Computers in Human Behavior 25 (2009) 887–896

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Computers in Human Behavior

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The relationships among service quality, perceived value, customer satisfaction,and post-purchase intention in mobile value-added services

Ying-Feng Kuo a,*, Chi-Ming Wu b, Wei-Jaw Deng c

a Department of Information Management, National University of Kaohsiung, 700, Kaohsiung University Road, Kaohsiung 811, Taiwanb Institute of Economics and Management, National University of Kaohsiung, Kaohsiung 811, Taiwanc Graduate School of Business Administration, Chung Hua University, Hsinchu 300, Taiwan

a r t i c l e i n f o a b s t r a c t

Article history:Available online 11 April 2009

Keywords:Mobile value-added servicesService qualityPerceived valueCustomer satisfactionPost-purchase intention

0747-5632/$ - see front matter � 2009 Elsevier Ltd. Adoi:10.1016/j.chb.2009.03.003

* Corresponding author. Tel.: +886 7 591 9513; faxE-mail address: fredkuo@nuk.edu.tw (Y.-F. Kuo).

The purposes of this study are to construct an instrument to evaluate service quality of mobile value-added services and have a further discussion of the relationships among service quality, perceived value,customer satisfaction, and post-purchase intention. Structural equation modeling and multiple regres-sion analysis were used to analyze the data collected from college and graduate students of 15 major uni-versities in Taiwan. The main findings are as follows: (1) service quality positively influences bothperceived value and customer satisfaction; (2) perceived value positively influences on both customersatisfaction and post-purchase intention; (3) customer satisfaction positively influences post-purchaseintention; (4) service quality has an indirect positive influence on post-purchase intention through cus-tomer satisfaction or perceived value; (5) among the dimensions of service quality, ‘‘customer service andsystem reliability” is most influential on perceived value and customer satisfaction, and the influence of‘‘content quality” ranks second; (6) the proposed model is proven with the effectiveness in explaining therelationships among service quality, perceived value, customer satisfaction, and post-purchase intentionin mobile added-value services.

� 2009 Elsevier Ltd. All rights reserved.

1. Introduction

Thanks to the fast growth of the mobile communication market,mobile phones that used to be exclusively held by business personshave become personal equipment closely integrated into every-one’s daily life (Olla & Patel, 2002) and relatively more frequentlyused than other mobile devices (Clarke, 2001). In Taiwan, due tothe lift of ban on mobile communication and the liberalization ofthe communication industry, the penetration rate of mobile phonehas reached 110% in 2003 (DGT, 2003). Furthermore, the release ofmobile number portability (MNP) and the licensing of VoIP (voiceover internet protocol) phone numbers with a prefix of 070 in2005 have dissolved the constraints set up by telecom serviceproviders and allowed consumers to have more options. As aresult, unable to rely solely on the conventional voice services,telecom service providers have been seeking for other opportuni-ties to increase their business revenue.

Mobile value-added services are digital services added tomobile phone networks other than voice services in which thecontents included can be either self-produced by mobile telecomservice providers or provided through strategic alliances with

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content providers. These services include games, icons, ringtones,messages, web browsing, SMS (short message service) coupons,and electronic transaction. They can bring five values to consum-ers: time-critical needs and arrangement, spontaneous needs anddecisions, entertainment needs, efficiency needs and ambitions,and mobility-related needs (Anckar & D’Incau, 2002). Thus, mobilevalue-added services will become new opportunities for telecomservice providers. However, mobile value-added services providedby telecom service providers can be classified into four types,namely information, communication, transaction, and entertain-ment, and this classification applies to almost all the providers.Although new services are being released at all times, whetherthey are appealing to consumers and can induce positive post-pur-chase intention after consumers have used them so as to effectivelyincrease revenue and sustainable development will be an impor-tant issue for telecom service providers.

Previous studies of marketing have pointed out that the key ofcorporate success and competitive advantage is the enhancementof service quality, perceived value, and customer satisfaction(Khatibi, Ismail, & Thyagarajan, 2002; Landrum & Prybutok,2004; Patterson & Spreng, 1997; Wang, Lo, & Yang, 2004; Yang &Peterson, 2004). As the number of studies of mobile telecomservice quality is still limited, and a definite set of measurementindices for the service quality of mobile value-added services is

888 Y.-F. Kuo et al. / Computers in Human Behavior 25 (2009) 887–896

not available, this study attempts to design a scale for measuringthe service quality of mobile value-added services and furtherexamines the relationships among service quality, perceived value,customer satisfaction, and post-purchase intention to find outwhich dimensions of service quality are significantly correlatedwith perceived value and customer satisfaction. The result can pro-vide valuable reference information for mobile value-added serviceproviders to manage their services and enhance their servicequality.

2. Literature review and hypothesis development

2.1. Service quality

Parasuraman, Zeithaml, and Berry (1985, 1988) conceivedthat service quality is the difference between customers’ expec-tation and their perceived performance of a service. Based onthis concept, Parasuraman et al. (1988) developed the SERVQUALmodel (including five dimensions, namely tangible, responsive-ness, reliability, assurance, and empathy) to measure servicequality. This model has drawn attention from the academicand the practical circles. However, many scholars have ques-tioned about the conceptual framework and measurement meth-od of this model. For instance, Cronin and Taylor (1992) pointedout that using service quality performance (SERVPERF, i.e. theperceived service in SERVUQAL) to measure service quality pro-duces better results of reliability, validity, and predictive powerthan using SERVQUAL. Some other studies (Boulding, Kalra, Rich-ard, & Zeithaml, 1993; McAlexander, Kaldenberg, & Koenig,1994; Parasuraman, Zeithaml, & Berry, 1994; Zeithaml, Berry, &Parasuraman, 1996) also maintained that SERVPERF is moreaccurate than SERVQUAL in the measurement of service quality,and SERVQUAL can provide better diagnostic information. In thestudies of the information industry, similar findings have beenproposed (Landrum & Prybutok, 2004; Pitt, Watson, & Kavan,1997; Van Dyke, Kappelman, & Prybutok, 1997), and Zeithaml,Parasuraman, and Malhotra (2002) proposed that it is not neces-sary to use customers’ expectation to measure the service qual-ity of a website. Therefore, this study will directly use perceivedservice quality to measure the service quality of mobile value-added services.

In the research of website service quality, various measurementdimensions have been proposed according to website properties.Kuo (2003) put forth a virtual community service quality scale,using advertising mail management, customer service manage-ment, online quality and information safety, webpage design andcontent, and extra function and service to evaluate the servicequality of a website. Yang, Cai, Zhou, and Zhou (2005) used usabil-ity, usefulness of content, adequacy of information, accessibility,and interaction to measure user’s perceived quality of informationpresenting web portals. From the perspective of transaction pro-cess, Bauer, Falk, and Hammerschmidt (2006) proposed eTran-sQual (including five quality aspects, namely functionality/design,enjoyment, process, reliability, and responsiveness) to measurethe quality of online shopping services. As to the quality of mobilecommunication services, Chae, Kim, Kim, and Ryu (2002) used con-nection quality, content quality, interaction quality, and contextualquality to measure the information quality of mobile networkingservices. Kim, Park, and Jeong (2004) examined the service qualityof mobile communication services in South Korea by call quality,value-added services, and customer support. Based on the afore-mentioned studies of website and telecom service quality, thisstudy further categorizes service quality factors into four dimen-sions, including content quality, navigation and visual design,management and customer service, and system reliability and con-nection quality.

2.2. Perceived value

Customer’s perceived value can be defined from the perspec-tives of money, quality, benefit, and social psychology. The Mone-tary perspective indicates that value is generated when less is paid(such as by using coupons or promotions) for goods (Bishop, 1984).In other words, it is the concept of consumer surplus in economics;perceived value is the difference between the highest price thatconsumers are willing to pay for a product or a service and theamount practically paid. According to the quality perspective, va-lue is the difference between the money paid for a certain productand the quality of the product (Bishop, 1984). That is, when lessmoney is paid for a high quality product, positive perceived valuewill be created. The benefit perspective indicates that perceivedvalue is customers’ overall evaluation of the utility of perceivedbenefits and perceived sacrifices (Zeithaml, 1988). In other words,consumers may cognitively integrate their perceptions of whatthey get and what they have to give up in order to obtaining goods.However, the sacrifice means more than the money paid for a cer-tain goods. Non-monetary costs, such as transaction cost, searchcost, negotiation cost, and time incurred during the purchase,should also be included (Cronin, Brady, Brand, Hightower, &Shemwell, 1997; Cronin, Brady, & Hult, 2000; Keeney, 1999; Zei-thaml, 1988). The social psychology perspective points out thatthe generation of value lies in the meaning of purchasing a certaingoods to the buyer’s community (Sheth, Newman, & Gross, 1991).That is, goods carrying particular meanings (such as social eco-nomic status and social culture) can increase the effect of socialself-concept (Sweeney & Soutar, 2001; Wang et al., 2004). In thisstudy, perceived value is the evaluation of the benefits of a productor a service by customers based on their advance sacrifices and ex-post perceived performance when they use mobile value-addedservices.

In the research of the relationships between service quality andcustomer’s perceived value in conventional retailing and onlineshopping, most of the empirical studies have pointed out that ser-vice quality will positively influence perceived value (Bauer et al.,2006; Brady, Robertson, & Cronin, 2001; Cronin et al., 1997,2000). Among the studies of the telecom industry, Wang et al.(2004) and Turel and Serenko (2006), respectively, investigatedthe mobile services in China and Canada and found out that servicequality is positively related to perceived value. Thus, Hypothesis 1is proposed as follows:

H1: Service quality positively influences perceived value inmobile value-added services.

2.3. Customer satisfaction

Customer satisfaction can be defined using the transaction-spe-cific perspective or cumulative perspective. The transaction-spe-cific perspective indicates that customer satisfaction is theevaluation based on the recent purchase experiences (Bouldinget al., 1993). Compared with the transaction-specific perspective,the cumulative perspective stresses overall evaluations, indicatingthat evaluations of customer satisfaction should be based on all thepurchase experiences of the customer, disregarding any specificpurchase experience (Johnson & Fornell, 1991). Parasuramanet al. (1988) argued that the cumulative perspective is more capa-ble of evaluating the service performance of firms and more effec-tive in predicting consumers’ post-purchase behaviors (Wang et al.,2004). Among the studies of customer satisfaction in the informa-tion industry, Lin and Wang (2006) revealed that customer satis-faction of mobile commerce is consumer’s total response to thepurchase experiences in a mobile commerce environment. There-

Y.-F. Kuo et al. / Computers in Human Behavior 25 (2009) 887–896 889

fore, in this study, customer satisfaction is defined as the total con-sumption perception of consumers when using mobile value-added services.

Previous studies of conventional retailing have pointed out thatservice quality positively influences customer satisfaction (Croninet al., 2000; Johnson & Fornell, 1991; Kristensen, Martensen, &Gronholdt, 1999). Similar conclusions have been proposed in thestudies of website and online shopping (Bauer et al., 2006; Collier& Bienstock, 2006; Hsu, 2006; Kuo, 2003; Lee & Lin, 2005; Park &Kim, 2006). Among the studies of the telecom industry, Wanget al. (2004) investigated the telecom industry in China, and Kimet al. (2004), Tung (2004), and Turel and Serenko (2006) investi-gated the mobile services in South Korea, Singapore, and Canada,respectively. These studies also supported that service quality pos-itively influences customer satisfaction. Thus, Hypothesis 2 is pro-posed as follows:

H2: Service quality positively influences customer satisfactionin mobile value-added services.

In the research of the relationships between perceived valueand customer satisfaction, empirical studies of the conventionalretailers discovered that perceived value positively influences cus-tomer satisfaction in most cases (Cronin et al., 2000; Eggert &Ulaga, 2002). A similar conclusion was also proposed in the studiesof online shopping websites and e-commerce (Hsu, 2006; Yang &Peterson, 2004). In the aspect of the telecom industry, Wanget al. (2004) (focusing the telecom industry in China), Tung(2004) (SMS service in Singapore), Lin and Wang (2006) (mobilecommerce in Taiwan), and Turel and Serenko (2006) (mobile ser-vices in Canada) all revealed that perceived value is positively re-lated to customer satisfaction. Thus, Hypothesis 3 is proposed asfollows:

H3: Perceived value positively influences customer satisfactionin mobile value-added services.

2.4. Post-purchase intention

Post-purchase intention is the tendency that consumers willpurchase the goods or services at the same shop and deliver theiruse experiences to friends and relatives (Cronin et al., 2000; Wanget al., 2004; Zeithaml et al., 1996). To evaluate post-purchaseintention, Zeithaml et al. (1996) adopted loyalty, switch, paymore, external response, and internal response to assess the eval-uation work. Boulding et al. (1993) used repurchase intention andword of mouth (WoM) to evaluate consumer’s post-purchaseintention. Repurchase intention is the process of an individualpurchasing goods or services from the same firm (Hellier, Geur-sen, Carr, & Rickard, 2003), and the reason for repurchase is pri-marily based on past purchase experiences. Compared with

Fig. 1. Researc

attracting new customers, enterprises can spend less on market-ing to retain old customers (Zeithaml et al., 1996). WoM is aprocess in which consumers who have used a certain product orservice pass their experiences through word of mouth to consum-ers planning to purchase the product or service (Westbrook,1987). Consumers who have not experienced or fully understoodthe properties of a certain product or service may usually relyon WoM to acquire information (Bansal & Voyer, 2000). Therefore,compared with external marketing strategies, WoM is moreimportant and influential to customer’s attitude and behavior(Harrison-Walker, 2001).

In previous studies, post-purchase intention has been fre-quently used to inspect service quality (Alexandris, Dimitriadis, &Markata, 2002; Boulding et al., 1993; Cronin & Taylor, 1992; Croninet al., 1997, 2000; Wang et al., 2004; Zeithaml et al., 1996), whichhas been considered as significantly and positively influential topost-purchase intention (Alexandris et al., 2002; Boulding et al.,1993; Cronin et al., 1997, 2000; Zeithaml et al., 1996). In otherwords, good service quality can induce positive post-purchaseintention of consumers. In the research of website and onlinestores, Kuo (2003) pointed out that the service quality of onlinecommunity is positively related to continuous use, referral, andloyalty. Lee and Lin (2005) found that the service quality of onlineshops positively influences post-purchase intention. Thus, Hypoth-esis 4 is proposed as follows:

H4: Service quality positively influences post-purchase inten-tion in mobile value-added services.

In recent years, corporate managers and marketing staffs haveused long-neglected perceived value to evaluate consumer’spost-purchase intention (Eggert & Ulaga, 2002; Lin, Sher, & Shih,2005; Patterson & Spreng, 1997; Petrick, 2002; Wang et al.,2004). In the discussion of the relationships between perceived va-lue and post-purchase intention, many scholars considered per-ceived value has direct effects on repurchase intention and WoM(Eggert & Ulaga, 2002; Lin et al., 2005; Petrick, 2002; Wang et al.,2004). Cronin et al. (2000) discovered in a cross-industrial researchthat perceived value has positive effects on post-purchase inten-tion. Wang et al. (2004) which focused on the telecom industryin China also supported that perceived value positively influencespost-purchase intention. Lin and Wang (2006) also revealed thatperceived value positively influences loyalty in the research of mo-bile commerce in Taiwan. Thus, Hypothesis 5 is proposed asfollows:

H5: Perceived value positively influences post-purchase inten-tion in mobile value-added services.

Many studies of satisfaction have pointed out a positive rela-tionship between customer satisfaction and post-purchase inten-tion (Brady et al., 2001; Cronin et al., 2000; Johnson & Fornell,

h model.

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1991). Consumers with a higher level of satisfaction tend to have astronger intention to repurchase and recommend the purchasedproduct (Zeithaml et al., 1996). In other words, when customer sat-isfaction is enhanced, repurchase can be more frequent. The extantstudies of e-retailing and online shopping also provided the similarconclusions (Collier & Bienstock, 2006; Lee & Lin, 2005). Among thestudies of the telecom industry, Gerpott, Rams, and Schindler(2001) and Tung (2004), respectively, examined the telecom indus-try in Germany and SMS service in Singapore. They also concludedthat customer satisfaction is positively related to post-purchaseintention. Moreover, other studies of the mobile services in Canadaand China also supported this argument (Turel & Serenko, 2006;Wang et al., 2004). Lin and Wang (2006) manifested a positive rela-tionship between customer satisfaction and customer loyalty inthe study of mobile commerce in Taiwan. Thus, Hypothesis 6 isproposed as follows:

H6: Customer satisfaction positively influences post-purchaseintention in mobile value-added services.

Fig. 1 shows the proposed model based on the above literaturereview.

3. Research methodology

3.1. Questionnaire design

The questionnaire used in this study was designed according torelated literatures and users’ and experts’ opinions. After the draftwas completed, a pretest was performed on experts and usersfamiliar with mobile value-added services to modify items withambiguous expressions. Therefore, questionnaire respondentscould understand the questions in the formal survey and the con-tent validity of the questionnaire could be ensured. The question-naire was composed of two sections. The first section wasintended to understand each respondent’s basic personal dataand usage of mobile phone and value-added services. All the mea-surement scales adopted were nominal. The second section mea-sured the respondent’s perception of each construct in theresearch model. All items were assessed using five-point Likertscales from 1 = ‘‘strongly disagree” to 5 = ‘‘strongly agree”. Table1 shows the research constructs and items included in the ques-tionnaire. Operationalizations of the research constructs are asfollows.

3.1.1. Service qualityService quality is operationalized using the SERVPERF model

due to the superiority of performance-based measures (Bouldinget al., 1993; Cronin & Taylor, 1992; Landrum & Prybutok, 2004;McAlexander et al., 1994; Parasuraman et al., 1994; Pitt et al.,1997; Van Dyke et al., 1997; Zeithaml et al., 1996, 2002). That is,perceived service quality will be used to measure the service qual-ity of mobile value-added services. The construct of service qualitywas initially grounded on the four examining dimensions (contentquality, navigation and visual design, management and customerservice, and system reliability and connection quality) and wasmeasured by 24 items adapted from Chae et al. (2002), Kuo(2003), Kim et al. (2004), and Yang et al. (2005), which dealt withthe service quality of mobile Internet, Website, mobile telecommu-nication, and Web portal services. These items were also modifiedto fit the mobile value-added services context.

3.1.2. Perceived valuePerceived value is trade-off between what customers receive,

such as quality, benefits, and utilities, and what they sacrifice, such

as price, opportunity cost, time, and efforts (Cronin et al., 1997,2000; Keeney, 1999; Zeithaml, 1988). In this study, perceived valueis the evaluation of the benefits of a product or a service by cus-tomers based on their advance sacrifices and ex-post perceivedperformance when they use mobile value-added services. Per-ceived value was measured by three-item measures adapted fromCronin et al. (2000), Tung (2004), and Wang et al. (2004). Theseitems were also modified in wording appropriate for mobile va-lue-added services context.

3.1.3. Customer satisfactionCustomer Satisfaction is customers’ cumulative impression of a

firm’s service performance (Johnson & Fornell, 1991). In terms ofmobile commerce, customer satisfaction is customer’s post-pur-chase evaluation and affective response or feeling to the overallproduct or service experience in a mobile commerce environment(Lin & Wang, 2006). In this study, customer satisfaction is definedas the total consumption perception of consumers when using mo-bile value-added services. The items measuring customer satisfac-tion were measured by three-item measures taken from previousmeasures of the overall level of user satisfaction in mobile services(Chae et al., 2002; Lin & Wang, 2006).

3.1.4. Post-purchase intentionPost-purchase intention is the tendency that consumers will

purchase the goods or services at the same shop and deliver theiruse experiences to friends and relatives (Cronin et al., 2000; Wanget al., 2004; Zeithaml et al., 1996). Items for the post-purchaseintention construct were measured by three-item measuresadapted from the previously validated inventory (Cronin et al.,2000; Wang et al., 2004; Zeithaml et al., 1996) and all items weremodified to fit the mobile value-added services context.

3.2. Research subjects and sampling method

This study was conducted in Taiwan because the high penetra-tion rate in mobile phone (110%) technologies and applications(DGT, 2003). According to a 2006 survey released by FIND (Fore-seeing Innovative New Digiservices, Taiwan), approximately57.7% of Taiwanese people have used mobile value-added servicesrecently, and most of them (54.2%) are in the age group of 21–30.Therefore, if this age group was selected for sampling, the resultwould be representative to a certain degree of the population.The above age group is mainly composed of college students andgraduate students, who may come from various regions of Taiwan.Thus, under limited research resources, 15 universities were se-lected, and undergraduates and graduate students in these univer-sities were the respondents of this study. In the formal survey, werequested teachers willing to assist our research to let us distributequestionnaires in class and retrieve them after students have col-lectively completed their answers. Before the formal survey, thepurpose of this study and notices were explained. Assistance wasfurther provided to the respondents during the survey to reducethe occurrence of invalid response. A total of 1100 questionnaireswere distributed, and the response rate was 100%. Excluding therespondents not in the selected age group (age 21–30) and thosewho have never used mobile value-added services before, a totalof 387 valid questionnaires were obtained.

4. Data analysis, results, and discussion

4.1. Sample characteristics

Among the samples collected, female respondents (58.4%) werethe majority. In terms of education background, undergraduate

Table 1Constructs and items included in the questionnaire.

Construct Item Measurement Reference

Service quality Content quality CQ1 This value-added service provides complete content Chae et al. (2002)CQ2 This value-added service provides appropriate content Kuo (2003)CQ3 This value-added service provides important content Kim et al. (2004)CQ4 This value-added service provides fashionable content Yang et al. (2005)CQ5 This value-added service provides regularly updated contentCQ6 I can fully understand the content provided

Navigation andvisual design

BV1 I can easily use the value-added service Chae et al. (2002)BV2 This value-added service is displayed in a harmonious way Kuo (2003)BV3 I can clearly understand the position of the screen I am currently browsing in the

navigation layoutKim et al. (2004)

BV4 The homepage of this value-added service can clearly present the location ofinformation

Yang et al. (2005)

Management andcustomer service

MS1 This telecom company provides diversified value-added services Chae et al. (2002)MS2 This telecom company provides multiple tariff options Kuo (2003)MS3 This telecom company provides good post-services Kim et al. (2004)MS4 I can easily alter the contract of value-added services Yang et al. (2005)MS5 When I have my contract altered, the telecom company still holds a friendly attitudeMS6 When any problem occurs, the telecom company can instantly cope with itMS7 This telecom company provides a FAQ for value-added services

System reliabilityand connection quality

SC1 This value-added service system is stable Chae et al. (2002)SC2 Error seldom occurs to this value-added service system Kuo (2003)SC3 This value-added service provides effective links Kim et al. (2004)SC4 I can easily return to the screen previously browsed Yang et al. (2005)SC5 It does not take too much time to download the information I needSC6 It does not take too much time to load the links I click onSC7 This value-added service system can instantly react to the data I input

Perceived value PV1 I feel I am getting good mobile value-added services for a reasonable price Cronin et al. (2000)PV2 Using the value-added services provided by this telecom company is worth for me to

sacrifice some time and effortsTung (2004)

PV3 Compared with other telecom companies, it is wise to choose this telecom company Wang et al. (2004)

Customer satisfaction CS1 I am satisfied with the value-added services provided by this telecom company Chae et al. (2002)CS2 I think this telecom company has successfully provided value-added services Lin and Wang (2006)CS3 This value-added service is better than expected

Post-purchase intention PI1 In the future, I will use the value-added services provided by this telecom company again Zeithaml et al. (1996)PI2 In the future, I will recommend the value-added services provided by this telecom

company to my relatives and friendsCronin et al. (2000)

PI3 In the future, I will continue to use the value-added services provided by this telecomcompany

Wang et al. (2004)

Y.-F. Kuo et al. / Computers in Human Behavior 25 (2009) 887–896 891

students accounted for 89.1%. Most of them were the subscribers ofChunghwa Telecom (54.5%). In terms of use time, over 90% (91.5%)of them used value-added services for less than 30 min a month,and nearly 60% (58.4%) of them used no more than 10 min a month.Most of them (81.4%) spent no more than NT$200 on value-addedservices a month. In terms of value-added services used, the topfive value-added services were ringtone (48.6%), multimedia mes-sage service (MMS) (48.4%), picture download (28.4%), musicdownload (28.2%), and auto answering message (13.2%). Over thepast 6 months, MMS was the most frequently used service(33.3%). Among the reasons why consumers did not use mobile va-lue-added services, need-irrelevant (48.0%) was the main cause,followed by lack of understanding about how to use them(40.7%) and high cost (33.0%). (The above three questions allowedmultiple answers.) In order to assess the representativeness of thesample, we collected and compared socio-demographical charac-teristics and the most popular mobile value-added services of therespondents with those reported in a survey of mobile data ser-vices use in Taiwan conducted by FIND (2007), one of the leadingorganizations for providing abundant and professional informationon Internet demographics and trends. Our comparison revealed aclose match between both samples.

4.2. Verification of the proposed model and hypotheses

This study employed structural equation modeling (SEM) toverify the proposed model and hypotheses and used LISREL 8.52as the analysis tool. The dimensions of service quality were ana-

lyzed first. Later, the research model was analyzed and verified.For parameter estimation, maximum likelihood method wasadopted. In the model fitness test, measurement model test andstructural model test were used.

4.2.1. Measurement model of service qualityThe exploratory factor analysis (EFA) was performed to purify

the scale of service quality in the hope of deleting the ‘‘garbageitems” which do not have the common core. Before EFA, a Bartlettsphericity test was performed to determine whether the data wereappropriate for factor analysis. In terms of service quality, a KMO(Kaiser–Meyer–Olkin) value of 0.90 and significance level of .00were obtained using Bartlett’s sphericity test, which suggests thatthe inter-correlation matrix contains sufficient common varianceto make factor analysis worthwhile. For EFA, the Principal Compo-nent Analysis, with varimax rotation and eigenvalue greater than 1and factor loadings greater than 0.4 was used (Kaiser, 1958). Forthe analysis of items, the corrected item-total correlation coeffi-cient less than 0.40 was used as the criterion to delete items, andwhether the removal of the item could significantly enhance thetotal reliability of the questionnaire was considered. This processwas iterated until an optimal result was obtained. Later, we usedCronbach’s a to test the reliability of the items. As suggested bythe results of EFA (Table 2), nine items were removed. The dimen-sion of ‘‘system reliability and connection quality” was divided intotwo dimensions, where SC1 and SC2 were integrated into thedimension of customer service, and SC5–SC7 composed a newdimension. Thus, according to the analysis results, the dimensions

Table 2EFA results of service quality.

Dimension Item Factor loading Item to total Eigenvalue Variance explained (%) Cronbach’s a

Customer service and system reliability (SQ1) MS5 0.76 0.65 3.13 20.86 0.85MS6 0.78 0.69MS7 0.60 0.59SC1 0.76 0.67SC2 0.76 0.66

Navigation and visual design (SQ2) BV1 0.68 0.62 2.62 17.46 0.83BV2 0.68 0.70BV3 0.84 0.69BV4 0.73 0.65

Content quality (SQ3) CQ1 0.82 0.76 2.46 16.43 0.86CQ2 0.83 0.77CQ3 0.79 0.70

Connection speed (SQ4) SC5 0.87 0.74 2.34 15.63 0.85SC6 0.85 0.77SC7 0.68 0.65

Cumulative variance explained: 70.37%

Table 3The fit indices and analysis results for measurement model of service quality.

Fit indices Recommendedvalue

Result

v2/df <3.00 1.58GFI (goodness of fit index) >0.90 0.96RMSEA (root mean square error of approximation) <0.08 0.04RMR (root mean square residual) <0.08 0.03NFI (normed fit index) >0.90 0.99NNFI (non-normed fit index) >0.90 0.99CFI (comparative fit index) >0.90 0.99

Table 4Standardized factor loadings, SMC, and CR for measurement model of service quality.

Dimension Item Factor loading t-value SMC CR

SQ1 MS5 0.71 14.58*** 0.50 0.98MS6 0.77 16.46*** 0.59MS7 0.78 16.86*** 0.61SC1 0.71 14.94*** 0.51SC2 0.69 14.26*** 0.47

SQ2 BV1 0.75 16.50*** 0.58 0.98BV2 0.88 20.84*** 0.77BV3 0.79 17.41*** 0.62BV4 0.74 16.17*** 0.55

SQ3 CQ1 0.93 23.85*** 0.86 0.98CQ2 0.93 23.84*** 0.86CQ3 0.81 19.42*** 0.66

SQ4 SC5 0.73 15.50*** 0.53 0.98SC6 0.79 17.53*** 0.63SC7 0.91 20.90*** 0.82

Note: CR = ðRkÞ2

½ðRkÞ2þRðhÞ�; k = factor loading; h = measurement error of each measured

variable.*** p < .001.

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were, respectively, renamed as ‘‘customer service and system reli-ability”, ‘‘navigation and visual design”, ‘‘content quality”, and‘‘connection speed”. These were consistent with our originaldimensions except for the system reliability and connection qual-ity. The Cronbach’s a coefficients ranged from 0.83 to 0.86, andthe cumulative variance explained was 70.37%.

To test normality assumptions underlying the maximum likeli-hood procedure, we used the multivariate normality test to examwhether the data were normal distributed. The result indicatedthat data were normal (p-value > .05). In the next step, we per-formed confirmatory factor analysis (CFA) to test the overall fit ofthe measurement model. In the beginning, v2 was used as the cri-terion to test the overall fit. If v2 was small and did not reach thelevel of significance, the overall fit of the model was good. How-ever, v2 was sensitive to sample size. When sample size was large,v2 would easily reach the level of significance, making the modelineffective as a result (Bentler & Bonnett, 1980). Thus, manyresearchers have proposed various fit indices to improve the draw-back that v2 is greatly affected by sample size and suggested thatvarious indices be considered before making the judgment of fit(Hair, Anderson, Tatham, & Black, 1998). Table 3 shows the com-mon fit indices, recommended values and analytical results formeasurement model of service quality. According to Table 3, allthe model-fit indices exceeded the respective common acceptancelevels (Hair et al., 1998), indicating that the measurement model ofservice quality exhibited a good fit with the data collected.

CFA can also be used to measure the reliability, convergent valid-ity, and discrimination validity of measurement model. As shown inTables 4 and 5, all the squared multiple correlations (SMC) of themeasured variables, excluding SC2 slightly smaller than the crite-rion (0.50), were larger than 0.50, and the composite reliability(CR) of the latent variables was higher than 0.6, indicating that allmeasures had good reliability (Bagozzi & Yi, 1988; Hair et al.,1998). Moreover, the completely standardized factor loadings allreached the level of significance. All the latent variables had a CRabove 0.60 and an average variance extracted (AVE) above 0.50,meaning that a good convergent validity could be obtained (Fornell& Larcker, 1981). Each latent variable’s AVE was larger than thesquared correlation between each pair of latent variables. Hence,the discrimination validity was adequate (Fornell & Larcker, 1981).

4.2.2. Total measurement modelIn this section, first we used the multivariate normality test to

exam whether the data were normal distributed. The result indi-cated that data were normal (p-value > .05), then CFA was em-

ployed to test the hypothesized relationships between measuredvariables and latent variables. Table 6 shows the common fit indi-ces, recommended values and analytical results for total measure-ment model. According to Table 6, all the model-fit indices werequalified with the recommended values (Hair et al., 1998), indicat-ing that the overall model fit was acceptable.

As shown in Tables 7 and 8, except for SQ4 with an SMC slightlysmaller than the recommended criterion, all the other items had avalue above 0.50, and the CR of latent variables was larger than0.60, indicating that all measures had good reliability (Bagozzi &Yi, 1988; Hair et al., 1998). Moreover, the completely standardizedfactor loadings all reached the level of significance. All the latent

Table 5The mean, standard deviation, and inter-variable correlations of service quality.

Mean SD SQ1 SQ2 SQ3 SQ4

SQ1 3.34 0.63 0.92SQ2 3.26 0.66 0.67 0.93SQ3 3.37 0.63 0.61 0.67 0.95SQ4 3.10 0.74 0.65 0.73 0.60 0.94

Note: (1) Diagonal elements (bold) show the average variance extracted (AVE). Off-diagonal elements show the shared variance.(2) AVE = ðRk2Þ

½ðRk2ÞþRðhÞ�; k = factor loading; h = measurement error of each measuredvariable.

Table 7Standardized factor loadings, SMC, and CR of the measurement model.

Construct Item Factor loading t-value SMC CR

SQ SQ1 0.79 16.92*** 0.63 0.98SQ2 0.81 17.37*** 0.66SQ3 0.73 15.82*** 0.54SQ4 0.66 13.94*** 0.44

PV PV1 0.75 16.29*** 0.56 0.97PV2 0.74 16.20*** 0.54PV3 0.83 18.84*** 0.69

CS CS1 0.77 17.25*** 0.59 0.98CS2 0.89 21.08*** 0.79CS3 0.86 20.07*** 0.73

PI PB1 0.90 22.74*** 0.80 0.98PB2 0.85 20.40*** 0.72PB3 0.96 25.17*** 0.92

*** p < .001.

Table 8The mean, standard deviation, and inter-variable correlations.

Mean SD SQ PV CS PI

SQ 3.27 0.53 0.92PV 3.03 0.64 0.67 0.95CS 3.25 0.62 0.72 0.78 0.93PI 3.26 0.72 0.63 0.80 0.78 0.94

Note: Diagonal elements (bold) show the average variance extracted (AVE). Off-diagonal elements show the shared variance.

Table 9The fit indices and analysis results of the structural model of the overall model.

Fit indices Recommendedvalue

Result

v2/df <3.00 2.65GFI (goodness of fit index) >0.90 0.95RMSEA (root mean square error of approximation) <0.08 0.07RMR (root mean square residual) <0.08 0.03NFI (normed fit index) >0.90 0.98NNFI (non-normed fit index) >0.90 0.99CFI (comparative fit index) >0.90 0.99

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variables had a CR larger than 0.60, and AVE larger than 0.50,meaning that good convergent validity could be obtained (Fornell& Larcker, 1981). Each latent variable’s AVE was larger than thesquared correlation between each pair of latent variables. There-fore, the discriminant validity was good (Fornell & Larcker, 1981).

4.2.3. Structural modelTable 9 shows the common model-fit indices, recommended

values and results of the test of structural model fitness. As shownin Table 9, comparison of all fit indices with their correspondingrecommended values (Hair et al., 1998), the evidence of a goodmodel fit was revealed.

Given the satisfactory fit of the model, the estimated path coef-ficients of the structural model were then examined to evaluatethe hypotheses. Fig. 2 shows the standardized path coefficients,t-values, and coefficients of determination (R2) of the latent vari-ables. Most of the hypotheses were strongly supported, expectfor hypothesis H4 (c = 0.00; t = 0.02). The research results and dis-cussions are shown as follows.

(1) The effect of service quality on perceived value

Service quality had a positive and significant effect on perceivedvalue (c = 0.67; t = 11.07). Thus, H1 was supported. This result isconsistent with those of previous studies on the telecom industry(Turel & Serenko, 2006; Wang et al., 2004). In other words, whentelecom companies provide good service quality in terms of mobilevalue-added services, perceived value can be enhanced.

(2) The effect of service quality and perceived value on cus-tomer satisfaction

As expected, customer satisfaction was significantly directlyinfluenced by both service quality (c = 0.37; t = 5.68) and perceivedvalue (b = 0.53; t = 7.52), so H2 and H3 were supported. These re-sults echo the findings of previous studies on the telecom industry(Tung, 2004; Turel & Serenko, 2006; Wang et al., 2004). Thus, whencustomers perceive higher service quality and value of mobile va-lue-added services, their satisfaction will be more positive.

(3) The effect of service quality, perceived value, and customersatisfaction on post-purchase intention

Table 6The fit indices and analysis results of the measurement model.

Fit indices Recommendedvalue

Result

v2/df <3.00 2.86GFI (goodness of fit index) >0.90 0.94RMSEA (root mean square error of approximation) <0.08 0.07RMR (root mean square residual) <0.08 0.03NFI (normed fit index) >0.90 0.98NNFI (non-normed fit index) >0.90 0.98CFI (comparative fit index) >0.90 0.99

Service quality had no significantly positive influence on post-purchase intention (c = 0.00; t = 0.02), so H4 was not supported.This means that the effect of service quality on post-purchaseintention was insignificant. Service quality has no significant influ-ence on post-purchase intention, probably due to the properties ofresearch samples. The respondents in this study were undergradu-ates and graduate students. In this era of information and technol-ogy, computer and new things would be frequently involved intheir life. Therefore, according to their life experiences, they wouldconsider that the service quality of the mobile value-added ser-vices should be equipped with these quality attributes, so the ser-vice quality of mobile value-added services cannot significantlyinfluence their post-purchase intention. Post-purchase intentionwas significantly directly influenced by both perceived value(b = 0.50; t = 6.44) and customer satisfaction (b = 0.39; t = 4.95),thereby confirming H5 and H6, respectively. These results are con-sistent with the findings of previous studies on the telecom indus-try (Lin & Wang, 2006; Wang et al., 2004). It implies that whencustomers have high perceptions of value and high levels of satis-faction with the mobile value-added services, they are more likelyto use or reuse the services again in the future or to encourage theirfriends and relatives to do so.

(4) The direct effect, indirect effect, and total effect of each con-struct on the post-purchase intention

Fig. 2. Hypotheses testing results.

Table 11

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Using the standardized path coefficients between constructs,the direct effect and the indirect effect of each construct on thepost-purchase intention can be calculated (Table 10). The total ef-fects of the constructs on post-purchase intention (direct effectplus indirect effect) can be ranked as follows: perceived value(0.71), service quality (0.62), and customer satisfaction (0.39). Thisresult is consistent with the argument of previous studies (Anckar& D’Incau, 2002; Keen & Mackintosh, 2001) which the most impor-tant focus of mobile commerce in the future is to create customervalue. By delivering higher values to customers, customers’ repur-chase intention can be increased and their positive words of mouthcan be transmitted to others (Eggert & Ulaga, 2002; Lin et al., 2005;Petrick, 2002). Although service quality has no direct and signifi-cant effect on post-purchase intention, service quality will indi-rectly influence post-purchase intention through perceived valueand customer satisfaction. Thus, good service quality is still criticalto the profit of firms and the maintenance of their competitiveadvantages and customer satisfaction is influential to long-termrelationships between firms and customers.

In addition, according to R2 results, 45.5% variance of perceivedvalue can be explained by service quality; 67.7% variance of cus-tomer satisfaction can be jointly explained by service quality andperceived value; 70% variance of post-purchase intention can bejointly explained by service quality, customer satisfaction, and per-ceived value. According to the above results, we can say the pro-posed model is effective in explaining the relationships amongthe service quality, customer satisfaction, perceived value, andpost-purchase intention in mobile value-added services.

4.3. The multiple regression analysis of the effects of service quality onperceived value and customer satisfaction

According to the path analysis (Fig. 2), service quality had sig-nificant and positive impact on perceived value and customer sat-isfaction. However, which dimension(s) of service quality willsignificantly influence perceived value and customer satisfaction

Table 10The direct effect, indirect effect, and total effect of each construct.

Direct effect Indirect effect Total effect

PV CS BI PV CS BI PV CS BI

SQ 0.67 0.37 0.00 0.36 0.62 0.67 0.73 0.62PV 0.53 0.50 0.21 0.53 0.71CS 0.39 0.39

is (are) unknown. Therefore, multiple regression analysis wasadopted to identify the influential dimension(s).

4.3.1. The multiple regression analysis of the effects of service qualityon perceived value

According to Table 11, the four dimensions of service quality allhad significantly positive effects on perceived value. In otherwords, customer service and system reliability (SQ1), navigationand visual design (SQ2), content quality (SQ3), and connectionspeed (SQ4) influenced the perceived value. The effect of ‘‘cus-tomer service and system reliability” ranked first (0.27), followedby ‘‘content quality” (0.15), ‘‘navigation and visual design” (0.15),and ‘‘connection speed” (0.14).

4.3.2. The multiple regression analysis of the effects of service qualityon customer satisfaction

As shown in Table 12, among the four dimensions of servicequality, except navigation and visual design (SQ2) (t = 0.75,p > .05), all the other dimensions had significantly positive effectson customer satisfaction. This means that customer service andsystem reliability (SQ1), content quality (SQ3), and connectionspeed (SQ4) will influence customer satisfaction, where ‘‘customerservice and system reliability” (0.41) ranked first, followed by‘‘content quality” (0.17) and ‘‘connection speed” (0.15).

5. Conclusions

In this study, a scale for measuring the service quality of mobilevalue-added services was proposed first. Through exploratory andconfirmatory factor analyses, we identified four dimensions of ser-vice quality, including customer service and system reliability,navigation and visual design, content quality, and connectionspeed. The final instrument showed adequate reliability and valid-

The multiple regression analysis of the effects of service quality on perceived value.

Dimension Standardized coefficients t

SQ1 0.27 5.02***

SQ2 0.15 2.52*

SQ3 0.15 2.81**

SQ4 0.14 2.66**

R2 = 0.32, Adj-R2 = 0.31, F = 44.82***

* p < .05.** p < .01.

*** p < .001.

Table 12The multiple regression analysis of the effects of service quality on customersatisfaction.

Dimension Standardized coefficients t

SQ1 0.41 8.27***

SQ2 0.04 0.75SQ3 0.17 3.41***

SQ4 0.15 3.00**

R2 = 0.41, Adj-R2 = 0.40, F = 65.00***

* p < .05.** p < .01.

*** p < .001.

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ity. Further, we also examined the relationships among servicequality, perceived value, customer satisfaction, and post-purchaseintention in mobile value-added services. The proposed modelwas strongly supported by the data collected in Taiwan. Servicequality positively influenced perceived value and customer satis-faction, indicating that when telecom companies provide good ser-vice quality, perceived value and customer satisfaction can beenhanced. Perceived value positively influenced customer satisfac-tion. In other words, higher perceived value can lead to higher cus-tomer satisfaction. Perceived value and customer satisfactiondirectly and positively influenced post-purchase intention, wherethe effect of perceived value was the largest, followed by that ofcustomer satisfaction. Service quality showed no directly positiveeffect on post-purchase intention. Although service quality hasno direct effect on post-purchase intention, service quality couldindirectly influence post-purchase intention through perceived va-lue and customer satisfaction.

According to the total effects of each construct on post-pur-chase intention, the constructs can be ranked as follows: perceivedvalue, service quality, and customer satisfaction. This result im-plies that if telecom companies attempt to induce positive post-purchase intention from mobile value-added service users, suchas the intention to refer or repurchase the service, improvementof perceived value should be prioritized. They can evaluatewhether the release of a certain value-added service can make cus-tomers feel the service is ‘‘more valuable than it costs”, the benefitsof the service for consumers, and the reasonableness of its price.Therefore, users not only use a certain service but also feel the va-lue added of the service, which elevates the simple use of service toenjoyment. In this way, the value-added service can create sticki-ness of users and even become a real competition advantage. Be-sides, service quality also plays an important role. According tothe importance of the four dimensions of service quality, telecomcompanies can improve the quality of value-added services.Through the results of multiple regression analysis, we found theeffects of ‘‘customer service and system reliability” on perceivedvalue and customer satisfaction were the largest. Therefore, thisdimension should be prioritized by telecom companies whenimproving the quality of mobile value-added services. They canreinforce their customer service through education training andtechnical support. For instance, with the aid of computer, customerservice staffs can quickly and accurately react to customer’s ques-tions, and the FAQ can be regularly updated to meet the presentneeds. In terms of the stability of wireless networks, the correct-ness and the stability of connections should be ensured. ‘‘Contentquality”, ‘‘navigation and visual design”, and ‘‘connection speed”ranked second, third, and fourth in terms of their influence on per-ceived value, respectively. However, navigation and visual designshowed no significant impact on customer satisfaction. In the as-pect of content quality, telecom companies should reinforce theircooperation with content providers and further evaluate the valueand usefulness of the value-added service content and whetherthey meet the demands of consumers to attract more users. In

the aspect of connection speed, various technologies should beintegrated and developed, and existing base stations should be up-graded to enhance connection speed. In the aspect of navigationand visual design, the limited display of mobile phones should beconsidered, so as to provide a comfortable and easy-to-operateuser interface. If telecom companies can reinforce their servicequality, then perceived value and customer satisfaction can be di-rectly improved, post-purchase intention can be indirectly posi-tively influenced, and business profit and competitiveness will beenhanced.

To address the limitation of our study we point out the follow-ing issues. Even though the study succeeds in validating the mea-surement scale of service quality in mobile value-added services,but the results obtained in the EFA developed to test the measure-ment model of service quality show off some weaknesses in theelaboration of the measurement scales for content quality, man-agement and customer service, and system reliability and connec-tion quality. Particularly, the need to remove nine items from thescales originally proposed for this constructs may raise doubtsabout content validity of the scales. This could be due to variousreasons. One of the reasons is that the measurement scale of ser-vice quality in mobile value-added services is designed accordingto the related literatures including Internet (such as Web portalsand Website) and mobile telecomm service quality, but the attri-butes of mobile value-added services are still a little different fromthe above services. This situation indicated that when rashlyapplying the service quality scale in Internet and Web environ-ment to measure mobile value-added service quality is inappropri-ate. The validated scale, the 15 items across four dimensions, inthis study can serve a useful instrument to measure the servicequality in mobile value-added services. Consequently, we can usea second-order factor model to test the stability of the scale of ser-vice quality in mobile value-added services. In addition, other largesamples should be gathered to confirm and refine, the factor struc-ture of the service quality scale in mobile value-added services,and to assess its reliability and validity.

For future research, we suggested that variables that affect con-sumer’s post-purchase intention (such as switch cost) should bediscussed to have more extensive understanding. In addition, somerespondents’ characteristics may affect on the results of multipleregressions for example gender, education, and level of value-added services usage. There variables may consider as control vari-ables to modify their effects. In the aspect of sampling respondents,this study selected only the main user group of mobile value-addedservices (university students and graduate students). Follow-upstudies can extend this scope to other consumer groups. Due tothe limitation of time, cross-sectional data collection method wasadopted. Thus, follow-up studies can collect longitudinal data tore-verify the proposed model or find out whether there is any dif-ference when applied to different consumer groups.

Acknowledgement

This research is supported by National Science Council (NSC 95-2416-H-390-006), Taiwan.

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