Journal of Business Research 66 (2013) 1153–1160

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Journal of Business Research

Understanding service quality in a virtual travel community environment

Statia Elliot a,1, Guoxin Li b,⁎, Chris Choi a,2

a School of Hospitality and Tourism Management, University of Guelph, Guelph, Canada N1G 2W1b School of Management, Harbin Institute of Technology, 13 Fayuan Street, Nangang District, Harbin, 150006, China

⁎ Corresponding author at: School of Management, HBox 1222, No. 13 Fayuan Street, Nangang District, Har451 86414042.

E-mail addresses: statia@uoguelph.ca (S. Elliot), lisapliguoxin@hit.edu.cn (G. Li), hwchoi@uoguelph.ca (C. Ch

1 Tel.: +1 519 824 4120x53970.2 Tel.: +1 519 824 4120x53370.

0148-2963/$ – see front matter © 2012 Elsevier Inc. Alldoi:10.1016/j.jbusres.2012.03.011

a b s t r a c t

a r t i c l e i n f o

Article history:Received November 2012Accepted April 2013Available online 15 May 2012

Keywords:Virtual travel communityConsumer behavior modelOnline travel agentService quality

Technological innovations in the tourism industry have significantly influenced the communication channelsbetween service providers and potential travelers. Virtual travel communities (VTCs) are now popular andinfluential venues for tourism information sharing, yet little is known about membership behavior. The pur-pose of this study is to test a new model of VTC beliefs, attitudes, and behaviors using structural equationmodeling. The model integrates measures proven in traditional consumer behavior theory, such as satisfac-tion, trust, and brand attitude, with behavioral measures unique to the virtual domain, such as stickiness.The results of an online survey of members of C-Trip, a Chinese VTC, indicate that the quality of the commu-nity significantly influences member satisfaction and trust. However, trust does not directly influence sitestickiness or intention to transact. Member satisfaction significantly influences site stickiness, whereastrust influences brand attitude, which in turn influences intention to transact. These relationships suggest aservice blueprint for site owners to ultimately stimulate online transactions.

© 2012 Elsevier Inc. All rights reserved.

1. Introduction

Technological innovations in the tourism sector have significantlyinfluenced the communication channels between service providersand potential travelers, yet supporting research is lacking in comparisonto its availability in other sectors, notably consumer retail goodssectors (Matzler, Grabner-Krauter, & Bidmon, 2008). Tourism is aninformation-rich industry, and more travelers rely heavily on the Inter-net as their single most important source of travel information to maketheir trip decision (Arsal, Backman, & Baldwin, 2008; Fodness &Murray,1998). The advent of virtual travel communities (VTCs) provides aunique platform for both tourism service providers and travelers toexchange travel information (Kim, Lee, & Hiemstra, 2004) by makingit easier for people to obtain information, maintain connections, devel-op relationships, and ultimately make travel-related decisions (Jiang,Mills, & Stepchenkova, 2008). Although the importance of VTCs is rec-ognized, few studies examine the behavior of these communities, andthe understanding of the members' needs remains fragmented (Illum,Ivanov, & Lian, 2010; Wang, Yu, & Fesenmaier, 2002). Yet, their powerand popularity suggest that VTCs have the potential to play an impor-tant role in customer relationshipmanagement and e-business strategy.

arbin Institute of Technology,bin, 150006, China. Tel.: +86

pleheart@gmail.com,oi).

rights reserved.

The relationship between technology and service performance, whenmeasured, indicates that technological capabilities positively impact per-formance, particularly if complementary to environmental contexts andmarketing capabilities, which can be effectively leveraged (Song, Droge,Hanvanich, & Calantone, 2005). To improve the success rate of serviceinnovations, Song, Di Benedetto, and Song (2009) demonstrate theimportance of service quality to improve customer satisfaction and per-formance. Customer evaluations of service quality in the e-commerceenvironment, given its perceived uncertainties, are also influenced bytrust, in both B2B marketplaces (Pavlou, 2002) and B2C (Pavlou, 2003).Ultimately, consumer beliefs about site quality, and attitudes of trustand satisfaction, influence behavior, and determine whether consumersstick to VC sites and transact online or not.

This study tests a newmodel of VTC beliefs, attitudes, and behaviorsand explores the relationships between these important elements ofconsumer behavior. The objectives are threefold: (i) to empiricallytest a VTC model that integrates measures of beliefs, attitudes, andbehaviors; (ii) to consider traditional and nontraditional consumerbehavior relationships within a virtual domain; and (iii) to betterunderstand the impact of these relationships, on intentions to transactas a measure of revenue generation.

2. Theoretical background

The Internet explosion of themid 1990s facilitated the emergence ofvirtual communities (VCs), which have developed at a rapid pace overthe past two decades. While several definitions of VCs exist withinthis relatively new field (Komito, 1998; Turban, King, Viehland, & Lee,

1154 S. Elliot et al. / Journal of Business Research 66 (2013) 1153–1160

2006), one of themost common is by Rheingold (1994), who defines anonline community as a

social aggregation that emerge[s] from the Net when enoughpeople carry on those public discussions long enough, with suffi-cient human feelings, to formwebs of personal relationships in cy-berspace. A virtual community (VC) is a group of people who mayor may not meet one another face to face, and who exchangewords and ideas through the mediation of computer bulletinboards and networks (pp. 57–58).

In the travel industry, VTCs have changed the nature ofcommunication between businesses and consumers. Within VTCs,large numbers of people can communicate with others as peers, with-out restrictions of time and distance, for travel-related purposes suchas obtaining travel information, maintaining connections, finding travelcompanions, providing travel suggestions, or simply having fun by tell-ing each other interesting stories of travel experiences (Wang et al.,2002). Members can join discussion forums and chat rooms for infor-mation sharing and communication and build their own travel pagesto showcase personal travel profiles, share travel tips, and create per-sonal travelogues (Wang & Fesenmaier, 2004).

In response to the emergence of VTCs, a number of large travel com-panies have integrated community functionalities into their websites(e.g., IgoUgo, BootsnAll, Travellerspoint, and Virtualtourist.com). TheseVTCs have revolutionized the way participants behave and interactwith each other in terms of the forms of communication, ways of acces-sing resources, and the rules for conducting business (Dellaert, 2000).Quite simply, VTCs have transformed the tourism industry (Kim et al.,2004). As one of the most popular forms of user-generated content(UGC), VTCs could be the most influential source for travel decisionmaking (Arsal et al., 2008; Chung & Buhalis, 2008).

Although the importance of VTCs has long been recognized, fewstudies have examined their business value. Understanding both the so-cial and business functions of VCs is extremely valuable for Internetvendors enthusiastic about better understanding market trends andconsumer preferences (Hagel & Armstrong, 1997). VCs as valuable busi-ness media have beenmore extensively researched in the general busi-ness literature outside of the tourism field (Wu, Chen, & Chung, 2010).The relatively small numbers of researchers who focus on the businessvalue specifically of VTCs identify factors that influence members topurchase products in a virtual domain (Kim et al., 2004). Wang andFesenmaier (2004) empirically test a conceptual framework of onlinetravel community member needs and propose that an understandingof member participation is vital to tourism marketing organizations,which are increasingly incorporating VTCs into their operations. Osti's(2009) study of VTC success factors found information reliability, navi-gational ease, frequency of posts, member respect, and user homogene-ity to be most important.

The majority of existing research focuses on Western customersand cultures, specifically American VC members, and less is knownabout Asian VC members (Kim, Ma, & Kim, 2005). Yet, social net-working has become a global phenomenon, and China now has thelargest number of Internet users in the world. VTCs are transforminghow the travel industry and the traveler function, and focused re-search is needed to better understand their role in tourism market-ing and management. To this end, this study explores VTC memberbeliefs, attitudes, and behaviors through a broad review of tourism,technology, and marketing-related literature and general theories ofconsumer behavior. The study proposes and tests a new modelwithin the context of a Chinese VTC.

3. Model development and hypothesis

To understand the nature and influence of relationships withinVTCs, key behavioral factors are modeled based on the review of

tourism, technology, marketing, and consumer behavior literature.The model incorporates three key elements: (i) the beliefs of commu-nity members, measured in terms of their assessment of VTC quality;(ii) the attitudes of community members, measured in terms of theirsatisfaction, trust, and brand attitude; and (iii) the behavior of commu-nity members, measured in terms of site stickiness and members' in-tentions to transact. Social-psychology researchers and practitionersrecognize the importance of understanding the relationship betweenbeliefs, attitudes, and behavior. In particular, the relationship betweenattitude and behavior has been debated in the academic literature sincethe 1930s. Early research indicates weak or no relationship betweenattitude and behavior (see Wicker, 1969 for more details), whereasAjzen and Fishbein (1977) provide evidence of a strong relationshipbetween attitude and behavior when there is high correspondence be-tween them, and appropriate measures are used. Social psychology fo-cuses largely on exploring the formation of attitude, defined as apersonal disposition toward engaging in behavior. Numerous re-searchers proposed and tested models to explain attitude formation(Allport, 1935; Thomas & Znaniecki, 1918). Fishbein (1963, 1965)and Fishbein and Ajzen (1975), Ajzen and Fishbein (1980) conductedsome of the most known studies on attitude, proposing a model ofattitude formation which suggests that the additive integration of indi-viduals' beliefs and their simultaneous, affective evaluation of thesebeliefs combine to determine attitudes toward actual behavior. Accord-ing to Ajzen and Fishbein (1980), and Fishbein and Ajzen (1975),individuals' beliefs predict individuals' attitudes. Subsequently, the atti-tude affects individuals' intention to act, which has a direct influenceon behavior. For the last four decades, based on the above assertions,numerous studies have examined the relationship between beliefs, at-titudes, intentions and behavior (Ajzen & Fishbein, 1977, 1980;Bagozzi, 1981; Bentler & Speckhart, 1979, 1981; Fishbein & Ajzen,1975).

One of the seminal works testing the interrelationship betweenbeliefs, attitudes, and behavior is the Theory of Planned Behavior(TPB). As the extension of the Theory of Reasoned Action (Fishbein& Ajzen, 1975), TPB (Ajzen, 1985) is a compelling model that spec-ifies salient beliefs that influence given behavioral perceptions, sub-sequent attitudes, and actual behavior. Many researchers adoptedTPB, either all or some of its constructs, or an extended model tobetter understand a wide range of human behavior related to cus-tomer satisfaction and retention (Guo, Xiao, & Tang, 2009), e-commerce adoption (Grandón, Nasco, & Mykytyn, 2011), interna-tional traveling (Martin, 2010), and e-purchasing behavior (Buttleand Bok, 1996). Although TPB is considered a suitable model forunderstanding individuals' actions, Ajzen (1991) criticizes his ownmodel, suggesting that its ability to accurately determine the rela-tionship between the constructs is not as strong as desired. In thisregard, this study adopts the basic approach of TPB (Belief→Attitude→ Intention) to propose a model to examine the relation-ship between beliefs, attitudes, and behaviors of virtual communitymembers based on previous studies (Chen & He, 2003; DeLone &McLean, 2003; Liu, Marchewka, Lu, & Yu, 2004; Shang, Chen, &Liao, 2006; Valck, Langerak, Verhoef, & Verlegh, 2007), appliedwithin a travel services context. Fig. 1 presents the resulting studymodel.

3.1. VTC quality beliefs

Members' beliefs about the quality of a VTC can influence individualattitudes, such as satisfaction and trust, and thus the overall sustain-ability of a community. Social online interaction supported by technol-ogy, including content management and website functionality, iscrucial to the success of VCs (Lin, 2007; Preece, 2000; Wang et al.,2002). Information quality refers to the quality of the information pro-vided by online services. It includes dimensions such as information ac-curacy, completeness, and currency and information presentation

H3a

H4a

H1b

H1f

H1e

H1d

H1c

H3c

H3b

H2a

H2b

H1a

InformationQuality

Service Quality

System Quality Satisfaction

Brand Attitude

Intention to Transaction

Trust

Stickiness

Control Variables: age, income, education, Internet usage

frequency, VC memberships

Fig. 1. A virtual travel community model of member behavior.

1155S. Elliot et al. / Journal of Business Research 66 (2013) 1153–1160

format (Nelson, Todd, & Wixom, 2005). System quality in a web-basedinformation systemmeasures the functionality of a website. System re-liability, convenience of access, response time, and system flexibilityare examples of qualities users value (DeLone & McLean, 2003;Nelson et al., 2005).

DeLone and McLean (2003) argue that service quality is a signifi-cant dimension of information system success in the e-commerce envi-ronment, where customer service is crucial. Service quality is importantin the VC context because online communication lacks face-to-face con-tact. Kuo (2003) notes thatwebsite usability and service quality are keyfactors that predict members' intentions to use VCs. Service qualitymeasures the overall support delivered by the website and includestrust, responsiveness, and personalization (Keating, Rugimband, &Quzai, 2003; Lee & Lin, 2005). Leimeister and Krcmar (2004) arguethat the success of a VC relies on management and service qualitysuch as offering up-to-date content, focusing on the needs of the mem-bers, involving members in activities, and handling member data seri-ously. Based on DeLone and McLean's (2003) classification of onlinequality components, Lin (2007) examines the impact of informationquality, system quality, and service quality on the sustainability ofVCs, and confirms the influence of online quality and service features.

In the context of VTCs, Jiang et al. (2008) define the attitude con-struct as “a psychological tendency to evaluate performance of thecommunity with some degree of favor or disfavor” (p. 50). Memberattitude can be measured in terms of satisfaction and trust. While sat-isfaction has been extensively researched in the e-commerce context,the exploration of members' satisfaction within the VTC context, andthe effect of satisfaction on future participation, is at a relatively na-scent stage (Valck et al., 2007). Valck et al. (2007) view satisfactionas an important indicator of a member's overall community evalua-tion and conceptualize different levels of member interactions withtheir VC. On these grounds, the hypotheses are as follows:

VTC Quality:

H1a. VTC system quality positively influences member satisfaction.

H1b. VTC system quality positively influences member trust.

H1c. VTC service quality positively influences member satisfaction.

H1d. VTC service quality positively influences member trust.

H1e. VTC information quality positively influences member satisfaction.

H1f. VTC information quality positively influences member trust.

3.2. VTC site behavior

The existence of a VC is dependent upon the community popular-ity and members' stickiness, defined as the ability of a company tokeep a customer and to get customers to return (Paul, 1999). A web-site has stickiness when a user always visits the same website, spendsmore than the average time browsing, and digs deeper into the site(Brock, 1997). Indicators of a user's stickiness are duration, frequency,and depth. Different levels of stickiness can lead to different inten-tions to exchange information, participate in online activities, andmake a transaction.

In the virtual domain, a sense of community is associated withmembers' purchasing behavior (Kim et al., 2004). Shang et al.(2006) created a VC of computer users to test a model of involvement,trust, and attitude toward the brand within the community. They ex-amined the effects of consumers' lurking and posting behavior in on-line consumer communities on brand loyalty and found that aparticipant's stickiness in the community directly affects their futureintentions to transact online. The hypotheses are as follows:

Site Stickiness:

H2a. Member satisfaction positively influences site stickiness.

H2b. Member trust positively influences site stickiness.Researchers also note relationships among brand, online search

action, and the intention to engage in online transactions (Chen &He, 2003). A brand can be defined as “a name, term, sign, symbol, ordesign, or combination of them which is intended to identify thegoods and services of one seller or group of sellers and to differentiatethem from those of competitors” (Kotler, 1991, p. 396). The impor-tance of brand knowledge to consumer decision making is well docu-mented (Alba & Chattopadhyay, 1985). Brand knowledge can directlyimpact consumers' intentions to adopt, or not adopt, a service (Chen& He, 2003). As members tend to interact with each other and con-gregate in brand-based VCs, that means a brand virtual community(BVC) can provide both social and business functions that are valu-able for brand corporate and Internet vendors to capture markettrends and consumer preferences (Hagel & Armstrong, 1997). Inother words, a positive brand attitude facilitates customer relation-ship management and enables businesses to attract, engage, and re-tain customers. BVCs can be viewed as focal points for not onlyattracting large numbers of customers, but also promoting products,establishing website stickiness, and understanding customer needs(Hagel & Armstrong, 1997; Wang et al., 2002). Although the

1156 S. Elliot et al. / Journal of Business Research 66 (2013) 1153–1160

importance of brand attitude is recognized, few studies have exam-ined its relationship to business service quality and members' inten-tion to transact. The hypotheses are as follows:

Intention to Transact:

H3a. Site stickiness positively influences intention to transact.

H3b. Member trust positively influences intention to transact.

H3c. Member brand attitude positively influences intention totransact.

Table 1Demographic characteristics of the C-Trip VTC sample.

Frequency Percent (%)

Gender (n=201)Male 107 53.2

3.3. Attitudes toward VTC

Formation and expansion of a VC depend on the willingness ofmembers to share information and services. Researchers find thattrust is a core component facilitating the anonymous interaction incommunities and e-commerce, and therefore trust building online isa common research topic (Cox, Burgess, Sellitto, & Buultjens, 2009;Hoffman, Novak, & Peralta, 1999; Luo, 2002; McKnight, Choudhury,& Krcmar, 2002; Tan & Thoen, 2001; Urban, Sultan, & Qualls, 2000;Ye & Emurian, 2005; Yoo & Gretzel, 2010). Observing that trust devel-opment is a complex process, researchers have investigated the for-mation of trust and its impact. McKnight et al. (2002) points outthat, when users develop trust in a website, they intend to continu-ously participate and conduct transactions with the content provider.Liu et al. (2004) also suggests that trust can lead to repeat purchasing,repeat website visiting, and website recommendation. Casalo, Flavian,and Guinaliu (2007) conducted a web survey using members of sev-eral free software VCs and found a positive and significant effect ofconsumer trust on loyalty.

Cox et al. (2009) found that, when making travel plans, hospitalityand tourism consumers consider government-sponsored tourismwebsites to be the most credible and trustworthy providers of UGC.This suggests that, while UGC may be increasingly popular, trust hasan impact on its actual influence. Yoo and Gretzel (2010) also foundtrust to be influenced by the source of UGC, with official destinationand travel agency websites ranked as more credible than personaland sharing websites such as YouTube. Notably, their study alsofound that with greater trust comes greater perceived benefits fromUGC use, suggesting the need for websites to make efforts to gain vis-itor trust (Yoo & Gretzel, 2010). To measure the relation betweentrust and service provider, brand attitude is used as an indicator.The hypothesis is:

Member Trust:

H4a. Member trust positively influences member brand attitude.

Female 94 46.8

Age (n=198) 18–58a 31.8 (7.6)b

Marital status (n=201)Yes 114 56.7No 87 43.3

Education (n=203)High school diploma 12 5.9College graduate 36 17.7Some university 106 52.2University graduate 49 24.1

Income (n=204)Below 2000 yuan 30 14.82001–5000 yuan 75 36.7Over 5000 yuan 99 48.5

Internet usage frequency (n=204)1–20 h per week 66 32.421–40 h per week 53 26.2Over 40 h per week 85 41.7

Number of VC memberships (n=204)1–3 104 51.0Over 3 100 49.0

a Range.b Mean (std. dev.).

4. Study method

The model test implements an online survey of C-Trip VC mem-bers. C-Trip is a mainland China travel agency that sells airline ticketsand accommodations and also runs a very large travel website(Leung, Law, & Lee, 2011). The survey instrument is a structuredquestionnaire comprising seven-item measurement scales for all la-tent variables. The measures for Quality, Trust, Satisfaction, andBrand Attitude use bipolar adjective scales, and the measures forStickiness and Intention to Transact use Likert-type scales. The ques-tionnaire has at least three items per latent variable to reduce factorindeterminacy (Ferguson, Partyka, & Lester, 1974). The English-language questionnaire was translated into Chinese, then back-translated into English to test for equivalency. The questionnairewas pretested with a small sample of Chinese students before fieldimplementation. The online questionnaire was uploaded on the web-site of Qualtrics.com and an invitation letter was posted to forums ofthe C-Trip VTC. A total of 243 questionnaires were collected; this was

reduced to 204 usable questionnaires through an empirical process ofdata cleaning.

Demographic characteristics indicate that the respondents are arelatively representative sample of the Chinese online population(Table 1). The majority of the respondents are married (56.7%),with males representing 53.2% of the sample. The average age is31.8 years, and respondents are highly educated (with 76.3% havingat least some university education), have relatively high income(almost half earn over 5000 yuan per year), and are heavy Internetusers (well over half spend more than 30 h per week on the Internet).The sample is split between those belonging to three or fewer VCs(51%) and those belonging to more than three (49%).

5. Results

Following the two-step approach recommended by Anderson andGerbing (1988) to analyze structural equation modeling, confirmato-ry factor analysis (CFA) with AMOS 17.0 was performed to assess thevalidity of all model latent constructs. Items of system quality, servicequality, information quality, trust, satisfaction, brand attitude, inten-tion to transact, and stickiness were modeled as reflective indicatorsof latent constructs respectively. These constructs were allowed toco-vary freely in the CFA model. The result suggests adequate modelfit (χ2/df=2.08, pb .001, CFI=0.94, IFI=0.94, NFI=0.896, TLI=0.93,RMSEA=0.07). Loadings for all variables are in an acceptable range,being above 0.60. Thus, allmeasures are retained in themodel. Compos-ite reliability measures range from 0.77 to 0.94, indicating strong reli-ability of the constructs. To assess discriminant validity, the averagevariance extracted (AVE) was computed for each construct. AVEsrange from 0.542 to 0.838, well above the conventional benchmark of0.50. Table 2 presents mean, standard deviation, factor loadings, com-posite reliability, discriminant validity, and model fit indices.

The VTC model test used structural equation modeling (SEM) tosimultaneously measure the hypothesized relationships betweenconstructs. The model produced χ2 (520)=1061.57, which equals aCMIN/DF ratio of 2.04, within the acceptable range of 1 to 3, indicat-ing a good fit. The model also produced a CFI=0.92, IFI=0.93,NFI=0.86, TLI=0.91; all these fit indices close to 1 indicates a goodfit, and an RMSEA=0.07, within the acceptable range of 0.08 and

Table 2Measurement model summary.

Online community quality Mean (std. dev.) Factor loadings Discriminant validity (AVE) Composite reliability Fit index

System quality χ2=784.59df=377χ2/df=2.08(pb .001)CFI=0.94IFI=0.94NFI=0.896TLI=0.93RMSEA=0.07

Easy to use 5.6 (1.23) .74 0.54 0.83Convenient to access 5.6 (1.33) .88Flexible 5.4 (1.23) .91Reliable 5.4 (1.41) .84

Service qualityVisually appealing 4.5 (1.40) .66 0.60 0.77Prompt service 4.9 (1.30) .83Well-organized 5.1 (1.32) .88Sincere in term of solving problems 5.4 (1.31) .85

Information qualityTimely 5.1 (1.29) .84 0.61 0.86Complete 5.0 (1.16) .88Accurate 5.3 (1.04) .84Useful 5.6 (1.07) .85

TrustTrustworthy 5.8 (1.22) .97 0.64 0.84Believable 5.8 (1.14) .99Does the job right 4.9 (1.21) .61

SatisfactionPleased 5.8 (1.04) .93 0.84 0.94Satisfied 5.8 (1.03) .95Contented 5.6 (1.08) .91

Brand attitudeGood 6.0 (1.02) .96 0.67 0.91Pleasant 6.0 (1.05) .97Like 6.0 (1.04) .97Favorable 6.0 (1.02) .93Positive 5.9 (1.10) .94

Intention to transactNext time I book a trip 5.0 (1.42) .97 0.84 0.94During the next 6 months 4.7 (1.42) .84If they offer what I am looking for 5.3 (1.53) .80

StickinessSpend more time 5.0 (1.28) .90 0.71 0.91Read more postings 5.3 (1.29) .94Increase my visits 5.1 (1.24) .94Continue to visit the site 5.4 (1.28) .87

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0.1, with p=0.000, further supports goodness of fit. While alternativemodels were considered, the original model was retained based on itstheoretical grounding and adequate measures of fit (Song &Montoya-Weiss, 2001) (Fig. 2).

The H1 relationships between VTC Quality, Satisfaction and Trustare all supported with path estimates that are reasonable and statisti-cally significant, except for H1b, System Quality to Trust. ServiceQuality significantly (at the 0.01 level) influences both Satisfactionand Trust, as does Information Quality (at the 0.05 level); whereasSystem Quality positively influences Satisfaction (0.21), but notTrust (−0.02). It seems that service factors such as appeal andpromptness have a greater influence on consumer attitudes than sys-tem factors such as flexibility and reliability.

Of the H2 relationships, Satisfaction to Stickiness and Trust toStickiness, only the first is statistically supported (0.55). Trust doesnot positively influence Stickiness (0.08), rejecting H2b, nor doesTrust influence Intention to Transact (−0.001), rejecting H3b. Therole of Trust in the VTC model is in its positive and strongly significantinfluence on Brand Attitude (0.76), supporting H4a. It seems that thepath to transactions is an indirect one, either through the creation of asticky site (supporting H3a), or, through a positive brand attitude(supporting H3c). Table 3 summarizes the results of the analysis ofhypothesized relationships.

6. Discussion

The results of the study partially support the hypothesized relation-ships between a member's perception of VTC quality and both theirsatisfaction with, and trust of, the community. Service quality is

particularly influential, suggesting that VTC operators pay greatattention to the visual appeal, prompt service, organization and prob-lem solving sincerity of their sites, calling for ongoing investments oftime and money in order to satisfy members, and gain their trust. Thisfinding is in accordwith previous studies.Member satisfaction is impor-tant as it directly influences behavior, ormember stickiness. The greaterthe satisfaction with a VTC, the more time spent, the more postingsread, the more visits and continuation of visits to the site by a member.These measures of stickiness are significant influencers on intention totransact, as stickymembers aremore likely to book travel products/ser-vices online. Thus, sticky websites are highly valuable to marketers, asthey encourage users to become more deeply involved, spend moretime browsing, and increase the likelihood of transactions. The strongrelationship from VTC service quality, to member satisfaction, to sticki-ness, and to transaction, suggests a service blueprint for VTC operatorsand site developers to follow. Indeed, it is an important strategy to im-plement as a means to compensate for the lack of face-to-face interac-tion. For instance, some online consumers are not comfortable withmaking problem inquiries via email communication, while others com-plain about the slow response times of e-mail and online form queries.To improve the speed and quality of such inquiries, some retailersoffer a live function (e.g. live chat, voice over Internet protocol(VOIP)). This function can enable greater interactivity with cus-tomers and thus improve service quality. In addition, according toLindgaard (2007), if a website's visual appeal creates a positiveimpression, then complaints against its system and usability short-comings may be diminished.

Interestingly, trust did not have a significant influence on stickinessor intention to transact, contrary to pastfindings (Casalo et al., 2007; Liu

SystemQuality

InformationQuality

ServiceQuality

Satisfaction

Trust

Stickiness

Intention to TransactBrand

Attitude

.23*

.42***

-.02

.74***

.20*.27***

.50***

.08

-.05

.60***

.24*

.84***

Age Education

Internet usage frequency

Income

VC membership

.14*-0.29 ..05

-.03

.01

Fig. 2. Virtual travel community model with structural equation model path coefficients. *** Path coefficient estimate is significant at pb0.01. * Path coefficient estimate is significantat pb0.05.

1158 S. Elliot et al. / Journal of Business Research 66 (2013) 1153–1160

et al., 2004; McKnight et al., 2002) that as cognitive resources are verylimited in the decisionmaking process, trust plays a critical role bymit-igating the risks of uncertainty and reducing perceived risk in the onlineshopping environment (Grabner-Kraeuter, 2002). The result may re-flect the particular parameters of this study, the nature of a travel virtualcommunity, and/or the influence of culture. For instance, the study find-ing can be explained by Zeithaml, Parasuraman, and Malhorta (2002).They argue that online consumers in a country with a large power dis-tance tend to positively behave toward expertise and authority. In thisinstance, a well-known online brand (i.e. C-Trip) is well received byconsumers in countries such as China, Korea and Japan. Accordingly,trust may not be a key factor directly affecting transaction intentionand stickiness. In the study context, Trust positively influences BrandAttitude, which in turn positively influences Intention to Transact.This is an interesting finding, suggesting the fragility of consumertrust, and perhaps reflecting the extreme cautiousness of Chinese con-sumers to transact online, even with somewhat trusted sources. Itseemsmore likely that the consumers' trust will help to build a positiveattitude toward brand, or site first (Chen, Wu, & Chung, 2008), then,transactions may follow. Once consumers are experienced purchasers,trust is gained. It is that first transaction that is the challenge set beforeVTC managers, and requires attention to quality, satisfaction and brandto overcome.

A notable limitation of the study is the collection of data through theVTC, whereby members self-select to participate. Also, it is difficult toassess the potential bias of thismethod, or even estimate an accurate re-sponse rate. Both the advantages of convenience and reach of virtualsurveying, and the disadvantages of reliability and validity, have beenwell documented (Illum et al., 2010). Given the goal of this study, to un-derstand VTC member behavior, the methodology was deemed appro-priate. A benefit to the particular VTC selected, based on China'spopular C-Trip site, is the insight the findings shed on this new yetpromising outbound market.

The VTC as a customer relationship management tool can enabletourism businesses to build customer satisfaction, and even trust, in avariety of ways. This new communication media can uniquely engagecustomers through in-depth, focused, and member-generated content.

VCs can enable Internet travel agents to promote product, increase cus-tomer participation and interaction, leading to greater site stickiness,and a better understanding of customer needs. VTCs may well be oneof the most effective business models in the technology age, providinggreat opportunity for both providers and customers alike.

Finally, this study only tested the proposed model with a Chinesesample. Based on previous research (Hofstede, Sternkamp, & Wedel,1999), culture plays a significant role in consumers' responses, fromdecision making and purchase intention, to stickiness and loyalty.However, few studies have focused on examining the impact of cul-ture specifically on purchase behaviors online. Thus, there is a needfor a better understanding of how culture affects online consumers'trust and other key variables. To address this gap, future studiescould test cultural differences using not only national boundaries,but also ethnic background and Hofstede's uncertainty avoidanceindex.

7. Conclusion

This study contributes to the growing body of servicesmarketing lit-erature on VTCs, specifically addressing gaps in the academic researchto date by integrating measures of beliefs, attitudes, and behaviors inonemodel. For practitioners, the identification of factors, such as servicequality, that strongly influence VTC member satisfaction can help tofocus technological resources in key areas. For academics, the resultsprovide additional insights into behavioral factors in a VTC environ-ment, helping to sort out relationships between traditional measuresand relatively new measures. For example, the behavioral measure ofstickiness is unique to this domain and appears to work well as an out-come in VTCmodeling. Trust, on the other hand, is a traditionalmeasurein behavioral models, yet its role in the VTC environment seems morecomplex and warrants further study.

In addition, this study includes the attitude measures (satisfaction,trust, and brand attitudes) and behavioral measures (stickiness andintention to transact), partially confirming that some relationshipsbetween behavioral measures and attitude measures can be used toestimate the potential profitability of VC membership. Also, marketers

Table 3Results of hypothesis analysis.

Hypothesis Est. S.E. C.R. P Support

H1: online community qualitya. System quality > Satisfaction .24 .11 2.27 .02 Moderateb. System quality > Trust −.02 .07 −.22 .82 Noc. Service quality > Satisfaction .47 .12 3.96 .00 Highd. Service quality > Trust .60 .11 5.50 .00 Highe. Information quality > Satisfaction .30 .11 2.72 .01 Moderatef. Information quality > Trust .16 .08 2.15 .03 Moderate

H2: Stickinessa. Satisfaction > Stickiness .54 .11 4.92 .00 Highb. Trust > Stickiness .12 .15 .84 .40 No

H3: intention to transacta. Stickiness > Intention to transact .66 .08 8.41 .00 Highb. Trust > Intention to transact −.08 .17 −.47 .64 Noc. Brand attitude > Intention to transact .28 .12 2.38 .02 Moderate

H4: trusta. Trust > Brand attitude 1.20 .13 9.45 .00 High

Control variablesAge > Intention to transaction −.00 .01 −.51 .61Education > Intention to transaction .10 .11 .96 .34Income > Intention to transaction .11 .05 2.49 .01Internet usage frequency > Intention to transaction .01 .04 .14 .89Number of VC memberships > Intention to transaction −.08 .14 −.53 .59

1159S. Elliot et al. / Journal of Business Research 66 (2013) 1153–1160

can develop strategies directly targeting VTC members, such as specialdiscounts, more discounts for active community members, early adapt-er programs (new travel package testing), and so on. Future studies arecalled for, as well as more sophisticated modeling, to expand the mea-surement of VTC member behavior and to conduct experiments acrossindustries, communities, and cultures. As consumers in general seekmore and more of their services online, a deeper understanding of theVTC environment will help support continued innovation in the crea-tion and management of quality service.

Acknowledgments

The first and second authors contributed equally to this article.The research was supported by the National Natural Science Founda-tion of China (grant no. 70803008) and China Postdoctoral ScienceFoundation (grant no. 201104426). The authors gratefully acknowl-edge the reading and revision suggestions by Michael Song from theUniversity of Missouri-Kansas City, and Rob Law from The HongKong Polytechnic University. The authors alone are responsible forall limitations and errors that may be related to the paper.

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