Effects of inertia and satisfaction in female online shoppers on repeat-purchase intention: The moderating roles of word-of-mouth and alternative attraction

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Effects of inertia and satisfaction

in female online shoppers on

repeat-purchase intention

The moderating roles of word-of-mouth and

alternative attraction

Ying-Feng Kuo

Information Management, National University of Kaohsiung,

Kaohsiung, Taiwan

Tzu-Li Hu

Market Intelligence & Consulting Institute, Institute for Information Industry,

Taipei, Taiwan, and

Shu-Chen Yang

Information Management, National University of Kaohsiung,

Kaohsiung, Taiwan

Abstract

Purpose – With the prevalence of the internet, whether various interactive relationship building between online channel and consumers may lead or not to profit has been paid much attention by researchers and practitioners. It is also to note that the ratio of female shoppers online has been increasing, and female shoppers now outnumber male shoppers online. Based on the perspective of switching path analysis technique (SPAT), the aim of this study is to explore the effects of consumer inertia and satisfaction on repeat-purchase intention among female online shoppers, and also to examine whether positive word-of-mouth and alternative attraction moderate the above relationships. Design/methodology/approach – Data were collected from a self-developed online survey system. The formal questionnaire consisted of three sections. The first section screened participants by gender and online shopping experience. The second section measured respondent perceptions of each construct in the research model. The last section aimed to understand respondent basic personal data. Findings – The study results indicate that both consumer inertia and satisfaction positively influence repeat-purchase intention, and that consumer inertia is more influential than satisfaction; moreover, positive word-of-mouth negatively moderates the relationship between consumer inertia and repeat-purchase intention, but positively moderates that between satisfaction and repeat-repeat-purchase intention; finally, alternative attraction does not moderate any of the above relationships significantly. Originality/value – To the authors’ knowledge, the difference between the direct effect of inertia and satisfaction on purchasing behavior has not been investigated. Based on the study findings, suggestions are made for shopping website operators.

Keywords Repeat-purchase intention, Consumer inertia, Customer satisfaction, Word-of-mouth, Alternative attraction, Consumer behaviour

Paper type Research paper

1. Introduction

With the increasing maturity of online payment and delivery systems, customer reliance on the internet for information seeking and transactions, and entry of many physical stores to the online shopping market, online stores have become an important channel for doing business. The growth of online shopping web sites has

The current issue and full text archive of this journal is available at www.emeraldinsight.com/0960-4529.htm

Received 18 July 2012 Revised 10 December 2012 Accepted 20 December 2012

Managing Service Quality Vol. 23 No. 3, 2013 pp. 168-187

rEmerald Group Publishing Limited 0960-4529

DOI 10.1108/09604521311312219

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also intensified competition among e-tailers. Maintaining customer repeat-purchase intention and avoiding significant switching behavior to sustain operations and gain competitive advantage is thus important for e-tailers.

Based on Keaveney’s (1995) framework for analyzing switching behavior, Roos (1999) proposed a switching path analysis technique (SPAT) in which three switching determinants were identified, a pushing determinant, a swayer, and a pulling determinant. In this study, the pulling determinant and swayer were employed to investigate the antecedents of online repeat-purchasing intention. Notably, several studies argued that trust (e.g. Lim et al., 2006) and satisfaction (e.g. Keiningham et al., 2007; Shim et al., 2002; Tsai and Huang, 2007) are one of the most important antecedents of customers’ repeat-purchase intention in online shopping. In addition to the above factors, inertia can drive customer repeat-purchases (e.g. Huang and Yu, 1999; Liu et al., 2007; Solomon, 2007; White and Yanamandram, 2004, 2007). Nevertheless, little is known regarding the relative explanatory power of consumer inertia. The “Law of Inertia” proposed by Issac Newton also has marketing implications. Consumers will stay with their current e-tailer as long as no other force compels them to change. Consumers who have high inertia will be reluctant to change even though the alternatives are more attractive (Liu et al., 2007). Solomon (2007) argued that inertia behaviors occur when consumers get used to a particular goods or service provider based on past consumption experience. Inertia makes consumers avoid dealing with unfamiliar providers and incurring considerable learning cost. That is, consumers who have high inertia unconsciously stick to their current goods and service providers and make their repeat-purchase decisions in a less deliberate manner (Gulati, 1995). In this study, inertia can be considered as a pulling determinant of SPAT. Without inertia a customer would probably have switched to other attractive service providers or goods sellers. Satisfaction, on the other hand, represents an overall evaluation of past experiences and happiness with a particular provider’s work (Seiders et al., 2005), can be regarded as another pulling determinant. Consumers who express high satisfaction will be inclined to transact with current provider repeatedly (no switching behavior) in order to obtain optimal outcome. Anderson and Srinivasan (2003) found that inertia will deter dissatisfied customers from moving to an alternative provider. With inertia, customers make repeat-purchase decisions in spite of their negative perceptions about the current goods and service provider (White and Yanamandram, 2004). That is, the purchasing behavior based on satisfaction is more thoughtful than that based on inertia. To our knowledge, the difference between the direct effect of inertia and satisfaction on purchasing behavior has not been investigated. Hence, this study examines the degrees to which consumer inertia and satisfaction affect repeat-purchase intention in online shopping.

Studies have shown that customers see word-of-mouth from other consumers as more trustworthy and reliable than promotion by advertisers or marketers. Especially after the emergence of the internet, the anonymity it offered increased consumer intention to publicly share their experiences and feedback, information which other consumers consider important in making purchase decisions (e.g. Athanasopoulou, 2008; Dellarocas, 2003; Park et al., 2007; Sen and Lerman, 2007; Sun et al., 2006). Positive word-of-mouth is a swayer in SPAT, that makes a customer patronize the switched-from e-tailer even after switching. This study views positive word-of-mouth as a moderator and examines its moderating effects on the relationship between consumer inertia and repeat-purchase intention, as well as that between satisfaction and repeat-purchase intention in the online shopping context.

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In a competitive business environment, customers may switch stores depending on which appears more attractive. Notably switching costs are insignificant in the online shopping context. Customers can easily switch service providers with the click of a mouse. Therefore, this study also uses alternative attraction, a negative swayer of SPAT, as a moderator to investigate its moderating effects on the relationship between consumer inertia and repeat-purchase intention, as well as that between satisfaction and repeat-purchase intention.

With the growing population of employed women and the prevalence of the internet, the ratio of female shoppers online has been increasing, and female shoppers now outnumber male shoppers online (Ministry of Economic Affair, R.O.C., 2009). A survey suggests that 80 percent of buying decisions are made or dominated by women (Popcorn and Marigold, 2000). While online transactions for female products (such as cosmetics, woman dressing, fashion accessories, etc.) are becoming more frequent and prevalent (Business Next, 2008), women now form an important consumer group in online shopping. This study thus focusses on female online shoppers.

To sum up, this study investigates and compares the effects of consumer inertia and satisfaction on repeat-purchase intention among female online shoppers. Furthermore, this study examines the moderating effects of positive word-of-mouth and alternative attraction on the above relationships. Based on the research findings, this study makes suggestions regarding management strategies for shopping web site operators. 2. Literature review and hypotheses development

2.1 Repeat-purchase intention and switching behavior

Repeat-purchase intention is the degree to which customers are willing to purchase the same product or service and it is a simple, objective, and observable predictor of future buying behavior (Lin and Liang, 2011; Jones and Sasser, 1995; Seiders et al., 2005).

Customer repeat-purchase intention is critical to store profitability ( Jones and Sasser, 1995; Oliver et al., 1997; Reichheld and Sasser, 1990). For example, Reichheld and Sasser (1990) pointed out that a 5 percent improvement in customer retention can increase profits by 25-85 percent, and the cost of attracting a new customer is about five times that of retaining an old one. Product or service providers thus can effectively increase profits and reduce costs as long as they can successfully retain customers and induce their repeat-purchase intentions.

Numerous factors affect repeat-purchase intention. Most service providers are concerned with retaining customers because competition is getting fierce and acquiring new customers is more costly. Several studies suggest that the determinants of repeat-purchase can be understood through examining customer’s switching behavior (e.g. Bansal et al., 2005; Keaveney, 1995; Roos, 1999). Bansal et al. (2005) employed the PPM (push, pulling, and moorings) migration model to investigate customers’ service provider switching behavior. The PPM model was also applied in some online consumer behavior studies (e.g. Hou et al., 2009; Zhang et al., 2008). It is important to note that switching behavior is not irrevocable. Service providers can make profits as long as their customers still patronize them even after the switching behavior happened. The PPM model cannot explain why customers go back to the service provider from which they have recently switched.

This study employed Roos’s (1999) SPAT which identifies three switching determinants, a pusher, a swayer, and a puller, to investigate the antecedents of online repeat-purchase intention. The three determinants were also discussed in later studies (e.g. Ta¨htinen and Halinen, 2002). A pusher is defined as the determinant perceived by

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the customer as the reason for switching to another service provider. A puller explains why customers go back to the service provider from which they have recently switched. A swayer has no power of its own to provoke switching or returning. It only mitigates or strengthens the switching decision, and may be positive or negative. In other words, it plays a moderating role. This study intends to investigate the factors that cause repeat-purchase intention, thus, the puller and the swayer are employed to develop our research model.

Based on the definition in Roos’s (1999) study, inertia and satisfaction can be regarded as pulling determinants. Roos (1999) argued that a swayer has little significance if detached from the context. Word-of-mouth stands for general others’ evaluations for an original service provider and alternative attraction reflects customers’ expected performance of possible alternative service providers. The interpretation of above evaluations and expected performance are different from person to person, thus, they cannot cause switching by themselves. In this study, word-of-mouth and alternative attraction are regarded as two swayers that moderates the effects of inertia and satisfaction.

Because this study focusses on the effects of consumer inertia and satisfaction on repeat-purchase intention among female online shoppers, and the moderating effects of word-of-mouth and alternative attraction, the following sections discuss consumer inertia, satisfaction, word-of-mouth, and alternative attraction, respectively, and also develop related hypotheses.

2.2 Consumer inertia

Inertia is the most efficient way when individuals believe they can confidently rely on a source of existing information to achieve objectives in a stable and reliable environment (Gulati, 1995). In a consumption context, consumer inertia refers to a fixed consumption model in which consumers unconsciously patronize the same store or purchase the same brand of products based on past consumption experience (Gulati, 1995; Oliver, 1999; Solomon, 2007).

According to Solomon (2007), inertia-driven consumers make buying decisions without much contemplation. Compared with other factors (such as cost), inertia tends to influence customers to re-patronize a store. For instance, customers may add frequently visited shopping web sites to their browser bookmarks to facilitate faster access (Anderson and Srinivasan, 2003). The reasons for consumers inertia shopping are as follows: first, reduced consumption time: consumers do not need to spend extra time to get used to a new web site, or to compare prices or services between stores; second, familiarity: the more frequently consumers re-patronize a store, the more familiar they are with that store. To avoid uncertainty regarding making deals with other stores, or the risks of switching to other stores, consumers prefer to maintain their existing transaction relationship with the current store; and third, perceived difference between the current store and alternatives is minimal (Anderson and Srinivasan, 2003; Liu et al., 2007; Oliver, 1999; Solomon, 2007; Tsai and Huang, 2007).

The above discussion indicates that after consumers have developed inertia to purchase products from a store, they will have higher intention to re-patronize the current store. Therefore, this study proposes H1, as follows:

H1. Consumer inertia positively influences repeat-purchase intention in online shopping.

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2.3 Customer satisfaction

In this customer-oriented era, all enterprises pursue customer satisfaction as essential to gaining sustainable growth and competitive advantages (Deng et al., 2010; Udo et al., 2010). Customer satisfaction can be defined from the transaction-specific and cumulative perspectives. The former indicates that customer satisfaction is an evaluation based on recent purchase experiences (Boulding et al., 1993), while the latter stresses the holistic evaluation of all aspects of consumption, indicating that customer satisfaction is an evaluation made on the basis of all customer experiences from past to present ( Johnson and Fornell, 1991). The cumulative perspective is better for evaluating firm service performance, and also more effective for predicting customer post-purchase behaviors ( Johnson et al., 2001; Liu et al., 2007). This study thus adopts the cumulative perspective and defines customer satisfaction as “an overall evaluation of past experiences with products or services purchased from a shopping website” (Cronin et al., 2000; Maxham and Netemeyer, 2002; Seiders et al., 2005).

Many studies have concluded that customer satisfaction is positively related to repeat-purchase intention (e.g. Brady et al., 2001; Cronin et al., 2000; Johnson and Fornell, 1991; Zeithaml et al., 1996), and is a determinant of long-term repeat-purchase (Ranaweera and Prabhu, 2003). That is, higher cumulative satisfaction can lead to higher repeat-purchase intention and frequency (Maxham and Netemeyer, 2002; Seiders et al., 2005). The same result is found among studies of e-retailing and online shopping (Collier and Bienstock, 2006; Lee and Lin, 2005). Therefore, this study proposes H2, as follows: H2. Customer satisfaction positively influences repeat-purchase intention in online

shopping. 2.4 Word-of-mouth

Word-of-mouth denotes individual evaluations of brands, products, services, or organizations that are made without commercial intention and diffused through face to face or other communication channels throughout social networks. Word-of-mouth is viewed as more reliable and trustworthy than messages from advertisers or marketers (Bansal and Voyer, 2000; Carl, 2006).

Recently, the rise of the internet has not only increased the speed and breadth of information diffusion but also motivated more consumers to anonymously share their comments regarding a product or service. These comments derive from personal experiences. Consumers view such feedback, whether positive or negative, as more reliable than recommendations from product or service providers, and use them as an important reference when making purchase decisions. Consequently, word-of-mouth significantly affects consumer purchase and post-purchase perceptions (Dellarocas, 2003; Park et al., 2007; Sen and Lerman, 2007; Sun et al., 2006).

The more word-of-mouth a consumer receives, the more they are likely to be affected by word-of-mouth (Bansal and Voyer, 2000). Even an already-established transaction relationship may be influenced by word-of-mouth under the following conditions: first, the source of word-of-mouth is a trustworthy third party (such as a relative or friend); second, the experience offered by the source of word-of-mouth can reduce customer uncertainty. For instance, customers are more willing to diffuse positive word-of-mouth when they have a satisfactory experience with a product or service (Leisen and Prosser, 2004). Also, customers have higher intention to spread word-of-mouth after a service recovery; third, the source of word-of-mouth can quickly respond to questions and offer important information (Silverman, 1997).

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Customers in whom repeat-purchase intention results from inertia or satisfaction can be inferred to have stronger repeat-purchase intention if they receive positive word-of-mouth from other customers. Therefore, this study proposes H3 and H4 as follows:

H3. Positive word-of-mouth positively moderates the relationship between consumer inertia and repeat-purchase intention in online shopping.

H4. Positive word-of-mouth positively moderates the relationship between customer satisfaction and repeat-purchase intention in online shopping.

2.5 Alternative attraction

Alternative attraction refers to the perceived possibility of obtaining more satisfactory services from an alternative service provider. Consumers see alternative attraction when other stores offer lower priced but higher quality products, or more attractive services, than the store they generally visit (Keaveney, 1995; Ping, 1993).

In a highly competitive environment, customers are easily affected by offers from numerous service providers and can easily switch to whichever provider is most attractive (Bansal et al., 2004; Jones and Sasser, 1995; Keaveney, 1995). Online shopping is also a highly competitive environment, in which information is highly penetrating and transparent. Alternative attraction may weaken customer repeat-purchase intention based on inertia or satisfaction and further motivate customers to switch to other more attractive stores. Therefore, this study proposes H5 and H6, as follows:

H5. Alternative attraction negatively moderates the relationship between consumer inertia and repeat-purchase intention.

H6. Alternative attraction negatively moderates the relationship between customer satisfaction and repeat-purchase intention.

Based on the above hypotheses, this study develops the following research model (Figure 1). Consumer inertia Customer satisfaction Repeat-purchase intention Positive word-of-mouth Alternative attraction H1 H2 H3 H5 H4 H6 Figure 1. Research model

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3. Research methodology 3.1 Questionnaire design

The draft questionnaire comprised valid and reliable questions extracted from the previous literature. Since the questionnaire from the literature was originally developed in English, a university graduate with special training in English-Chinese translation translated it into Chinese. Another trained translator then performed a back-translation to ensure that the original translation was accurate. To ensure the adequacy and clarity of each question and identify potential problems in the questionnaire, this study invited four experts and ten female shoppers familiar with online shopping to review the questionnaire, helping to modify ambiguous expressions. This helped ensure the respondents understood the survey questions, and hence the content validity of the questionnaire.

The questionnaire was then adopted in a pilot test involving 120 undergraduate and graduate female students from two universities in Taiwan. For the item analysis, the corrected item to total correlation coefficient o0.40 was used as the criterion for item deletion, and whether the removal of the item could significantly enhance the total reliability of the questionnaire was considered. Subsequently, this study used Cronbach’s a to test the construct reliability. According to the results of the above analysis, no items were deleted and all constructs had Cronbach’s a coefficients exceeding the 0.70 threshold, revealing considerable reliability (Nunnally, 1978).

Data were collected from a self-developed online survey system. The formal questionnaire consisted of three sections. The first section screened participants by gender and online shopping experience. Participants were asked whether they had purchased goods from any of the top five online shopping web sites in Taiwan, namely Yahoo!Kimo Shopping, PChome Shopping, Books.com.tw, PayEasy, and ETMall. In order to avoid small inertia variance and concentration of participant choices of shopping web sites, participants were not allowed to directly identify the sites they frequently visited (frequent visits might be highly related to high inertia). Participants were asked whether they had previously visited each of the five popular web sites, with these web sites being asked in random order. For example, a participant may see a list with Yahoo!Kimo as the first web site and another participant may see a list with ETMall as the first web site. As soon as participants gave a positive answer for any web site they were guided to the second section of the survey; meanwhile, respondents who had not visited all the five web sites were asked to specify one web site they frequently visited and then proceed to the second section. Participants without experience of online shopping, since they were not the primary target of this study, were asked to terminate the survey immediately. The second section measured respondent perceptions of each construct in the research model. All items were assessed using five-point Likert scales ranging from 1¼ “strongly disagree” to 5 ¼ “strongly agree.” Table I lists the research constructs and items included in the questionnaire. The last section aimed to understand respondent basic personal data, and all the measurement scales adopted were nominal.

3.2 Research subjects and sampling method

The research subjects were female consumers with online shopping experience. In order to recruit online respondents, the survey was thus administered online rather than in paper format. The URL of the online survey was posted on the e-shopping

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section of a Taiwan well-known electronic bulletin board, ptt (bbs://ptt.cc), as well as several online shopping web forums, to solicit participation of female online shoppers. We also announced that this study will hold a lucky draw of gift certificates of convenience store after questionnaire completion in order to solicit more online users to participate in this study. Some 770 responses were ultimately obtained, of which 749 were valid.

4. Results

4.1 Sample characteristics

Table II lists the respondent demographics. Most respondents were aged 21-25 years old (60.2 percent). In terms of educational level, most were university or college

Construct Item Measurement Reference

Repeat-purchase intention

RI1 I will continue to purchase goods from “this shopping website” in the future

Tsai and Huang (2007) RI2 I anticipate repeat purchasing from “this

shopping website” in the near future RI3 I expect to repeat purchase from “this

shopping website” in the near future Consumer

inertia

CI1 Unless I am very dissatisfied with “this shopping website”, changing to a new one would be a bother

Anderson and Srinivasan (2003), Liu et al. (2007) CI2 I will access “this shopping website”

very intuitively when I need to purchase goods online

CI3 To my mind, the cost of time, money, and effort for switching to other shopping web sites is high

Customer satisfaction

CS1 It is a smart decision to purchase goods from “this shopping website”

Cronin et al. (2000), Maxham III and Netemeyer, (2002), Seiders et al. (2005)

CS2 My experience with “this shopping web site” is pleasant.

CS3 Overall, I am satisfied with “this shopping website”

Positive word-of-mouth

WOM1 Other consumers think it is worth to purchase goods from “this shopping website”

Maxham III and Netemeyer (2002), Leisen and Prosser (2004)

WOM2 Other consumers have positive comments for “this shopping website”

WOM3 Other consumers consider purchasing goods from “this shopping website” as a pleasant experience

Alternative attraction

AA1 Many shopping websites that are better than “this shopping website” are available for my choice

Ping (1993), Bansal et al. (2004)

AA2 I can obtain more satisfactory services from other shopping websites than from “this shopping website”

AA3 I can enjoy more benefits from other shopping websites than from “this shopping website”

Table I. Constructs and items included in the questionnaire

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educated (78.2 percent), with those with a graduate school education accounting for the second largest group (19.1 percent). Most respondents (41.7 percent) had a monthly discretionary income of NTD 5,001-15,000. The most popular shopping web site among the respondents was Yahoo!Kimo shopping web site (35.0 percent).

4.2 Factor analysis and common method bias

Exploratory factor analysis (EFA) was performed to assess the dimensionality of the constructs used. For EFA, principal component analysis, with Varimax rotation and eigenvalue 41 and factor loadings exceeding 0.4 was used (Kaiser, 1958). Additionally, any items which were cross-loaded on two factors with a factor loading 40.4 were removed. For item analysis, a corrected item to total correlation coefficient ofo0.40 was used as the criterion for item deletion, and whether the removal of the item could

Demographic profile Frequency %

Age Less than 15 2 0.3 16-20 130 17.4 21-25 451 60.2 26-30 149 19.9 31-35 13 1.7 36-40 0 0 41 or above 4 0.5 Educational level

High school or below 20 2.7

University/college 586 78.2

Graduate school 143 19.1

Occupation

Student 470 62.8

Military, civil, and teaching staff 59 7.9

Agricultural, forestry, fishery, and husbandry industry 1 0.1

Manufacturing industry 29 3.9

Finance and banking industry 20 2.7

Service industry 76 10.1

Freelance 64 8.5

Others 30 4.0

Monthly discretionary income (NT dollar)

Less than 5,000 197 26.3 5,001-15,000 312 41.7 15,001-25,000 87 11.6 25,001-35,000 94 12.6 35,001-45,000 39 5.2 45,001-55,000 15 2.0 55,001 or above 5 0.6

Shopping web sites

Yahoo!Kimo Shopping 262 35.0 PChome Shopping 96 12.8 Books.com.tw 221 29.5 PayEasy 131 17.5 ETMall 39 5.2 Note: n¼ 749 Table II. Demographic characteristics of survey respondents

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significantly enhance the total reliability of the questionnaire was considered. This process was iterated until an optimal result was obtained.

Two measures were used to test the appropriateness of factor analysis. The Kaiser-Meyer-Olkin (KMO) overall measure of sampling adequacy (MSA) was 0.864, and thus fell within the acceptable level. Additionally, Bartlett’s test of sphericity was 6,155.16, significant at p¼ 0.000, showing a significant correlation among the variables (Hair et al., 1998). Table III lists the result of factor analysis. According to the above analytical results, no item was deleted and items loaded onto the factors they were expected. All five factors had eigenvalues 41, explaining 78.14 percent of the total variance. Based on the results, the convergent and discriminant validity have been established. A reliability coefficient (Cronbach’s a) was computed for each factor to estimate the reliability of each scale. The Cronbach’s a coefficients ranged from 0.74 to 0.86, revealing good reliability (Nunnally, 1978).

Given that both the dependent variables and independent variables were measured using a single instrument at a single point in time, the occurrence of common methods bias was still possible. This study employed two steps to rule out common methods bias. First, as suggested by Chang et al. (2010), respondents were assured of the anonymity and confidentiality of this study, that there are no right or wrong answers, and that they should answer as honestly as possible. Second, Harman’s one-factor test was performed after data collection (Podsakoff et al., 2003). All items were entered into an unrotated EFA to determine whether a single factor emerges or a single factor accounts for the majority of the variance. In our test, four factors emerged, the largest of which accounted for 40 percent of the variance. The results indicate that common methods bias is not an issue in this study.

4.3 Hypotheses testing

Hierarchical moderator regression analysis (HMRA) was conducted for hypothesis testing. The HMRA used CI and CS as independent variables, RI as the dependent variable, and positive WOM and AA as the two moderator variables. Interaction terms

Construct Item Factor loading Item-to-total correlation Eigenvalue Variance explained (%) Cronbach’s a Alternative attraction AA1 0.829 0.646 2.733 18.22 0.856 AA2 0.911 0.779 AA3 0.903 0.763 Customer satisfaction CS1 0.781 0.574 2.475 16.50 0.838 CS2 0.912 0.767 CS3 0.913 0.770 Positive word of mouth WOM1 0.777 0.560 2.363 15.75 0.820 WOM2 0.892 0.719 WOM3 0.902 0.748 Repeat-purchase intention RI1 0.820 0.580 2.129 14.20 0.761 RI2 0.840 0.611 RI3 0.807 0.571

Consumer inertia CI1 0.877 0.668 2.021 13.47 0.736 CI2 0.785 0.526

CI3 0.765 0.498 Cumulative variance explained: 78.14%

Table III. Results of factor analysis

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were then included in the model. To represent these interaction terms, the variables were first mean-centered to reduce multicollinearity and then multiplied together (Aiken and West, 1991). The analysis models are as follows:

Model 1: Y¼ b0þ b1 X1þ b2 X2 Model 2: Y ¼ b0þ b1 X1þ b2 X2þ b3 M

Model 3: Y¼ b0þ b1 X1þ b2 X2þ b3 M þ b4 X1 M þ b5 X2 M ; where b0is the intercept; b1, b2, b3, b4, b5the coefficient; Y the RI; X1the CI; X2the CS;

M the moderator, positive WOM or AA.

According to Tables IV and V, the VIF waso10, indicating no multicollinearity problems among the variables (Hair et al., 1998) and the Durbin-Watson coefficient was between 1.5 and 2.5, indicating no autocorrelation. Table IV indicated that the addition of interaction terms to the main effect relationship significantly increased the amount of variance explained for RI (DR2¼ 0.006, DF ¼ 0.009, po0.01). Two key determinants of consumer RI, CI (b1¼ 0.460, po0.001), and CS (b2¼ 0.379, po0.001),

both remained significant even after the variance was explained by the interaction terms. Thus H1 and H2 were supported. More interestingly, the effect of CI on RI is stronger than that of CS.

Model 2 investigates the main effect of positive WOM. The analytical results show that positive WOM did not explain significant incremental variance (DR2¼ 0.001, DF¼ 0.167, p40.05) beyond that accounted for by CI and CS. As expected, the direct effect of positive WOM on RI (b3¼ 0.048, p40.05) was not significant.

In Model 3, the interaction term of CI and positive WOM was significantly and negatively related to RI (b4¼ 0.067, po0.05). This did not support H3. That is, CI

and positive WOM interact to negatively predict consumer RI. The interaction term of CS and positive WOM was significantly related to RI (b5¼ 0.076, po0.01). This

supported H4, which hypothesized that CS and positive WOM would interact to predict consumer RI.

Model Variable Standardized coefficient VIF R2 DR2 DF Durbin-Watson Model 1 RI¼ b0þ b1 CI þ b2 CS CI 0.461*** 1.196 0.513 – – 1.890 CS 0.392*** 1.196 Model 2 RI¼ b0þ b1 CI þ b2 CS þ b3 WOM CI 0.463*** 1.197 0.514 0.001 0.167 CS 0.359*** 2.089 WOM 0.048 1.869 Model 3 RI¼ b0þ b1 CI þ b2 CS þ b3 WOM þ b4 CI  WOM þ b5 CS  WOM CI 0.460*** 1.198 0.521 0.006** 0.009** CS 0.379*** 2.224 WOM 0.042 1.889 CI*WOM 0.067* 1.186 CS*WOM 0.076** 1.295

Notes: RI, repeat-purchase intention; CI, consumer inertia; CS, customer satisfaction; WOM, positive word-of-mouth. *po0.05; **po0.01; ***po0.001

Table IV.

Moderated regression analysis of the effect of positive word-of-mouth on repeat-purchase intention

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According to the results of Models 2 and 3, positive WOM is a pure moderator in this model based on the criteria of Sharma et al. (1981). A pure moderator is a variable that can change the form of the relationship between predictor and dependent variables. The interaction of the moderator and predictor variables causes this kind of change. However, a pure moderator is not directly related to the predictor or dependent variables.

To better understand the nature of the effect of these interactions, this study plots each relationship on a y-axis of repeat-purchase intention and an x-axis of CI and CS for high and low positive WOM (plus and minus one standard deviation from their mean) (Aiken and West, 1991; Cohen and Cohen, 1983). Figure 2 shows the relationship between CI and RI at two levels of positive WOM. Notably, the figure shows that the relationship between CI and RI was stronger among female shoppers who perceived a low level of positive WOM than among those who perceived high positive WOM. Figure 3 shows the relationship between CS and RI at two levels of positive WOM. The figure indicates that RI increases via CS and at a greater rate for those with higher positive WOM.

The results for Model 3 listed in Table V indicated that the addition of the interaction terms CI AA and CS  AA to the original model did not significantly increase the variance explained for RI (DR2¼ 0.001, DF ¼ 0.379, p40.05), nor were the standard regression coefficients of the interaction terms significant (b4¼ 0.042, p40.05;

Repeat-purchase intention 4.5 High positive WOM Low positive WOM 3.728 4.039 3.984 3.544 Low High Consumer inertia 4 3.5 3 Figure 2. Moderating effect of positive word-of-mouth (WOM) on the relationship between consumer inertia and repeat-purchase intention Model Variable Standardized coefficient VIF R2 DR2 DF Durbin-Watson Model 1 RI¼ b0þ b1 CI þ b2 CS CI 0.461*** 1.196 0.513 – – 1.904 CS 0.392*** 1.196 Model 2 RI¼ b0þ b1 CI þ b2 CS þ b3 AA CI 0.413*** 1.330 0.531 0.018*** 0.000*** CS 0.362*** 1.249 AA 0.148*** 1.256 Model 3 RI¼ b0þ b1 CI þ b2 CS þ b3 AA þ b4 CI  AA þ b5 CS  AA CI 0.416*** 1.339 0.532 0.001 0.379 CS 0.359*** 1.394 AA 0.146*** 1.263 CI AA 0.042 1.413 CS AA 0.025 1.574

Notes: RI, repeat-purchase intention; CI, consumer inertia; CS, customer satisfaction; AA, alternative attraction. *po0.05; **po0.01; ***po0.001

Table V. Moderated regression analysis of the effect of alternative attraction on repeat-purchase intention

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b5¼ 0.025, p40.05). AA did not significantly moderate the relationship between two

independent variables CI and CS and RI. Thus, neither H5 nor H6 was supported. The main effect of AA was investigated in Model 2. The analytical results show that AA explained a significant incremental variance (DR2¼ 0.018, DF ¼ 0.000, po0.001) beyond that accounted for by CI and CS. As expected, the standard regression coefficient (b3¼ 0.148, po0.001) was significant. AA thus acts as an independent

antecedent variable of RI.

5. Discussions and implications

Figure 4 summarizes the results of hypotheses testing. The figure shows that consumer inertia and customer satisfaction positively and significantly influenced repeat-purchase intention. Furthermore, positive word-of-mouth negatively moderated the relationship between consumer inertia and repeat-purchase intention and positively moderated that between customer satisfaction and repeat-purchase intention. Alternative attraction did not significantly moderate the relationships between consumer inertia and repeat-purchase intention, or between customer satisfaction and

Repeat-purchase intention 4.5 High positive WOM 3.460 3.396 4.132 4.306 Low positive WOM 4 3.5 3 Low High Customer satisfaction Figure 3. Moderating effect of positive word-of-mouth (WOM) on the relationship between customer satisfaction and repeat-purchase intention

Notes: Dotted line (---) indicates that the path relationship is insignificant.

Value within the parenthesis is t-value. *p<0.05; **p<0.01; ***p<0.001

Consumer inertia Customer satisfaction Repeat-purchase intention Positive word-of-mouth Alternative attraction 0.461*** (16.520) 0.392*** (14.039) –0.067* (–2.407) 0.025 (0.793) 0.076** (2.637) –0.042 (–1.393) Figure 4.

Hypotheses testing results

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repeat-purchase intention. Additionally, consumer inertia and customer satisfaction jointly explained the 51.3 percent variance of repeat-purchase intention.

The results indicate that consumer inertia positively affects repeat-purchase intention in online shopping. This finding echoes the results of previous studies (e.g. Liu et al., 2007; Solomon, 2007; White and Yanamandram, 2004) and suggests that the more customers visit an online store due to inertia, the more likely that they will repeatedly purchase goods from this store. This tendency is probably attributed to customer perceptions of least efforts required for consumption from that store and higher familiarity with the store. Customer satisfaction also positively affects repeat-purchase intention. Consumers have higher intention to re-patronize stores with which they are most satisfied. This finding is consistent with the conclusions of several prior studies (e.g. Brady et al., 2001; Cronin et al., 2000; Johnson and Fornell, 1991; Maxham and Netemeyer, 2002; Ranaweera and Prabhu, 2003; Seiders et al., 2005; Zeithaml et al., 1996).

Notably consumer inertia influences repeat-purchase intention more than does satisfaction. To enhance customer repeat-purchase intention, online store managers should make more efforts to develop consumer consumption inertia while improving customer satisfaction. The online store managers can increase web site efficiency by shortening the effort needed to search for products and browse product pages; shorten the effort required for shoppers to familiarize themselves with their store; improve their store by learning from competitors and thus minimizing perceived differences from other stores; develop and manage customer relationships through offering distinctive favors, such as shortest delivery time and post-purchase service, to establish higher switch cost; and offer one-stop shopping services. Past studies on customer repeat-purchase intention focussed mainly on how to improve customer satisfaction and ignored consumer inertia, despite it being more influential than satisfaction. Therefore, besides customer satisfaction, online store managers should pay attention to the formation of consumer inertia to effectively increase customer repeat-purchase intention and economic gains.

The study findings also suggest that positive word-of-mouth negatively moderates the relationship between consumer inertia and repeat-purchase intention. A plausible explanation for this phenomenon is that customers who are used to re-patronizing a certain online store may feel unfairly treated when they hear others had better experiences with the store in question. These customers then speculate that the store does not offer consistent services or products to all customers and thus have reduced intention to repeat-purchase goods there. To avoid customer perceptions of providing inconsistent services or products, online stores should endeavor to maintain fair, stable, and consistent services for all customers spending the same money. For instance, customers at the same membership level should consistently be given discounts, after-sales services, and transaction handling services. That is, positive word-of-month from other customers negatively affects frequent customers whose repeat-purchase intention is built upon inertia. Another explanation may be that customers who receive weak positive word-of-mouth do not have sufficient information for making repeat-purchase decisions. Consumer inertia is thus a highly important determinant of repeat-purchasing intention when the positive word-of-mouth is rather low. Under the situation of high positive word-of-mouth, inertia is not the only determinant and the importance of inertia is diminishing. Some customers who have low inertia receive higher positive word-of-mouth may exhibit stronger repeat-purchase intention; the statistical effect of word-of-mouth on the dependent variable is then weakened.

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Additionally, positive word-of-mouth positively moderates the relationship between satisfaction and repeat-purchase intention. This finding implies that customers whose intention to repeat-purchase goods from a store is based on satisfaction will feel more confident in their choice when they hear positive word-of-mouth about the store from other customers thus increasing their intention to re-patronize the store.

As shown in Table IV, positive word-of-mouth does not significantly and directly affect repeat-purchase intention in Model 2, but exerts significant moderating effects in Model 3, suggesting that positive word-of-mouth is a pure moderator. That is, word-of-mouth does not exhibit significant effect on online consumer’s repeat-purchase intention. This finding seems contrary to that of prior studies in which word-of-mouth is critical for e-tailers (e.g. Park et al., 2007; Park and Lee, 2008). Kuan and Bock (2007) suggest that word-of-mouth is especially important for an e-tailer when consumers have not interacted with that e-tailer previously. This study, however, addresses the effect of word-of-mouth in an established online transaction relationship. Before a stable relationship with a certain store is established, customers may doubt the credibility of word-of-mouth about that store. Besides, online shopping involves risk and uncertainty. Customers are not easily affected solely by word-of-mouth. However, after establishing a firm relationship with the store (as shown in Model 3), they can compare the word-of-mouth of others with their past experiences with that store and thus be affected by word-of-mouth in making repeat-purchase decisions.

Alternative attraction does not significantly moderate the relationship between consumer inertia and repeat-purchase intention, or between customer satisfaction and repeat-purchase intention. Although previous studies have shown that customers may switch to other stores which offer more attractive services than the one they used to visit ( Jones and Sasser, 1995), the findings of this study suggest that customers whose repeat-purchase intention is based on either inertia or satisfaction may not easily be affected even when they learn that they can obtain more attractive services or products from competing stores. The persistence of such customers can be attributed to risks of switching and unfamiliarity with other stores. For instance, customers need to bear additional risks (such as providing personal data to unfamiliar stores or making online transactions through unfamiliar processes) or switching costs (such as spending more time and efforts on learning the new user interface) when they switch to other more attractive stores. Since they are already used to the current store and satisfied with it, alternative attraction is unlikely to reduce the intention of such customers to re-patronize that store. That is, compared with alternative attraction (outside-pulling force), customer satisfaction and consumer inertia (inside-pulling force) are more influential in terms of increasing profits and reducing costs. This finding also supports the conclusion of previous studies on repeat-purchase intention – the cost of attracting a new customer is about five times the cost that of retaining an old one; a 5 percent improvement in customer retention can increase profits by 25-85 percent (Reichheld and Sasser, 1990). Thus, it can be concluded that customer retention depends not only on improving satisfaction but also on strengthening consumer inertia, which can minimize the effects of alternative attraction.

Table V shows that alternative attraction significantly and directly affects repeat-purchase intention in Model 2, but it has no significant moderating effect in Model 3. Before establishing a firm transaction relationship with the current store, customers may easily be affected by better price or service offers from other stores. In the internet

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environment it is possible to switch to other stores with the click of a mouse. Customers can easily switch to other more attractive stores. However, after establishing a firm transaction relationship with the current store (situation in Model 3), customers consider risks and switching costs and become less susceptible to alternative attraction.

Despite our efforts to conduct this research in a careful manner, it is still subject to various limitations, including insufficient human resources, material resources, and time, and its inclusion of only top five online shopping web sites in Taiwan. Hence, the generalizability of the results for other online shopping web sites should be further examined. Besides, the research sample comprised only female customers. Future studies could also include male customers to compare the gender differences in the effects of inertia and satisfaction. As this study empirically found that consumer inertia influences repeat-purchase intention more than does satisfaction, future researchers can further explore the formation of consumer inertia and factors affecting inertia to provide more advanced managerial suggestions to online store operators.

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About the authors

Ying-Feng Kuo is a Professor in Department of Information Management, National University of Kaohsiung, Taiwan. Formerly, he was a chairman of Information Management at National University of Kaohsiung and Kun Shan University as well as Industrial Management at Shu-Te University, Taiwan. He received his PhD and MS in Industrial Engineering from the University of Texas at Arlington. He has published in more than 70 referred journals (e.g. International Journal of Information Management, Electronic Commerce and Research Applications, Computers in Human Behavior, Service Industries Journal, Total Quality Management and Business Excellence, Technovation, Journal of Intelligent and Fuzzy Systems, Expert Systems with Applications, Journal of Information Management, Journal of e-Business and others) and conference papers. His current research interests are in electronic commerce, online community, service quality management, internet consumer behavior and managerial decision making. Ying-Feng Kuo is the corresponding author and can be contacted at: fredkuo@nuk.edu.tw

Tzu-Li Hu is a Research Associate in Market Intelligence & Consulting Institute, Institute for Information Industry, Taiwan. He received his MS in Information Management from National University of Kaohsiung, Taiwan. His current research interests are in electronic commerce and internet consumer behavior.

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Shu-Chen Yang is an Associate Professor in Department of Information Management, National University of Kaohsiung, Taiwan. He received his PhD and MS in Information Management from the National Central University, Taiwan. He has published in more than 30 referred journals (e.g. Psychology and Marketing, International Journal of Information Management, Journal of Management, Journal of Business Ethics, Asia Pacific Management Review, Journal of Information Management, Journal of e-Business, Management Review, Internet Research and others) and conference papers. His current research interests are in electronic commerce, online community, internet consumer behavior and knowledge management.

To purchase reprints of this article please e-mail: reprints@emeraldinsight.com Or visit our web site for further details: www.emeraldinsight.com/reprints

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Table II lists the respondent demographics. Most respondents were aged 21-25 years old (60.2 percent)

Table II

lists the respondent demographics. Most respondents were aged 21-25 years old (60.2 percent) p.8
Table III. Results of factor analysis

Table III.

Results of factor analysis p.10
Table IV.

Table IV.

p.11
Table V. Moderated regression analysis of the effect of alternative attraction on repeat-purchase intention

Table V.

Moderated regression analysis of the effect of alternative attraction on repeat-purchase intention p.12
Figure 4 summarizes the results of hypotheses testing. The figure shows that consumer inertia and customer satisfaction positively and significantly influenced repeat-purchase intention

Figure 4

summarizes the results of hypotheses testing. The figure shows that consumer inertia and customer satisfaction positively and significantly influenced repeat-purchase intention p.13

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