The development of service quality dimensions for internet service ...

Journal of Retailing and Consumer Services ∎ (∎∎∎∎) ∎∎∎–∎∎∎

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The development of service quality dimensions for internet service providers: Retaining customers of different usage patterns Paramaporn Thaichon n, Antonio Lobo 1, Catherine Prentice 2, Thu Nguyen Quach 3 Faculty of Business & Enterprise, Swinburne University of Technology, Melbourne, Victoria 3122, Australia

art ic l e i nf o

a b s t r a c t

Article history: Received 1 April 2014 Received in revised form 18 June 2014 Accepted 18 June 2014

This study examines the relationships among relevant service quality dimensions of Internet service providers (ISP) and their customers’ perceived value, trust and commitment. Data was collected from residential Internet users in Thailand. The final usable sample size was 1507. The analyses include segmenting ISPs’ customers on the basis of their usage pattern and evaluating their perceptions of Internet service quality dimensions. In addition, several alternatives models were compared using structural equation modelling to confirm the mediation effects. An ISP’s service quality is influenced by the following four dimensions (a) network quality, (b) customer service and technical support, (c) information quality and (d) security and privacy. The findings reveal that while all dimensions have positive effects on trust, only network quality, information support and privacy influence customer value significantly and information support is the only dimension which is directly related to commitment. Additionally, the effects of customer service and information support on value vary across customers of different Internet usage patterns. The contribution of the present paper stems from the simultaneous modelling of a range of mediation effects which can better help explain the impact of service quality dimensions on customers’ cognitive and affective evaluations in high-tech service settings. & 2014 Elsevier Ltd. All rights reserved.

Keywords: Customer commitment Value Trust Service quality Internet usage Internet service provider (ISP)

1. Introduction Service quality is an important differentiator in a competitive business environment, and a driver of service-based businesses (Zhao and Benedetto, 2013). By enhancing service quality, businesses can influence customers’ value (Lai et al., 2009), trust (Sabiote and Roman, 2009), and commitment (Fullerton, 2005). These are important for business success and long term customer loyalty (Prentice, 2013). However, very few studies have assessed how different aspects of Internet service providers’ (ISP) service quality would influence their customers’ value, trust, and commitment (Thaichon et al., 2014; Vlachos and Vrechopoulos, 2008). ISPs may benefit from obtaining accurate information regarding their customers’ assessments of their brand’s delivered service quality; such information may enable service brand managers to formulate appropriate marketing strategies in order to achieve competitive advantage and long term profitability. This paper attempts to fill this important research gap

n

Corresponding author. Tel.: þ 61 3 92145266; fax: þ 61 3 9214 5293. E-mail addresses: [email protected] (P. Thaichon), [email protected] (A. Lobo), [email protected] (C. Prentice), [email protected] (T.N. Quach). 1 Tel.: þ 61 3 92148535. 2 Tel.: þ 61 406 627622. 3 Tel.: þ 61 3 92145266.

by investigating the effects of ISPs’ service quality on their customers’ value, trust, and commitment in the high-tech Internet services. Service quality measures how well the service delivered matches customer expectations (Zhao and Benedetto, 2013). In addition to SERVQUAL, E-S-QUAL has been developed by Parasuraman et al. (2005) as an attempt to capture the measurement of service quality in the new information age. However, owing to the very special nature of the services offered by ISPs, their service quality cannot be effectively measured by SERVQUAL or E-S-QUAL (He and Li, 2010; Thaichon et al., 2014). SERVQUAL and E-S-QUAL focus on service providers who operate via the Internet platform (Vlachos and Vrechopoulos, 2008) but not those who actually provide the Internet connection and platform activities. Numerous studies have been done in the telecommunications industry, especially in the mobile telephony market (He and Li, 2010). However, several basic differences exist between Internet services and other telecommunications services. For example, mobile phone service quality includes value-added services (e.g. SMS, MMS, WAP, GPRS) or mobile devices (Santouridis and Trivellas, 2010), which are not applicable in the case of ISPs. In addition, as the nature of home Internet services is Internet related, privacy and security are more prominent when assessing an ISP’s service quality as compared to assessing service quality of other telecommunications services such as mobile and television services. An ISP’s server contains account information of many

http://dx.doi.org/10.1016/j.jretconser.2014.06.006 0969-6989/& 2014 Elsevier Ltd. All rights reserved.

Please cite this article as: Thaichon, P., et al., The development of service quality dimensions for internet service providers: Retaining customers of different usage.... Journal of Retailing and Consumer Services (2014), http://dx.doi.org/10.1016/j.jretconser.2014.06.006i

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P. Thaichon et al. / Journal of Retailing and Consumer Services ∎ (∎∎∎∎) ∎∎∎–∎∎∎

Fig. 1. The proposed conceptual model.

users, which might put customers’ personal data at risk if unauthorised access is granted (Rowe et al., 2011). Moreover, being more active than other telecommunications service providers regarding online activities, ISPs observe and monitor traffic flowing through their networks; hence, they are able to detect suspicious traffic spikes, and can either stop malicious traffic or provide timely warnings to their customers (Rowe et al., 2011). A study in 2004 reports that 66 per cent of consumers would switch to other ISPs who offered more secured Internet service (Streamshield, 2004). Therefore, it can be concluded that customers perceive ISPs protection from privacy invasion and cybercrime as necessary and important. A recent consumer study demonstrates that the more regularly customers access the Internet, the more they need and appreciate online help (Oracle, 2012). Additionally, not every customer in the telecommunications services, other than Internet services, has access to online information support, especially in developing countries. In other words, customers of other telecommunications services might not perceive information support as important as customers of an ISP do. For example, in the mobile telephony services context, there are more than 84 million mobile subscribers in Thailand and 134 million subscribers in Vietnam (CIA, 2013). However, only 31.9 per cent of the Thai population (NBTC, 2013) and 40 per cent of Vietnamese users (Freedom, 2012) use online services via their mobile phones. On the other hand, the number of residential Internet users account for 26 per cent (approximately 20 million users) and 36 per cent (approximately 30 million users) of the population in Thailand and Vietnam respectively (WorldBank, 2014). As such, in contrast to ISP users who generally take advantage of online information support, the majority of mobile phone service customers would most likely ignore the online information support. Hence, it can be assumed that customers of an ISP are more likely to access the company’s website to look for information support as compared to customers of other telecommunication services. On the basis of above discussion, this study aims to provide a more holistic picture on the unique dimensions of an ISP’s service quality. Several researchers (Ringle et al., 2013) suggest that studying a single homogenous population in path models is insufficient to understand the path relationships as customers’ characteristic and

the nature of their demand for services differ (Mazzoni et al., 2007; Ringle et al., 2013). Segmentation is the process of subdividing a heterogeneous market into homogeneous groups of customers who have similar characteristics or who respond to marketing activities in the same way (Ko et al., 2012). Nevertheless, there is hardly any evidence of how effective segmentation is operationalised for an ISP’s customers. This study segments customers of ISPs based on their usage pattern, which is one of the most logical basis of segmentation in similar types of services (Mazzoni et al., 2007; Wedel and Kamakura, 2003). Segmenting markets by consumption patterns is a relatively intuitive step toward comprehending customers (Weinstein, 2002). By categorising customers into usage groups, service providers can create suitable marketing strategies for each segment. Furthermore, segmentation by usage is helpful in assessing the profitability of customer retention, as well as developing retention strategies (McDougall, 2001). In a similar vein, Weinstein (2002) concludes that usage analysis can support customer retention accomplishments. Based on the foregoing discussion, the objectives of this research study are threefold: first, to identify the relevant service quality dimensions for an ISP; second, to evaluate their effects on an ISP’s customer’s value, trust, and commitment; and third, to investigate service perceptions of different market segments. In order to achieve the research objectives stated above, a model is proposed as depicted in Fig. 1. Section 2 reviews the literature and develops hypotheses. Next, data collection and analysis using the structural equation modelling technique of comparing alternative mediation models are reported including the testing of hypotheses. The paper concludes with a discussion of the results, implications of the research as well as limitations and future research direction.

2. Literature review and development of hypotheses Customer commitment is influenced by customers’ value (Tai, 2011), trust (Wu et al., 2010) and service quality (Thaichon et al., 2012). Customer commitment has been defined as a customer’s conviction to maintain a relationship that might produce functional and emotional benefits (Tuškej et al., 2013). Lin and Wu (2011) consider customer commitment as a customer’s persistent



wish and attempt to retain a relationship with a service provider. Trust is known to be a foundation of a long-term relationship, possibly building an advanced exchange relationship between buyers and sellers (Hong and Cho, 2011). Customer trust refers to the customers’ perceptions of attributes of service providers, including the ability, integrity, and benevolence of the providers (Deng et al., 2010). Additionally, customer trust relates to the perception of customers relating to the ability of a brand to fulfil its promise while expertise refers to a brand’s capability of realising its promises (Ou et al., 2011). Customer value has often been described as an exchange between what customers receive and what customers have to give to purchase a service (Lai et al., 2009; Shirin and Puth, 2011; Tam, 2012). Sweeney and Soutar (2001) introduce four types of values based on the work by Sheth et al. (1991). These are functional value involving performance quality, cost/sacrifice value involving price/value for money, emotional value referring to feelings or affective states generated by a service, and social value relating to an enhancement of social self-concept. This study deals only with functional value (performance quality) and cost/sacrifice value (price/value for money) which are directly related to an ISP’s service quality and are considered to be important with respect to customers’ usage intentions and behaviour in the telecommunications sector (Kim, 2012; Vlachos and Vrechopoulos, 2008). Previous research reveals that overall service quality in the telecommunications industry is associated with customers’ perceptions of a stable and strong network quality (Lai et al., 2009), ready-to-serve customer support team (Aydin and Özer, 2005), informative quality (Thaichon et al., 2014) and a high level of security and privacy that is trusted by customers (Roca et al., 2009). This study intends to identify this unique service quality measurement model using the abbreviated acronym “NCIS Quality Model” and each of the dimensions of this model are now discussed. In the telecommunications market, network quality is one of the most important drivers of overall service quality (Vlachos and Vrechopoulos, 2008). In the Internet service industry, network quality includes the quality and strength of the network signal, number of errors, downloading and uploading speed (Thaichon et al., 2012). Breaks in Internet connectivity can lead to poor perceptions of network quality in the customer’s perspective. In this respect, timely recovery of network connectivity is essential. Factors contributing to benefits or sacrifices in the relationship between customers and service providers lead to different perceptions of customer value (Wang and Lo, 2002). He and Li (2010) suggest that network quality is a positive driver of customer value in the mobile phone services sector in Taiwan. In addition, a service provider whose core performance meets or exceeds the expectations of customers is likely to develop more trusting relationships with its customers (Eisingerich and Bell, 2008). Hence, it can be posited that network quality is positively related to customer trust. On the other hand, attachment to the company can be built through cognitive evaluation of service performance (Fullerton, 2005). Although scant evidence has been found in the direct relationship between network quality and commitment in the Internet services market, Fullerton (2005) concludes that service quality is a direct antecedent of customer commitment which is also supported by Thaichon, Lobo and Mitsis (2014). Consistent with the foregoing discussion, the following hypotheses are postulated: H1a. Network quality is positively related to customer value. H1b. Network quality is positively related to customer trust. H1c. Network quality is positively related to customer commitment.

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Telecommunication companies can offer additional service attributes (Wang and Wu, 2012) to enhance the quality of their services (Tam, 2012) such as superior customer service and aftersales support. Customer service and technical support provide touch points between the company and their customers, and is a critical dimension of service quality in the Turkish telecommunications industry (Aydin and Özer, 2005). Customers pursue cordial relationships with the company which gives due importance to their thoughts, emotions and concerns (Eisingerich and Bell, 2008). These authors suggest that considerate, caring and responsive customer service can stimulate confidence in customers. Previous research reveals that customer service influences customer commitment in UK retail banks (Malhotra et al., 2013). Also, customer service has an impact on customer trust in financial services and on value perceptions of customers in the communication services in the United States (Blocker, 2011). Hence, the following hypotheses have been developed: H2a. Customer service and technical support are positively related to customer value. H2b. Customer service and technical support are positively related to customer trust. H2c. Customer service and technical support are positively related to customer commitment. The combination of information and communication technology generates massive impact on society by connecting businesses and their customers via the Internet (Asmussen et al., 2013). In fact, many businesses rely on the Internet as a main communication channel (Lee et al., 2012). Information quality refers to the accuracy, completeness, presentation and format of the information given by service providers (Elliot et al., 2013), and has been considered as an important component of service quality (Yang et al., 2005). Information quality positively influences customer trust in the Chinese online travel agency business (Elliot et al., 2013). In addition, Kim and Niehm (2009) reveal that perceived information quality is significantly related to perceived value in the apparel retailing sector. Customers can evaluate value directly or indirectly via information provided by the websites (Grewal et al., 2003).Moreover, information quality reflects quality of the service (Kim and Niehm, 2009), and service quality has an influence on perceived value (Parasuraman and Grewal, 2000; Tam, 2004), as well as, customer commitment (Fullerton, 2005; Morgan and Hunt, 1994). Hence, it is reasonable to assume that the quality of information is related to perceived value and customer commitment. The foregoing discussion supports the following hypotheses: H3a. Information quality is positively related to customer value. H3b. Information quality is positively related to customer trust. H3c. Information quality is positively related to customer commitment. Security and privacy are associated with customers’ feelings of protection and safety during their transactions and usage (Vlachos and Vrechopoulos, 2008). Security of payments and privacy of personal information are positively related to service quality in e-commerce (Ha and Stoel, 2012). A trustworthy e-commerce service provider is often associated with fewer privacy concerns (Cases et al., 2010). Wu et al. (2010) report that website privacy policies enhance trust among virtual community members. Moreover, customers tend to believe that it is safe to purchase services from providers who possess good reputation with regards to their security practice (Roca et al., 2009). In other words, a transparent and reliable security and privacy policy is likely to generate favourable perceptions of overall ISP’s service quality. Hence,


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security and privacy are identified as dimensions of service quality (Ladhari, 2010; White and Nteli, 2004). By utilising the existing evidence on the relationship between service quality, and value (Tam, 2012; Wang and Wu, 2012) and commitment (Fullerton, 2005), this study proposes that security and privacy are also related to value and commitment. Based on the foregoing discussion the following hypotheses have been developed: H4a. Security and privacy are positively related to customer value. H4b. Security and privacy are positively related to customer trust. H4c. Security and privacy are positively related to customer commitment. The proposed conceptual model showing the relationships of the various constructs discussed so far is depicted in Fig. 1. The second set of constructs in Fig. 1 explores the underlying relationships between customers’ evaluations of value, trust and commitment. Previous research has shown that customer commitment is positively related to repeat purchase, and propensity to stay in the relationship (Fullerton, 2005). Relationship commitment is driven by functional and emotional benefits (Tuškej et al., 2013). In addition, consumer buying behaviour theory suggests that customers consider both the loss and gain of a certain decision (Hauser and Wernerfelt, 1990; Ratchford, 1982). Furthermore, customer value involves perceived trade-off between benefits and sacrifices in relationships (Blocker, 2011). Therefore, the value obtained by remaining with the company may enhance motivations for customer commitment (Lacey, 2007). Musa et al. (2005) support this view by asserting that the perception of value is derived from the direct sales consumption experience and has a positive effect on relational commitment. Tai (2011) confirms that relationship commitment is positively influenced by the functional and relational value in the information sharing services. Previous research postulates that there is a positive relationship between perceived value and customer trust, as perceived value can enhance customers’ perceptions of their service providers’ ability, reliability and benevolence, thereby increasing their confidence in purchasing the service (Chen and Chang, 2012). In line with this thinking, Chen and Chang (2012) report that perceived value has a positive effect on trust in a green marketing context as well as in mobile telecommunication services (Karjaluoto et al., 2012). Hence, the literature review informs the following hypotheses: H5. Perceived value is positively related to customers’ commitment.

present (Wu et al., 2010). Based on the above discussions, the following has been hypothesised: H7. Customers’ trust is positively related to their commitment. Different customers have distinctive needs and require tailored approaches (Mazzoni et al., 2007; Ringle et al., 2013). Based on their usage pattern, ISP customers are generally segmented to be heavy, medium and light users. On average, an Internet user spends from 9 h to as much as 20 h weekly (ACMA, 2012). Heavy users are those who spend more than 29 h on the Internet every week, whilst light users are those who use the Internet for less than 9 h per week (Assael, 2005). A study by Electronic Transactions Development Agency (ETDA, 2013) reveals that in general Thai Internet users who spend less than 11 h per week online account for 35.7 per cent; those who spend between 11 and 20 h per week online make up 25.8 per cent; 10.7 per cent of Thai users spend 21–41 h on the Internet weekly; and 27.8 per cent spend more than 41 h weekly. This study adapting usage segmentation from previous research, categorises three main groups of Internet users which are: light (i.e. less than 9 h per week), medium (i.e. 9– 29 h per week), and heavy users (i.e. more than 29 h per week). Furthermore, it is most likely that each specific service quality dimension distinctively impacts customers’ value, trust and commitment, depending on differently segmented groups of customers (Ringle et al., 2013). Hence, the following has been hypothesised: H8. The relationships between service quality dimensions and value, trust and commitment differ across different segments. 3. Method 3.1. The study sample To test the hypotheses, an online survey was designed and conducted in all regions of Thailand. Thailand is ranked third in South East Asia by way of residential Internet usage with an estimated 17,483,000 Internet users in 2009 (CIA, 2013) and over 24 million Internet users in 2012 (IWS, 2013). The number in 2012 represented over one-third of the Thai population. The competition among residential Internet service providers in Thailand is intense. Currently there are three majors ISPs and sixteen smaller ones across the country (Thaichon and Quach, 2013). In this highly competitive market, the churn rate of Internet users was approximately 12 per cent in 2009 (Thaichon et al., 2012). This scenario, therefore, poses huge challenges to ISPs especially in the area of customers’ repurchase intention.

H6. Perceived value is positively related to customers’ trust. Customer trust is a primary element of long-term relationship marketing and an essential antecedent of purchase behavior (Benedicktus, 2011). Customer trust can be evaluated by exploring how customers feel about their service provider in terms of the company’s honesty, responsibility and professional manners, and if the customers think that the firm understands and cares about them (Chiou, 2004). Trust together with commitment is important in establishing a long-term business relationship (Morgan and Hunt, 1994). Recent research demonstrates that the more a customer trusts the service provider, the more affectively committed he or she becomes (Perry et al., 2004). In fact, trust shows a positive and significant effect on the customers’ commitment in virtual community services (Wu et al., 2010), and in the Greek business-to-business services (Perry et al., 2004). Rutherford (2012) states that customer commitment increases as trust in the salesperson increases. This study considers trust as a precursor of commitment, which involves vulnerability and sacrifice. Commitment will most likely develop in relationships in which trust is

3.2. Measures Vlachos and Vrechopoulos (2008) connection quality scale was used to measure network quality. This scale examines connection quality for mobile phone services which is very similar to the nature of an ISP’s network quality. The customer service scale was sourced from Wolfinbarge and Gilly’s (2003) scale which addresses both customer service and technical support in an ISP’s offerings. Four different scales relating to information quality from Chae et al. (2002), Lin (2007), Kim and Niehm (2009) and Vlachos and Vrechopoulos (2008) were considered. After a thoughtful analysis, the Kim and Niehm’s (2009) information quality scale was selected as this scale has higher factor loadings (.80 .83), and Cronbach’s alpha (α ¼ .96). Vlachos and Vrechopoulos (2008) privacy scale was selected. The scale’s measurement items investigate whether customers feel safe parting with information during transactions, and also seek their opinions on security features of an ISP.



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Table 1 Instrument items and reliability indices.

NQ

CS

IW

SP

TRU

COM

VAL

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 This ISP provides sufficient information This ISP provides up-to-date information This ISP provides relevant information I feel that my personal information is protected at this ISP I feel that my financial information is protected at this ISP I feel that the transactions with this ISP are secured I trust this ISP I feel that I can rely on this ISP service I feel that this ISP will not deceive me in any way I feel involved with this ISP I am very proud to have this company as my service provider I feel attached to this ISP This Internet package is worth my money I would consider this Internet package to be a good buy I feel that I purchase a good Internet package with a reasonable price

.67 .83 .82 .89 .89 .80 .84 .82 .82 .74 .76 .90 .92 .93 .80 .86 .74 .89 .96 .96 .95

.82

.82

.60

.89

.90

.74

.86

.86

.68

.83

.85

.65

.90

.92

.78

.86

.87

.69

.96

.96

.89

Notes: FL ¼factor loadings, α¼ Cronbach’s alpha, CR ¼Construct reliability, AVE¼ Average variance extracted, CS ¼Customer Service; NQ ¼Network Quality; IW¼ Information Quality; SP ¼Security and Privacy; COM¼ Commitment; VAL ¼Value; TRU¼ Trust.

To assess customer’s trust, Aydin and Özer (2005) scale was selected. Items in this scale examine customers’ perceptions of a company’s honesty, responsibility and professional dealings. Customer commitment was operationalised using Eisingerich and Rubera’s (2010) scale which has relatively strong factor loadings (.716–.852) and reasonable Cronbach’s alpha (α ¼.865). Eisingerich and Rubera’s (2010) scale examines into customers’ feelings and sense of belonging to their service provider. Finally, Kim and Niehm’s (2009) perceived values scale was selected. This scale investigates customers’ evaluations toward the service offerings in terms of value for money. Respondents were required to rate their perceptions for every item using a Likert scale which was anchored at 1 for strongly disagree and 5 for strongly agree. These statements originally in English were translated into Thai language by a professional translator. The translated versions were then crosschecked by three other bilingual researchers to ensure face and content validity. The items of the survey are depicted in Table 1.

3.3. Data collection Data was collected from residential Internet users in Thailand. A selective customer database of a well-established major ISP in Thailand was utilised as the sampling frame. This database included customers throughout Thailand who were not locked into any fixed term contract with the ISP. It was a requirement that the participants were over 18 years of age and they should have used home Internet services. The web link of the online survey was relayed by the chosen ISPs to households in the sampling frame. The university’s Opinion platform was kept live for a period of three months. The final usable sample size was 1507. Approximately 55.1 per cent of the respondents were male, and 44.9 per cent were female. In the age profile, 22 per cent of the respondents were between 18 and 28 years, 38.8 per cent between 29 and 39 years, 24.5 per cent between 39 and 49 years and 14.7 per cent were 50 years or older. In terms of internet usage, 27.7 per cent of the respondents were light users, 23 per cent were medium users and 49.3 per cent were heavy users.

4. Data analysis 4.1. Factor analysis and validity testing Table 1 demonstrates the measurement items of each construct. Confirmatory factor analysis (CFA) using AMOS Version 20 (Analysis of Moment Structures) confirmed that the first-order four-factor model of ISP’s service quality (λ2(50) ¼233.11, po .0005, CFI ¼ .98, TLI ¼.98, SRMR¼ .027, and RMSEA ¼.05) produced a better fit than the second order service quality model (λ2(48)¼ 238.77, p o.0005, CFI¼.98, TLI¼ .97, SRMR¼ .03, and RMSEA¼ .05). Hence the first-order model was used for the subsequent analysis. Table 1 illustrates the 7 constructs and their Cronbach’s alpha, construct reliability and average variance extracted (AVE). It can be seen that factor loadings of all measurement items well exceeded the recommended .4 cut-off (Nunnally, 1978) and were statistically significant. The AVE for each factor was greater than .50, showing sufficient convergent validity (Fornell and Larcker, 1981). Discriminant validity was examined by calculating squared correlations coefficients between each pair of constructs and comparing them with the corresponding average variances extracted (AVE) of each construct. As all squared correlations coefficients were below AVEs, discriminant validity was confirmed (Fornell and Larcker, 1981). The composite reliability was also satisfactory. Furthermore, the correlations among all variables presented in Table 2 ranging from .53 to .89 were below the .90 cut-off (Tabachinick and Fidell, 2001), showing neither redundancy nor violation of multi-collinearity. 4.2. Structural modelling Structuring alternate and competitive models are recommended by Cronin et al. (2000) and McKenzie (1998), with a view to obtaining better explanations for the relationships amongst constructs. In addition to the Main Research Model (MRM), this study tests four other alternative models with service quality dimensions acting as initiators in the development of commitment. The MRM is the proposed model of this study and is illustrated in Fig. 1. The alternative models (Fig. 2) are Direct



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Effects Model (DEM), Simple Mediation Model (SMM), Double Mediation Model (DMM) and Real Mediation Model (RMM). The DEM presents one-way direct effects of the independent constructs (i.e. service quality dimensions, value, and trust) on the dependent construct (i.e. commitment). The second model, SMM, introduces the effect of service quality dimensions on commitment through value as a mediator. The third alternate view demonstrated in the DMM proposes that trust mediates the effect of value on commitment, whereas value is a mediator of the relationship between service quality dimensions and commitment. Finally, the RMM posits direct effects of service quality dimensions on value and trust, direct effects of value on trust, and direct effects of both value and trust on customer commitment. As the research model involves several latent constructs and different levels of mediating relationships, structural equation modelling (SEM) is the appropriate method of data analysis. The proposed main research model and other alternatives were tested using SEM with maximum likelihood estimation. Bias correct bootstrapping was also conducted to assist the mediation test as mentioned by Preacher and Kelley (2011).

Table 2 Correlations among all variables.

SP IW CS NQ VAL TRU COM

SP

IW

CS

NQ

VAL

TRU

COM

.60a .77a .61a .58a .64a .78a .65a

.68a .75a .69a .66a .80a .70a

.74a .60a .53a .67a .54a

.60a .67a .69a .58a

.89a .74a .66a

.78a .82a

.69a

Notes: CS ¼ Customer Service; NQ¼ Network Quality; IW¼ Information Quality; SP ¼Security and Privacy; COM¼ Commitment; VAL ¼ Value; TRU¼ Trust. a

All correlations are significant at the .01 level (2-tailed).

Both the DEM and the SMM resulted in a poor fit (λ2(177)¼ 3716.30, GFI¼ .83, TLI ¼.84, CFI¼ .87, RMSEA ¼.12, AIC ¼3824.30; and λ2(177)¼ 2721.51, GFI¼ .90, TLI¼.89, CFI¼.90, RMSEA ¼.10, AIC ¼2829.51 respectively). Although having slightly improved fit indices, the DMM (λ2(176) ¼1657.26, GFI ¼.91, TLI¼ .93, CFI¼.94, RMSEA¼ .08, AIC ¼1767.26) was also not satisfactory in representing the influence of service quality dimensions on customer commitment. This conclusion is further confirmed by better fit indices in the RMM as the chi square statistics decreased to λ2(172) ¼ 988.78, and other fit indices were much improved: GFI¼.94, TLI ¼.93, CFI¼.97, RMSEA ¼ .06, AIC ¼1106.78. The fit indices in the RMM and the main research model (MRM) were very similar. Because they were nested models, a chi square difference test was conducted to compare these models. The results show that the difference in Chi square test was significant when comparing the MRM with RMM (λ2(4)¼15.91; p ¼.00). As a result, the more complicated model, which is MRM, is preferred. In other words, the importance of the comprehensive direct and indirect effects of network quality, customer service and technical support, information quality, and privacy and security on customer commitment is established. For the MRM, although the Chi square statistics was significant (p ¼.000), which can be explained owing to the relatively large sample size (41000), other fit indices (CMIN/DF ¼5.79, GFI¼.94, TLI¼.96, CFI¼.97, RMSEA ¼.06, AIC ¼1098.87) indicate that the structural model was a good fit to the data. The model explained 76.6 per cent of the variance in trust (R2 ¼.77), 55 per cent in value (R2 ¼.55) and 73.3 per cent in commitment (R2 ¼.73). Whereas all service quality dimensions directly influenced customer trust, only the effects of network quality, information quality, and privacy and security on value were found to be significant. Also, the direct impact on customer commitment was confirmed only for information quality. The results are depicted in Table 3. Table 4 shows the standardised total, direct, and indirect effects of the predictors on their criterions based on the results of bias corrected bootstrapping. As Tables 3 and 4 demonstrate, mediating

Direct Effect Models

Simple Mediation Model

Double Mediation Model

Real Mediation Model

Fig. 2. Alternative models.


P. Thaichon et al. / Journal of Retailing and Consumer Services ∎ (∎∎∎∎) ∎∎∎–∎∎∎ Table 3 Regression weights in the main research model. Path o— o— o— o— o— o— o— o— o— o— o— o— o— o— o—

VAL VAL VAL VAL TRU TRU TRU TRU TRU COM COM COM COM COM COM

NQ CS IW SP NQ CS IW SP VAL NQ CS IW SP TRU VAL

Estimates

S.E.

C.R.

.43 .02 .26 .34 .12 .11 .27 .33 .22 .05 .10 .17 .09 .80 .07

.04 .05 .07 .05 .03 .03 .05 .03 .02 .03 .04 .06 .04 .05 .03

11.59 .36 3.79 7.37 4.37 3.54 5.64 9.77 10.35 1.60 2.78 3.12 2.30 15.62 2.85

p nnn

.72 nnn nnn nnn nnn nnn nnn nnn

.11 .01 .00 .02 nnn

.00

Notes: CS ¼Customer Service; NQ¼ Network Quality; IW ¼Information Quality; SP ¼ Security and Privacy; COM¼ Commitment; VAL ¼ Value; TRU¼ Trust. Full model: λ2(168) ¼ 972.87, CMIN/DF¼ 5.79, GFI ¼.94, AGFI¼ .92, TLI¼ .96, CFI¼ .97, RMSEA ¼ .06, 90% CI ¼ .05:.06; SRMR ¼.03. nnn

p r .001.

7

conditions set up by Baron and Kenny (1986) were satisfied. Except for customer service and technical support, the relationship of all the other service quality dimensions with trust was mediated by value. While network quality and security and privacy fully manifested their effects on commitment through trust and value, trust and value partly mediated the relationship between information quality, and commitment. Apart from that, trust was also a partial mediator in the relationship between value and commitment. Drawing upon the above findings, network quality directly influenced customer trust and value, and indirectly affected customer commitment, supporting H1a and H1b, and partially confirming H1c. The influence of customer service and technical support was only found significant on trust; thus, H2a and H2c were rejected whereas H2b received support. The direct effects of information quality on trust, value, and commitment were significant, providing evidence for H3a, H3b, and H3c. Security and privacy was positively and directly related to trust and value while only the indirect effect of this dimension on commitment was evidenced. Therefore H4a, and H4b were supported, and H4c was partially confirmed. In addition, direct relationships between trust and commitment, between value and commitment, and between

Table 4 Standardized total, direct and indirect effects in the main research model. VAL

TRU

Std. DE

Std. IE

nnn

NQ CS IW SP VAL TRU

.38 .01 .19nn .28nnn – –

.00 .00 .00 .00 – –

Std. TE

Std. DE

nnn

.38 .01 .19nn .28nnn – –

nnn

.12 .10nnn .23nnn .31nnn .26nnn –

COM Std. IE

Std. TE

nnn

.10 .01 .05nn .07nnn .00 –

Std. DE

nnn

.05 .09 .15n .09 .09 .83

.22 .09nn .28nnn .38nnn .26nnn –

Std. IE nnn

.22 .08nnn .25nnn .34nnn .21nnn .00

Std. TE .16nnn .01 .40nnn .25nnn .30nnn .83nnn

Notes: CS ¼Customer Service; NQ ¼Network Quality; IW¼ Information Quality; SP ¼ Security and Privacy; COM¼Commitment; VAL ¼Value; TRU¼ Trust; Std. DE ¼ Standardized Direct Effect; Std. IE ¼Standardized Indirect Effect; Std. TE ¼Standardized Total Effect. n

pr .05. p r.01. nnn p r .001. nn

Table 5 Structural results for different internet user groups in the main research model. Path VAL VAL VAL VAL TRU TRU TRU TRU TRU COM COM COM COM COM

o— o— o— o— o— o— o— o— o— o— o— o— o— o—

NQ CS IW SP NQ CS IW SP VAL NQ CS IW SP VAL

Light users

Medium users

Heavy users

.36nnn .09 .22 .37nnn .07nnn .15n .29nnn .34nnn .20nnn .04 .11 .26nn .09 .02

.53nnn .14 .09 .40nnn .07 .05 .31nn .32nnn .24nnn .13 .06 .17 .04 .13n

.43nnn .14n .40nnn .30nnn .16nnn .13nn .24nnn .33nnn .24nnn .06 .10 .11 .10 .08n

COM o — TRU .77nnn Goodness of fit indices

λ2(168) ¼ 422.10, CMIN/DF ¼ 2.51, GFI ¼.91, AGFI¼ .88, TLI¼ .96, CFI¼ .97, RMSEA ¼.06, 90% CI ¼ .05:.07

.89nnn

.74nnn

λ2(168) ¼ 359.17, CMIN/DF¼2.14, GFI¼ .91, AGFI¼ .88, TLI¼ .96, CFI¼ .97, RMSEA ¼.06, 90% CI ¼.05:.07

λ(168) ¼ 588.98, CMIN/DF ¼ 3.51, GFI¼ .93, AGFI¼ .90, TLI ¼.96, CFI ¼.97, RMSEA ¼ .06, 90% CI ¼.05:.06

Chi Square Δλ2(28)¼ 46.14, p ¼ .02 difference test Notes: CS¼ Customer Service; NQ ¼Network Quality; IW ¼Information Quality; SP ¼ Security and Privacy; COM¼Commitment; VAL ¼Value; TRU¼Trust. n

p r .05. pr .01. nnn p r.001. nn



8

5

Table 6 Results of regressing value against customer service and information quality. Adjusted R square

Light users

.24

Medium users

.25

F

Information quality

Light users

Medium users

.63

1

127.80 (Constant)

1.06

0

204.03 (Constant)

.35

Customer service 229.22 (Constant) Information quality 165.40 (Constant) Information quality

Heavy users

.37

Medium users Heavy users 1

3.96

2

3

4

5

Information Quality

.60

Fig. 4. The relationship between information quality, and perceived value among light, medium and heavy users.

.52 .69 .74 3.13 .25

442.17 (Constant) Information quality

.66 .75

5 4

Value

Light users

2

.96

.27

.32

3

156.28 (Constant) Customer service

Customer service Heavy users

4

Unstandardized coefficients

Value

Customer service

Usage group

This result suggests that the effects of customer service, and information quality on value were not the same for people from different Internet usage groups (Δχ2(2)¼7.84, p ¼.02 in the path of customer service and technical support to value; Δχ2(2) ¼7.30, p¼ .03 in the path of information quality to value). These findings demonstrate that the two paths were not invariant among customers from different Internet usage groups, indicating a strong moderating effect of Internet usage. As illustrated in Table 6, Figs. 3 and 4, heavy users were distinctively different from the others. Specifically, customer service had a negative influence on value among heavy users. Meanwhile, the positive impact of information quality on value perceived by heavy users was more significant as compared to that manifested by light and medium users. On the basis of these findings, H8 was partially supported.

3 Light users

2 1 0

5. Discussion

Medium users Heavy users 1

2

3

4

5

Customer Service Fig. 3. The relationship between customer service and perceived value among light, medium and heavy users.

trust and value were also established as shown in Table 3, confirming H5, H6, and H7. Additionally, H8 postulated the moderating effect of Internet usage on the paths from service quality elements towards value, trust and commitment. To examine the interaction effect, this study split the sample into three groups based on their usage level (light users: less than 9 h a week, medium user: 9–29 h a week; and, heavy users: more than 29 h a week) and determined paths at different levels of the moderating variable. As indicated in Table 5, the main research models of Internet usage show reasonable fit to the data. In addition, the difference of overall Chi square test was significant. To further confirm the differences in each structural path between the three groups of users, the structural models were separated for the three subsamples. The moderating effect was tested by constraining the twelve paths (from four quality dimensions to trust, value and commitment) to be equal, using the chisquare difference test for the effect of Internet usage. An unconstrained model that simultaneously fit all three usage groups was run and the paths of interest were fixed to be invariant in all groups to arrive at a constrained model (Cunningham, 2010). In the twelve models, only two models testing the paths from customer service, and information quality towards value resulted in significant difference in the chi-square test.

This study aims to examine the individual influence of each service quality dimension on an ISP’s customers’ affective and cognitive evaluation. The results confirm that network quality, customer service and technical support, information quality, and privacy and security have different influences on perceived value, trust and commitment. In addition, it was revealed that only information quality directly influenced customer commitment. The effect of customer service and technical support was found significant only on trust. The findings are considered reasonably robust as the model explained a considerable portion of variance in customer perceived value, trust and commitment. 5.1. The relationship between service quality dimensions and perceived value As expected network quality was the strongest predictor of perceived value. An ISP’s service package is considered to be of high value when it includes good network quality. This is likely as network quality is the core element of an ISP’s service offerings. The strength and stability of the network is usually the primary concern of customers when evaluating an ISP’s service. This finding echoes Tam’s (2012) research which highlights that network quality is an antecedent of perceived value. Similarly, security and privacy as well as information quality were also significant antecedents to value. This conclusion is not surprising considering the information era today. Customers are well aware of the importance and integrity of the personal information given to their service providers. They also assess the value of the service through the quality and quantity of the information they acquire from the providers. High-quality information tailored to the customer’s needs and wants allows customers to diminish the costs of seeking and handling information.



Surprisingly, the direct effect of customer service and technical support on value was not evident from the results. This finding contrasts the work of Blocker (2011) who found that customer service was directly related to perceived value. This can be explained because of the fact that in Asian cultures customers typically tend to avoid in-person interaction with the company’s personnel, especially when they experience lack of resources and support from the service provider. This is typical in Asian countries and is generally attributed to customers’ ego and value orientations (Neuliep, 2012). 5.2. The relationship between service quality dimensions and trust Although all the service quality dimensions influenced customers’ trust towards the service provider, security and privacy had the greatest influence. Internet services are characterised by a unique combination of online and offline transactions. Customers are able to choose to conduct their business with the ISPs online or using brick-and-mortar retailers. However, the role of Internet has become considerably important, especially to time-poor customers, and this has resulted in numerous cases of illegal disclosure of personal data as well as exposure of transaction data. Customers are highly concerned about the integrity of their ISP and the manner in which their privacy and security are guaranteed. In fact, millions of dollars were stated lost due to cybercrime in 2012 (IC3, 2012). In addition, according to a report by the Financial Times, basic personal information of customers is available at a cheap cost (Steel, 2013). As a result, the guarantee of privacy and security can enhance trust of an ISP’s customers which is similar to the findings of Cases et al. (2010). Moreover, an ISP’s customers are unable to see the company’ tangibles, making it harder for them to decide if a service provider is trustworthy. Hence, customers establish trust in their service provider on the basis of the network performance together with usability and clarity of the available information. Customers usually believe that a company which delivers stable and strong network quality is reliable. Similarly, as the information is adequate and transparent, customers will most likely perceive the company as being trustworthy. In addition, customer service and technical support have an impact on customer trust. In other words, customer trust increases through superior customer services. This is evident as customer service personnel are an important conduit between users and service providers. When customers experience superior service and technical support in handling their enquiries and complaints, they perceive the ISP to be reliable and sincere, which then translates into trust. 5.3. The relationship between service quality dimensions and commitment Interestingly, among four ISP service quality dimensions, only the direct effect of information quality on commitment was found significant. Customers develop a sense of belonging and commitment to an ISP based on perceived quality and quantity of the information made available to them by the company. This conclusion, once again, confirms the importance of information in servicing customers. Lack of support for the direct effects of other service quality dimensions on commitment may be possible as customers in Thailand consider network quality, customer service and privacy and security to be similar among major ISPs. This stems from compulsory industry standards and limited number of service providers. On the other hand, although only the effect of customer service on trust is evident, it is critical to remember that perceptions of customer service might influence other affective responses excluded in this study (for example, satisfaction), which would have an impact on commitment.

9

The absence of direct effects of network quality, and privacy and security on commitment was compromised by other indirect effects. The results reveal that value and trust were the precursors to commitment, thereby being the mediators in the relationship between network quality, and privacy and security and commitment. In fact the relationship between information quality and commitment was also partly mediated by customer trust and value. Furthermore, whilst both of the direct effect of value on trust and the effect of trust on commitment were significant, the direct effect of value on commitment also remained significant, signaling the presence of a partially mediated effect rather than an alternative fully mediated relationship. Hence, value had both a direct and an indirect effect on commitment. Table 4 indicates the direct and indirect effects of each predictor on the criteria constructs. Service quality dimensions influences cognitive evaluations (i.e. value), and affective responses (i.e. trust and commitment) which are important antecedents of customer loyalty. 5.4. Segmentation analysis The invariance tests for different Internet usage groups reveal some interesting findings. Internet usage moderated the effects of customer service and technical support, and information quality on value. Customer service and technical support had a negative effect on perceived value of heavy users. Also, information quality determined how heavy users perceive the value of their service package while this effect was absent in the other two groups. One possible explanation is that heavy users are usually conversant with technology and might consider the help from customer service personnel unnecessary. Moreover, they tend to spend considerable time on the Internet and prefer online tools, for example websites, to more conservative means of communications, for example face to face customer service. In addition, as mentioned earlier, Asian customers are more likely to avoid direct communications with an ISP’s staff in the interest of their ego. Heavy users usually take pride in their Internet expertise and hesitate to admit that they lack knowledge. As a result, customer service might not add any value to the service package provided for heavy users.

6. Theoretical and managerial implications 6.1. Theoretical implications Though widely discussed in the relevant literature, service quality is rarely researched in the ISP context. The findings of the study demonstrate several theoretical contributions. Drawing upon previous studies (e.g. Aydin and Özer, 2005; Lai et al., 2009; Roca et al., 2009) and taking the unique characteristics of ISP services into account, the current study identifies ISP’s service quality dimensions, namely network quality, customer service and technical support, information quality, and privacy and security, and examines the relationships among ISP service quality dimensions, perceived value, customer trust and commitment. Finding of the significant effects on customer cognitive and affective responses exerted by identified service quality factors has implications for customer satisfaction and loyalty research. Establishment of the mediation model enriches service quality literature and provides insights into customer commitment research by incorporating customer value and trust into the quality–commitment relationship. This finding presents a new perspective on and challenge to the traditional chain relationship of service quality, customer satisfaction and commitment (loyalty or retention) proposed by Heskett et al. (1994). Furthermore, this study extends the service quality literature by including customer segmentation in the analysis. Consistent


10


with the findings of Homburg and Giering (2001) this study confirms the mediating role of customer characteristics in the relationship among psychological factors. Internet usage mediates the link between customer service and technical support, as well as information quality and perceived value. The effects of customer service and technical support, and information quality on perceived value differ across different Internet usage groups. 6.2. Managerial implications This research has developed an understanding of customer behaviour relating to home Internet services. The results emphasise the critical role of service quality and its dimensions, and highlight the significance of ISPs dedicating resources in improving the service quality dimensions. Nowadays customers are geared with available information and tools to make comparisons between services and companies. Hence it is crucial that companies invest in improving service quality in order to induce positive cognitive and affective responses of customers and eventually increase their loyalty. The findings suggest that management should guarantee network consistency and reliability, and satisfactory Internet speed. As network quality becomes more reliable among service providers, a transparent policy of privacy protection and ensured transaction security can provide ISPs with a strong point of differentiation. Findings from this study also highlight the need for management to establish easy access and informative channels, especially websites, in order to provide adequate information suited to customer needs and wants. The company should keep in mind that these channels are means of information provision and communications, hence they should have user friendly interfaces and reliable functions. Furthermore, ISPs should attend to the aspects pertaining to customer service and technical support. Customer service and technical support personnel should also be readily available. Staff with appropriate skills and competence must demonstrate sincere interest in dealing with problems or issues in their responses to customers’ enquiries and complaints. This study suggests that ISPs delve deeper into customer segmentation in order to effectively and efficiently understand the market and develop appropriate marketing strategies for different segments. In particular, ISPs should distinguish heavy Internet users from other segments. On the basis of the finding that customer service and information support made exclusive and significant contribution to the heavy users’ perception of value, ISPs should offer different service package for these segments with a view to differentiating service offering and maximizing use of organizational resources. For example, information package could be designed in accordance with different levels of Internet usage and knowledge, such as novice, intermediate and expert. Communications material should be tailored to suit each segment on the basis of their characteristics. Similarly, customer care could be varied by offering customers with a wide range of alternatives, for instance, face to face consultation, online support, and one-off or periodical follow-ups relating to technical issues. These would eventually result in significant long-term profitability. Noticeably, service providers should be aware of the negative effect of customer service on value among heavy users. Many ISPs attempt to create rapport with their customers through follow-up calls or emails which might have adverse effects on heavy users due to the orientations of value and face (Neuliep, 2012). Consequently, communications with heavy users should be succinct and covert. It is posited that heavy Internet users tend to be less comfortable communicating and establishing relationships offline (Thayer and Ray, 2006). Therefore, instead of face to face consultation, online support could be a more appropriate alternative for heavy users. While being convenient, interactions through website environment may be more favourable for this group of Internet users as heavy

Internet users spend considerable time on the Internet and are most likely to prefer Internet related activities to non-Internet ones (Assael, 2005). Hence, it is advisable for ISPs to focus on the design of the information support platform in both online and offline environment in order to increase the perceived value of heavy users. In contrast to heavy users, light users should be approached differently. Light users tend to emphasise security and privacy in their evaluation of service value. Therefore, a thorough explanation of security and privacy policy are recommended for light users. In addition, information support demonstrates a significant effect on commitment among light users. ISPs could provide information package tailored to the needs and wants of customers in this Internet usage group. Light users are usually novices, and more likely to prefer simple information than complicated descriptions. Hence, any communications material intended for this segment should be specialised at the beginner level. Moreover, although it is not directly related to value and commitment, customer service is an important factor of trust, a precursor to customer commitment. Since light users spend the least time online among the three usage segments, they probably choose face to face support over online support. Follow-up calls and emails are also a good idea to improve customer experience with an ISP service. Finally, whereas network quality is the predominant factor in determining value, privacy and security has the strongest influence on trust of medium users. In addition to clarify the transparency and reliability of the company’ security and privacy practice, an ISP could also promote high network quality via their communications to medium users, for example, brochures or flyers. Medium users are expected to have better knowledge of Internet than light users albeit less knowledgeable than heavy users. Therefore, intermediate level of information is considered appropriate. On the other hand, the role of customer support appears to be less significant in the development of medium users’ commitment towards their ISP. Although service quality dimensions demonstrate various effects on the outcome variables among the different segments, ISPs should endeavor to ensure delivering optimal services to these segments and avoid perceived prejudice by customers which may affect their perception of the firm’s service quality and subsequently their commitment.

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