The role of service value and switching barriers in an integrated model of behavioural intentions

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The role of service value and switching

barriers in an integrated model of

behavioural intentions

Department of Marketing and Logistics , Vanung University , Tanyuan, Taiwan

b

Department of Marketing , Guanghua School of Management , Peking University , Beijing, China

c

Department of Transportation Technology and Management , National Chiao Tung University , Hsinchu, Taiwan

Published online: 20 Oct 2011.

To cite this article: Tim Lu , Rungting Tu & William Jen (2011) The role of service value and

switching barriers in an integrated model of behavioural intentions, Total Quality Management & Business Excellence, 22:10, 1071-1089, DOI: 10.1080/14783363.2011.614867

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The role of service value and switching barriers in an integrated

model of behavioural intentions

Tim Lua, Rungting Tuband William Jenc∗

a

Department of Marketing and Logistics, Vanung University, Tanyuan, Taiwan;bDepartment of Marketing, Guanghua School of Management, Peking University, Beijing, China;cDepartment of Transportation Technology and Management, National Chiao Tung University, Hsinchu, Taiwan

There are many ways to increase consumers’ purchase intentions, and most studies explore this issue by adopting the ‘quality – satisfaction – behavioural intentions’ paradigm. However, this approach may overlook a few important factors including customer sacrifice, perceived value, and switching barriers. This study proposes an integrated model that incorporates the service value and switching barriers perspectives to further examine the formation of behavioural intentions. Specifically, our research model synthesises two key determinants of service values (service sacrifice and service quality) and the two main switching barriers (switching cost and alternative attractiveness). Furthermore, because previous studies on the links among these constructs are rather divergent and fragmented, we build structural causal relationships among these factors to explain consumers’ decision processes. Several competing theories are also presented and compared to this new research model. The results, as hypothesised, suggest that service value is the most effective predictor of satisfaction and customers’ behavioural intentions; the findings also support previous research indicating that cognitive evaluations precede emotional responses. We highlight the delivery of service value as a strategic imperative and stress the research stream of service value measurement.

Keywords:service quality; service value; satisfaction; switching barriers; behavioural intentions

Introduction

Research has shown that customer behavioural intention can directly influence a com-pany’s profits (Cronin, Brady, & Hult, 2000; Oh, 1999; Zeithaml, 1988). Specifically, the company makes a profit from customers’ favourable behavioural intentions, which refers to customers’ willingness to praise the company, recommend them to other custo-mers, remain loyal to them, and increase the volume of their purchase (Cronin & Taylor, 1992; Cronin et al., 2000; Zeithaml, Berry, & Parasuraman, 1996). Most researchers argue that the more favourable a customer’s behavioural intentions, the greater the possibility of that customer’s retention, and the less likely a switch to a competitor, securing financial benefits to the firms currently serving the customers. Previous research has also shown that one or both of the following strategies are often used to encourage desirable consumer behavioural intentions (Fornell, 1992; Patterson & Smith, 2003): establishing customers’ true attitudinal loyalty so that they have little interest in competitors’ offerings (Bolton & Drew, 1991; Cronin et al., 2000); and/or raising switching barriers so that customers are

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∗_{Corresponding author. Email: wljen@mail.nctu.edu.tw}

unlikely to change to an alternative provider (Balabanis, Reynolds, & Simintiras, 2006; Jones, Mothersbaugh, & Beatty, 2000; Liu, Leach, & Bernhardt, 2005; Wathne, Biong, & Heide, 2001).

To better understand the influence of customers’ true attitudinal loyalty, researchers have started to focus on the cognitive aspects of decision making. Among these studies, there has been a great body of research emphasising the relationships among service quality, satisfaction, and behavioural intentions (Choi, Cho, Lee, Lee, & Kim, 2004; Cronin et al., 2000; Dabholkar, Shepherd, & Thorpe, 2000; Oliver, 1993). Research indi-cates that when customers receive higher service quality, they are more satisfied and have more positive behavioural intentions. The ‘quality – satisfaction – behavioural intentions’ paradigm mainly concentrates on the benefit aspect of service. However, other research suggests that the sacrifice aspect, including price, time, and effort, should also be included (Dodds, Mornoe, & Grewal, 1991; Grewal, Monroe, & Krishnan, 1998; Zeithaml, 1988). Adopting this view, researchers further propose that service value, i.e. the trade-off between perceived benefits and perceived sacrifice (PB/PS), can provide a more compre-hensive viewpoint of behavioural intentions (Bolton & Drew, 1991; Grewal et al., 1998; Monroe, 1991). Some researchers emphasise that service value is a strategic imperative for behavioural intentions (Caruana, Money, & Berthon, 2000; Choi et al., 2004; Lapierre, Filiatrault, & Chebat, 1999; Pura, 2005). However, there is no consistent conclusion regarding the relationships among quality, sacrifice, value, satisfaction, and behavioural intentions. This study aims at providing a more complete view of consumer behavioural intentions by integrating service value into the ‘quality – satisfaction – behavioural inten-tions’ paradigm and further examining the interrelationships among the factors.

Furthermore, due to the high costs of attracting new customers and the infeasibility of satisfying every customer, companies also raise different barriers to encourage customer loyalty, such as imposing higher switching costs or developing alternative ties to mers, e.g. frequent flier programmes or loyalty programmes, in an effort to ‘lock’ custo-mers into an existing relationship with that service provider (Bansal & Taylor, 1999; Jones et al., 2000; Patterson & Smith, 2003). When switching barriers are high, customers find it difficult to defect even if they are not very satisfied or experience short-term fluctuations in service quality (Ranaweera & Prabhu, 2003; Wathne et al., 2001). Despite the fact that the role of switching barriers has received more attention recently, there is little knowledge regarding the relationship between switching barriers and customers’ attitudinal loyalty. The complex interrelationships among service sacrifice, service quality, service value, sat-isfaction, switching barriers, and behavioural intentions are particularly not well under-stood. Moreover, few existing studies have empirically investigated these constructs in an integrated framework.

The main purpose of this study, as indicated, is to propose an integrated model that incorporates service value and switching barriers perspectives into the ‘quality – satisfac-tion – behavioural intensatisfac-tions’ paradigm (see Figure 1). Our model helps to (1) extend exist-ing consumer behavioural intentions models which focus mostly on satisfaction, and (2) clarify the relationships among service sacrifice, service quality, service value, satisfac-tion, and switching barriers in the prediction of behavioural intentions.

Theoretical background

Relationships among service quality, satisfaction, and behavioural intentions

There are several areas of study regarding customers’ behavioural intentions, such as total quality management, customer satisfaction management, and customer value management.

These research streams often begin with the study of service quality and then carry through to satisfaction research, which is called the ‘quality – satisfaction – behavioural intentions’ paradigm (Caruana et al., 2000; Dabholkar et al., 2000; Lapierre et al., 1999). Existing lit-erature notes past use of expectancy disconfirmation theory in interpreting the two concepts of service quality and satisfaction (Oliver, 1993; Parasuraman, Zeithaml, & Berry, 1988; Rust & Oliver, 1994); however, since the two constructs share similar properties, research-ers have not always been able to distinguish between them. Several studies attempt to provide conceptual and operational distinctions between service quality and satisfaction. For example, some researchers indicate that expectation in service quality can be viewed as either ‘ideal’ or ‘should’, whereas expectation in satisfaction refers to ‘desired’ or ‘will’ (Boulding, Kalra, Staelin, & Zeithaml, 1993). Some also argue that dimensions under-lying service quality are rather specific, whereas satisfaction judgements have a broader range of dimensions which also include service quality aspects (Oliver, 1993). Furthermore, satisfaction assessments require customer experience, whereas service quality does not (Cronin & Taylor, 1992; Parasuraman et al., 1988).

The undistinguishable characteristics of service quality and satisfaction further induce argument surrounding causality. Traditionally, research has suggested that satisfaction would lead to an overall evaluation about service quality (Boulding et al., 1993; Fornell, 1992; Parasuraman et al., 1988). From this viewpoint, satisfactory service quality experi-ences may lead consumers to develop and modify their global attitude in the long run, such that an accumulation of specific evaluation (satisfaction with a transaction) results in a global evaluation (service quality) (Lapierre et al., 1999; Ledden, Kalafatis, & Samouel, 2007). Thus, satisfaction is treated as an antecedent of service quality. However, a dominant view on the relationship between service quality and satisfaction is that service quality represents a cognitive judgement, whereas satisfaction is more of an effective evaluation (Cronin et al., 2000; Oliver, 1993). According to the multi-attribute atti-tude model framework, i.e. cognitionaffectconation model (Wilkie, 1986), service quality is an antecedent to satisfaction and in turn, drives behavioural intentions. Moreover, a theoretical justification also be attributed to the appraisalemotional responsecoping framework (Bagozzi, 1992), which indicates that satisfaction strongly mediates the effect of service quality on behavioural intentions. Hence, the relationship of service qual-itysatisfactionbehavioural intentions has been treated as a research paradigm, and several studies have found empirical support for this model (Brady, Robertson, &

Figure 1. Conceptual model.

Cronin, 2001; Choi et al., 2004; Dabholkar et al., 2000; Yu, Wu, Chiao, & Tai, 2005). According to previous research results, when customers perceive higher service quality, they feel more satisfied and exhibit more positive behavioural intentions. Building upon these findings, the first two hypotheses in this study are as follows:

H1: Customers’ perception of service quality has a positive impact on satisfaction. H2: Customers’ satisfaction has a positive impact on behavioural intentions.

Relationships among service sacrifice, service quality, and service value

In marketing literature, scholars highlight the delivery of service value as a strategic imperative for achieving customer loyalty and reducing defection rates (Lapierre et al., 1999; Ledden et al., 2007; Liu et al., 2005). These studies suggest that service value leads to favourable behavioural intentions (Wang, Lo, Chi, & Yang, 2004). Other research further indicates that service value represents a new paradigm that creates and sustains a competitive advantage and requires a more comprehensive approach than a simple focus on service quality or satisfaction (Ruiz, Gremler, Washurn, & Carrio´n, 2008).

Service value serves as a widespread construct which simultaneously integrates custo-mers’ perception of benefits and sacrifices. Researchers also reached considerable agree-ment in conceptualising service value as the trade-off between benefits and sacrifices: Zeithaml (1988) defines value as ‘customer’s overall assessment of the utility of a product based in perceptions of what is received and what is given’; Monroe (1991) defines customer perceived value as the relationship between PB/PS; and Day (2000) introduces the value equation as a mechanism for understanding service value, with the value represented as the difference between PB – PS. Hence, customers cognitively combine their perceptions of what they can get (i.e. benefits and/or gain) and what they have to give up (i.e. sacrifices and/or loss) in order to obtain a service. Furthermore, mar-keters can increase the value of their service by improving the service benefits, reducing sacrifice through productivity, or a combination of both.

Although previous research indicates that the key components of service value are PB and PS, service value has most often been operationalised in terms of the trade-off between service quality and price (Bolton & Drew, 1991; Dodds et al., 1991; Grewal et al., 1998; Zeithaml, 1988). Bolton and Drew (1991) have shown that quality and price perception influence value perceptions. Lapierre et al. (1999) suggest that perceived value of a service results in part from service quality. Caruana et al. (2000) refers to the idea that cus-tomers buy bundles of attributes that together represent a certain level of service quality offered by a firm at a certain price level. According to previous research, service quality is most commonly used to represent PB, and serves as an important element of service value. Researchers further offer significant empirical evidence in support of a relationship between service quality and service value (Cronin et al., 2000; Lam, Shankar, Erramolli, & Murthy, 2004; Oh, 1999; Yang & Peterson, 2004). Hence, when customers get better service quality from a firm, they perceive the firm offers a valuable service. Thus,

H3: Customers’ perception of service quality has a positive impact on service value.

Although service quality is an important element of service value, research suggests that price is another crucial component, in which consumers pay to acquire a service. Pre-vious research defines service sacrifice as what is given up or cost to acquire a service (Heskett, Sasser, & Hart, 1990; Zeithaml, 1988). In this regard, sacrifices can be divided into two types: monetary prices and non-monetary prices (Choi et al., 2004;

Wang et al., 2004). The former can be assessed by a direct measure of the dollar price of the service, and the latter can be defined as the time, effort, and energy invested by a cus-tomer to obtain that service. Since it can be difficult to measure non-monetary prices, most studies use monetary prices to assess service sacrifice (e.g. Heskett et al., 1990; Oh, 1999). However, researchers also indicate that for some customers or in certain situations, non-monetary prices could prove more important than non-monetary prices. For example, time-constrained consumers patronise convenience stores and increasingly shop online to save time (Ruiz et al., 2008; Wang et al., 2004). In this context, both time spent making the buying decision and time spent waiting to access, receive, and complete the service are relevant (Berry, Swider, & Grewal, 2002). Service sacrifice is one of the key determi-nants of customer perceived service value, and many empirical studies have confirmed the relationship between these two variables. These findings suggest that the more customers have to give up or sacrifice to acquire services, the less value they perceive in the services. Hence, we have:

H4: Customers’ perception of service sacrifice has a negative impact on service value.

Previous research also consistently agrees that service value is a stable construct with which to predict satisfaction and customers’ behavioural intentions (Anderson & Sriniva-san, 2003; Brady et al., 2001; Cronin et al., 2000; Grewal et al., 1998; Liu et al., 2005; Pura, 2005; Yang & Peterson, 2004). These studies find that customers’ value perceptions can increase satisfaction, brand preference, and willingness to buy, while decreasing cus-tomers’ search intentions for alternatives. Many empirical evidences from the health care service, sport service, telephone service, insurance service, and mobile service industries further support the relationship between service value and behavioural intentions (e.g. Bolton & Drew, 1991; Brady et al., 2001; Cronin et al., 2000; Yu et al., 2005). Thus, we propose that:

H5: Customers’ perception of service value has a positive impact on satisfaction.

H6: Customers’ perception of service value has a positive impact on behavioural intentions.

Relationship between service barriers and behavioural intentions

Emerging research suggests that two strategies are often used to increase customers’ behav-ioural intentions: (1) increasing customer satisfaction so that customers are willing to stay; and (2) making any switch difficult for the customer (Balabanis et al., 2006; Huang, Cheng, & Farn, 2007; Jones et al., 2000; Ranaweera & Prabhu, 2003). Studies further argue that sat-isfaction plays a lesser role when switching barriers are high. Jones et al. (2000) notes the importance of switching barriers in potentially fostering greater customer retention in general and helping companies to weather short-term fluctuations in service quality that might otherwise result in defection. Sharma and Patterson (2000) also indicate that custo-mers often face a considerable risk in switching to an alternative service provider because it is difficult to evaluate a service before actually purchasing it. It is sometimes a case of the ‘devil you know is better than the devil you don’t’. Research further shows that switching barriers explain more of the variation in repurchasing behaviour than satis-faction (Burnham, Frels, & Mahajan, 2003; Patterson & Smith, 2003). Hence, not only sat-isfaction but also switching barriers should be taken into account in forming a more complete framework for studying customers’ behavioural intentions.

Previous research notes that switching barriers refer to many factors, making it difficult or costly for customers to change providers (Jones et al., 2000). Many studies focus

discussion on the property of switching costs, i.e. costs incurred when a customer changes from one service provider to another. Researchers find that switching costs not only have economic in nature, but can have psychological and emotional characters as well (Burnham et al., 2003; Jones, Mothersbaugh, & Beatty, 2002; Sharma & Patterson, 2000; Yang & Peterson, 2004). For instance, customers wishing to switch to other service providers may be forced to terminate a current relationship in which they have already invested a great deal. Researchers view switching costs as sunk costs which rep-resent a customers’ perception of the non-recoupable money, time, and emotional effort involved in establishing and maintaining a friendly, quasi-social relationship with a service provider (Patterson & Smith, 2003). Furthermore, customers must secure an alternative prior to changing service providers. Hence, customers have to spend money, time, and effort, i.e. search costs, in order to look for an acceptable alternative service pro-vider (Jones et al., 2000). Other research also indicates that switching costs can not only be categorised into financial switching and procedural switching costs (i.e. search cost), but also relational switching costs causing psychological or emotional discomfort due to a loss of identity and the breaking of brands (Burnham et al., 2003; Vasudevan, Gaur, & Shinde, 2006). Hence, as the switching costs of an activity increase, the likelihood of customers engaging in such behaviour should diminish, and they are ‘locked’ into a relationship with the incumbent service provider.

Past research also emphasises that alternative attractiveness is an important factor when customers consider switching providers (Jones et al., 2000; Patterson & Smith, 2003; Wathne et al., 2001; Yim, Cjan, & Hung, 2007). Alternative attractiveness is con-ceptualised as the customer’s estimate of the likely satisfaction available in an alternative relationship (Patterson & Smith, 2003; Sharma & Patterson, 2000). Competitors encou-rage customers to switch by offering superior service quality, reputation, and brand image than those of the existing provider. Some new competitors even conduct marketing programmes to captivate customers, such as lower prices or enhanced value offers through a service portfolio whose breadth exceeds that of the current provider in meeting future needs (Wathne et al., 2001). Empirical evidence suggests that when the alternative attrac-tiveness increases, the expressed customer satisfaction with the existing provider decreases (Patterson & Smith, 2003; Yim et al., 2007) and repurchase intentions increase (Jones et al., 2000; Wathne et al., 2001). Comprehensively, if customers are either unaware of attractive alternatives or simply do not perceive them as any more attractive than the current relationship, they are likely to remain in the current relationship.

Although most studies show that switching barriers play a critical role in the process of improving customer behavioural intention, the effect of switching barriers on behavioural intentions is less than conclusive. One research stream indicates that switching barriers has main/direct effect on customer behavioural intentions (Burnham et al., 2003; Jones et al., 2002; Liu et al., 2005; Wathne et al., 2001), whereas the other proposes the moderating/ interaction effect of switching barriers (Balabanis et al., 2006; Jones et al., 2000; Patterson & Smith, 2003; Ranaweera & Prabhu, 2003; Sharma & Patterson, 2000). The former view-point suggests switching barriers represent one of the main antecedents of customer behav-ioural intentions: the higher the switching barrier, the more a customer is forced to stay with the incumbent provider (Jones et al., 2002; Liu et al., 2005) and the less the likelihood of the customer defecting (Wathne et al., 2001). Other studies find that switching barriers have moderate effects on the link between satisfaction and retention (Balabanis et al., 2006; Jones et al., 2000; Ranaweera & Prabhu, 2003) and the relationship between trust and commitment (Sharma & Patterson, 2000). These research results show that when switching barriers are substantial or the switching process especially painful, customers

remain in relationships with service providers even if not highly satisfied. Patterson and Smith (2003), however, propose that there are no significant interaction effects between satisfaction and switching barriers. On the basis of the foregoing arguments and evidence, we further propose:

H7: Customer perception of switching cost has a positive impact on behavioural intentions. H8: Customer perception of alternative attractiveness has a negative impact on behavioural intentions.

Competing models

As shown in the literature review, many researchers have clarified the relationships among service quality, service value, satisfaction, switching barriers, and such conse-quences as repurchase intention, customer loyalty, and switching behaviour. However, a detailed assessment of previous research shows little uniformity regarding which of the aforementioned variables, or combinations therein, directly affect behav-ioural intentions. For example, if the purpose of the research is to evaluate customer satisfaction implications, then the associated model tends to be ‘satisfaction domi-nated’, in which the major link to behavioural intentions is through satisfaction. This could also be found in the studies that focus on either service value or switching bar-riers. To better understand the underlying relationships among these variables, an inves-tigation of a more collective model is needed (Jones et al., 2000). We, in this study, construct three competing models inspired by the different focus areas of previous and designed according to the nature of the relationship as found in the dominant litera-ture of that area.

The first model seen in Figure 2 is derived from satisfaction literature, where satisfac-tion is suggested to lead directly to customer behavioural intensatisfac-tions (Butcher, Sparks, & O’Callghan, 2001; Dabholkar et al., 2000; Eggert & Ulaga, 2002; Hsu, Chen, & Hsueh, 2006; Lapierre et al., 1999; Yim et al., 2007; Yu et al., 2005). The second model is based on service value literature, which defines service value as the primary and direct link to customer behavioural intentions (Bolton & Drew, 1991; Choi et al., 2004; Grewal et al., 1998; Ruiz et al., 2008; Sweeney, Soutar, & Johnson, 1999; Wang et al., 2004; Zeithaml, 1988). The third model emanates from switching barriers literature which indicates that switching barriers represent the key antecedent of behavioural inten-tions (Burnham et al., 2003; Jones et al., 2002; Lam et al., 2004; Liu et al., 2005; Patterson & Smith, 2003; Ranaweera & Prabhu, 2003; Wathne et al., 2001).

With our hypotheses, we propose a fourth model which suggests that all three variables (service value, satisfaction, and switching barriers) directly lead to favourable behavioural intentions. We believe this fourth model goes further in exploring the relationship between these variables than the three existing competing models described above and thus account for a greater share of variance in customer behavioural intentions.

Research design

Methodology selection and data collection

Our integrated model explores many causal relationships among constructs (e.g. service quality, service value, and behavioural intentions), and a series of linear equations need to be solved. Moreover, each construct can be measured through indicators. Therefore,

on the basis of research model formulation, structural equation modelling (SEM) serves as a suitable method to examine our theoretical model, largely because SEM results can easily help explain the pattern of a series of interrelated dependence relations simul-taneously between a set of latent constructs, each measured by one or more indicators (Reisinger & Turner, 1999).

Previous studies also suggest that SEM is the superior method on both theoretical and empirical statistical ground than other approaches (Iacobucci, Saldanha, & Deng, 2007; Reisinger & Turner, 1999). More specific, SEM makes it possible to identify measurement errors, while other methods (e.g. regression) typically do not take into account measure-ment error in the observed variables and thus susceptible to these biases (Steenkamp, & Baumgartner, 2000). Furthermore, SEM is a covariance-based modelling technique which is more focused on explaining the phenomena than on predicting specific outcome variables.

This study selected coach passengers in Taiwan as research subjects. To receive passenger transportation service, customers must physically enter the service system, and they must be prepared to spend time interacting and working with the service provider (Lovelock & Wirtz, 2007). Customers’ high involvement could result in a more accurate evaluation of the service, and, in turn, help the researchers better understand their behav-ioural intentions. Considering the population distribution, transfer distances, and popular routes in Taiwan, we selected the passengers of a middle distant route (Taichung – Kaoh-siung) and a long distant route (Taipei – Tainan) as our investigation subjects.

The questionnaire was distributed randomly in waiting areas. We asked respondents to fill out the questionnaire after they finished their trip and then mailed the completed

Figure 2. Proposed model.

questionnaires back to us. This helped to avert responses based on recollections of a remote consumption experience, and avoid responses from non-users. However, mail survey might cause the low response rate. Furthermore, because a meaningful SEM analy-sis tends to require a larger sample of 200 subjects and above (Hatcher, 1994), we distrib-uted 500 questionnaires in each city of the routes. A total of 2000 questionnaires were distributed, and we obtained a valid sample of 543 passengers, and both routes were in excess of 200 subjects (Taichung – Kaohsiung route: 257 subjects, Taipei – Tainan route: 286 subjects). Among the respondents, 52.7% were male, 40.1% were students, and 61.7% were between ages 20 – 29.

Measurement

Multi-item scales validated in previous studies are selected and modified to support our study setting. The questionnaire contains items of seven constructs including service sacri-fice, service quality, satisfaction, service value, switching costs, alternative attractiveness, and behavioural intentions (see Appendix). All indicators are adopted from relative studies and further modified to fit for the coach service and cultural context to ensure content val-idity of the indicators.

For measuring service quality, abundant evidence in the existing literature supports the use of expectancy disconfirmation theory (Oliver, 1993; Parasuraman et al., 1988; Rust & Oliver, 1994). We follow these studies and measure both perceived and expected perform-ance of all service quality items. Research further suggests that service quality can be divided into several sub-dimensions, and a hierarchical approach is more suitable for representing the relationships among service quality, its sub-dimension, and associated indicators (Brady & Cronin, 2001; Rust & Oliver, 1994). We follow the literature and measure service quality along three primary dimensions: interaction quality, physical environment quality, and convenience quality (Brady & Cronin, 2001; Rust & Oliver, 1994). We use four items to measure interaction quality (V1– V4), four items to

measure physical environment quality (V5– V8), and four items to measure convenience

quality (V9– V12) (Brady & Cronin, 2001; Jen, Tu, & Lu, 2011; Lin, Lee, & Jen, 2008;

Rust & Oliver, 1994).

Furthermore, previous studies indicate that service sacrifice can be categorised into monetary prices and non-monetary prices (Choi et al., 2004; Wang et al., 2004). Hence, items that represent customers’ perception of the monetary and non-monetary prices are used as indicators of the service sacrifice construct. Monetary price is measured by the dollar price of the service (V13). Time spent was utilised to assess the non-monetary

price associated with the service (V14– V16) (Choi et al., 2004; Jen et al., 2011; Lin

et al., 2008; Wang et al., 2004).

Our assessment of satisfaction is based on effective evaluation (Oliver, 1993), reflect-ing the degree to which customers believe that possession of a service evokes positive feel-ings (Rust & Oliver, 1994). In this context, we adapt the measurement developed by Cronin et al. (2000) and Westbrook and Oliver (1991), which uses three emotion words (V17– V19). Previous research consistently agreed that service value is the trade-off

between PB/PS (Day, 2000; Monroe, 1991; Zeithaml, 1988). Following these studies, we include three direct measures of service value (V20– V22) (Bolton & Drew, 1991;

Dodds et al., 1991; Jen et al., 2011; Lin et al., 2008).

In our study, switching barriers consist of both switching costs and alternative attrac-tiveness. We define switching costs as customer perceptions regarding the time, money, and effort associated with changing service providers, and alternative attractiveness as

customer perceptions regarding the extent to which competitors offer equal or superior ser-vices. Following these definitions, we adopt three items to measure switching costs (V23–

V25), and another three items to assess alternative attractiveness (V26– V28) (Jen et al.,

2011; Jones et al., 2000; Lin et al., 2008; Patterson & Smith, 2003).

Customer behavioural intention was operationalised with three items (V29– V31)

including loyalty, recommendations to others, and increased spending on the existing service provider (Jen et al., 2011; Lin et al., 2008; Zeithaml et al., 1996). We use a five-point, Likert-type response format, ranging from ‘strongly agree’ to ‘strongly dis-agree’ to measure all items. The links between the eight constructs and their indicators, and the research hypotheses are shown in Figure 3.

Research results Measurement model

We utilised a two-step modelling approach following Anderson and Gerbing (1988). The measurement model was tested by confirmatory factor analysis (CFA), and the quality of the measurement model was assessed on reliability, convergent validity, and discriminant validity (Table 1). The level of internal consistency in each construct is acceptable, with Cronbach’s alpha estimates ranging from 0.676 (alternative attractiveness) to 0.908 (sat-isfaction). Furthermore, we computed a composite reliability index for each latent factor included in the model. The estimations of composite reliability ranged from 0.729 (alternative attractiveness) to 0.931(service quality), suggesting acceptable measurement reliabilities. Consequently, these results reflect the internal consistency of the indicators. Convergent validity for the measurement model was supported by a good overall fit: x2_{¼ 872.392, df ¼ 410 (P , 0.001), CFI (comparative fit index) ¼ 0.945, GFI}

(good-ness of fit index) ¼ 0.905, NFI (Bentler – Bonett normed fit index) ¼ 0.902, IFI (Bollen’s incremental fit index) ¼ 0.945, RMSEA (root mean square error of approxi-mation) ¼ 0.046. Moreover, all factor loadings for the indicators assessing the same latent variable are statistically significant (see Table 1), also serving as evidence in

Figure 3. Measurement model and research hypotheses.

supporting the convergent validity of our measurement model (Anderson & Gerbing, 1988; Hatcher, 1994). To assess discriminant validity, we computed the average variance extracted (AVE), which should be greater than the variances shared between the construct (Fornell & Lacker, 1981). This comparison can be incorporated into a correlation matrix (see Table 2), including the correlations between different constructs in the off-diagonal

Table 1. The property of CFA results.

Latent variable Standardised factor loadings Cronbach’s a Composite reliability AVE

Service quality IQ 0.734∗∗∗ 0.885 0.931 0.819 PEQ 0.892∗∗∗ CQ 0.818∗∗∗ Interaction quality V1 0.618∗∗∗ 0.744 0.790 0.488 V2 0.550∗∗∗ V3 0.740∗∗∗ V4 0.706∗∗∗

Physical environment quality

V5 0.648∗∗∗ 0.772 0.796 0.494 V6 0.620∗∗∗ V7 0.740∗∗∗ V8 0.704∗∗∗ Convenience quality V9 0.665∗∗∗ 0.849 0.845 0.578 V10 0.735∗∗∗ V11 0.814∗∗∗ V12 0.796∗∗∗ Service sacrifice V13 0.669∗∗∗ 0.803 0.843 0.574 V14 0.774∗∗∗ V15 0.742∗∗∗ V16 0.674∗∗∗ Satisfaction V17 0.903∗∗∗ 0.908 0.914 0.781 V18 0.944∗∗∗ V19 0.776∗∗∗ Service value V20 0.837∗∗∗ 0.846 0.870 0.513 V21 0.851∗∗∗ V22 0.748∗∗∗ Switching cost V23 0.645∗∗∗ 0.780 0.758 0.488 V24 0.738∗∗∗ V25 0.832∗∗∗ Alternatives attractiveness V26 0.423∗∗∗ 0.676 0.729 0.677 V27 0.763∗∗∗ V29 0.744∗∗∗ Behavioural intentions V29 0.901∗∗∗ 0.861 0.861 0.710 V30 0.925∗∗∗ V31 0.699∗∗∗

Notes: IQ, interaction quality; PEQ, physical environment quality; CQ, convenience quality.

∗∗∗_{P , 0.001.}

elements of the matrix, and the square roots of AVE for each of the constructs along the diagonal. After examining the results, we infer that the constructs have adequate discrimi-nant validity.

Structural equation model

Having established confidence in our measurement model, we examined the four struc-tural equation models by estimating path coefficients, model fit index, and R2value. Each model was tested on the whole sample (n = 543), and the results of the model compari-sons are shown in Table 3. The path estimates showed that service quality had a positive impact on satisfaction in four models (H1supported), and results from the satisfaction

model (Model 1) and the research model (Model 4) both indicated that satisfaction is a determinant of behavioural intentions (H2supported). These results agreed with the

‘quality – satisfaction – behavioural intentions’ paradigm. For the paths leading to service value, the results across the four models supported the trade-off relationships among service value, service quality, and service sacrifice. As we hypothesised, service quality had a positive effect on service value (H4 supported), whereas sacrifice

had a negative influence on service value (H5 supported). Furthermore, the positive

link between service value and satisfaction (H3 supported) was consistently significant

across the four models.

Regarding the impact of service value and switching barriers, the path estimates from the value model (Model 2) and the research model (Model 4) both suggested that service value had a positive effect on behavioural intentions (H6supported). Moreover, the results

across the four models consistently showed that switching costs had a positive impact on behavioural intentions (H7supported), whereas alternative attractiveness exerted a

nega-tive influence on behavioural intentions (H8supported). Comprehensively, except for the

statistically insignificant relationship between service value and switching costs in the switching barrier model (Model 3), all other links are significant in the four models.

Although most of the relationships in the four models are significant, we compare their power of explanation by examining model fit indices and R2value. The results show that thex2 value for the research model is 973.620 with df = 417. This is compared to x2 values for the competing models ranging from 1018.158 with df = 418 (the satisfaction model) to 1359.682 with df = 421 (the switching barrier model). Our research model yields a better x2/df ratio. The research model also shows a better fit in terms of good-ness-of-fit indices than the other three models, with CFI = 0.934, IFI = 0.934,

Table 2. Discriminant validity.

SQ SS SA SV SWC AA BI SQ 0.901 SS 20.453 0.757 SA 0.559 20.652 0.884 SV 0.582 20.762 0.798 0.831 SWC 0.174 20.160 0.413 0.344 0.716 AA 20.227 0.100 20.338 20.343 20.189 0.798 BI 0.533 20.564 0.720 0.754 0.455 20.337 0.823

Notes: SQ, service quality; SS, service sacrifice; SA, satisfaction; SV, service value; SWC, switching cost; AA, alternatives attractiveness; BI, behavioural intentions. The bold numbers on the diagonal are the square root of the AVE. Off-diagonal elements are correlations among constructs.

TLI (Tucker –Lewis index) ¼ 0.926, RMSEA ¼ 0.050. Moreover, the relative ability of our research model to explain variation in behavioural intentions was 0.630 (as measured by R2 value), accounting for a greater share of the data than the competing models, whose R2 values ranged from 0.400 (the switching barrier model) to 0.619 (the value model). Conse-quently, we inferred that the research model yields a better fit to data and accounts for a greater share of the variance in behavioural intentions than the three competing models.

Research model description

In order to further affirm our theory, specific discussion of the hypotheses’ results will be restricted to the research model (see Figure 4). Path coefficients from the results showed that service value had a greater effect (g = 0.726, t = 14.200, P , 0.001) on satisfaction than service quality (g = 0.136, t = 2.815, P , 0.01). The R2of satisfaction was 0.666, indicating that the independent variables (service quality and service value) accounted for 66.6% of the variance in satisfaction. As for the determinants of service, the path esti-mation suggested that service sacrifice (g = 20.637, t = 211.604, P , 0.001) had a stron-ger effect on service value than service quality (g = 0.321, t = 6.431, P , 0.001). Thus, we inferred that customers were more concerned about the sacrifice aspects, such as monetary price and time, than the benefit aspects when evaluating the value of a service. The R2of

Table 3. Results of model comparisons.

Fit/path The research model The satisfaction model The value model

The switching barrier model x2 /df 973.620/417 1018.158/418 1103.011/418 1359.682/421 CFI 0.934 0.929 0.919 0.889 IFI 0.934 0.929 0.919 0.889 TLI 0.926 0.921 0.910 0.877 RMSEA 0.050 0.051 0.055 0.064 SQSA 0.136∗∗ 0.131∗∗ 0.674∗∗∗ 0.820∗∗∗ SVSA 0.726∗∗∗ 0.673∗∗∗ – – SWCSA – 0.199∗∗∗ – – AASA – 20.085∗ – – SQSV 0.321∗∗∗ 0.298∗∗∗ 0.140∗ 0.609∗∗∗ SASV – – 0.473∗∗∗ – SSSV 20.637∗∗∗ 20.651∗∗∗ 20.388∗∗∗ 20.345∗∗∗ SWCSV – – 0.110∗∗ – AASV – – 20.131∗∗∗ – SASWC – – – 0.429∗∗∗ SVSWC – – – 0.059 SAAA – – – 20.272∗∗∗ SVAA – – – 20.209∗∗ SABI 0.236∗∗∗ 0.745∗∗∗ – – SVBI 0.503∗∗∗ – 0.787∗∗∗ – SWCBI 0.198∗∗∗ – – 0.452∗∗∗ AABI 20.075∗ – – 20.361∗∗∗ R2(BI) 0.630 0.555 0.619 0.400

Notes: SQ, service quality; SA, satisfaction; SV, service value; SWC, switching cost; AA, alternatives attractiveness; SS, service sacrifice; BI, behavioural intentions.

∗_{P , 0.1.} ∗∗_{P , 0.01.} ∗∗∗_{P , 0.001.}

service value was 0.694, indicating that service quality and service sacrifice accounted for 69.4% of the variance in service value. Furthermore, in terms of predictors of behavioural intentions, the results from our research model indicated that service value had the greatest influence on behavioural intentions (g = 0.503, t = 7.346, P , 0.001), with the switching cost component of switching barriers (g = 0.198, t = 5.355, P , 0.001) having greater effect than the alternative attractiveness component (g = 20.075, t = 22.018, P , 0.1). These findings showed that switching barriers could discourage customer disloyalty, but service value was more useful in encouraging favourable consumer behavioural intentions. The R2 of behavioural intentions was 0.630, indicating that satisfaction, service value, switching cost and alternative attractiveness accounted for 63.0% of the variance in service value.

Discussion

The objective of this study was to examine the relationships among service sacrifice, service quality, service value, satisfaction, switching barriers, and behavioural intentions. Our research model incorporated the service value theory and the switching barriers theory into the ‘quality – satisfaction – behavioural intentions’ paradigm. The results presented in the previous sections suggest that this research model fits well and outperforms the com-peting models in explaining the data collected. More specifically, according to our results, service value is the most important predictor of satisfaction and customers’ behavioural intentions. Hence, our findings provide support for previous research indicating that cog-nitive evaluations precede emotional responses (Bagozzi, 1992; Wilkie, 1986). In our model, we also propose service value as a trade-off between service quality and service sacrifice. Further, this study conducts a hierarchical approach to assess service quality. Our empirical results indicate that service quality perceptions are composed of multiple sub-dimensions: interaction, physical environment, and convenience quality. Moreover, the empirical results show that customers perceived the service as more valuable when they received better quality of service and when they did not need to sacrifice much to acquire the service. However, if companies wish to charge higher or premium prices,

Figure 4. Results of the research model testing. Note:∗P , 0.1;∗∗P , 0.01;∗∗∗P , 0.001.

they will have to provide value-added service to justify customers’ sacrifice. For example, in the airline industry, customers might choose to spend double the amount of money in purchasing a business-class ticket rather than economy class, in an effort to obtain better service values through monetary sacrifice, i.e. in exchange for better check-in service (convenience quality), spacious accommodation on the plane (physical environ-ment quality), and a more personable and responsive service (interaction quality).

For switching barriers, we find that customers are indeed more likely to stay with current service providers when the trouble of switching providers increases, e.g. when switching costs increase and/or the attractiveness of alternatives decreases. Therefore, the optimal strategy for service providers is to both provide value-added service to custo-mers and increase switching costs. For example, in order to reduce alternative attractive-ness, companies could develop customised services that cannot be made available through other firms. Companies can also adopt bucket pricing strategies, which encourage custo-mers to pay a larger amount in advance for more service which, in turn, impose higher switching costs. It is worthwhile to note that such efforts to lock in customers can be short-term orientated and should only be used in addition to providing customers with higher satisfaction through better service values.

Suggestions for future research

As we propose in the model, service value is composed of service sacrifice and service quality, and service value is a specified higher-level construct. Equity theory indicates that customers are concerned about whether the sacrifice is fair, right, and/or deserved. Hence, future research could investigate different properties of service value. For example, customers might evaluate the fairness for how much they have to pay or how long they have to wait to obtain a service respectively. Furthermore, customers might also assess whether they are paying for interaction quality or convenience quality, which may potentially have different evaluations on values. Future research could also explore the effect of different service values on customers’ intentions.

Furthermore, research suggests that the nature of switching barriers provides no intrin-sic benefits and creates feelings of entrapment including through high membership and application fees (Jones et al., 2000; Ranaweera & Prabhu, 2003). These ‘negative’ barriers may possibly do more harm than good in the long run. For example, customers may remain with the present service provider but may not provide positive word of mouth references. Hence, future research studies could emphasise some ‘positive’ barriers, such as interper-sonal relationships, which provide intrinsic benefits. Moreover, studies could further compare the effect of ‘negative’ and ‘positive’ barriers on customers’ behavioural intentions.

Future research could also consider some objective barriers, such as competitive situ-ations. Studies might conduct a comparative analysis of a low-competition market (e.g. oligopoly industry) and a high-competition market (e.g. perfectly competitive industry). By comparing the perceived switching barriers and the objective switching barriers, we could obtain a deeper understanding of customers’ intentions.

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Appendix: The survey items

1. Service Quality

1.1 Interaction Quality

V1: Drivers appreciate the safety of passengers when they get on/ off the vehicle. V2: Drivers are polite and friendly to communicate with passengers.

V3: Drivers drive smoothly, and road craft is fine.

V4: Drivers drive on right route and never fail to stop when passengers want to get on.

1.2 Physical Environment Quality

V5: Vehicles are clean inside.

V6: Noise on the vehicle isn’t too loud.

V7: The equipment in the vehicle fulfills passengers’ needs. V8: The air conditioning is very comfortable.

1.3 Convenience Quality

V9: The places of stops or stations are proper and convenient. V10: The information about routes is marked clearly.

V11: The company will have fast notification on the cars when the routes and schedules are changed.

V12: The company will quickly correct the information at stops or stations when the routes and schedules are changed.

2. Service Sacrifice

V13: The fare charged to travel by this coach company is high. V14: The time required to arrive at the station is high. V15: The time required to wait at the station is high. V16: The time required to travel in the coach is high.

3. Satisfaction

V17: I felt interesting to travel by this company’s coach. V18: I felt enjoyable to travel by this company’s coach. V19: I felt surprised to travel by this company’s coach.

4. Service Value

V20: The company’s service offered is valuable.

V21: The company’s service based on certain price is acceptable.

V22: It is worthier to travel by this company’s coach than by other coach companies.

5. Switching Cost

V23: It would be a hassle for me to get information about other companies. V24: For me, it would take a lot of costs to travel by other coach companies. V25: For me, it would be a high risk to travel by other coach companies.

6. Alternatives Attractiveness

V26: If I needed to change coach companies or modes, there are other good companies or modes to choose from.

V27: I would probably be happy with the services of another coach company or mode. V28: Compared to this coach company, there are other coach companies or modes with

which I would probably be equally or more satisfied.

7. Behavioural Intentions

V29: I would like to travel by this coach company again. V30: I would like to recommend this coach company to others.

V31: I would like to buy a monthly ticket or become a member of this coach company.