Information Systems Frontiers 6:4, 325–340, 2004C© 2004 Kluwer Academic Publishers. Manufactured in The Netherlands.

An Integrated Framework for Service Quality, CustomerValue, Satisfaction: Evidence from China’sTelecommunication Industry

Yonggui Wang∗

International Business School, Nankai University, Tianjin,China 300071 & Department of Management Sciences, CityUniversity of Hong Kong, ChinaE-mail: msygwang@cityu.edu.hk

Hing-Po Lo and Yongheng YangDepartment of Management Sciences, City University of HongKong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong, ChinaE-mail: mshplo@cityu.edu.hkE-mail: msyhyang@cityu.edu.hk

Abstract. Service quality, customer satisfaction and customervalue have become the priority of both manufacturers and serviceprovider in the increasingly intensified competition for customersin today’s customer-centered era. However, findings regardingservice quality, customer satisfaction and customer value arerather divergent and related studies are fragmented, especiallyfor the complicated interrelationships among them. Thus, lessis known about the relative impacts of quality-related factorson customer value and customer satisfaction up to now and themoderating role of customer value in the relationship betweenservice quality and customer satisfaction has been neglected.Further, it is very difficult to find related studies, supported byevidence, that focus on service quality, customer satisfaction andcustomer value, and their influences on customer behavior in-tentions in the telecommunication industry. In this paper, muchattention is paid to the measurement model of service quality inChina’s mobile communication market based on the well-knownSERVQUAL model, but with reasonable modification on the ba-sis of focus group discussions and expert opinions to reflect thespecific industry attributes and the special culture of China. Bytaking a disaggregated approach, the key drivers of service qual-ity, customer value and customer satisfaction are first identifiedand the impact of customer perceived sacrifice on customer valueis emphasized. Then attention is given to the systematic study ofthe dynamic relationships among them, especially the moderat-ing effect of customer value on the relationship between servicequality and customer satisfaction, which is followed by the exam-ination of their influences on behavior intentions of customers.Results are based on the development of structural equation mod-els using Partial Least Square technique.

Key Words. service quality, customer value, customer satisfac-tion, behavioral intentions, moderating effect, telecommunica-tion industry, China

1. Introduction

It has been well known that customer-perceived servicequality, customer value and satisfaction have been themost important success factors of business competi-tion for either manufacturers or service providers (e.g.,Buzzell and Gale, 1987; Zeithaml, 1996; Bolton andDrew, 1991; Parasuraman et al., 1988, 1991, 1997).Such factors are becoming the priority of managersin the increasingly intensified competition for cus-tomers in the customer-centered era of today and fu-ture (Zeithaml, 1988; Bolton and Drew, 1991; Ravaldand Gronroos, 1996; Woodruff, 1997; McDougall andLevesque, 2000; Lapierre, 2000; Oh, 1999). However,many different conclusions have been made regard-ing service quality, customer satisfaction and customervalue and related studies are rather fragmented, es-pecially for the complicated interrelationships amongthem. Furthermore, for the extant studies focusing onthe relationships among service quality, customer valueand satisfaction, few empirical studies take an disag-gregated approach and examine the decomposed effectsof service quality on customer value and customer sat-isfaction. Thus, less is known about the relative im-pacts of quality-related factors on customer value andcustomer satisfaction up to now. So the key drivers of

∗To whom correspondence should be addressed.

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customer perceived service quality, customer value andcustomer satisfaction still need more extensive in-depthstudies. In addition, although attention has been givento the rather neglected aspect in the discussion of cus-tomer evaluation of services and products recently, i.e.,customer value, its possible role as a moderating vari-able between quality and satisfaction appears to havereceived even less attention, which is in fact should beone of the most important points to help us understandthe complicated relationships among them. For exam-ple, even for the model proposed by Oh (1999) basedon the so called “a holistic perspective”, the moderatingrole of customer value is neglected. As an exception,Caruana et al. (2000) make the moderating role of cus-tomer value as the focus. However, in their paper, asingle-item measure is used for the complex construct“customer value”, although the shortcomings of single-item measure are discussed by themselves and manyother related studies.

Therefore, this study makes several contributions torelevant studies as follows. First, by taking a disaggre-gated approach, the impacts of service quality on bothcustomer value and customer satisfaction are decom-posed, which helps to understand how service-relatedfactors may influence customer value and customer sat-isfaction, and provides researchers and managers morepractical guidance to improve them. Second, in theidentification of the key drivers of customer value, wetry not to only combine both product-related factors andservice-related factors but also include one more im-portant factor, i.e., customer perceived sacrifice, whichhelps to integrate extant research and provides a morecomprehensive picture about how customer value canbe influenced. Third, in the discussion of the compli-cated relationship among customer perceived servicequality, and customer satisfaction, not only the mediat-ing role but also the moderating role of customer valueare explored by using the PLS-based product indica-tor approach based on multiple-items measures of allconstructs involved. Fourth, the influences of servicequality, customer value and customer satisfaction onbehavioral intentions of customers are combined andexamined in an integrated framework. Furthermore, inthis paper, unlike others, we attempt to conceptualizefactors of service quality (tangible, reliability, respon-siveness, assurance and empathy)1 as antecedents tocustomers’ overall evaluation of service quality, ratherthan as dimensions or components of the construct, andmuch effort has been made to replicate and extend theextant studies of service quality. “Tangibles”, “relia-

bility”, responsiveness”, “assurance”, “empathy” and“network quality” are identified as the quality-relatedfactors by modifying the famous SERVQUAL scaleof service quality based on extensive literature review,group discussions and expert opinions to reflect thespecific attributes of mobile communication market ofthe telecommunication industry and the special cultureof China. In addition, by collecting data from the gen-erally neglected but important industry, i.e., telecom-munication industry of China, it helps to verify thegeneralizability of relevant research findings in a devel-oping country, China. Therefore, this study necessarilycomplements related studies in both the academic andpractical spheres, and helps to test relevant findings asreported in the literature, which adds valuable insightsto the development of theory (Easley et al., 1994).

The remainder of the paper is organized as follows.The following section provides the theoretical back-ground of customer perceived service quality, customervalue and customer satisfaction. The next section de-velops related hypotheses and presents the integratedframework that shows the hypothesized relationships,which is followed by the methodology and measuresof the survey in this study. Then both the measurementmodel and the hypothesized relationships are empiri-cally tested based on the evidence from China’s mo-bile communication market. Results are reported anddiscussed, and limitations and directions for future re-search are indicated.

2. Theoretical Background

2.1. Customer perceived service qualityAlthough more research findings concerning qualityhave appeared in the past two decades, it is still worthnoting here that there are several distinct conceptual-izations of quality (Holbrook, 1994). In marketing andeconomics, quality often has been viewed as depen-dent on the level of product attributes. In operationsmanagement, quality is defined as having two primarydimensions, fitness of use2 and reliability (To what ex-tent is the product free from deficiencies?).3 In serviceliterature, quality is viewed as an overall assessment(Parasuraman, Zeithaml and Berry, 1988). The mostcomprehensive definition of quality is the one proposedby Garvin (1988) with the following eight attributes:performance, features, conformance, reliability, dura-bility, serviceability, aesthetics and customer-perceivedquality.4 However, since this study focuses on China’s

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mobile communication market within the telecommu-nication industry, durability and aesthetics may not berelevant. As for the attribute “performance”, networkquality is used, since almost all the participants in thefocus groups and the pilot study believed network qual-ity is one of the most important factors associated withthe quality of mobile communication service. Other at-tributes can find their counterparts in related studies ofcustomer perceived service quality.

In fact, with the role of customers changing gradu-ally, customer perceived service quality has been givenincreasing attention for its specific contribution to thecompetitiveness of business and there have been a va-riety of studies on different issues concerning servicequality over recent years. Traditionally service qualityhas been defined as the difference between customerexpectations and perceptions of service (Parasuraman,Berry and Zeithaml, 1988, 1991). These researchersbelieve that measuring service quality as disconfirma-tion (the difference between perceptions and expecta-tions) is valid and allows service providers to identifyseveral gaps in the service provided. However, mostof these studies have proved a poor fit for the discon-firmation model. As a result, their SERVQUAL scalehad been criticized by more and more researchers forits use of gap scores, measurement of expectations,positively and negatively worded items, the generaliz-ability of its dimensions, and the defining of a baselinestandard for good quality (Cronin and Taylor, 1992;Brown, Churchill and Peter, 1993; Oliver, 1993). Fur-ther, problems of reliability, discriminant validity andvariance restriction exist because of the computed dif-ference scores. As a result, some researchers have triedto combine expectations and perceptions into a singlemeasure to alleviate these problems, and found thatthis outperforms the SERVQUAL scale in terms ofboth reliability and validity (Babakus and Boller, 1992;Brown, Churchill and Peter 1993; Dabholkar et al.,2000). Therefore, we will adopt the latter and measureservice quality with customer perceptions only.

2.2. Customer valueDriven by demanding customers, keen competition andrapid technological change, more and more firms aresearching for new ways to achieve, retain, upgrade andleverage competitive advantages. As some researchershave concluded (Day, 1990; Slater, 1997), creating su-perior customer value is a major goal for market-drivenfirms. In fact, delivering superior customer value isinevitably becoming one of the most important suc-

cessful factors for any firm now and in the future,due to its significant impact on behavior intentions ofcustomers. As a result, many firms are transformingtheir focus from looking internally within the organi-zation for improvement by way of quality management,downsizing, business process reengineering or leanproduction and agile manufacturing to pursuing supe-rior customer value delivery (Day, 1990; Gale, 1994;Naumann, 1995; Butz and Goodstein, 1996; Woodruff,1997). Therefore, learning about customer value andrelated knowledge, which can provide sufficient cus-tomers voice to guide managers how to respond, isplaying an ever important role in a firm’s increasinglycompetitive environments.

Although the significance of customer value iswidely recognized, the growing body of research aboutcustomer value is quite fragmented and the definitionof customer value is divergent. Zeithamml (1988) con-siders value as the customer’s overall assessment ofthe utility of a product based on the perception of whatis received and what is given. Dodds et al. (1991) ar-gue that buyers’ perceptions of value represent a trade-off between the quality or benefits they receive in theproduct relative to the sacrifice they perceive in payingthe price. Gale (1994) considers it as market perceivedquality adjusted for relative product price. Butz andGoodstein (1996) define it as an emotional bond estab-lished between a customer and a producer after the cus-tomer has used a salient product or service produced bythat supplier. Woodruff (1997) defines customer valueas a customer perceived preference for and evaluationof those product attributes, attribute performances, andconsequences arising from use that facilitate achiev-ing the customer’s goals and purposes in use situa-tions. This is based on customer perspectives of valuederived from empirical research into how customersreally think about value. However, it is obvious thatthere are some areas of consensus among the differ-ent concepts mentioned above. For example, customervalue is inherent in or linked through use to certainproducts or services; customer value is something per-ceived by customers rather than objectively determinedby sellers or other stakeholders, and those perceptionprocesses typically involve a trade-off between whatcustomers receive, such as quality, benefits, and utili-ties, and what they sacrifice, such as price, opportunitycost, and maintenance and learning cost. In this study,we concur with the majority of researchers who definecustomer value in terms of get (benefit) and give (sacri-fice) components (Woodruff, 1997; Slater, 1997; Day,

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1990; Zeithaml, 1988) although some researchers ar-gue that perceived value is made only of benefits (Huntand Morgan, 1995; Hamel and Prahalad, 1994).

2.3. Customer satisfactionConsumer satisfaction has long been recognized inmarketing thought and practice as a central conceptas well as an important goal of all business activities(Anderson et al., 1994; Yi, 1990). Consumer satisfac-tion has different levels of specificity in various studies.Although satisfaction with, say, a product attribute, asales-person, and a consumption experience may beuseful, at a more fundamental level, it should be seenas satisfaction with a product, whether a commodity orservice.

There are at least two different conceptualizations ofcustomer satisfaction. One is transaction-specific, theother is cumulative (Boulding et al., 1993; Andreassen,2000). On the one hand, from a transaction-specificperspective, customer satisfaction is viewed as a post-choice evaluative judgment of a specific purchase oc-casion (Oliver, 1977, 1993). Up to now, behavioralresearchers have developed a rich body of literaturefocusing on the antecedents and consequences of thistype of customer satisfaction at the individual level (Yi,1990). On the other hand, cumulative customer satis-faction is an evaluation based on the overall purchaseand consumption experiences with a product or serviceover time (Fornell et al., 1996; Johnson and Fornell,1991; Anderson et al., 1994), which is more funda-mental and useful than transaction-specific consumersatisfaction in predicting subsequent consumer behav-iors and a firm’s past, present and future performance. Itis the cumulative customer satisfaction that motivatesa firm’s investment in customer satisfaction. So hereour theoretical framework treats customer satisfactionas cumulative.

3. Integrative Framework and Hypotheses

3.1. The key drivers of customerperceived service qualityAs Dabholkar et al. (2000) have suggested and tested,factors associated with service quality (e.g. reliabil-ity, responsiveness, tangibles, assurance and empthy)are antecedents to customer perceived service qualityrather than as dimensions or components of the con-struct. At the same time, results of focus group dis-cussions and our pilot study show that network quality

is another important quality-related factor that drivescustomer perceived service quality. Thus we can havethe hyptheses as follows.

Hypothesis 1(a). “Tangibles” is a significant driver ofcustomer perceived service quality.

Hypothesis 1(b). “Reliability” is a significant driver ofcustomer perceived service quality.

Hypothesis 1(c). “Responsiveness” is a significantdriver of customer perceived service quality.

Hypothesis 1(d). “Assurance” is a significant driver ofcustomer perceived service quality.

Hypothesis 1(e). “Empathy” is a significant driver ofcustomer perceived service quality.

Hypothesis 1(f). “Network quality” is a significantdriver of customer perceived service quality.

3.2. The key benefit versus sacrifice drivers ofcustomer valueBased on the discussions of the definition of customervalue, it is clear that factors influencing the benefitscustomers receive or sacrifices customers have to makewill cause different evaluations of customer value, eventhough different customers may form different opin-ions over time. For example, product related factorssuch as product quality, and product customization,quality-related factors such as responsiveness, flexibil-ity, reliability and technical competencies and relation-ship related factors such as image, time/effort/energyand solidarity are all customer value drivers or sources(Lapierre, 2000; Bolton and Drew, 1991; Zeithaml,1988). In this study, we use quality-related factorsto represent most of the positive benefit drivers ofcustomer value since most of them have alreadybeen included in the quality-related factors as men-tioned above. Thus the following hypotheses can beproposed.

Hypothesis 2(a). “Tangibles” is a significant driver ofcustomer value.

Hypothesis 2(b). “Reliability” is a significant driver ofcustomer value.

Hypothesis 2(c). “Responsiveness” is a significantdriver of customer value.

Hypothesis 2(d). “Assurance” is a significant driver ofcustomer value.

Hypothesis 2(e). “Empathy” is a significant driver ofcustomer value.

Hypothesis 2(f). “Network quality” is a significantdriver of customer value.

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On the other hand, as we have discussed above, cus-tomer value is related not only to what customers canget, but also to what they have to give up; in other words,customer perceived sacrifice. For example, Lapierre(2000) identifies the key drivers of customer perceivedvalue and clarifies sacrifice as one of the two key factors(the other is benefits). Sacrifice refers to what is givenup or sacrificed to acquire a product or service (Hes-kett, Sasser and Hart, 1997; Zeithaml, 1988). However,not only is price considered an element of sacrifice,but other non-monetary factors are also believed to beclosely related to sacrifice. In fact, many customerscount time rather than dollar cost as their most pre-cious asset. Therefore, generally speaking, it is clearthat there are two broad kinds of sacrifice: monetarycosts and non-monetary costs. The former can be as-sessed by a direct measure of the dollar price of theservice or product and the latter can be defined as thetime, effort, energy, distance and conflict invested bycustomers to obtain products or services or to establisha relationship with a supplier (Ruyter et al., 1997).

Hypothesis 3. Customer perceived sacrifice is a keydriver of customer value.

3.3. The key quality-related driversof customer satisfactionAs for the key quality-related drivers of customer satis-faction, Oliver (1993) first suggests that service qualityis the antecedent to customer satisfaction regardless ofwhether these constructs are measured for a given ex-perience or over time. Up to now, other researchershave found empirical support for the point of viewmentioned above (Anderson et al., 1994; Fornell et al.,1996; Spreng and Mackoy, 1996). Therefore, similarto the identification of the key quality-related driversof customer value, we can also propose that quality-related factors are the key drivers of customer satis-faction. For example, a customer who obtain a reliableproduct/service in the proper time and place supportedby quick responses of a product/service provider whenhe/she have any inquiry or problem tends to exhibithigher satisfaction. Therefore, the following hypothe-ses can be formed.

Hypothesis 4(a). Tangibles has a significantly positiveinfluence on customer satisfaction.

Hypothesis 4(b). Reliability has a significantly positiveinfluence on customer satisfaction.

Hypothesis 4(c). Responsiveness has a significantlypositive influence on customer satisfaction.

Hypothesis 4(d). Assurance has a significantly positiveinfluence on customer satisfaction.

Hypothesis 4(e). Empathy has a significantly positiveinfluence on customer satisfaction.

Hypothesis 4(f). Network quality has a significantlypositive influence on customer satisfaction.

3.4. Relationships among customer service quality,customer value and customer satisfactionThe expectany/disconfirmation paradigm provides thetheoretical basis for the link between customer per-ceived service quality and satisfaction (Yi, 1990). Andas mentioned above, this link is surported by other stud-ies, wherein customer satisfaction is a consequence ofservice quality. On the other hand, utility theory, whichlies at the foundation of modern microeconomic the-ory, argues for an association between quality and valueand this is also supported by many other studies (Doddset al., 1991; Oh, 1999). Thus the following hypothesescan be proposed.

Hypothesis 5. Customer perceived service quality con-tributes positively to customer satisfaction.

Hypothesis 6. Customer perceived service quality con-tributes positively to customer value.

Like service quality, perceived value should alsobe positively related to consumer satisfaction (Fornellet al., 1996). This is reasonable, and it has been shownthat consumer satisfaction depends on value to someextent (Caruana et al., 2000; De Ruyter et al., 1997;Rust and Oliver, 1994). For example, Rust and Oliver(1994) note that value, like quality, is an encounterspecific input to satisfaction, which implicates the pos-itively link between customer value and satisaction.Bojanic (1996) finds strong positive association be-tween customer value and satisfaction in four lodg-ing markets segmented by price. At the same time,the service management literature argues as well thatcustomer satisfaction is the result of a customer’s per-ception of the value (Hallowell, 1996; Fornell et al.,1996). Therefore, customer value is viewed as anotherkey driver of consumer satisfaction. Thus the followinghypothesis can be proposed.

Hypothesis 7. Customer value contributes postively tocustomer satisfaction.

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However, besides the above-mentioned relation-ships, customer value play an important moderatingrole in the service quality-customer satisfaction rela-tionship as well, which is tested and concluded byCaruana et al. in 2000 although single-item measure isused for the construct of customer value. Just as whatRuyter et al. (1997) point out in discussing the findingsof their combined service quality/satisfaction model,who find that an increase in service quality leads to anincrease in satisfaction, “the reverse need not necessar-ily be the case”. Low service quality may result in highsatisfaction. Customers may not always buy the highestquality service. That is, convenience, price, availabil-ity may enhance satisfaction without actually affect-ing customer perceptions of quality. Similarly, thereis experiential evidence in practical business world.Customer perceived service quality may be somewhatlower, however, the prices are very competitive, thevalue received is higher and favorable level of satisfac-tion can be achieved. Thus we can form the hypothesisas follows.

Hypothesis 8. Customer value moderates the relation-ship between customer perceived service quality andcustomer satisfaction.

3.5. Impacts on behavior intentions of customersAlthough “research examining the effects of customervalue and customer satisfaction on behavior intentionshas received very limited attention in the marketingliterature”, Rust and Oliver’s (1994) call did not gounanswered. Bagozzi’s model suggests that initial ser-vice evaluation leads to an emotional reaction, which,in turn, drives behavior. It has also been suggested thatcustomer value leads to favorable behavior intentions(Chang and Wildt, 1994; Cronin, et al., 2000; Gale,1994). Similarly, customer satisfaction drives favor-able behavior intentions (Anderson and Fornell, 1994;Andreassen, 2000; Hallowell, 1996). Therefore, thefollowing hypotheses can be proposed.

Hypothesis 9. Customer perceived service quality hasa positive influence on the behavior intentions ofcustomers.

Hypothesis 10. Customer value has a positive influenceon the behavior intentions of customers.

Hypothesis 11. Customer satisfaction has a positive in-fluence on the behavior intentions of customers.

Based on the comprehensive literature review andthe in-depth discussions mentioned above, an integra-tive framework for customer perceived service qual-ity, customer value and customer satisfaction, and theirimpacts on behavioral intentions of customers is pre-sented as Fig. 1, in which the relevant hypotheses to beexamined empirically are shown.

4. Methodology

4.1. Sample and proceduresIn order to collect enough data of high quality totest our hypotheses, a face-to-face customer surveywas conducted by adopting the availability samplingtechnique based on the measurement refinement re-sults of the pilot study with customers of both ChinaMobile and China Unicom, the two monopoly com-panies in China that compete with each other in themobile communication market. Subjects were askedto assess items of different constructs such as fac-tors viewed as antecedents of service quality, customersacrifice, customer perceived service quality, customersatisfaction, and customer value in terms of their per-ceptions, based on a seven-point scale. The descrip-tors ranged from “strongly disagree”, “somewhat dis-agree”, “slightly disagree”, “neutral”, “slightly agree”,“somewhat agree” and “strongly agree”. A total of 348were considered valid and were used to develop struc-tural equation models with a PLS-Graph.

The reason for us to choose mobile communica-tion market in China is as follows. First, this is oneof the most important service markets but often ne-glected by most of previous studies. This service maynot only have significant influences on the life qual-ity of people, but also affect the operational activitiesof firms since it cannot be imagined if there were nomobile communication between managers and man-agers, managers and employees, employees and em-ployees, and so on. Second, over the last decade, thecentralized telecommunication monopoly in China hasbeen changed and a relatively open and free compet-itive market is gradually coming into being. As a re-sult, many functional and fundamental changes havetaken place in telecommunication reforms and moreand more attention has been paid to the improvementof customer perceived service quality, customer valueand customer satisfaction in order to build superiorcompetitive advantages by way of effective customer

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Fig. 1. Service quality, customer value, satisfaction and behavioral intentions of customers: an integrated framework.

acquisition and retention with the increasingly intensi-fied domestic competition. Furthermore, with China’sentry into WTO, it is inevitable for China’s domestictelecommunication companies to meet ever-keener for-eign competition as a result of the agreement that jointventures can be established freely in Beijing, Shanghaiand Guangzhou, with a maximum foreign equity of25% for the first year following China’s entry. It hasbecome imperative for domestic firms to focus on theimprovement of service quality in order to deliver supe-rior customer value, achieve higher customer satisfac-tion and keep customers or attract potentially profitablenew customers, which signifies the significance and ur-gent need of this study. Third, China’s telecommuni-cation industry has been growing fastest in the worldover the past 20 years and the mobile communicationmarket has been the most active and has attracted muchattention all over the world among so many markets inChina’s telecommunication industry. For example, theannual increase in GSM users in China has been over200% since 1990, a potential market that is attractingmany giant global corporations to compete there (Tian-jin Daily). Up to now, there are nearly 0.1 billion GSM

users of China Mobile, a newly established companyformed from a restructuring of China Telecom in 1999,which has taken up all the mobile business of the latter,making it the largest mobile communication networkin the world. Furthermore, it has been forecasted thatmobile communication users in China will amount to0.3 billion in the next few years. Therefore, such astudy is very useful for those foreign firms that intendto compete in China’s mobile communication marketin the near future. Besides, such a choice also makesit possible for us to verify the generalizability of rele-vant findings of extant studies in a rather new context,a developing country.

4.2. MeasurementsMany of the instruments used to measure the constructsinvolved in this study are adapted from existing litera-ture and others are developed based on the extant con-ceptual studies and and focus group discussions, whichare refined by a pilot study of 80 customers in China.

Specific items were developed for each quality-related factor, i.e., “tangibles”, “reliability”, “re-sponsiveness”, “assurance”, “empathy”, and “network

332 Wang, Lo and Yang

quality”, either by modifying SERVQUAL or from ourfocus group discussions. “Tangibles”, “reliability”, “re-sponsiveness”, “assurance” and “empathy” were eachmeasured by related parts of SERVQUAL with smallwording changes. “Network quality” was created usingtwo items from focus group comments by asking cus-tomers to evaluate the quality of their chosen networkbased on their overall experience and the call quality oftheir chosen network. As for customer perceived sacri-fice, three items were used to evaluate the price charged,time required and effort needed to receive the chosenservice, which were adapted from the work of Croinin,et al. (2000). For behavior intentions, three items sim-liar to those reported and used throughout the servicemarketing literature were adopted (Croinin and Taylor,1992; Zeithaml et al., 1996; Cronin et al., 2000).

Customer perceived quality, customer value andcustomer satisfaction often consist of single item mea-sures in previous studies, especially for the last twoconstructs (Babakus and Boller, 1992; Bolton andDrew, 1991; Boulding et al., 1993; Croin and Taylor,1992; Spreng and Mackoy, 1996; Oh, 1999; Caruana,et al., 2000). However, there are many shortcomingsto meaure a construct with a single item (Churchill,1979). For exmaple, it often fails to capture the rich-ness and complexity of a theoretical construct or la-tent variable that is not directly measurable (Fornellet al., 1996). Furthermore, since all survey variablesare believed to be measured with certain degree of er-rors (Fornell et al., 1996), single-item scales cannotassess or average out the variance due to random er-rors, specific items, and method factors (Yi, 1990). Asa result, the reliability of single-item scales is diffi-cult to assess and, even if assessed in some studiesusing the only available test-retest reliability estimate,most estimates of this kind are so low and indicate thatthe scales should be used with caution (Yi, 1990). Incontrast, some studies employing multi-item scales tomeasure perceived quality and consumer satisfactionshow that multi-item scales are significantly more reli-able than the single-item scales (Dabholkar et al., 2000;Dabholkar et al., 1996; Spreng and Mackoy, 1996;Cronin et al., 2000). In this study, therefore, we tryto integrate distinct conceptualizations of constructsdiscussed above and measure them with multi-itemscales. Based on our preliminary pilot study and lit-erature review, three items are retained to evaluate cus-tomer perceived service quality, customer value andcustomer satisfaction respectively in the final survey

stage, which are shown in Table 1. For customer per-ceived service quality, repondents were asked to givetheir assessment in term of “excellent overall service”,“service of a very high quality”, and “superior servicein every way”, which were adapted from the work ofDabholkar et al. (2000) and similar to other overall ser-vice quality indicators used elsewhere in the literature(Cronin and Taylor, 1992). For customer value, threeitems were included in the survey to ask customers toevaluate whether “overall, the chosen service is valuefor money”, “the chosen service is worth what is givenup such as time, energy and effort” (Cronin et al., 2000)and “comparing with offerings of major competitors,the transaction with the mobile communication serviceprovider is a good choice. For customer satisfaction,three items were used with endpoints “completely sat-isfied/completely dissatified”, “very pleased/very dis-pleased” and “absolutely delighted/absolutely terrible”(Wrestbrook, 1980; Dabholkar et al., 2000).

5. Empirical Results

There are two types of estimation techniques for anSEM. The first type is the maximum likelihood (ML)based covariance structure analysis method that is doc-umented in software such as LISREL, AMOS and EQS(Bollen, 1989; Joreskog, 1970). Another type is the par-tial least squares (PLS) based variance analysis method(Chin, 1998; Fornell and Cha, 1994; Wold, 1986),which is implemented in such programs as LVPLS andPLS-Graph. Although the PLS method is not as popularas the ML method in the SEM field, it does provide “away to avoid problems of improper solutions and factorindeterminacy as well as the violations of distributionalassumptions” (Fornell and Cha, 1994) which may beassociated with the ML method. As for the testing ofmoderator effects, although traditional techniques suchas analysis of variance (ANOVA, MANOVA, MAN-COVA, ANCOVA) or moderated multiple regression(MMR) are frequently used, however, they may notbe able to detect such interaction effects under con-ditions of measurement errors. For example, accord-ing to what has been concluded by Chin et al. (2003),studies using analysis of variance approaches fail toreport effect size estimates on the one hand while theregression and path analysis techniques, which do pro-vide beta path coefficients, have few significant terms,small effect sizes and low statistical power. By

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Table 1. Confirmatory factor analysis results and relevant composite reliability

Constructs Compositeand Items Loading T-value reliability

Tangible 0.861. The physical facilities are visually appealing 0.84 30.282. Materials associated with the mobile service are visually appealing 0.88 73.763. The employees are well dressed and neat in appearance 0.72 14.92

Reliability 0.844. When the service provider promises to do something by a certain time, it does so 0.79 28.745. When customers have a problem, the service provider shows a sincere interest in solving it 0.88 54.046. The service provider deliver its services at the times it promises to do so 0.80 17.407. The service provider always performs the service right the first time 0.84 35.88Responsiveness 0.918. The employees tell me exactly when services will be performed 0.89 56.579. The employees give me a prompt service 0.85 53.00

10. The employees are always willing to help me 0.93 110.3511. The employees are never too busy to respond to my requests 0.89 53.96Assurance 0.8712. The employees instill confidence in customers 0.90 73.3413. Customers feel safe in transactions with the service provider 0.86 43.5114. The employees are consistently courteous with customers 0.86 48.1915. The employees have knowledge to answer customers’ questions 0.79 20.52Empathy 0.9016. The service provider gives customers individual attention 0.82 28.0117. The service provider has customers’ best interest at heart 0.89 59.0518. The employees understand customers’ specific needs 0.84 29.9919. The service provider has operating hours and location convenient to all its customers 0.86 50.3520. The employees give their personal attention 0.83 30.58Network quality 0.9021. The quality of the specific chosen network is always good 0.94 84.3722. The call quality of the specific chosen network is always good 0.96 195.88Customer perceived service quality 0.8623. The mobile communication provider always deliver excellent overall service 0.89 58.1724. The offerings of the service provider are of high quality 0.76 19.4125. The mobile communication provider deliver superior service in every way 0.81 27.26Customer perceived sacrifice 0.8126. Please give your evaluation in terms of the price charged by the provider 0.87 20.0827. Please give your evaluation in terms of the time require to obtain the offerings 0.72 18.8328. Please give your evaluation in terms of the effort needed to receive the chosen offerings. 0.69 17.67Customer value 0.8729. Overall, the chosen offerings are value for money 0.92 57.9230. The chosen offerings are worth what is given up such as time, energy and effort 0.95 166.0831. Comparing with major competitors, the transaction with the provider is a good choice 0.89 89.06Customer satisfaction 0.8732. I am completely satisfied with the services delivered by the service provider 0.89 83.6533. I feel very pleased with delivered services 0.83 65.3634. I feel absolutely delighted 0.80 19.46Behavioral intentions of customers 0.8335. I would like to repurchase the offerings from the service provider 0.87 73.2636. I would like to recommend the mobile communication service to others 0.81 56.2137. I would like to keep close relationship with the service provider 0.79 49.72

comparison, the PLS method has been gaining interestand used in recent years because of its ability to modellatent constructs under conditions of non-normalityand small to medium sample sizes, which is prefer-

able to techniques such as regression assuming errorfree measurement. Therefore, in this paper, the PLSmethod is used to estimate the models using the surveydata.

334 Wang, Lo and Yang

5.1. Assessment of measurement propertiesFollowing the two-step approach recommended byAnderson and Gerbing (1988), the adequacy of eachmulti-item scale in capturing its construct was assessedusing the measurement model of all constructs, bychecking internal consistency reliability, convergentvalidity and discriminant validity, before testing thehypotheses via the causal model.

Firstly, the composite reliability for internal consis-tency was demonstrated, since values for all constructswere above the suggested threshold of 0.70, with a min-imum of 0.81 (see Table 1). Secondly, the standardizedfactor loadings for all items were above the suggestedcut-off 0.60 (Hatcher, 1994), with a minimum of 0.69,and all were significant (P < 0.005), showing strongevidence of convergent validity. At the same time, asshown in Table 2, the average variance extracted (AVE)of each construct in our model was more than 0.60,which meets the criterion of a construct’s AVE; i.e.it should be, at least, higher than 50% to guaranteemore valid variance explained than error in its mea-surement (Fornell et al., 1994; Fornell and Larcker,1981). Thirdly, apart from the above-mentioned con-vergent validity, the constructs should also show highdiscriminant validity. According to Fornell and Cha(1994) and Fornell and Larcker (1981), this can be ev-idenced by the fact that the square root of the AVEof each construct is generally higher than the correla-tions between it and any other constructs in the model,which is presented in Table 2. That is, the constructs areboth conceptually and empirically distinct from eachother. Furthermore, the magnitude of R square valuesfor latent endogenous variables show that our modelhas strong predictive power. For example, the six fac-

Table 2. Correlation coefficients and the square root of AVE for all constructs in the study

1 2 3 4 5 6 7 8 9 10 11

1. Tangibles 0.822. Behavior intentions 0.55 0.813. Customer value 0.57 0.66 0.864. Network quality 0.48 0.48 0.56 0.895. Customer satisfaction 0.61 0.62 0.43 0.50 0.876. Reliability 0.63 0.54 0.45 0.45 0.69 0.817. Responsiveness 0.64 0.53 0.60 0.49 0.59 0.56 0.908. Assurance 0.68 0.71 0.56 0.39 0.73 0.62 0.62 0.849. Empathy 0.49 0.57 0.66 0.42 0.54 0.48 0.56 0.64 0.88

10 Customer perceived sacrifice −.60 −.57 −.51 −.35 −.66 −.57 −.60 −.67 −.67 0.7811 Customer perceived service quality 0.72 0.56 0.49 0.66 0.68 0.76 0.67 0.82 0.58 −.69 0.81

Notes: Interrelations are included in the lower triangle of the matrix, and the square root of AVE is on the diagonal.

tors associated with service quality explained 83.6% ofthe variance in overall service quality, quality-relatedfactors, customer perceived sacrifice and service qual-ity accounted for 67.2% of the variance in customervalue, quality-related factors, customer perceived ser-vice quality and customer value explained 71% of thevariance in customer satisfaction and after the moder-ating effect of customer value was included, the vari-ance explained in customer satisfaction was up to about76%, which is shown in Table 4. Furthermore, cus-tomer perceived service quality, customer satisfactionand customer value explained 67.8% of the variance inthe behavior intentions of customers in China’s mobilecommunication market.

5.2. Hypotheses testing of direct effectsIn order to test the direct effects, a structural equationmodel was developed by using PLS-Graph and the re-sults are shown in Table 3. It can be seen in Table 3that nearly all the standardized path coefficients (ex-cluding responsiveness) relating the six factors associ-ated with quality to customer perceived service qual-ity have the expected positive sign and are statisticallysignificant. Among them, network quality and empa-thy are significant at p < 0.01 level, while tangible,assurance and reliability are significant at P < 0.05level, which shows that hypothesis 1a, 1b, 1d, 1e and1f are strongly supported with hypothesis 1c as an ex-ception. In other words, in China’s mobile communica-tion market, both empathy and network quality are thetwo most important drivers of customer perceived ser-vice quality (β = 0.356 and t = 2.51, and β = 0.265and t = 3.06 respectively) with tangible, assurance andreliability coming second (β = 0.203 and t = 2.09,

Service Quality, Customer Value, and Satisfaction 335

Table 3. Assessment of direct effects by using PLS-GRAPH

StandardizedHypotheses Constructs and relationships path coefficient T -values Assessment

H1a Customer perceived ← Tangible 0.203 2.09∗ sservice quality

H1b Customer perceived ← Reliability 0.269 2.27∗ sservice quality

H1c Customer perceived ← Responsiveness 0.055 0.45 noservice quality

H1d Customer perceived ← Assurance 0.198 2.27∗ sservice quality

H1e Customer perceived ← Empathy 0.356 2.51∗∗ sservice quality

H1f Customer perceived ← Network quality 0.265 3.06∗∗ sservice quality

H2a Customer value ← Tangible 0.301 2.55∗∗ sH2b Customer value ← Reliability 0.023 0.059 noH2c Customer value ← Responsiveness 0.006 0.01 noH2d Customer value ← Assurance 0.141 0.94 noH2e Customer value ← Empathy 0.523 4.26∗∗ sH2f Customer value ← Network quality 0.251 3.64∗∗ sH3 Customer value ← Customer sacrifice −0.375 −2.36∗∗ sH4a Customer satisfaction ← Tangible 0.139 1.51† sH4b Customer satisfaction ← Reliability 0.296 5.18∗∗ sH4c Customer satisfaction ← Responsiveness 0.045 0.54 noH4d Customer satisfaction ← Assurance 0.410 4.29∗∗ sH4e Customer satisfaction ← Empathy 0.041 0.26 noH4f Customer satisfaction ← Network quality 0.203 3.66∗∗ sH6 Customer value Customer perceived 0.137 2.38∗ s

service qualityH9 Behavior intentions ← Customer perceived 0.106 1.32 no

service qualityH10 Behavior intentions ← Customer value 0.474 6.22∗∗ sH11 Behavior intentions ← Customer satisfaction 0.418 5.30∗∗ s

∗Significant at P < 0.025 level.∗∗Significant at P < 0.01 level.†Significant at P < 0.1 level.

β = 0.198 and t = 2.27, and β = 0.269 and t = 2.27respectively). As for the key drivers of customer value,the standardized path coefficients show that networkquality, empathy and tangibles have a significantly pos-itive influence on customer value in China’s mobilecommunication market, while we find no evidence tosupport the influence of reliability, responsiveness andassurance, which implies that hypothesis 2a, 2e and 2fare strongly supported but hypothesis 2b, 2c and 2dare not supported. On the other hand, customer per-ceived sacrifice has a significantly negative impact oncustomer value, which indicates that hypothesis 3 isstrongly supported. Among the key drivers of customervalue, empathy and network quality are the two mostimportant positive drivers (β = 0.523 and t = 4.26,

and β = 0.251 and t = 3.64 respectively) while cus-tomer perceived sacrifice is the most important nega-tive driver (β = −0.375 and t = −2.36). In contrast,our structural equation model shows no evidence ofthe relationship between empathy and customer satis-faction, and responsiveness and customer satisfactionwhile the influence of reliability, assurance, tangible,and network quality on customer satisfaction is foundto be significant, which indicates that hypothesis 4a, 4b,4d, and 4f are strongly supported and hypothesis 4c and4e are not supported. Among these quality-related fac-tors, assurance is the most important driver of customersatisfaction (β = 0.410 and t = 4.29), and reliabilitycomes the second (β = 0.296 and t = 5.18). For theimpacts on behavior intentions of customers, results in

336 Wang, Lo and Yang

Table 3 show that hypothesis 10 and 11 are stronglysupported, which implies significant influences of cus-tomer value and customer satisfaction on behavior in-tentions of customers. Comparatively speaking, theimpact of customer value is a little larger than thatof customer satisfaction, and the path coefficients are0.474 (t = 6.22) and 0.418 (t = 5.30) respectively. Bycontrast, we find no evidence to support hypothesis 9,which implies that the impact of customer perceivedservice quality on behavior intentions is not supported.However, this does not mean that customer perceivedquality is not important, and it can also exert indirectimpact on behavior intentions of customers by affect-ing customer value and customer satisfaction, whichwill be reported later.

5.3. Hypotheses testing of the moderating effectAfter examining the direct effects, let’s begin to checkthe moderating effects. In doing so, two structural equa-tion models were developed. One is the “main-effectsmodel” excluding interaction terms and the other isthe “interaction model” including the interaction effect.Since PLS does not make any distributional assump-tions and traditional parametric tests are inappropriate,a bootstrapping method of sampling with replacementwas used and standard errors computed on the basisof 500 bootstrapping runs. During the process of in-teraction model building, as in regression analysis, thepredictor and moderator variable are multiplied to ob-tain the interaction terms. As suggested by Chin et al.(2003), we standardized the indicators prior to multi-plying them. The results of both “main-effects model”and “interaction model” are reported in Table 4, whichshows only highly related constructs.

It is obvious that customer perceived service qual-ity contributes positively and significantly to customer

Table 4. Assessment of main effects and moderating effects by using PLS-GRAPH

Main-effects model Interaction model

Hypotheses Exogenous variables Path coefficients T -value Path coefficients T -value

H5 Customer perceived service quality 0.382 6.29∗∗ 0.317 3.99∗∗H7 Customer value 0.450 8.50∗∗ 0.452 4.39∗∗H8 Customer perceived service −0.177 −2.32∗

quality X customer valueR square 0.7133 0.7591

∗Significant at P < 0.025 level.∗∗Significant at P < 0.01 level.

satisfaction not only in the “main-effects model” butalso in the “interaction model”, and path coefficientis 0.382 (t = 6.29) and 0.317 (t = 3.99) respectively,which implies that hypothesis 5 is strongly supported.Similarly, the impact of customer value on customersatisfaction is also statistically significant both in the“main-effects model” and the “interaction model”, andthe path coefficient is 0.450 (t = 8.50) and 0.452(t = 4.39) respectively, which implies that hypothesis7 is strongly supported. Furthermore, by checking theinteraction effect, we find that the path coefficient is−0.177 and t = −2.32. At the same time, as recom-mended by Chin et al. (2003), the overall effect sizecan be calculated as follows:

f 2

=[

R2(Interaction-model) − R2(Main-effects-model)

[1 − R2(Interaction-model)]

]

We find that the value of f 2 in our model is 0.190, whichis larger than the suggested threshold of 0.15 (Cohen,1988). So we can make the conclusion that a mediummoderating effect is detected. Therefore, as hypothe-sized, customer value moderates the relationship be-tween perceived quality and customer satisfaction andthus hypothesis 8 is strongly supported.

6. Conclusions and Implications

By taking a disaggregated approach, we find that not allquality-related factors contribute to customer perceivedservice quality, customer value and customer satisfac-tion equally, which provides more useful and practicalsuggestions for researchers and managers in improving

Service Quality, Customer Value, and Satisfaction 337

service quality, creating and delivering superior cus-tomer value, and achieving high customer satisfaction.Furthermore, in addition to the direct interrelationshipsamong customer perceived service quality, customervalue and customer satisfaction (including the medi-ating role of customer value between customer per-ceived service quality and customer satisfaction), themoderating effect of customer value on the relation-ship between customer perceived service quality andcustomer satisfaction is detected based on our struc-tural equation models. Besides, we find that only theimpacts of customer value and customer satisfaction onbehavior intentions of customer are statistically sup-ported. However, it is found that customer perceivedservice quality may influence behavior intentions ofcustomers indirectly by affecting customer value andcustomer satisfaction.

On the one hand, such findings can be used to ex-plain the competitive behaviors of the two monopolymobile communication firms in China, China Mobileand China Unicom. Although there are at present onlytwo players in China’s mobile communication market,the competition between them is more intense thanever. They compete not only in the improvement ofcustomer perceived service quality based on networkquality via a large amount of investment in network ex-tension and upgrading, but also in customer retentionand acquisition via direct and indirect price reductionto lower customer perceived sacrifice and to deliversuperior customer value. The rationale of this is sup-ported by our research since network quality is one ofthe most important drivers of overall service quality,customer value and customer satisfaction while cus-tomer perceived sacrifice (including price) has a signif-icantly negative impact on customer value, and thus in-fluences customer satisfaction and behavior intentionsof customers indirectly and negatively. Furthermore,the competition in the mobile communication marketwill, inevitably, grow much more intense in the nextfew years, following China’s formal entry into WTOin December 2001, since a general agreement was ar-rived at that China’s telecommunication service sector,like other sectors, would allow foreign companies tocompete gradually. Thus, more foreign giants will in-creasingly be involved. Therefore, firms expecting tobuild and maintain competitive advantages in this mar-ket must try their best to improve service quality, de-liver superior customer value, achieve higher customersatisfaction, and turn behavior intentions of customerinto the true purchasing behavior.

On the other hand, our research enables firms tocompete more effectively and efficiently. According toour research, as both China Mobile and China Unicomhave been doing, priority should be given to how toreduce customer perceived sacrifice and improve net-work quality, since mobile communication customersin China perceive these as the key factors influenc-ing their evaluation of customer value, customer satis-faction, or service quality, which, in turn, drive themto make actual purchasing decisions. Also, attentionmust be given to another important factor, that is, “tan-gibles” since this influences not only service quality,but also customer value and customer satisfaction. Atthe same time, firms should devote themselves to fac-tors such as “empathy” in order to improve customervalue, even though “empathy” is not significant in cus-tomer’s perception of customer satisfaction. Neverthe-less, “empathy” plays a part in superior customer value,which helps customers make their purchasing deci-sions, and also fosters higher customer satisfactionindirectly. Similarly, China’s mobile communicationservice suppliers should not neglect reliability, since re-liability contributes positively and significantly to cus-tomer satisfaction, despite its not being a significantlyinfluential factor of customer value.

Moreover, based on what has been achieved on theinterrelationships among customer perceived servicequality, customer value and customer satisfaction, theintegrated framework developed in this study enable usto show the existence of significant moderating role ofcustomer value in addition to the direct influence of cus-tomer perceived service quality on customer value andthat of customer value on customer satisfaction, whichrepresent an important addition in our understandingof the interrelationships among these three constructs.The negative regression coefficient for the interactionbetween quality and value implies that this factor canhave a negative impact on satisfaction, which corre-sponds with what Caruana et al. (2000) argue for. Thishas important implications for management as wellsince all the three constructs have increasingly playedkey roles in competition and are believed to have asignificant effect on customer retention and ultimatelylong term profitability. It indicates that although cus-tomers may believe that a firm provides high levels ofservice quality, it does not necessarily follow that satis-faction will be high and they will conduct a transactionwith that firm. For example, if prices are perceived to behigh, this may still result in a negative effect on satisfac-tion. Therefore, satisfaction does not depend on service

338 Wang, Lo and Yang

quality alone and higher levels of quality are consid-ered worthwhile to the extent that customers believethat value is being enhanced. The results also providea basis for understanding the role of low price or lowcost strategy in customer satisfaction and intense com-petition since customer value will be perceived higherwith reduced customer perceived sacrifice and similarquality level, while the contribution of perceived qual-ity to customer satisfaction should become a little lessas a result of the negative moderating effect of cus-tomer value. Finally, although our empirical modelswere developed based on the data collected in China’smobile communication market, these findings can alsobe applied to other industries.

7. Limitations and Directionsof Future Research

This research has successfully applied what has beenfound in service market literature to one sector of thetelecommunication industry in China. It has also pro-vided a comprehensively integrated framework for un-derstanding the dynamic relationships among quality-related factors, customer perceived service quality,customer perceived sacrifice, customer value, cus-tomer satisfaction and behavior intentions of cus-tomers. However, these results need to be interpretedwithin the limitations of the study. For example, a lim-itation of this study is its cross-sectional design, whichmeans that an important step for further research isthe collection and analysis of longitudinal data. Sec-ondly, although constructs of customer perceived ser-vice quality, value and satisfaction are conceptualizedseparately and steps have been taken to reduce multi-collinearity, they are related and some effects may stillbe present. So the coefficient of the interaction term forvalue must be interpreted with caution since the mod-erator is correlated to both satisfaction and customerperceived service quality. Thirdly, the findings need tobe confirmed by further evidence from other regionsgiven the difference in values and cultures among dif-ferent regions. Furthermore, the results should also betested by evidence from other industries. Finally, fur-ther research should extend our integrative frameworkand take other variables such as corporate reputation,customer loyalty and other performance measures intoconsideration to provide practical managers more use-ful suggestions to acquire new customers and retainexisting customers.

Acknowledgment

We wish to acknowledge the National Natural Sci-ence Foundation of China and China-Canada Univer-sity Industry Partnership Program (NSFC-CCUIPP)(70142023), National Natural Science Foundation ofChina (70202002), the National Social Science Foun-dation of China (02CJL004), the Sumitomo Foundationof Japan (018006), and the Research Grants Councilof the HKSAR, China (Cityu 1129/02E). We grate-fully acknowledge the three anonymous reviewers fortheir valuable advice about further improvement of thismanuscript. We also wish to thank Dr. Wynne Chin ofthe University of Houston for kindly permitting us touse his PLS-Graph package.

Notes

1. “Tangibles” refers to the appearance of physical facilities, equip-ment, personnel, and written materials; “reliability” refers to theability to perform the promised service dependably and accu-rately; “responsiveness” refers to the willingness to help cus-tomers and provide prompt service; “assurance” refers to the em-ployee’s knowledge and courtesy, and their ability to inspire trustand confidence; “empathy” refers to giving caring, individualizedattention to customers (Parasuraman et al., 1988).

2. It refers to whether the product or service does what it is supposedto do and possesses features that meet the needs of customers.

3. It represents to what extent the product is free from deficiencies.4. Performance means a product’s primary operating characteris-

tics; features refer to the additional features or the “bells andwhistles” of the product; conformance represents the extent towhich a product’s design and operating characteristics meet theestablished standards; reliability indicates the probability a prod-uct will operate properly over a specified period of time understated conditions of use; durability means the amount of use theconsumer gets from a product before it physically deteriorates oruntil replacement is preferable; serviceability refers to the speed,competence and courtesy of repair; aesthetics shows how a prod-uct appeals to our five senses and customer-perceived qualityindicates a customer’s perception of a product’s quality based onthe reputation of the firm.

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Yonggui Wang is an associate professor of Interna-tional Business School, Nankai University, China. He

received his second Ph.D. degree from City Univer-sity of Hong Kong. He specializes in Service Opera-tion Management & Marketing, Corporate Strategy &Dynamic Competitive Advantages, and Customer re-lation Management and Business Statistics with aboutten academic papers published in leading journals suchas Journal of Engineering and Technology Manage-ment, The Journal of Management Development, In-ternational Journal of Managing Service Quality andso on.

Hing-Po Lo is Head and an associate professor of theDepartment of Management Sciences, City Universityof Hong Kong. As an applied statistician, he has beenchampioning the application of statistics through teach-ing, research, and consultancy for thirty years. In par-ticular, he specializes in the use of statistical modelingin Marketing Research and Transportation.

Yongheng Yang is an associate profesor of Quality Man-agement in the Department of Marketing at NanjingUniversity. He received his Master degree in BusinessAdministration from Nankai University, and PhD inService Operation from City University of Hong Kong.His research interests include business statistics, ser-vice quality management, operations management, andinformation system modeling.