The Moderating Role of Age in Customer Loyalty ...

Services Marketing Quarterly

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The Moderating Role of Age in Customer Loyalty Formation Process Paramaporn Thaichon, Antonio Lobo & Thu Nguyen Quach To cite this article: Paramaporn Thaichon, Antonio Lobo & Thu Nguyen Quach (2016) The Moderating Role of Age in Customer Loyalty Formation Process, Services Marketing Quarterly, 37:1, 52-70, DOI: 10.1080/15332969.2015.1112184 To link to this article: http://dx.doi.org/10.1080/15332969.2015.1112184

Published online: 30 Jan 2016.

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Date: 30 January 2016, At: 21:09

SERVICES MARKETING QUARTERLY , VOL. , NO. , – http://dx.doi.org/./..

The Moderating Role of Age in Customer Loyalty Formation Process Paramaporn Thaichona , Antonio Lobob , and Thu Nguyen Quachb

Downloaded by [Swinburne University of Technology] at 21:09 30 January 2016

a School of Marketing, S P Jain School of Global Management, Sydney, Australia; b Faculty of Business and Enterprise, Swinburne University of Technology, Melbourne, Australia

ABSTRACT

KEYWORDS

This study attempts to investigate the effects of an Internet service provider’s network quality and customer service on their customers’satisfaction, commitment, behavioral loyalty, and attitudinal loyalty, as well as the moderating effect of age. Data obtained from 1,989 Internet users was analyzed using statistical techniques including bias corrected bootstrap and structural equation modeling with multiple group analysis. The findings confirm that satisfaction mediated the relationship between network quality and customer service, and customer commitment. It is also revealed that the influence of satisfaction and commitment on behavioral and attitudinal loyalty varied across different groups of customers characterized by their age.

Age profile; attitudinal loyalty; behavioral loyalty; customer commitment; customer satisfaction; Internet service provider

Introduction Scholars haveempirically demonstrated that customer loyalty is a key factor in improving a company’s economic and competitive position (Kuo, Hu, & Yang, 2013). It is more valuable for a service provider to maintain and develop long-term relationships with customers rather than focus on attracting short-lived ones (Rafiq, Fulford, & Lu, 2013). Customer loyalty is important especially during times of economic austerity and increasing competition (Dick & Basu, 1994). It has been determined that customer acquisition costs approximately 5 times more than customer retention (Christodoulides & Michaelidou, 2010). For example, a 1% increase in the customer retention rate generally results in a 5% decrease in the cost of customer acquisition (Han, Lu, & Leung, 2012). This also results in an increase of approximate 5% in an ISP’s profit, which reduces the pressure of seeking and acquiring new customers (Spiller, Vlasic, & Yetton, 2007). Service quality is an important point of differentiation in a competitive setting, and a key driver of service-based businesses (Parasuraman, Zeithaml, & Berry, 1988). By enhancing service quality, providers influence customers’ satisfaction

CONTACT Paramaporn Thaichon [email protected] Dr., Sydney Olympic Park, NSW , Australia ©  Taylor & Francis Group, LLC

S P Jain School of Global Management,  Figtree


SERVICES MARKETING QUARTERLY

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(Thaichon, Lobo, and Mitsis 2014), commitment (Morgan & Hunt, 1994), and customer loyalty (Kuo et al., 2013). Though previous research has established the link between overall service quality and loyalty (Kuo et al., 2013), there have been limited academic studies investigating the effects of specific Internet service provider’s (ISP’s) service quality dimensions on customer loyalty in the high-tech residential Internet services context (Vlachos & Vrechopoulos, 2008). With the increase in technology-enabled services, the attention of the services literature has shifted to measurement and operationalization of service quality elements predominant in different types of services (Carlson & O’Cass, 2011; Ganguli & Roy, 2010; Kurt & Atrek, 2012). SERVQUAL and ES-QUAL have been developed by Parasuraman et al. (1988) and Parasuraman, Zeithaml, and Malhotra (2005), respectively, as an attempt to fully capture quality in generic types of services. Scholars have recently attempted to develop service quality measurement scales in different high-tech services. Some of these are Shamdasani, Mukherjee, and Malhotra (2008) in self-service Internet technologies and Vlachos and Vrechopoulos (2008) in mobile telephony services. Nevertheless, the area associated with the measurement of service quality of Internet services has been somewhat neglected (He & Li, 2010; Thaichon, Lobo, Prentice et al., 2014). Several basic differences exist between Internet services and other telecommunications services: for example, service quality in mobile telephony includes valueadded services [e.g., SMS (Short Message Service), MMS (Multimedia Messaging Service), WAP (Wireless Application Protocol), and GPRS (General Packet Radio Service)] or mobile devices (Santouridis & Trivellas, 2010), all of which are not applicable in the case of the service quality of an ISP. In home Internet services, information support on websites is crucial when assessing an ISP’s service quality, but this might not be relevant for other telecommunication services such as television transmission. Therefore, Thaichon, Lobo and Mitsis (2014) have introduced specific service quality dimensions for an ISP called the NCIS quality model, which comprises (a) network quality, (b) customer service, (c) information support, and (d) privacy and security. Network quality is the core service performance of an ISP (Thaichon, Lobo, and Mitsis 2014). Customer service is proven to have the most significant effect on satisfaction and loyalty in contexts such as technology-based banking (Ganguli & Roy, 2011). Moreover, website support and privacy have been well discussed in the Internet services contexts (Kim & Stoel, 2004; Vlachos & Vrechopoulos, 2008). For this reason, this study focuses on network quality, as well as customer service. These two dimensions are part of the NCIS quality model, hence their influence on customer loyalty would indeed be interesting to explore. Based on the aforementioned discussion, this study attempts to address the identified gap by investigating the effects of an ISP’s network quality and customer service on their customers’ satisfaction, commitment, and behavioral and attitudinal loyalty. Secondly, it endeavors to examine how customers’ satisfaction and commitment are related to their behavioral and attitudinal loyalty. Finally, it intends to investigate the moderating effect of the age profile of customer on the relationship

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between their satisfaction and commitment on their behavioral and attitudinal loyalty. The following section reviews the literature and develops related hypotheses. Next, data collection and analysis using the structural equation modeling technique of comparing alternative mediation models are reported including the testing of hypotheses. The article concludes with a discussion of the results, implications of the research as well as limitations, and future research direction.


Literature review Customer loyalty comprises of attitudinal and behavioral loyalty, which are associated with elements of customer evaluations, namely customer satisfaction, customer commitment, network quality, and customer service. The concept of loyalty has been researched in the literature for almost a century, since brand insistence was presented by Copeland in 1923 (Jacoby & Chestnut, 1978). Most of the early research merely explained loyalty merely in terms of behavioral aspects (i.e., repeated purchase) until Jacoby and Chestnut (1978) introduced a new conceptualization of loyalty that incorporated both attitudinal and behavioral elements. Dick and Basu (1994) conjecture that consumer loyalty includes both a favorable attitude and repeat purchase, which is empirically evidenced by a study of East, Gendall, Hammond, and Lomax (2005). True loyalty can be considered as an attitude-based behavior of loyalty, whereas the inertial repurchase with slight or no loyal attitude is referred to as spurious loyalty (Kim, Morris, & Swait, 2008). Attitudinal loyalty is evaluated by customers’ inner thoughts of attachment, and positive word-of-mouth and recommendations (Zeithaml, Berry, & Parasuraman, 1996). In addition, behavioral loyalty refers to customer retention or repurchase behavior (Zeithaml et al., 1996). Satisfaction is an affective evaluation based on the purchase and consumption experiences of a service or product (Qayyum, Khang, & Krairit, 2013). Oliver (1997) defines customer satisfaction as customers’ response to the condition of fulfillment, and customers’ judgment of the fulfillment state. In addition, customer satisfaction is described as the feeling that arises in a customer’s mind when the service performance exceeds the customer’s expectations (Senic & Marinkovic, 2013). Lin and Wang (2006) referred to customer satisfaction in mobile phone commerce as the cumulative affective response or impression that customers have towards their experience with all aspects of the products and services. Furthermore, in the area of psychology, the commitment concept is deemed as possessing intentional aspects, supported by Kiesler’s definition of commitment: “the pledging or binding of an individual to behavioral acts” (1971, p. 30). Therefore, customer commitment is defined as a customer’s enduring desire to maintain a relationship. Commitment signifies a high level of relational bonding and is vital for prosperous long-term relationships. Network quality in the telecommunications sector involves the strength of the network signals, number of errors, and downloading and uploading speed (Thaichon and Quach 2015). Errors in Internet connectivity may lead to poor perceptions of service quality, which could reduce the level of customer satisfaction (Vlachos &



55

Vrechopoulos, 2008). A study in the Singaporean mobile services context conducted by Kau and Loh (2006) suggested that successful service recovery alone does not restore customer satisfaction to the prefailure levels. It is therefore vital for service providers to provide superior services in the first place (Kau & Loh, 2006). In fact, a higher level of network quality leads to greater customer commitment (Thaichon et al., 2013). Despite scant empirical research on network quality in an ISP’s services, this study proposes that network quality has a direct and positive relationship with customer satisfaction and commitment. Moreover, the fundamental network quality characteristics in high-tech Internet services such as connectivity quality and speed of Internet affect customers’ behavioral loyalty (Seo, Ranganathan, & Babad, 2008). Miranda-Gumucio, Gil-Pechuan, and Palacios-Marques (2012) also endorsed that network quality is one of the most important drivers of attitudinal customer loyalty in prepaid cell phone services. Based on extant literature, the following battery of hypotheses are developed: H1: Network quality is positively associated with customer satisfaction. H2: Network quality is positively associated with customer commitment. H3: Network quality is positively associated with customers’ attitudinal loyalty. H4: Network quality is positively associated with customers’ behavioral loyalty.

In addition to network quality, Abdolvand, Charkari, and Mohammadi (2006) suggested that ISPs should invest in customer service in order to improve overall service quality, which in turn leads to customer satisfaction and commitment. Customer service involves a careful deliberation, design, and combination of specific tangible fundamentals of the service offerings (Malhotra, Mavondo, Mukherjee, & Hooley, 2013). In particular, technical support is an exclusive component of customer service in the Internet services sector. Molina, Martín, Santos, and Aranda (2009) contended that companies can increase customer satisfaction by providing reliable customer service with timely responses to customers’ inquiries. In Internet banking services, dimensions such as reliability and responsiveness are found to be predictors of customer satisfaction (Ramseook-Munhurrun & Naidoo, 2011). Customer service is also a critical factor in establishing customer commitment in the UK retail-banking context (Malhotra et al., 2013). A study in the Spanish grocery industry by Molina et al. (2009) concludes that customer service can be viewed as one of the primary factors in maintaining satisfied and attitudinally loyal customers. Similarly, it is proven that customer service is the major determinant of behavioral loyalty, while deficiencies in the quality of customer service are the prime reasons for customers switching service providers (Lounsbury et al., 2012). In light of the previous discussion, the following battery of hypotheses are formulated: H5: Customer service is positively associated with customer satisfaction. H6: Customer service is positively associated with customer commitment. H7: Customer service is positively associated with customers’ attitudinal loyalty.

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H8: Customer service is positively associated with customers’ behavioral loyalty.

The endogenous or outcome construct of the proposed framework is customer loyalty. As mentioned previously, customer loyalty is used by businesses to develop long-term relationships with their customers, and this comprises of attitudinal and behavioral loyalty (Goldsmith, Flynn, Goldsmith, & Stacey, 2010). Cheng, Lai, and Yeung (2008) remarked that customers who experience high levels of satisfaction would most likely stay with their existing providers and maintain their service subscriptions in the Hong Kong Internet services context. A study in the Malaysian mobile services context found that when customers feel satisfied they become more devoted, and are more likely to continue doing business with their incumbent provider (Mokhtar, Maiyaki, & Mohd Noor, 2011). Likewise, customer satisfaction is positively related with behavioral loyalty in the services sectors of Brazilian and France (Matos & Leis, 2013). A study conducted in Tehran reported that satisfaction of university students has a direct and positive effect on their attitudinal loyalty (Kheiry, Rad, & Asgari, 2012). Similarly Frank and Enkawa (2009) have concluded that customer satisfaction can increase repurchase behavior and positive word-ofmouth. Therefore, the following has been hypothesized: H9: Customer satisfaction directly leads to attitudinal loyalty. H10: Customer satisfaction directly leads to behavioral loyalty.

Customer commitment exists when customers think that the relationship is significant enough to deserve expending effort at maintaining that relationship in the long term. Customer satisfaction is generally regarded as an influencer in the formation and development of affective commitment (Johnson, Sivadas, & Garbarino, 2008). Bansal, McDougall, Dikolli, and Sedatole (2004) demonstrate that the stronger the satisfaction of customers, the more committed they become. Similar findings were revealed by research conducted in the Greek financial and entertainment services (Dimitriades, 2006); Canadian hairstyling, auto repair, and financial services (Fullerton, 2011); and Dutch health insurance and banking sectors (Bügel, Buunk, & Verhoef, 2010). Drawing upon the extant research, the following has been hypothesized: H11: Customer satisfaction directly leads to customer commitment.

Several studies have reported a positive correlation between customer commitment and customer loyalty (Cater & Zabkar, 2009; Fullerton, 2005). Cater and Zabkar (2009) stated that affective and calculative commitment positively affects customers’ repurchase in the European services context. Similarly, Fullerton (2005) demonstrated that customer commitment positively influences behavioral intentions. Besides, the more time and effort customers have invested in the relationship with their provider, the less inclined they are to terminate that relationship (Bügel et al., 2010). In other words, customers with high levels of commitment would be unwilling to switch to another provider (Cater & Zabkar, 2009). A study in the Western European mass transit services provides evidence of positive and significant



57

Figure . Proposed conceptual model.

impacts of affective commitment on both attitudinal loyalty and behavioral loyalty (Evanschitzky, Iyer, Plassmann, Niessing, & Meffert, 2006). Hence, the following has been hypothesized: H12: Customer commitment directly leads to attitudinal loyalty. H13: Customer commitment directly leads to behavioral loyalty.

Based on the discussion so far on network quality, customer service, customer satisfaction, customer commitment, behavioral loyalty, and attitudinal loyalty and the interactions between these constructs, a conceptual model has been developed which is depicted in Figure 1. In order to obtain more insightful understanding of the links between customer loyalty and its antecedents, scholars have examined the role of moderators in these relationships. For example, Rust and Zahorik (1993) assessed the length of patronage, whereas Homburg and Giering (2001) considered personal characteristics such as variety seeking, age, and income. In addition it is of interest that different customers have distinctive needs and require tailored approaches (Mazzoni, Castaldi, & Addeo, 2007). Customers differing in their age may possess different attitudes and buying behaviors towards particular high-tech services and their providers (Cardoso, Costa, & Novais, 2010; Hervé & Mullet, 2009). In line with this thinking Homburg and Giering (2001) argued that older customers are limited in their information processing ability as compared to younger ones, which results in critical differences in their affective responses and loyalty. Results from a study in member-based services organizations by Daughtrey, Vowles, and Black (2013) confirmed that as customers age, they tend to be more loyal to their service provider and are less likely to terminate their memberships. Hence, this study attempts to investigate the age profile of customers as a moderator of the link between their satisfaction and commitment on both attitudinal and behavioral loyalty. In this process, customers have been segmented in the following age categories: 18–28 years, 29–39 years, and older

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than 39 years. Related to the above discussion, the following battery of hypotheses have been proposed: H14: Age moderates the effects of satisfaction on attitudinal loyalty. H15: Age moderates the effects of commitment on attitudinal loyalty. H16: Age moderates the effects of satisfaction on behavioral loyalty. H17: Age moderates the effects of commitment on behavioral loyalty.


Method Data collection

Data was collected from residential Internet users in all regions of Thailand. The customer database of well-established major ISPs in Thailand was utilized, which included customers from all over the country and they were representative of the Thai population. The respondents were those who were not locked into any contracts, therein being free to switch to competing ISPs. The survey instrument was administered online. A total of 8,000 surveys were distributed in all geographical regions of Thailand. The final usable sample size was 1,989. In terms of respondents’ profiles, 51.4% of the total respondents were male and 48.6% were female. The age group of 18–28 made up 27.3% of the total respondents, 38.4% were 29–39 years old, and 34.3% were 40 years or older. Only 13.4% were students and 62.8% of respondents were employed full time. Measures

Respondents were required to rate their perceptions for items on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree). The constructs of the conceptual model were operationalized using multi-item measurement scales, which were obtained from extant literature as follows: Vlachos and Vrehopoulos’s (2008) connection quality scale was used to operationalize network quality. This scale examines the quality for mobile phone services, which is very similar to the offerings of ISP’s. The customer service scale was taken from Wolfinbarge and Gilly (2003), which addresses both customer service in the ISP’s offerings. To measure customer’s satisfaction, Chiou’s (2004) scale was selected. Customer commitment was operationalized using Eisingerich and Rubera’s (2010) scale, which has high Cronbach’s alpha (α = .865). Its items examine customers’ commitment, feelings, and sense of belonging to their service provider. Chiou’s (2004) satisfaction scale has been selected to be used in this study. The purpose of this scale is to investigate the extent of customers’ satisfaction with the ISP and their service offering. The loyalty scale from Kim and Niehm (2009) was selected for attitudinal loyalty since it has strong factor loadings (.71 to .95) and Cronbach’s alpha (α = .93). Also, this scale covers all the aspects of attitudinal loyalty and assists to determine if customers consider themselves to be


59

loyal patrons of an ISP. Zeithaml et al. (1996) behavioral loyalty scale was selected to investigate another aspect of customer loyalty. These scales are well known and most cited in the loyalty literature. It has reasonable factor loadings (.74 to .94) and investigates whether the customer intends to remain with a particular ISP in the next few years.

Analysis and results


Factor analysis and validity testing

In order to carry out exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) (Kline, 2005), two sets of data were generated through randomly splitting the sample into two halves. With the first split data set (n = 987), all of the items measuring each construct were subjected to an exploratory factor analysis. Varimax rotation using the Kaiser criteria of eigenvalues greater than 1 (Hair, Anderson, Tatham, & Black, 1998) to determine scales’ dimensionality was employed. The factor analysis resulted in an eight-factor solution with the items loading on appropriate factors consistent with the theoretically hypothesized relationships. The total variance explained by the eight factors was 63.41%. The results depicted in Table 1 reveal that a single factor solution was obtained for each construct. In other words, each of the eight scales was unidimensional. With the second split data set (n = 972), confirmatory factor analysis (CFA) was performed to examine whether theoretical relationship between items and their hypothesized factors were supported by the data (Cunningham, 2010). AMOS Version 20 (Analysis of Moment Structures) was used to analyze the data. Table 2 depicts all the eight factors (constructs of the proposed conceptual model) and the reliability indices for each of the factors (i.e., Cronbach’s alpha, construct validity and average variance extracted [AVE]). Table 3 shows that the variables were significantly correlated (p < .01). As a rule of thumb, correlations among items greater than 0.9 signal the presence of multicollinearity (Tabachinick & Fidell, 2001). The correlations among predictors were well below the 0.9 cutoff. Thus, it was concluded that problematic multicollinearity was not present in the data. Discriminant validity was examined by calculating the squared correlations coefficients between each pair of constructs and confirming that they were lower than the corresponding AVE for each construct (Fornell & Larcker, 1981). All factor loadings depicted in Table 2 were relatively high and adequate convergent validity was ensured.

Hypotheses tests

The measurement models were then assembled using structural equation modeling with maximum likelihood estimation. Bias correct bootstrapping was also conducted to assist the mediation test as advocated by Preacher and Kelley (2011). Although the chi-square statistic was significant (p = .000), which was owing to

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Table . Exploratory factor analyses (EFA) results. Factor Item


NQ

I do not experience any Internet disconnection from this ISP The Internet downloading and uploading speed meet my expectations The Internet speed does not reduce regardless peak or off-peak hours CS Customer service personnel are knowledgeable Customer service personnel are willing to respond to my enquiries My technical problems are solved promptly SAT I am happy about my decision to choose this ISP I believe that I did the right thing when I chose this ISP Overall, I am satisfied with this ISP COM I feel involved with this ISP I am very proud to have this company as my service provider I feel attached to this ISP AL I consider myself to be a loyal patron of this ISP I would say positive things about this ISP to other people I would recommend this ISP to someone who seeks my advice BL I would consider this ISP as my first choice to buy services I would do more business with this ISP in the next few years I would do less business with this ISP in the next few years (-) Explained variance (%)













. . . . . . . . . . . . . . . . . . .

.

.

.

.

.

Note. NQ = network quality; CS = customer service; SAT = customer satisfaction; COM = customer commitment; AL = attitudinal loyalty; BL = behavioral loyalty.

the relatively large sample size (>1000), other fit indices (CMIN/df = 11.304, GFI = .922, AGFI = .890, TLI = .948, CFI = .959, RMSEA = .072, 90% CI [.069, .075]) indicate that the structural model was a good fit to the data. The results in Table 4 demonstrate that the model explained 89.3% of the variance in attitudinal loyalty (R2 = .893), and 92.5% towards behavioral loyalty (R2 = .925). Network quality only directly influenced customer satisfaction, whereas customer service had a significant impact on both customer commitment and satisfaction. Customer satisfaction and commitment were determinants of attitudinal and behavioral loyalty. Although the direct effects of network quality and customer service on the two aspects of loyalty were not confirmed, the results from bias corrected bootstrapping revealed their significant indirect effects on attitudinal and behavioral loyalty. Table 5 shows the total, direct, and indirect effects of the predictors on their criterions. The following hypotheses received support: H1, H5, H6, H9, H10, H11, H12, and H13. To further confirm the effects of network quality and customer service on the dependent variables in the proposed model, the fit indices of the full model and mediation model were compared in Table 4, following the recommendations of Rust, Zahorik, and Keiningham (1995). The mediating model shows slightly superior fit


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Table . Instrument items and reliability indices.

NQ

CS


SAT

COM

AL

BL

Items

FL

α

CR

AVE

I do not experience any Internet disconnection from this ISP The Internet downloading and uploading speed meet my expectations The Internet speed does not reduce regardless peak or off-peak hours Customer service personnel are knowledgeable Customer service personnel are willing to respond to my enquiries My technical problems are solved promptly I am happy about my decision to choose this ISP I believe that I did the right thing when I chose this ISP Overall, I am satisfied with this ISP I feel involved with this ISP I am very proud to have this company as my service provider I feel attached to this ISP I consider myself to be a loyal patron of this ISP I would say positive things about this ISP to other people I would recommend this ISP to someone who seeks my advice I would consider this ISP as my first choice to buy services I would do more business with this ISP in the next few years I would do less business with this ISP in the next few years (-)

.

.

.

.

.

.

.

. . .

.

.

.

. . .

.

.

.

. . .

.

.

.

.

.

.

. . . .

. . . .

Note. α = Cronbach’s alpha; CR = construct reliability; AVE = average variance extracted; NQ = network quality; CS = customer service; SAT = customer satisfaction; COM = customer commitment; AL = attitudinal loyalty; BL = behavioral loyalty.

criteria (CMIN/df, AGFI, TLI, RMSEA) than the full model. In addition, the difference in chi-square test was not significant. It can then be concluded that there was no significant difference between the full and mediation model. Hence the more parsimonious model is preferred (Cunningham, 2010).

Table . Descriptive statistics and intercorrelations.

CS NQ SAT COM AL BL

CS

NQ

SAT

COM

AL

BL

.593 . . . . .

. .736 . . . .

. . .865 . . .

. . . .687 . .

. . . . .773 .

. . . . . .574

Note. The lower-left triangle elements (italicized) are correlations among construct. The upper-right triangle elements are the squared correlations among constructs. All correlations are significant at the . level (two-tailed). CS = customer service; NQ = network quality; SAT = satisfaction; COM = commitment; AL = attitudinal; BL = behavioral.

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Table . Results of hypothesis tests. Full model


Path SAT SAT COM COM COM AL AL BL BL AL BL BL AL

⇐ ⇐ ⇐ ⇐ ⇐ ⇐ ⇐ ⇐ ⇐ ⇐ ⇐ ⇐ ⇐

NQ CS CS NQ SAT SAT COM SAT COM NQ CS NQ CS

Regression weight

S.E.

C.R.

. . . −. . . . . . . −. −. .

. . . . . . . . . . . . .

. . . −. . . . . . . −. −. .

Mediating model β

p

∗∗∗ . ∗∗∗ . ∗∗∗ . . −. ∗∗∗ . ∗∗∗ . ∗∗∗ . ∗∗∗ . ∗∗∗ . . . . −. . −. . .

Regression weight

S.E.

C.R.

p

β

. . .

. . ∗∗∗ . . ∗∗∗ . . ∗∗∗

. . .

. . . . .

. . ∗∗∗ . . ∗∗∗ . . ∗∗∗ . . ∗∗∗ . . ∗∗∗

. . . . .

Note. CS = customer service; NQ = network quality; SAT = satisfaction; COM = commitment; AL = attitudinal; BL = behavioral. Full model: λ ()= ., CMIN/df = ., GFI = ., AGFI = ., TLI = ., CFI = ., RMSEA = ., % CI [., .]; mediated model: λ()= ., CMIN/DF = ., GFI = ., AGFI = ., TLI = ., CFI = ., RMSEA = ., % CI = [., .]; chi-square difference test: λ () = ., p = ..

Table . Standardized total, direct and indirect effects of NQ, CS and COM on the criterions. Network quality

SAT COM AL BL

Customer service

Satisfaction

Std. DE

Std. IE

Std. TE

Std. DE

Std. IE

Std. TE

Std. DE

Std. IE

Std. TE

R

.?? −. . −.

. . . .

. . . .

. . . −.

. . . .

. . . .

— . . .

— . . .

— . . .

. . . .

Note. SAT = satisfaction; COM = commitment; AL = attitudinal; BL = behavioral; Std. DE = standardized direct effect; Std. IE = standardized indirect effect; Std. TE = standardized total effect. ∗ p ࣘ .. ∗∗ p ࣘ .. ∗∗∗ p ࣘ ..

Moderation analysis

H14 to H17 postulate the moderating effect of age on the paths from commitment and satisfaction towards attitudinal and behavioral loyalty. To analyze the interaction effect, we divided the sample into three categories based on their age (Category 1: 18–28 years, Category 2: 29–39 years; and Category 3: greater than 39 years) and compared paths at different levels of the moderating variable. As depicted in Table 6, the indices reveal that the models show acceptable fit to the data. Interestingly, the effects of satisfaction of customers over 39 years on loyalty were insignificant while those effects were significant in the other two age categories. The paths of commitment towards attitudinal and behavioral loyalty were still significant but appeared to be stronger as age increased. To ascertain the differences between the three structural models, they were separately analyzed for the three subsamples. The moderating effect was tested by constraining the four paths (i.e., from commitment and satisfaction to attitudinal and behavioral loyalty) as being equal, using the chi-square difference test for the effect of age. An unconstrained model that simultaneously fits all three age categories was run and the paths of interest were fixed to be invariant in all categories to arrive at

C.R.

p

. . . . . . . . −. −. . . . . . . . . . . . −. −. . −. −. . −. −. . . . . λ () = ., df =, CMIN/df = ., GFI = ., AGFI = ., TLI = ., CFI = ., RMSEA = ., % CI [., .]

Regression weight

Group  (–) C.R.

p

. . . . . . −. −. . . . . . . . . . . . . −. . . . . −. −. . . . . λ () = ., df =, CMIN/df = ., GFI = ., AGFI = ., TLI = ., CFI = ., RMSEA = ., % CI [., .]

Regression weight

Group  (–)

Note. CS = customer service; NQ = network quality; SAT = satisfaction; COM = commitment; AL = attitudinal; BL = behavioral. ∗ p ࣘ .. ∗∗ p ࣘ .. ∗∗∗ p ࣘ ..

SAT ⇐ NQ SAT ⇐ CS COM ⇐ CS COM ⇐ NQ COM ⇐ SAT AL ⇐ SAT AL ⇐ COM BL ⇐ SAT BL ⇐ COM AL ⇐ NQ BL ⇐ CS BL ⇐ NQ AL ⇐ CS Goodness of fit indices

Path

Table . Structural results for different age groups.

C.R.

Group  (+) p

. . . . . . . . . . . . −. −. . . . . . . . . −. −. . . . . −. −. . -. -. . λ () = ., df =, CMIN/df = ., GFI = ., AGFI = ., TLI = ., CFI = ., RMSEA = ., % CI [., .]

Regression weight


SERVICES MARKETING QUARTERLY 63

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a constrained model (Cunningham, 2010). The result of the chi-square difference test shows that the difference between the unconstrained and constrained model was significant, suggesting that the effects of commitment and satisfaction on loyalty were not the same for different age categories, χ 2(2) = 27.108, p = .000 in the path of satisfaction to attitudinal loyalty; χ 2(2) = 29.112, p = .000 in the path of satisfaction to behavioral loyalty; χ 2(2) = 29.034, p = .000 in the path of commitment to attitudinal loyalty; χ 2(2) = 39.711, p = .000 in the path of commitment to behavioral loyalty. These findings show that the four paths were not invariant among customers from different age categories, indicating a strong moderating effect of age category, supporting H14 to H17. Discussion This study advances the literature associated with the influences of two important service quality elements—network quality and customer service—pertaining to loyalty of an ISP’s customer. The findings are considered relatively robust since the model explained a substantial portion of variance in both types of loyalty. The findings demonstrate that customer satisfaction depends on how customer service personnel handle and respond to customer enquiries and whether the customer services team and the ISP shows a sincere interest in solving customer enquiries. Ramseook-Munhurrun and Naidoo (2011) reported similar findings. The results also reveal that network quality is positively associated with customer satisfaction. This proves that customers pay considerable attention to the Internet downloading and uploading speed, as well as to the stability of the Internet connection. The link between customer satisfaction and commitment was also established mirroring the study by Fullerton (2011). Additionally, the effect of customer service on commitment was significant echoing the findings of a study by Malhotra et al. (2013). This is plausible as ISP services are high-tech industries, wherein customers usually face complex technical issues as well as lengthy contracts. Customer service is therefore an effective tool to win the hearts and minds of customers. However, the relationship between network quality and commitment was not confirmed. In recent times, network quality has improved and is of consistent standard, which makes it less likely a point of differentiation to determine how committed customers are towards the service provider. In fact, the results indicate that network quality manifests its effects on customer commitment via customer satisfaction. If customers perceive that network quality is superior, they would be delighted, and continue their subscription with that ISP. The results reveal that satisfaction and commitment are the precursors to attitudinal loyalty and behavioral loyalty. They were the mediators in the relationship between customer service, and attitudinal and behavioral loyalty. Similarly, the relationships between network quality and attitudinal and behavioral loyalty were fully mediated by customer satisfaction. Furthermore, satisfaction has both a direct and an indirect effect on attitudinal and behavioral loyalty indicating a partially mediated effect by commitment. Table 4 shows the direct and indirect effects of



65

each variable on the criteria constructs. The comparison between the full model and mediating model illustrated in Table 3 confirms these relationships. Network quality and customer service as cognitive evaluations results in customer affective responses (i.e., customer satisfaction and commitment), which eventually lead to customer loyalty. The invariance test for different age groups reveals some interesting insights. The age profiles show a negative moderating effect on the relationship between customer satisfaction and attitudinal and behavioral loyalty. Satisfaction was a strong predictor for loyalty of younger customers and was less important or even insignificant for older age groups. Additionally, the moderating effects of the age profile on the link between commitment and attitudinal and behavioral loyalty were strengthened as the age increased. Older customers appear to behave more rationally. Only if they feel a sense of belonging to the service provider, they intend to associate with the ISP and engage in repeat purchase. Satisfaction alone is inadequate for the older age groups to determine their loyalty but instead manifests its effects on loyalty via commitment. For example, the results show that respondents older than 39 years did not consider satisfaction as a determinant of either their decision to stay with a service provider, or their feeling of liking or being associated with a particular ISP. Theoretical and managerial implications Theoretical implications

Extant research has endeavored to explore customer loyalty and its antecedents (Thaichon et al., 2012). The findings of this study demonstrate several theoretical contributions. It advances the literature associated with service quality factors, in particular network quality and customer service, and customer loyalty in the hightech services industry. It has provided an understanding of the relationships between network quality, customer service and customers’ satisfaction, commitment, and attitudinal and behavioral loyalty. Furthermore, this study extends the service quality literature by including customer segmentation in the analysis. Consistent with the findings of Homburg and Giering (2001) it confirms the moderating role of customer characteristics in the relationship among psychological factors. The age profiles mediate the link between network quality, customer service, customer satisfaction, customer commitment to behavioral, and attitudinal loyalty. The effects of network quality, customer service, customer satisfaction, and customer commitment on behavioral and attitudinal loyalty differ across different age categories. Managerial implications

This study has developed an understanding of consumer buyer behavior relating to home Internet services in Thailand. These results highlight the pivotal role of network quality and customer service, and underscore the importance of management devoting resources to improve their network quality as well as customer service.


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Nowadays customers have easy access to detailed information and they can easily make comparisons between services and companies. Hence it is critical that IPSs concentrate on developing service quality in order to evoke positive feelings of customers and eventually enhance their loyalty. Management must ensure consistent and reliable networks in addition to reasonable Internet speed. It is important to keep abreast of the general standards in the industry. Moreover, ISPs need to make sure that their customer service staff are ready, qualified, empowered, and willing employees who demonstrate a sincere interest in solving problems or issues when responding to customers’ enquiries and concerns. Although network quality did not have any direct effect on commitment towards ISPs due to reasons mentioned previously, core Internet performance is a valid determinant of customer satisfaction, a precursor of commitment. In addition, customer service is an important antecedent to customer affective responses including both satisfaction and commitment. As network quality becomes more reliable among service providers, superior customer service can provide ISPs with a strong basis for differentiation. Despite the insignificant direct links with both types of loyalty, the indirect effects of network quality and customer service on loyalty suggest that the importance of these two quality factors cannot be neglected. The current research also emphasizes the important mediating effect that customer satisfaction and commitment has regarding loyalty, confirming the results of recent research by Fullerton (2005). This indicates that managers should try to enhance customer satisfaction and commitment in order to increase customer loyalty. Moreover, in line with Homburg and Giering (2001), this study highlights the importance of customer characteristics as moderators in the relationship between psychological and behavioral variables. Age was found to be a moderator in the link between commitment and satisfaction, and attitudinal and behavioral loyalty. It was revealed that customers in different age groups behave differently in terms of their affective responses and loyalty. The results show that as customers age, the effect of satisfaction on loyalty decrease as opposed to the effect of commitment on loyalty. As a result, aiming for only satisfaction is inadequate for ISPs to maintain a loyal customer base, especially for customers over 39 years old. As such, ISPs should focus on factors that enhance commitment of that mature segment, such as trust and switching costs (Tsai, 2011). This, therefore, signals ISPs to give due importance to customer segmentation in order to effectively and efficiently target the market. In short, apart from improving network quality and customer service, marketers of ISPs should consider customers’ personal information when exploring their loyal attitudes and behaviors. Such information can help ISPs better segment customers and create suitable marketing strategies. In general it is critical for ISPs to make customers more central in their daily operations, which would give them fair competitive advantage. Beneficiaries of this study are various stakeholders in Thailand, including customers of ISPs, ISPs themselves, the government, and other commercial interests.


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Limitations and future research directions This study has a few inherent limitations. Firstly, the choice of country (Thailand) might restrict the generalizability of the findings. In the interest of generalization, replication of this study should be made to test the robustness of this model in other neighboring countries such as Vietnam, Cambodia, and Burma. Secondly, the model in this study includes only two factors of service quality (network quality and customer service) and only two types of affective responses by customers (satisfaction and commitment). An investigation on other service quality factors as well as affective responses could be a fruitful research area and a step in the direction of addressing the aforementioned limitations. Thirdly, this study merely investigated ISP’s customers based on their age. It might be more desirable to include other demographic factors or lifestyles in order to obtain detailed insights about the role of segmentation in the ISP context. Finally, a longitudinal study of ISP’s customers in Thailand might be useful to capture the varying patterns of customers’ purchase behavior over time.

References Abdolvand, N., Charkari, M., & Mohammadi, R. (2006). Technical model for improving customer loyalty with m-commerce: Mobile service providers. International Journal on Computer Science and Information Systems, 1(2), 50–62. Bansal, H. S., McDougall, G. H. G., Dikolli, S. S., & Sedatole, K. L. (2004). Relating e-satisfaction to behavioral outcomes: An empirical study. Journal of Services Marketing, 18(4), 290–302. Bügel, M., Buunk, A., & Verhoef, P. (2010). A comparison of customer commitment in five sectors using the psychological investment model. Journal of Relationship Marketing, 9, 2–29. Cardoso, P. R., Costa, H. S., & Novais, L. A. (2010). Fashion consumer profiles in the Portuguese market: Involvement, innovativeness, self-expression and impulsiveness as segmentation criteria. International Journal of Consumer Studies, 34, 638–647. Carlson, J., & O’Cass, A. (2011). Developing a framework for understanding e-service quality, its antecedents, consequences, and mediators. Managing Service Quality, 21(3), 264–286. Cater, B., & Zabkar, V. (2009). Antecedents and consequences of commitment in marketing research services: the client’s perspective. Industrial Marketing Management, 38(7), 785–797. Cheng, T., Lai, L., & Yeung, A. (2008). The driving forces of customer loyalty: A study of Internet service providers in Hong Kong. International Journal of E-Business Research, 4(4), 26–42. Chiou, S. (2004). The antecedents of consumers’ loyalty toward Internet service providers. Information & Management, 41, 685–695. Christodoulides, G., & Michaelidou, N. (2010). Shopping motives as antecedents of e-satisfaction and e-loyalty. Journal of Marketing Management, 27(1/2), 181–197. Cunningham, E. (2010). A practical guide to structural equation modelling using Amos (1 ed.). Melbourne, Australia: Statsline. Daughtrey, C. L., Vowles, N. S., & Black, G. S. (2013). The effects of membership and demographics on consumer satisfaction and loyalty in service organizations. Services Marketing Quarterly, 34(4), 292–308. Dick, S., & Basu, K. (1994). Customer loyalty: Toward an integrated conceptual framework. Journal of the Academy of Marketing Science, 22(2), 99–113. Dimitriades, Z. S. (2006). Customer satisfaction, loyalty and commitment in service organizations Some evidence from Greece. Management Research News, 29(12), 782–800.


68

P. THAICHON ET AL.

East, R., Gendall, P., Hammond, K., & Lomax, W. (2005). Consumer loyalty: singular, additive or interactive? Australasian Marketing Journal, 13(2), 10–26. Eisingerich, A., & Rubera, G. (2010). Drivers of brand commitment: A cross-national investigation. Journal of International Marketing, 18(2), 64–79. Evanschitzky, H., Iyer, G. R., Plassmann, H., Niessing, J., & Meffert, H. (2006). The relative strength of affective commitment in securing loyalty in service relationships. Journal of Business Research, 59(12), 1207–1212. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. Frank, B., & Enkawa, T. (2009). Economic influences on perceived value, quality expectations and customer satisfaction. International Journal of Consumer Studies, 33, 72–82. Fullerton, G. (2005). How commitment both enables and undermines marketing relationships. European Journal of Marketing, 39(11), 1372–1388. Fullerton, G. (2011). Creating advocates: The roles of satisfaction, trust and commitment. Journal of Retailing and Consumer Services, 18, 92–100. Ganguli, S., & Roy, S. K. (2010). Service quality dimensions of hybrid services. Managing Service Quality, 20(5), 404–424. Ganguli, S., & Roy, S. K. (2011). Generic technology-based service quality dimensions in banking. Impact on customer satisfaction and loyalty. International Journal of Bank Marketing, 29(2), 168–189. Goldsmith, R. E., Flynn, L. R., Goldsmith, E., & Stacey, E. C. (2010). Consumer attitudes and loyalty towards private brands. International Journal of Consumer Studies, 34, 339–348. Hair, J., Anderson, R., Tatham, R., & Black, C. (1998). Multivariate data analysis (5th ed.). Upper Saddle River, NJ: Prentice Hall. Han, S., Lu, S., & Leung, S. (2012). Segmentation of telecom customers based on customer lifetime value by decision tree model. Expert Systems with Applications, 39, 3964–3973. He, H., & Li, Y. (2010). Key service drivers for high-tech service brand equity: The mediating role of overall service quality and perceived value. Journal of Marketing Management, 27(1-2), 77–99. Hervé, C., & Mullet, E. (2009). Age and factors influencing consumer behaviour. International Journal of Consumer Studies, 33, 302–308. Homburg, C., & Giering, A. (2001). Personal characteristics as moderators of the relationship between customer satisfaction and loyalty—an empirical analysis. Psychology & Marketing, 18, 43–66. Jacoby, J., & Chestnut, R. W. (1978). Brand loyalty: Measurement and management. New York, NY: John Wiley & Sons. Johnson, M. S., Sivadas, E., & Garbarino, E. (2008). Customer satisfaction, perceived risk and affective commitment: an investigation of directions of influence. Journal of Services Marketing, 22(5), 353–362. Kau, A., & Loh, E. W. (2006). The effects of service recovery on consumer satisfaction: A comparison between complainants and non-complainants. Journal of Services Marketing, 20(2), 101–111. Kheiry, B., Rad, B., & Asgari, O. (2012). University intellectual image impact on satisfaction and loyalty of students (Tehran selected universities). African Journal of Business Management, 6(37), 10205–10211. Kiesler, C. A. (1971). The psychology of commitment: experiments linking behavior to belief. New York, NY: Academic Press. Kim, J., Morris, D., & Swait, J. (2008). Antecedents of true brand loyalty. Journal of Advertising Research, 37(2), 99–117. Kim, H., & Niehm, L. S. (2009). The impact of website quality on information quality, value, and loyalty intentions in apparel retailing. Journal of Interactive Marketing, 23(3), 221–233.



69

Kim, S., & Stoel, L. (2004). Apparel retailers: Web site quality dimensions and satisfaction. Journal of Retailing and Consumer Services, 11, 109–117. Kline, R. B. (2005). Principles and practice of structural equation modeling. New York, NY: Guilford Press. Kuo, Y., Hu, T., & Yang, S. (2013). Effects of inertia and satisfaction in female online shoppers on repeat-purchase intention. The moderating roles of word-of-mouth and alternative attraction. Managing Service Quality, 23(3), 168–187. Kurt, S. D., & Atrek, B. (2012). The classification and importance of E-S-Qual quality attributes: An evaluation of online shoppers. Managing Service Quality, 22(6), 622–637. Lin, H. H., & Wang, Y. S. (2006). An examination of the determinants of customer loyalty in mobile commerce contexts. Information & Management, 43(3), 271–282. Lounsbury, J. W., Foster, N., Carmody, P. C., Kim, J. Y., Gibson, L. W., & Drost, A. W. (2012). Key personality traits and career satisfaction of customer service workers. Managing Service Quality, 22(5), 517–536. Malhotra, N., Mavondo, F., Mukherjee, A., & Hooley, G. (2013). Service quality of frontline employees: A profile deviation analysis. Journal of Business Research, 66, 1338–1344. Matos, C. A., & Leis, R. P. (2013). The antecedents of complaint behaviour for Brazilian and French consumers of services. International Journal of Consumer Studies, 37, 327–336. Mazzoni, C., Castaldi, L., & Addeo, F. (2007). Consumer behavior in the Italian mobile telecommunication market. Telecommunications Policy, 31(10/11), 632–647. Miranda-Gumucio, L., Gil-Pechuan, I., & Palacios-Marques, D. (2012). An exploratory study of the determinants of switching and loyalty in prepaid cell phone users. An application of concept mapping. Heidelberg, Germany: Springer-Verlag. http://dx.doi.org/10.1007/s11628-0120177-3 Mokhtar, S., Maiyaki, A., & Mohd Noor, N. (2011). The relationship between service quality and satisfaction on customer loyalty in Malaysian mobile communication industry. School of Doctoral Studies (European Union) Journal, 2(3), 32–38. Molina, A., Martín, V. J., Santos, J., & Aranda, E. (2009). Consumer service and loyalty in Spanish grocery store retailing: an empirical study. International Journal of Consumer Studies, 33, 477– 485. Morgan, M., & Hunt, D. (1994). The commitment-trust theory of relationship marketing. Journal of Marketing, 58(3), 20–38. Oliver, L. (1997). Satisfaction: A behavioural perspective on the consumer. Singapore: McGrawHill. Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing and Consumer Services, 64(1), 12–40. Parasuraman, A., Zeithaml, V. A., & Malhotra, A. (2005). E-S-QUAL. A multiple-item scale for assessing electronic service quality. Journal of Service Research, 7(10), 1–21. Preacher, K. J., & Kelley, K. (2011). Effect size measures for mediation models: quantitative strategies for communicating indirect effects. Psychological Methods, 16(2), 93–115. Qayyum, A., Khang, D. B., & Krairit, D. (2013). An analysis of the antecedents of loyalty and the moderating role of customer demographics in an emerging mobile phone industry. International Journal of Emerging Markets, 8(4), 373–391. Rafiq, M., Fulford, H., & Lu, X. (2013). Building customer loyalty in online retailing: The role of relationship quality. Journal of Marketing Management, 29(3/4), 494–517. Ramseook-Munhurrun, P., & Naidoo, P. (2011). Customers’ perspectives of service quality in Internet banking. Services Marketing Quarterly, 32(4), 247–264. Rust, R. T., & Zahorik, A. J. (1993). Customer satisfaction, customer retention, and market share. Journal of Retailing, 69(193–215).


70

P. THAICHON ET AL.

Rust, T., Zahorik, J., & Keiningham, L. (1995). Return on quality (ROQ): Making service quality financially accountable. Journal of Marketing, 59(2), 58–70. Santouridis, I., & Trivellas, P. (2010). Investigating the impact of service quality and customer satisfaction on customer loyalty in mobile telephony in Greece. The Total Quality Management Journal, 22(3), 330–343. Senic, V., & Marinkovic, V. (2013). Patient care, satisfaction and service quality in health care. International Journal of Consumer Studies, 37, 312–319. Seo, D., Ranganathan, C., & Babad, Y. (2008). Two-level model of customer retention in the US mobile telecommunications service market. Telecommunications Policy, 32, 182–196. Shamdasani, P., Mukherjee, A., & Malhotra, N. (2008). Antecedents and consequences of service quality in consumer evaluation of self-service Internet technologies. The Service Industries Journal, 28(1), 117–138. Spiller, J., Vlasic, A., & Yetton, P. (2007). Post-adoption behavior of users of Internet Service Providers. Information & Management, 44(1), 513–523. Tabachinick, B. G., & Fidell, L. S. (2001). Using multivariate statistics. Boston, MA: Allyn and Bacon. Thaichon, P., Lobo, A., & Mitsis, A. (2012). Investigating the antecedents to loyalty of internet service providers in Thailand: developing a conceptual model. Paper presented at ANZMAC at Adelaide, Australia, 3–5 December 2012. Thaichon, P., Lobo, A., & Mitsis, A. (2014). An empirical model of home internet services quality in Thailand. Asia Pacific Journal of Marketing and Logistics, 26(2), 190–210. Thaichon, P., Lobo, A., Prentice, C., & Quach, T. N. (2014). The development of service quality dimensions for internet service providers: Retaining customers of different usage patterns. Journal of Retailing and Consumer Services, 21(6), 1047–1058. Thaichon, P. & Quach, T. N. (2015). The relationship between service quality, satisfaction, trust, value, commitment and loyalty of Internet service providers’ customers. Journal of Global Scholars of Marketing Science, 25(4), 295–313. Tsai, S. P. (2011). Fostering international brand loyalty through committed and attached relationships. International Business Review, 20(5), 521–534. Vlachos, A., & Vrechopoulos, P. (2008). Determinants of behavioral intentions in the mobile Internet services market. Journal of Services Marketing, 22(4), 280–291. Wolfinbarge, M., & Gilly, C. (2003). eTailQ: Dimensionalizing, measuring and predicting etail quality. Journal of Retailing, 79(3), 183–198. Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1996). The behavioral consequences of service quality. Journal of Marketing, 60(2), 31–46.