. 2002. Customer loyalty in B 228

ecommerce: An exploration of its antecedents and consequences. Journal of Retailing, 78(1), 41-50. Vázquez-Casielles, R. 2009. Customer satisfaction and switching barriers: Effects on repurchase intentions, positive recommendations, and price tolerance. Journal of Applied Social Psychology, 39(10). Verhwf, P.C., & Donkers, B. 2001. Predicting customer potentid value an application in the insurance industry. Decision Support Systems, 32, 189-199 Wang, Y. D., & Emurian, H. H. 2005a. An overview of online trust: Concepts, elements, and implications. Computers in Human Behavior, 21(1), 105-125. Wang, Y. D., & Emurian, H. H. 2005b. Trust in e-commerce: consideration of interface design factors. Journal of Electronic Commerce in Organizations, 3(4), 42-60. Wang, Y.S., Wu, S.C., Lin, H.H., & Wang, Y.Y. 2011. The relationship of service failure severity, service recovery justice and perceived switching costs with customer loyalty in the context of e-tailing. International Journal of Information Management, 31, 350-359. Wangenheim, F.V. 2003. Situational characteristics as moderators of the satisfaction-loyalty link: An investigation in a business-to-business context. Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior, 16, 145-156. Weun, S., Beatty, S. E., & Jones, M. A. 2004. The impact of service failure severity on service recovery evaluations and post-recovery relationships. Journal of Services Marketing, 18(2),133-146. Williamson, O. E. 1975. Markets and hierarchies: Analysis and Antitrust Implications. NY: Free press. Wu, L., Liu, L., & Li, J. 2005. Evaluating Customer Lifetime Value for Customer Recommendation, Services Systems and Services Management. Proceedings of ICSSSM '05. 2005 International Conference. B 229