Electronic Commerce Research and Applications 9 (2010) 171–182

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Electronic Commerce Research and Applications

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Virtual travel communities and customer loyalty: Customer purchaseinvolvement and web site design

Manuel J. Sanchez-Franco *, Francisco Javier Rondan-Cataluña 1

Facultad de Ciencias Economicas y Empresariales, Universidad de Sevilla, Avda. Ramon y Cajal, n1, 41018-Sevilla, Spain

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

Article history:Received 21 October 2008Received in revised form 28 May 2009Accepted 28 May 2009Available online 11 June 2009

Keywords:DesignEmpirical researchOnline servicesPartial least squaresPurchase involvementRelationship qualitySatisfactionTrustVisual aestheticsUsability

1567-4223/$ - see front matter � 2009 Elsevier B.V. Adoi:10.1016/j.elerap.2009.05.004

* Corresponding author. Tel.: +34 954 557542; fax:E-mail addresses: majesus@us.es (M.J. Sanc

(F.J. Rondan-Cataluña).1 Tel.: +34 954 554427; fax: +34 954 556989.2 Virtual communities have acted as a virtual meet

interests, build relationships, create fantasies or conArmstrong 1997).

Our research examines the influence of purchase involvement and design variables in the affective accep-tance of online services, in particular, virtual travel communities. Few studies have focused directly ondesign variables, visual aesthetics and usability, and the consequences on satisfaction of adopting auser-centered perspective. We propose an integrative model of relationship quality to provide an expla-nation of overall satisfaction through the influence of usability and visual aesthetics. We also suggest thatpurchase involvement moderates the strength of the relationships between design variables and satisfac-tion. Partial least squares (PLS) is used to estimate the parameters of the structural model and develop amulti-group analysis. The results provide strong support for the proposals. Design variables, satisfactionand trust lead the users to develop high customer loyalty; and, purchase involvement is an importantmoderator to engage in online service relationships. Our investigation contributes to the growing litera-ture by examining the influence of purchase involvement in developing virtual relationships.

� 2009 Elsevier B.V. All rights reserved.

1. Introduction

In recent years, the tourism sector has been experiencing con-tinuous transformation processes caused by the development andacceptance of Information Technologies. According to the AIMC-EGM, Internet Audiences Survey 2007, more than 52% of online buy-ers in Spain search for information on destinations or check pricesor schedules via the Internet, as well as travel packs, airline tickets,hotel rooms, or car rentals. Online tourism services must developan affective relationship with their customers. Online travel com-munities2 can play the role of a magnet that attracts travellers in avery efficient way, ‘‘thereby enabling tourism organizations to usethe concept of community as a basis for their relationship marketingactivities” (Wang and Fesenmaier 2004). In fact, customer collabora-tion is a prerequisite to strong marketing relationships. For instance,Virtual Tourist and Lonely Planet have integrated community func-tionalities into their Web sites to enhance customers’ travel informa-tion searching experience and trust; to build a sense of belonging;

ll rights reserved.

+34 954 556989.hez-Franco), rondan@us.es

ing place for people to shareduct transactions (Hagel and

and consequently to continue using online services offered. Theirgoal is to retain to their travellers.

The growing use of online community functionalities, however,raises the question of what encourages members to interact endur-ingly. Indeed, there is still a lack of research that analyses whichare the main drivers of members’ loyalty (Ridings et al. 2002, Sang-wan 2005). On the one hand, relationship quality (composed ofsatisfaction, trust and commitment) addresses the relationaldynamics. In particular, customer satisfaction and trust arerelevant drivers of affective commitment, especially true for onlinehigh-search/experience services, such as travel services (Bart et al.2005). Virtual communities – and travel sites – are characterisedby high information and information risk on the site. Virtual com-munities open up possibilities of online information searches asalternatives to experience, and help to satisfy different types ofcustomers’ needs: sharing resources, establishing relationships,trading and living fantasies (Hagel and Armstrong 1997). Likewise,the lack of face-to-face interaction in virtual tourist communitiesincreases the perceived risk of the relationship between customersand other virtual members.

Nevertheless, relationship quality is certainly not the only issue.Beyond relationship quality and assuming that the study of virtualcommunities is still at the exploratory stage, Wang et al. (2002)propose on the other hand, that the success of a virtual communitydepends partially on the customer profile, and website design. In

172 M.J. Sanchez-Franco, F.J. Rondan-Cataluña / Electronic Commerce Research and Applications 9 (2010) 171–182

particular, atmospherics of the Web site are captured by the per-ceptions of visual aesthetics and usability. The competition movesaway from functionality and utility alone, towards the customers’experiences and their emotions. Firstly, when ‘‘companies succeedin not only satisfying certain needs but also making the interac-tions pleasurable, people are more inclined to stay loyal” (Pullmanand Gross 2004). In this regard, ‘‘[v]isual design is one of the mostimportant factors that can influence these feelings in the virtualworld” (Tractinsky and Lowengart 2007). Secondly, despite recentinterest in visual aesthetics in online design, research should notforget the importance of traditional usability concerns; that is, anappreciation of only hedonic benefits weakens the understandingof the drivers of customer acceptance. ‘‘Information and communi-cation technology that is difficult to learn and difficult to use willinduce negative emotions (. . .) and thus generate avoidance behav-iour toward technology use” (Zhang 2007). Navigation and presen-tation will be even more important for Web sites with highinformation content, such as a virtual community (Bart et al. 2005).

Finally, customer involvement is also acquiring a growing rele-vance. Customers join communities in order to learn from othercustomers’ experiences or acquire information, and involvementhas been considered a main reason why customers look for serviceinformation (Shang et al. 2006). In particular, purchase involve-ment relates to uncertainty and plays a role in how customers willperceive a risk, and seek information; that is, it could provide ameans of segmenting tourist markets. Among highly-involvedtravellers, planning a pleasure trip involves a relatively high per-ceived risk of making a bad decision, investing a significant amountof time searching for information. When travellers consider tripevents in the near future, they could focus on concrete and instru-mental features because of the high elaboration likelihood. Con-versely, lower-involved travellers could focus, for instance, onaffective features because of the low elaboration likelihood in infor-mation processing (Petty and Cacioppo 1981, 1983). Research,therefore, suggests that purchase involvement-based attractiontravellers can be best understood along a continuum consistingof two polar ends: active (highly involved) and passive (lower in-volved) online-travellers. Virtual communities should be designedaccording to the customers’ involvement levels.

The following questions are, therefore, relevant. What role dovisual aesthetics and usability play? In particular, what form doesthe relationship between the design dimensions (visual aestheticsand usability) and the relationship quality take in the online envi-ronment? Would the relationship between visual aesthetics-usability and satisfaction be stronger or weaker when purchaseinvolvement is stronger compared to weaker assessment? Afterinterpreting the empirical results, we discuss the implicationsand conclude with a summary of this research.

2. Theory and research hypotheses

2.1. Relationship quality

Relationship quality can be defined as ‘‘the degree of appropri-ateness of a relationship to fulfil the needs of the customer associ-ated with that relationship” (Hennig-Thurau and Klee 1997). Threedimensions of relationship quality have traditionally been pro-posed: satisfaction, trust, and commitment. In particular, our re-search focuses on the non-economic aspects of overallsatisfaction, which is defined as ‘‘a channel member’s positive affec-tive response to the non-economic, psychosocial aspects of its rela-tionship, in that the interactions with the exchange partner arefulfilling, gratifying, and easy” (Geyskens et al. 1999).

Virtual tourist communities become applications essential forsatisfying customers’ needs: communication, information, and

entertainment (Wang et al. 2002). Virtual communities make iteasier for travellers to obtain information from others’ leisure-based experiences, maintain connections, and deepen relation-ships. For instance, at Virtual Tourist, online customers share realadvice on places they have travelled as well as their hometowns;they research and plan trips, and they interact with other avid trav-ellers via forums and email. In fact, this ‘‘exchange of information ismade significantly more efficient in the online environment”(Wang and Fesenmaier 2004). Information quality is assuredthrough discussions and thus depends on the community’s liveli-ness. In summary, when travellers are asked about overall satisfac-tion, they will likely to comment on global impressions and generalexperiences with other members. In this way, conceptualising sat-isfaction as the outcome of one single encounter in terms of eco-nomic satisfaction might be too restrictive.

Enhancing the interactive features and service-related informa-tion on online services does not necessarily indicate that customerswill be loyal to them, however (Sanchez-Franco in press). There-fore, our study proposes evaluating the effect of trust on customerloyalty. Members of a travel community often share high levels ofpersonal information. The importance of privacy in determiningonline trust is even greater for categories with personal informa-tion at risk, for example, travel sites, than it is for other online cat-egories (Bart et al. 2005). The perceived risk of a relationship ishigh due to the lack of face-to-face contact (Ridings et al. 2002)when community members are still unfamiliar with one another.

Trust is based on favourable expectations about the intentionsand behaviour of the other. Consequently, trust will be defined asthe customer’s perception of different aspects of the virtual com-munity (and its members); and decrease the uncertainty of therelationships between the customer and other members (Casalóet al. 2008). For instance, trust is essential in virtual communitieswhere the absence of workable rules makes a reliance on the so-cially acceptable behaviour of others (Ridings et al. 2002). UnlikeExpedia’s Trip Advisor or Yahoo Travel, which post anonymous re-views, Virtual Tourist’s information comes from members whomaintain a profile. So someone curious about the validity of anopinion can see whether he/she has anything in common withthe reviewer. Likewise, Booking.com provides hotel reviews andratings from readers, as well as a simplified way for readers to posttravel journals, photos and itineraries. In fact, travellers usually va-lue the post-trial experience (either from other travellers or a neu-tral evaluative report) more than the information offered bysuppliers (Bei et al. 2004).

We have limited our study to the consideration of trust basedon integrity and benevolence – affective trust, as opposed to trustbased on competence or cognitive trust (McAllister 1995, Mayeret al. 1995). Cognitive trust is more important at the start of a rela-tionship (and may become definitive in one or a few interactions),while the contribution of affective trust increases as the relation-ship intensifies since it is based on interpersonal relationships. Inthe context of this study, affective trust is defined as emotionalbonds between two parties who express genuine care and concernfor each other’s welfare (McAllister 1995). Without positive recip-rocation the virtual community would not exist (benevolence).Furthermore, the existence of norms of reciprocity, closely linkedwith benevolence, allows the virtual community to function sin-cerely or integrity. That is, although customer satisfaction canbring them to virtual communities, members will not take part ifthey do not trust each other. Additionally, a sense of belongingmight exist among members of virtual communities despite thelack of face-to-face social interaction between members, who areessentially a group of strangers.

In this regard, commitment is defined as the desire to maintain arelationship in the future; in other words, a sense of affective engage-ment with other virtual members defined as the psychological ten-

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dency to get close to others (Morgan and Hunt 1994). Geyskens et al.(1996), amongothers, suggest the existence of two differentcommit-ment traits: affective and calculated. In this study, we understandaffective commitment to be a concept referring to the desire to con-tinue a relationship that affectively benefits us, beyond the instru-mental value that it represents or calculative commitment. In short,affective commitment will favour customer identification with a vir-tual community and the establishment of enduring relationships be-tween them. Calculated commitment will tend to produce falseloyalty behaviours, and it is positively associated with opportunisticbehaviours and a search for alternatives. The more customers con-sider themselves as members of a virtual community, the more likelythey will develop positive perceptions toward the community andpossess a continued intention to use that virtual community, that is,affective commitment.

Concisely, as Tax et al. (1998) show, one variable frequentlyassociated with commitment is satisfaction. Satisfaction reinforcesthe users’ decision to participate in the process of the service beingoffered, committing themselves progressively. Moreover, user sat-isfaction is also linked to trust (Geyskens et al. 1996), as satisfac-tion is thought of as its antecedent. Satisfaction is an evaluationof the behaviour displayed by the object of trust and therefore,constitutes an antecedent. Likewise, a relationship in which mu-tual trust exists between the parties will generate sufficient valuefor both parties to be prepared to maintain their commitment.When participants trust in other community members, they willbe more inclined to participate and feel a sense of belonging.Ganesan and Hess (1997) explain this relationship in three basicpoints: trust reduces the perceived risk that one of the partiesmight behave opportunistically; it increases the confidence thatshort-term sacrifices will be compensated in the long term and willhave been worthwhile; and the transaction costs within the rela-tionship are reduced.

2.2. Website design: visual aesthetics and usability

Visual aesthetics. In our research, we will specifically analyse thevisual aesthetics defined as ‘‘an artistically beautiful or pleasingappearance” (American Heritage Dictionary of the English Language).That is, the extent to which a user believes a virtual community tobe aesthetically pleasing to the eye, as a relevant demonstration ofthe valence of the perceived affective quality. In this regard, asTractinsky et al. (2000) argue, visual aesthetics is an essential factorthat affects other, subsequent perceptions. If customers find virtualcommunities’ appearances pleasing, it is likely that both their stateof mind and subsequent implied evaluations will be favourably en-hanced and it will influence all of the members’ emotions and theirinclination to transact business (Browne et al. 2004). Furthermore,research indicates that first aesthetic impressions are used later informing judgments and formed very quickly (Lindgaard et al.2006) to establish a preference that is difficult to change. They tendto receive an essential weight in the decision-making processes, apositive or negative halo. Visual aesthetics of interactive servicesis then essential for online customers and researchers alike.

Usability. In recent years research has shown how usability hasbecome a main concern into Information Technologies (Flaviánet al. 2006). ISO 9241 defines usability as the ease with which a per-son can employ a product in order to achieve a goal in a particularcontext. The usability of a technological system involves efficacy,efficiency and customer satisfaction with their specific proposedaims. Moreover, a Web site with an adequate level of usabilityforms a basic factor in making the business tangible. Therefore,the usability of a virtual community should be regarded as a keyissue in the marketing strategy of any business operating in onlineenvironment. ‘‘When consumers visit Web sites with high informa-tion content, they may perceive that the Web sites that have a good

appearance and layout and that are capable of taking visitors totheir desired destination with a minimum number of clicks aretrustworthy” (Bart et al. 2005).

To sum up, the usability of a virtual community refers specifi-cally to the ease with which the customer is able to learn to usethe system and memorise its basic routine operations. That is, ausable virtual travel community will be one where members areable to communicate with each other, find tourist content, andbrowse the community sections with ease.

2.3. Relationships between visual aesthetics, usability and satisfaction

As Tractinsky et al. (2006) show, the effects of visual appearanceare not limited to perceptions between humans; they also extendto nature and architecture, and the visual appearance of onlineapplications. In fact, customers expect to find aesthetic designs inthe online services they request, and demonstrate greater satisfac-tion when the design meets the aesthetic expectation. The positiveemotions prompted by aesthetics improve the experiences ofinterest and enjoyment, as well as the satisfaction derived fromthe activity (Isen and Reeve 2005, Westbrook 1987). Customerscould then be able to demonstrate their liking of virtual touristcommunities even before examining their content. Conversely,unfavourable affective reactions would negatively condition cus-tomer satisfaction despite any possible functional merits or the va-lue of travel services offered.

In short, as Lindgaard (2007) comments, a pleasant experiencesuch as navigating a ‘beautiful’ Web site shows to be intrinsicallyconnected to customer satisfaction. From the above argumentsabove, we hypothesise the following relationship:

� Hypothesis 1 (The aesthetics to satisfaction hypothesis). Aesthet-ics has a positive influence on satisfaction.

Usability and satisfaction. Navigation, service information andvirtual community design are critical factors for customer satisfac-tion. In particular, usability is a key aspect for achieving an overallsatisfaction. Cristobal et al. (2007) suggest that Web site usability –although it is in itself an insufficient condition for satisfaction – isone of the determining factors of a Web site’s quality. In otherwords, ‘‘easier access to information typically increases satisfactionwith the shopping process, thereby increasing overall customersatisfaction” (Shankar et al. 2003). In brief, usability reduces searchcosts as well as possible errors; that is, though website design maynot guarantee customer’s satisfaction (there are other factors) ithas a direct influence (Flavián et al. 2006). From the above argu-ments, we hypothesise the following relationship:

� Hypothesis 2 (The usability to satisfaction hypothesis). Usabilityhas a positive influence on satisfaction.

Aesthetics and usability. Previous research has analysed the rela-tionship between visual aesthetics and perceived usability. Tract-insky (1997) establish the existence of a significant relationshipbetween aesthetic and apparent usability, prior to using the sys-tem. Likewise, Tractinsky et al. (2000) found that the perceivedusefulness and visual aesthetics of a system are related; these cor-relations persisted even after users had used the system. When acustomer believes that virtual communities are pleasing and inter-esting, that person would be able not to notice the difficulty ofinteracting with them. Van der Heijden (2003) also found empiricalevidence that perceived attractiveness of the Web site influencedperceived ease of use. To sum up, perceived aesthetics influencescustomers’ judgments regarding online usability: what is beautifulis usable (Tractinsky et al. 2000). From the above arguments, wehypothesise the following relationship:

174 M.J. Sanchez-Franco, F.J. Rondan-Cataluña / Electronic Commerce Research and Applications 9 (2010) 171–182

� Hypothesis 3 (The aesthetics to usability hypothesis). Aestheticshas a positive influence on usability.

2.4. Purchase involvement

Purchase involvement relates to ‘‘the level of concern for, orinterest in, the purchase process triggered by the need to considera particular purchase” (Beatty et al. 1988). Higher levels of eco-nomic and time concerns are associated with higher levels of pur-chase involvement. Likewise, in situations that involve uncertainty,customers are even more concerned about their behaviour in orderto avoid negative consequence. Perceived risk is often used to de-fine purchase involvement (Houston and Rothschild 1978).

With regard to this fact, large online travel-related companieshave integrated community functionalities into their Web sitesto enhance customers’ travel information searching experience.‘‘Involvement can thus be seen as the ultimate motivation for con-sumers to participate in a virtual community” (Shang et al. 2006).In particular, members characterised by high levels of purchaseinvolvement will thus engage in relatively extensive destinationselection processes. Tourists have already pre-decided to travel;are searching for additional information via active external infor-mation search; and are engaged in goal-directed behaviours suchas pre-purchase deliberation (Hoffman and Novak 1996). Mem-bers’ messages are mainly sought to answer specific questionsfor decision-making or to reduce associated risks. Concisely, whenmembers claim to be situationally involved in a destination-deci-sion, they are not only merely thinking about it, but also are ac-tively doing something with it (high elaboration likelihood inprocessing and the elaboration likelihood model, ELM) (Petty andCacioppo 1981, 1983). Travellers who are situationally involvedare engaged in goal-directed behaviours (Hoffman and Novak1996).

Conversely, lower-involved travellers will focus on the abstractand affective features. Low involved travellers (related to tradi-tional non-goal-directed behaviours) do not perform an extensivesearch, and rarely evaluate travel destinations in depth (e.g. desti-nation features) before making purchase decisions because of theabsence of sufficient customer motivation or perceived risk. Lowinvolved members, who merely surf the Internet, may view brows-ing process as an end in itself and a mere use of time. Less involvedtravellers therefore, play a passive role in virtual communities forentertaining themselves, or simply satisfying their curiosity. Inother words, they prefer lurking, because they have nothing tosay or because they are just learning about the community (Rafaeliet al. 2004). When pre-trip decisions are perceived to have minimalmotivation, or risk associated with them, travellers tend to gatherscarce evaluative information about choice destinations.

Moderating effects of purchase involvement on the relationship be-tween aesthetics and satisfaction. As one point of view, the ELM hasbeen identified as a useful framework applicable to a discussion ofinteraction effects of online visual aesthetics and usability. Specif-ically, in the peripheral route, attitudes towards a service (e.g. avirtual travel community) are based on emotional responses toperipheral cues and on a relatively cursory consideration of mes-sage content. Less involved customers will rely on heuristics, suchas design features (e.g. graphics, visual layout), music, endorsercharacteristics, etc., to make a decision.

With regard to this, less purchase-involved travellers may sim-ply be surfing the Internet to enjoy themselves. Less-involvedmembers will remember the visual aesthetic appeal of images,even when they forget the associated content; and surf over to vir-tual communities because of multisensory, fantasy, and emotive is-sues. Among low involved members, usage of the virtualcommunity is in read-only mode that involves merely viewing

the site’s material. These behaviours would include connectingand visiting the community), and browsing. Conversely, whenmembers are situationally involved, strong arguments are moreeffective, despite the presence of peripheral cues such as visualaesthetics. Members will thus be likely to engage in elaborateinformation processing.

In this regard, the works of Petty and Wegener (1999) andTractinsky and Lowengart (2007) were thus foundational to ourhypothesis: ‘‘Under low elaboration conditions, people tend to usesimple methods to judge objects. In such cases, people may base theirjudgment on the first argument processed (e.g. site attractiveness) oron a cursory analysis of the source (Petty and Wegener 1999). Sinceaesthetics is probably the easiest site attribute to judge, it is likely to beover weighted in low elaboration conditions”.

In short, under low elaboration process, information is pro-cessed using simple cues. Aesthetic properties fit this characterisa-tion. High purchase involvement on the other hand, might weakenthe relationship between visual aesthetics and satisfaction. Fromthe preceding arguments, we hypothesise the followingrelationship:

� Hypothesis 1a (The user’s purchase involvement negative mod-eration hypothesis). The user’s purchase involvement weakens thepositive relationship between aesthetics and satisfaction.

Moderating effects of purchase involvement on the relationship be-tween usability and satisfaction. Another point of view is that hightemporary stimulation associated with purchase occurs when sig-nificant uncertainties are associated with the purchase decision(Houston and Rothschild 1978). For instance, purchase involve-ment includes a customers’ concern for reducing the risk associ-ated with the selection of travel services. Due to the highpotential for loss in the case of high involvement purchases,customers are also more inclined to assign more weight to thedimensions of risk importance and risk probability in theirdecision-making (Pavlou et al. 2007).

In this regard, more involved travellers will seek key destina-tion features via virtual communities as media because of theconvenience of easy information availability and selection. Virtualcommunity activities, such as discussions in newsgroups, chatrooms, or other online forums, will be directly affected by indi-vidual members’ needs, expressed by their goal-directed behav-iours. As Flavián et al. (2006) argue, usability will: reduce thelikelihood of error; be related to a customers’ ability to knowwhere they are at any time and what can be done; and offer acomfortable atmosphere that might favour a more positive cus-tomers disposition. For instance, shopbot technologies and pricecomparison engines allow easy price and features comparison ofdifferent principals’ and intermediaries’ destinations (e.g. TravelJungle, Traveljungle.co.uk).

In brief, higher purchase-involvement users will search for cuesrelated to a specific purchase in order to make the appropriatechoice; search will improve their efficiency relative to the achieve-ment of their objectives. Visual aesthetics would interfere with thisgoal. As Eroglu et al. (2001) suggest, ‘‘[u]nder these circumstances,the online stimulus with low relevance to completing the shoppingtask is likely to interfere with the elaborate information-processinggoals of the high involvement shopper.” Specific informationsearch is characterised as reflecting situational involvement, andseeking utilitarian benefits. Therefore, perceived usability is likelyto be over weighted in high elaboration conditions. From the pre-ceding arguments, we hypothesise the following relationship:

� Hypothesis 2a (User’s purchase involvement positive modera-tion hypothesis). The user’s purchase involvement strengthensthe positive relationship between usability and satisfaction.

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3. Data and methods

3.1. Sample

We validated our theoretical model and the hypotheses dis-cussed above through non-probabilistic sampling and self-selec-tion. Respondents were asked to fill out an online questionnaire.Online survey methods are gaining acceptance in IS research,thanks to lower costs, faster responses, and elimination of geo-graphical limits. The data collection process was also programmedto list the questions in a random order for each participant, avoid-ing potential systematic biases in the data and other cognitive con-sistency patterns (Podsakoff et al. 2003). The survey was publishedon a Web server in the consumer behaviour research laboratory ofa Spanish university. Access to the survey was facilitated by pro-viding text links inserted on travel discussion forums, mailing lists,etc.

This study includes the respondents of virtual travel communi-ties, where members maintain affective bonds, find travel firms,provide travel tips and suggestions, or simply having fun by tellingeach other interesting stories and sharing travel experiences. Theregistered respondent must chose a virtual travel community thatis the one they visit most often, and well known in the sector(Flavián et al. 2006). Overall, 92% of the respondents indicated on-line travel sites as their essential method for, for instance, seekingtravel tips and suggestions.

The exclusion of invalid questionnaires resulted in two sam-ples: high purchase involvement sample (n = 159) and low pur-chase involvement sample (m = 165). The socio-demographiccharacteristics of the sample by purchase involvement levels areincluded in Table 1. As it was not possible to assess the reliabilityor possible bias of non-random samples statistically, we comparedsome of the survey results with available information about thepopulation. The background proportion is consistent with surveysof typical Internet users in Spain.

3.2. Measurement scales

This study used an extended model based on: Flavián et al.(2006) for its usability and satisfaction scales; McKnight et al.(2002) for trust theory; Cyr et al. (2006), Lavie and Tractinsky(2004) and Van der Heijden (2003) for their design aestheticsscales; Kumar et al. (1995) for their commitment scale; andGanesh et al. (2000) for their handling of purchase involvement.

Table 1Socio-demographic characteristics of study sample by cluster.

Variables Population internet (%) Sample (%)

Gendera

Male 57.1 55.5Female 42.9 44.5

Agea

14–19 15.0 8.720–24 13.5 13.125–34 29.6 24.035–44 21.8 21.645–54 13.3 16.655–64 5.2 13.4+65 1.6 2.5

Educationb

Less than high school 10.2 7.0High school graduate 42.9 39.1College/university 46.9 53.9

a AIMC – EGM: Internet Audiences, Oct–Nov 2007 (accessed on March 20, 2008 at: hb AIMC. 10th Internet User profile, Oct–Dec 2007 (accessed on March 20, 2008 at: htt

We made minor changes in terms of adding and adapting itemsfor each needed construct based on a thorough and extensive liter-ature review. See Appendix C. The survey concluded with demo-graphic items. All of the survey items are five-point Likert-typescales, ranging from ‘‘strongly disagree”, 1, to ‘‘strongly agree”, 5.

Ten business and marketing professors in e-commerce andtourism validated the suitability of the wording and format, andthe extent to which the measures represent all facets of the keyconstructs. Furthermore, a total of fifteen virtual travel communitymembers were selected for a personal interview during the firstphase of the study. The primary purpose of this procedure was toclarify ambiguous and non-discriminating items and to eliminateany implicit discrepancies. A test of the measures was also con-ducted on a sample of professionals chosen.

3.3. Model analysis

We propose a structural equation model (SEM) with partialleast squares (PLS) estimation to assess the relationships betweenthe constructs, together with the predictive power of the researchmodel. PLS was proposed by Herman Wold as an analytical alterna-tive for situations where the theory is weak and the availablevariables or measures would be unlikely to conform to a rigor-ously-specified measurement model.

Whereas covariance-based SEM techniques such as LISREL andEQS use a maximum likelihood function to obtain estimators in mod-els, the component-based PLS uses a least squares estimation proce-dure. PLS avoids many of the restrictive assumptions underlyingcovariance-based SEM techniques such as multivariate normalityand large sample size. Furthermore, in our study the trust constructis measured with formative indicators, including benevolence andintegrity. PLS is thus appropriate for analyses of measurement mod-els with both formative and reflective items (Diamantopoulos andWinklhofer 2001). As formative constructs, they cannot be easilymodelled using LISREL and other covariance-based approaches;these implicitly assume all indicators to be reflective. This generaladvantage is highly relevant for this research.

Although there have been a fair number of conceptual discus-sions of the differences between formative and reflective measure-ment models, perhaps the most comprehensive list of criteria hasbeen provided by Jarvis et al. (2003). They provide decision rulesfor examining the formative versus the reflective construct issuein modelling. In formative indicator models, the direction of causal-ity flows from the indicators to the emergent construct, and the

Clusters

High purchase involvement Low purchase involvement(n = 159) (49.1%) (n = 165) (50.9%)

55 5645 44

9.7 7.814.3 12.025.9 22.220.0 23.215.3 17.812.8 14.02.0 3.0

5.5 8.538.2 40.056.3 51.5

ttp://www.aimc.es/).p://www.aimc.es/).

Table 2Measurement model: full sample.

Latent dimension Loadingsa Composite reliability AVE

A. Individual item reliability-individual item loadings. Construct reliability andconvergent validity coefficients

Aesthetics 0.933 > 0.7 0.778 > 0.5AE1 0.844AE2 0.869AE3 0.902AE4 0.910

Commitment 0.909 > 0.7 0.770 > 0.5COMM1 0.848COMM2 0.901COMM3 0.882

Satisfaction 0.949 > 0.7 0.822 > 0.5ST1 0.860ST2 0.926ST3 0.942ST4 0.896

Usability 0.933 > 0.7 0.665 > 0.5USA1 0.821USA2 0.821USA3 0.799USA4 0.856USA5 0.864USA6 0.751USA7 0.788

Emergent dimension Weights -.- -.-

TrustBenevolence 0.811*

Integrity 0.284***

Aesthetics Commitment Satisfaction Trust Usability

B. Discriminant validity coefficientsAesthetics 0.882Commitment 0.569 0.877Satisfaction 0.653 0.672 0.907Trust 0.444 0.602 0.492 -.-Usability 0.446 0.550 0.543 0.466 0.815

Panel B: Diagonal elements (bold) are the square root of average variance extracted(AVE) between the constructs and their measures. Off-diagonal elements are cor-relations between constructs.

a Panel A: All loadings are significant at p < 0.001 (based on t(499), one-tailedtest).

* p < 0.001 (based on t(499), one-tailed test).*** p < 0.05 (based on t(499), one-tailed test).

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indicators, as a group jointly determine the conceptual and empir-ical meaning of the construct. This is unlike reflective measures,where a change in the latent construct affects the measures. There-fore, a construct should be modelled as having formative indicatorsif the indicators are the defining characteristics of the construct andchanges in them would cause alterations in the construct. Droppingan indicator from the measurement model may alter the conceptualmeaning of the underlying variable. Formative indicator models donot imply the presence of covariation. This is because the emergentconstruct in a formative indicator model is the dependent variableand the indicators are the independent variables. On the contrary,in reflective indicator measurement models, a change in any oneof the indicators should be accompanied by similar changes in allof the indicators. Finally, a construct should be modelled as havingformative indicators if the indicators do not necessarily have thesame antecedents and consequences (Podsakoff et al. 2003).

Mayer et al. (1995) noted that trust’s beliefs are not trust per se,but they help in building the foundations of trust. These character-istics are related, but separable. Together, they explain a large partof the variation in trustworthiness while maintaining parsimony.Also, Ganesan (1994) investigated the two facets independentlyand concluded that both of them demonstrated different relation-ships with other variables. Furthermore, each of these separatetrust dimensions of benevolence and integrity will be well capturedby reflective items in our research. The items are interchangeableand one or more could be removed with the remaining items stillbeing consistent and coherent (Petter et al. 2007). That is to say,an increase in the value of the dimension translates into an increasein the value for all the items representing the dimension.

Finally, the proposed model and hypothesis testing was con-ducted using software SmartPLS 2.0.M3 (Ringle et al. 2005) to ana-lyse the data. We analyse and interpret our PLS model in twostages: the assessment of the reliability and validity of the mea-surement model, and the assessment of the structural model. Thissequence ensures that the constructs’ measures are valid and reli-able before the researcher should attempt to draw conclusionsregarding relationships between the constructs. The stability ofthe estimates was tested via a bootstrap re-sampling procedureinvolving 500 sub-samples.

3.4. The measurement

Once we clarified our scales, our next step was to test their psy-chometric properties. For constructs using formative measures forthe emergent construct of trust, the weights provide informationabout the makeup and relative importance for each indicator inthe creation and formation of the component. Besides, it is neces-sary to bear in mind that no interdependencies among the forma-tive dimensions can be assumed, since the construct is viewed asan effect rather than a cause of the item responses. Therefore,the indicators are not necessarily correlated and, consequently, tra-ditional reliability and validity assessment have been argued asinappropriate and illogical.

Furthermore, the two-dimensionality of the trust-based scaleproposes that this higher-order emergent construct will be mod-elled by a number of first-order latent dimensions (benevolenceand integrity). The items for trust’s dimensions will optimally beweighted and combined using the PLS algorithm to create latentvariables scores. The dimensions, or first-order factors, will thusbecome the observed indicators of second-order factor. The result-ing scores reflect the underlying dimensions more accurately thanany of the individual items by accounting for the unique factorsand error measurements that may also affect each item.

For those constructs with reflective measures for latent con-structs, we examine the loadings, which can be interpreted in thesame way as the loadings in a principal component analysis. In this

case, the individual reflective item reliability is considered ade-quate when an item has a factor loading that is greater than 0.7on its respective construct. All the reflective individual itemloadings in our final model are above 0.7. We have checked thesignificance of the loadings with a re-sampling procedure (500sub-samples) for obtaining t-statistic values. They all are signifi-cant. Construct reliability is assessed using the composite reliability(qc). Research suggests 0.7 as a benchmark for a modest reliabilityapplicable in the initial stages of research. In our research, all of thelatent constructs are reliable. They all have measures of internalconsistency that exceed 0.7 (qc).

Average variance extracted (AVE) assesses the amount of vari-ance that a construct captures from its indicators relative to theamount due to measurement error. It is recommended that AVEshould be greater than 0.50, meaning that 50% or more varianceof the indicators should be accounted for. AVE measures for latentconstructs exceeding 0.65. Therefore, the convergent validity of thelatent constructs of the model is supported.

Discriminant validity indicates the extent to which a given con-struct is different from other latent variables. To assess discrimi-nant validity, AVE should be greater than the variance sharedbetween the latent construct and other latent constructs in themodel. All latent constructs satisfy this condition. (See Table 2).For this reason, the discriminant validity of the latent constructsof the model is sustained.

A. Full Sample

B. High Purchase-Involvement Sample

C. Low Purchase-Involvement sample

Fig. 1. Extended relationship quality model: results. ap < 0.001, bp < 0.01, cp < 0.05,ns = not significant (based on t(499), one-tailed test).

M.J. Sanchez-Franco, F.J. Rondan-Cataluña / Electronic Commerce Research and Applications 9 (2010) 171–182 177

3.5. The structural model

As mentioned above, the bootstrap re-sampling procedure (500sub-samples) is used to generate the standard errors and the t val-ues, which will allow the b coefficients to be made statisticallysignificant.

The research model appears to have an appropriate predictivepower for endogenous constructs to exceed the required level of0.10 for the R2 value. On the other hand, another measure that sup-ports these positive results is the Q2 test of predictive relevance forthe endogenous constructs (Geisser 1975, Stone 1974). A Q2 greaterthan 0 implies that the model has predictive relevance, whereas aQ2 less than 0 suggests that the model lacks predictive relevance.In general, the results confirm that the structural model has satis-factory predictive relevance for the endogenous variables: usability(0.145), satisfaction (0.360), trust (0.080), and commitment (0.392).

As indicated, in an extended relationship quality model, satis-faction and trust have significant impacts on commitment, withpath coefficients of 0.496 (t = 5.399, p < 0.001) and 0.358(t = 3.448, p < 0.001). (See Fig. 1a). Satisfaction also has a significanteffect on trust (b = 0.492; t = 5.388, p < 0.001). Moreover, the re-sults give a standardized beta of 0.514 (t = 5.259, p < 0.01; Hypoth-esis 1) from aesthetics to satisfaction, 0.315 (t = 3.224, p < 0.001;Hypothesis 2) from usability to satisfaction, and 0.446 (t = 4.920,p < 0.001; Hypothesis 3) from aesthetics to usability.

3.6. Multi-group analysis

Finally, our study developed an e-tourist customer typology. A to-tal of six items, adapted from previous studies to measure the pur-chase involvement construct (Ganesh et al. 2000), were employedto conduct clustering analysis and to identify customer segmentsbased on their purchase involvement levels.3 Three methods are com-monly used: hierarchical, k-means and two-step methods. In this study,the two-step method is used; the algorithm included in the two-stepcluster method is very suitable for the segmentation analysis.

Each cluster represents a different purchase involvement-basedprofile of the sample on online travel services. The choice of a sim-ilarity measure and the determination of the number of clusterswere based on the log-likelihood distance and Schwarz’s Bayesianinformation criterion (BIC), respectively. The two-step cluster anal-ysis yielded two clusters based on BIC (1215.590) and with thehighest log-likelihood distance measures (ratio of distance mea-sures = 2.020). The findings indicated an increasing trend in BICchanges from �198.254 (two-cluster model solution withBIC = 1215.590) to �63.139 (three-cluster model solution withBIC = 1152.451) to �11.418 (four-cluster model solution withBIC = 1141.034) and so forth. Consequently, there was strong evi-dence to support the optimal two-cluster solution. See Table 1.

The hypotheses formulated regarding the intensity of the rela-tionship between both samples (the effect of levels of purchaseinvolvement on aesthetics and usability, related to Hypothesis 1aand Hypothesis 2a, respectively) are examined statistically bycomparing the b coefficients of the structural model. The statisticalcomparison is made following the procedure suggested byChin (2000) to develop a multi-group analysis and imple-mented in Keil et al. (2000). We use the following form of the

t-statistic, t ¼ bhigh inv�blow inv

Sp�ffiffiffiffiffiffi1mþ1

n

p , with m + n � 2 degrees of freedom, a

pooled estimator for the variance, given by Sp ¼

3 The scale for purchase involvement meets the criteria of reliability and validityestablished in the literature with qc = 0.81 and AVE = 0.52.

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiðm�1Þ2ðmþn�2Þ � SE2

high inv þðn�1Þ2ðmþn�2Þ � SE2

low inv

q. These enable us to evaluate

the effects of high and low purchasing involvement).4 For

4 We have also tested the psychometric properties of measurement modelscorresponding to both samples. The measurement models are confirmed withadequate convergent and discriminant validity with respect to the measurement of allconstructs in the structural model. See Appendices A and B .

Table 3t-Tests for multi-group analysis.

Hypotheses Standard errors (SE) Sp bH � bL t-Value Supported H0

High involvement Low involvement

AEST ? SAT H1a 0.109 0.073 1.168 �0.345 �2.656* SupportedUSAB ? SAT H2a 0.105 0.086 1.214 0.288 2.136** Supported

* p < 0.01 (based on t(406), one-tailed test).** p < 0.05 (based on t(406), one-tailed test).

178 M.J. Sanchez-Franco, F.J. Rondan-Cataluña / Electronic Commerce Research and Applications 9 (2010) 171–182

additional information on PLS, the interested reader should visit thefollowing website: disc-nt.cba.uh.edu/chin/plsfaq/plsfaq.htm.

Hypotheses 1a and 2a were supported; purchase involvementtypes positively moderate the relationship between aesthetics andsatisfaction. According to our findings, the greater the customer’spurchase involvement, the less will be the relationship between aes-thetics and satisfaction. Moreover, purchase involvement increasesthe impact of usability to satisfaction. (See Table 3 and Fig. 1).

4. Discussion

Members participate in virtual communities through the ex-change of informational cues and other expressive means thatare available in the community. The online community as a cus-tomer relationship management (CRM) tool will enable travel busi-nesses to retain customers by facilitating relationship-buildingwith other members, and highly-involved customers to seek ac-tively information about trips. In this context, the aim of this paperwas to propose and verify an integrative model of relationshipquality based on the explanation of overall satisfaction throughthe influence of instrumental cues for usability, and hedonic cuesfor visual aesthetics, and the analysis of the moderating role of pur-chase involvement in each of the relationships established be-tween design variables and satisfaction. On the one hand,‘‘understanding the marketing potential of a virtual communityis only half way to capitalizing on the benefits it can generate;the other half mainly depends on the design and maintenance ofthe community” (Wang et al. 2002). On the other hand, it was alsonecessary to understand purchase involvement levels of travellers.

Online design is an interesting research area related to onlinemarketing and consumer behaviour. While empirical evidence dem-onstrates the importance of design on our ordinary lives, tradition-ally there has been an absence of studies for the related onlineissues. A review of the literature shows that previous research hasnot reached a consensus on the relationships among usability, visualaesthetics and overall satisfaction among high and low purchase-involvement customers. In particular, our research fills this gapand demonstrates that the proposed model predicts customer com-mitment. Also virtual travel community design, especially aestheticsand usability, has a statistically relevant and significant effect on sat-isfaction. In addition, purchase involvement has a relevant moderat-ing effect in each of the relationships established between the designdimensions and satisfaction. Perceived usability had a stronger ef-fect on online satisfaction for customers with high purchase involve-ment, and a weaker effect for low purchase-involved customers.Visual aesthetics, thus, has a significant statistical relationship withbrowsing, while community usability is linked to search activity. On-line travel community managers should take these differences intoconsideration when designing and providing community services.

The moderating effects of purchase involvement on the relationshipbetween visual aesthetics and satisfaction. Less involved members donot spend much effort performing goal-oriented tasks like lookingfor specific travel information. Rather they spend more time inlurking activities in travel communities. In this regard, our researchstresses the essential role of visual aesthetics among less purchase-involved members. Virtual travel communities should be visually

aesthetic. The integration of visual aesthetic cues, as an essentialdriver in the interaction model, proved to be an important issuein understanding travel community members’ satisfaction, as wellas facilitating their affective commitment. Customers with a lowlevel of purchase involvement base their judgment on first impres-sions like site attractiveness, or on a cursory analysis of the source.Visual aesthetics and other hedonic cues will affect pleasure andarousal for customers with low levels of involvement. Emotionsassociated with services also will play an important role in formingsatisfaction and, consequently, behavioural intentions.

Nevertheless, beyond the importance of visual aesthetics, man-agers should explore integrated ways to strengthen the social iden-tity of members. In fact, lurking could be a behaviour thatjeopardize communities’ existence. ‘‘Participation in the activitiescarried out in a virtual community is one of the most importantfactors for the development and sustainability of virtual communi-ties” (Casaló et al. 2008). In this regard, virtual communities shouldprovide valuable site content by offering customised cues abouttravel services or making special offers to members. Managersshould encourage customers to participate actively by stimulatingtheir involvement, motivation and capacity; to evaluate positivelythe virtual community; and to guarantee the community’s survivalin the long term. Furthermore, members will probably recommendthe virtual community to others. Enhancing customer involvementthrough increasing motivation and familiarity with the virtualcommunity can be seen as important initiatives that promotemember participation and help them to avoid considering compet-ing virtual communities (Sanchez-Franco in press).

In short, virtual travel communities might include locating spe-cific threads of interest related to the community topic, other he-donic cues for enjoyment and entertainment purposes, and theuse of experts in a particular area to interact with communitymembers. As Mathwick et al. (2001) comment: ‘‘as the customercrosses the line from spectator to participant, their role shifts fromone of distanced appreciation of aesthetic elements to co-produc-ers of value” related to playfulness. Customers will ‘‘explore anew world of fantasy and entertainment where they can try outnew persons and engage in role-playing games where everythingseems possible” (Wang et al. 2002).

The moderating effects of purchase involvement on the relationshipbetween usability and satisfaction. Utilitarian benefits are also criti-cal issues. Our research thus accepts the relevance of usability invirtual travel communities. Among high purchase-involvementusers intangible-dominant goods require different risk-reductionstrategies. Higher purchase-involvement customers will searchcues related to a specific purchase in order to make the appropriatedestination choice by looking for and exchanging travel informa-tion and tips. This pertains to the improvement in efficiency thatit represents for the achievement of their objectives. Also, highpurchase-involvement customers take part in discussions in orderto inform and influence other members about travel services.

When active travellers perceive that a community is usable tothem, they will tend to actively view and explore the cognitive cuesmore often. So displaying and updating content is essential forencouraging activity among members. Virtual communitiesallow more highly-involved customers to exceed their expectations,

M.J. Sanchez-Franco, F.J. Rondan-Cataluña / Electronic Commerce Research and Applications 9 (2010) 171–182 179

which may lead to increased satisfaction, and consequently increasethe benefit to customers of long-term relations with virtual commu-nities. Furthermore, virtual communities should ‘‘help both infor-mation seekers and information providers to achieve their goals.For example, they can include a search engine or a FAQ system inthe BBS (Bulletin Boards Communities) in order to facilitate informa-tion seekers to obtain valuable information” (Jin et al. 2007). Ostromand Iacobucci (1995) suggest that friendliness of the service person-nel, which centers on the interpersonal interaction between the ser-vice provider and the customer, is very important for experienceservices. Although formal systems will never be able to satisfy mostinformation needs, information retrieval is easier if information isprovided according to personal preferences.

To sum up, the intangibility of tourism services makes thedevelopment of a relationship approach a strategic imperative, ifsuch electronic services are to achieve long-term profitability.The findings of this study have important implications for cus-

Appendix A. Measurement model: high purchase involvement

Latent dimension Loadingsa

A. Individual item reliability-individual item loadings. Construct reliabilityAesthetics

AE1 0.820AE2 0.870AE3 0.896AE4 0.912

CommitmentCOMM1 0.797COMM2 0.851COMM3 0.876

SatisfactionST1 0.844ST2 0.908ST3 0.939ST4 0.875

UsabilityUSA1 0.816USA2 0.785USA3 0.827USA4 0.823USA5 0.851USA6 0.779USA7 0.796

Emergent dimension Weights

TrustBenevolence 0.750*

Integrity 0.407**

a All loadings are significant at p < 0.001 (based on t(499), one-tailed test).* p < 0.001 (based on t(499), one-tailed test).

** p < 0.01 (based on t(499), one-tailed test).

Aesthetics Commitment

B. Discriminant validity coefficientsAesthetics 0.875Commitment 0.443 0.842Satisfaction 0.487 0.581Trust 0.523 0.648Usability 0.388 0.508

Diagonal elements (bold) are the square root of average variance extracelements are correlations between constructs.

tomer loyalty towards online travel services. However, it is advis-able to set out some limitations. First, the model does not includeall the relevant variables that explain the formative process of thecustomers’ affective commitment. We recommend that future re-search include symbolism in the model, along with usability andvisual aesthetics. Second, user perception of and attitude towardwebsite use are dynamic. To this end, we suggest that future inves-tigations should measure relationship quality at different times.Third, it is necessary to validate and generalize the results in futureinvestigations. Fourth, an important limitation of the study is theresult of the self-selection process of the respondents. These prob-lems are difficult to avoid in studies of online communities sincethe members cannot be forced to fill out a survey questionnaire.Fifth, following the suggestions of Flavián et al. (2006), we mustpoint out that the majority of individuals who participated wereSpanish-speaking. While the sample size enabled us to make gen-eralizations, it may not hold for different nationalities.

Composite reliability AVE

and convergent validity coefficients0.929 > 0.7 0.765 > 0.5

0.879 > 0.7 0.709 > 0.5

0.940 > 0.7 0.796 > 0.5

0.931 > 0.7 0.658 > 0.5

-�- -�-

Satisfaction Trust Usability

0.8920.550 -�-0.582 0.502 0.811

ted (AVE) between the constructs and their measures. Off-diagonal

Appendix B. Measurement model: low purchase involvement

Latent dimension Loadingsa Composite Reliability AVE

A. Individual item reliability-individual item loadings. Construct reliability and convergent validity coefficientsAesthetics 0.930 > 0.7 0.770 > 0.5

AE1 0.851AE2 0.874AE3 0.889AE4 0.895

Commitment 0.916 > 0.7 0.784 > 0.5COMM1 0.857COMM2 0.917COMM3 0.881

Satisfaction 0.948 > 0.7 0.819 > 0.5ST1 0.868ST2 0.931ST3 0.933ST4 0.886

Usability 0.935 > 0.7 0.674 > 0.5USA1 0.816USA2 0.831USA3 0.772USA4 0.879USA5 0.878USA6 0.780USA7 0.785

Emergent dimension Weights -�- -�-

TrustBenevolence 0.919*

Integrity 0.116ns

a All loadings are significant at p < 0.001 (based on t(499), one-tailed test).* p < 0.001 (based on t(499), one-tailed test).

Aesthetics Commitment Satisfaction Trust Usability

B. Discriminant validity coefficientsAesthetics 0.877Commitment 0.591 0.886Satisfaction 0.734 0.677 0.905Trust 0.326 0.511 0.386 -�-Usability 0.467 0.562 0.479 0.417 0.821

Diagonal elements (bold) are the square root of average variance extracted (AVE) between the constructs and their measures. Off-diagonalelements are correlations between constructs.

Appendix C. Items

CommitmentCOMM1. Even if I could, I would not leave this virtual tourist community; I like having a relationship with itCOMM2. I want to continue being a member of this community; my relationship with it really is gratifyingCOMM3. My affective bonds with this virtual tourist community are the main reason why I continue to use its serviceTrustBenevolence (BEN) – Integrity (INT)TR-BEN1. I believe that this virtual tourist community would act in my best interestTR-BEN2. If I required help, this virtual tourist community would do its best to help meTR-BEN3. This virtual tourist community is interested in my well-being, not just its ownTR-INT1. This virtual tourist community is truthful in its dealings with meTR-INT2. I would characterise this virtual tourist community as honestTR-INT3. This virtual tourist community would keep its commitments to me

180 M.J. Sanchez-Franco, F.J. Rondan-Cataluña / Electronic Commerce Research and Applications 9 (2010) 171–182

Appendix C (continued)

TR-INT4. This virtual tourist community is sincere and genuineSatisfactionST1. I think that I made the correct decision to use this virtual tourist communityST2. The experience that I have had with this virtual tourist community has been satisfactoryST3. In general terms, I am satisfied with the way that this virtual tourist community has carried out transactionsST4. In general, I am satisfied with the service I have received from this virtual tourist communityVisual aestheticsAE1. The screen design (i.e., colours, boxes, menus, etc.) is attractiveAE2. This virtual tourist community looks creatively designedAE3. The graphical design is originalAE4. The overall look and feel of this virtual tourist community is visually appealingUsabilityUSA1. In this virtual tourist community everything is easy to understandUSA2. This virtual tourist community is simple to use, even when using it for the first timeUSA3. It is easy to find the information I need from this virtual tourist communityUSA4. The structure and contents of this virtual tourist community are easy to understandUSA5. It is easy to move within this virtual tourist communityUSA6. The organisation of the contents of this virtual tourist community makes it easy for me to know where I am when navigating itUSA7. When I am navigating this virtual tourist community, I feel that I am in control of what I can doUSA8. Downloading pages from this virtual tourist community is quickPurchase involvementPI1. I frequently compare the travel data, information, tips, prices, etc. offered by various virtual tourist communities and other travelguidesPI2. I visit multiple virtual tourist communities and other travel guides in order to make the appropriate choicePI3. I compare the travel data, information, tips, prices, etc. of various virtual tourist communities and other travel guides before I designmy definitive travelPI4. After deciding on my travel, I discuss my choice with family and friendsPI5. After deciding on my travel, I compare my travel plan with other optionsPI6. After deciding on my travel, I weigh the pros and cons of my decision

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References

Bart, Y., Shankar, V., Sultan, F., and Urban, G. Are the drivers and role of online trustthe same for all web sites and customers? A large-scale exploratory empiricalstudy. Journal of Marketing, 69, 4, 2005, 133–152.

Beatty, S. E., Homer, P., and Kahle, L. R. The involvement–commitment model:theory and implications. Journal of Business Research, 16, 2, 1988, 149–167.

Bei, L.-T., Chen, E. Y. I., and Widdows, R. Consumers’ online information searchbehavior and the phenomenon of search vs. experience products. Journal ofFamily and Economic Issues, 25, 4, 2004, 449–467.

Browne, G. J., Durrett, J. R., and Wetherbe, J. C. Consumer reactions toward clicksand bricks: investigating buying behaviour on-line and at stores. Behaviour andInformation Technology, 23, 4, 2004, 237–245.

Casaló, L. V., Flavián, C., and Guinalíu, M. Fundaments of trust management in thedevelopment of virtual communities. Management Research News, 3, 5, 2008,324–338.

Chin, W. W. Frequently asked questions: partial least squares and PLS-Graph. Homepage, 2000. Available at disc-nt.cba.uh.edu/chin/plsfaq.htm. Accessed on June 1,2009.

Cristobal, E., Flavián, C., and Guinalíu, M. Perceived e-service quality (PeSQ):measurement validation and effects on consumer satisfaction and web siteloyalty. Managing Service Quality, 17, 3, 2007, 317–340.

Cyr, D., Head, M., and Ivanov, A. Design aesthetics leading to m-loyalty in mobilecommerce. Information and Management, 43, 8, 2006, 950–963.

Diamantopoulos, A., and Winklhofer, H. Index construction with formativeindicators: an alternative to scale development. Journal of Marketing Research,38, 2, 2001, 269–277.

Eroglu, S. A., Machleit, K. A., and Davis, L. M. Atmospheric qualities of onlineretailing: a conceptual model and implications. Journal of Business Research, 54,2001, 177–184.

Flavián, C., Guinalíu, M., and Gurrea, R. The role played by perceived usability,satisfaction, and consumer trust on website loyalty. Information andManagement, 43, 1, 2006, 1–14.

Ganesan, S. Determinants of long-term orientation in buyer–seller relationships.Journal of Marketing, 58, 2, 1994, 1–19.

Ganesan, S., and Hess, R. Dimensions and levels of trust: implications forcommitment to a relationship. Marketing Letters, 8, 4, 1997, 439–448.

Ganesh, J., Arnold, M. J., and Reynolds, K. E. Understanding the customer base ofservice providers: an examination of the differences between switchers andstayers. Journal of Marketing, 64, 7, 2000, 65–87.

Geisser, S. The predictive sample reuse method with applications. Journal of theAmerican Statistical Association, 70, 1975, 320–328.

Geyskens, I., Steenkamp, J.-B. E. M., Scheer, L. K., and Kumar, N. The effects of trustand interdependence on relationship commitment: a Trans-Atlantic study.International Journal of Research in Marketing, 13, 4, 1996, 303–317.

Geyskens, I., Steenkamp, J.-B. E. M., and Kumar, N. A meta-analysis of satisfaction inmarketing channel relationships. Journal of Marketing Research, 36, 5, 1999,223–238.

Hagel, J., III, and Armstrong, G. A. Net Gain: Expanding Markets through VirtualCommunities. Harvard Business School Press, Boston, MA, 1997.

Hennig-Thurau, T., and Klee, A. The impact of customer satisfaction and relationshipquality on customer retention: a critical reassessment and model development.Psychology and Marketing, 14, 8, 1997, 737–765.

Hoffman, D. L., and Novak, T. P. Marketing in hypermedia computer-mediatedenvironments: conceptual foundations. Journal of Marketing, 60, 3, 1996, 50–68.

Houston, M. J., and Rothschild, M. L. Conceptual and methodologicalperspectives on involvement. In S. C. Jain (ed.), Research Frontiers inMarketing: Dialogues and Directions, American Marketing Association, Chicago,IL, 1978, 184–187.

Isen, A. M., and Reeve, J. The influence of positive affect on intrinsic and extrinsicmotivation: facilitating enjoyment of play, responsible work behavior, and self-control. Motivation and Emotion, 29, 4, 2005, 297–325.

Jarvis, C. B., Mackenzie, S. B., and Podsakoff, P. M. Critical review of constructindicators and measurement model misspecification in marketing research.Journal of Consumer Research, 30, 2, 2003, 199–218.

Jin, X.-L., Cheung, C. M. K., Lee, M. K. O., and Chen, H.-P. Factors affecting users’intention to continue using virtual community. In The 9th IEEE InternationalConference on E-Commerce Technology and The 4th IEEE International Conferenceon Enterprise Computing, E-Commerce and E-Services (CEC-EEE 2007), 239–246.

Keil, M., Tan, B. C. Y., Wei, K. K., Saarinen, T., Tuunainen, V., and Wasenaar, A. Across-cultural study on escalation of commitment behaviour in softwareprojects. MIS Quarterly, 24, 2, 2000, 299–325.

Kumar, N., Scheer, L. K., and Steenkamp, J-B. E. M. The effects of perceivedinterdependence on dealer attitudes. Journal of Marketing Research, 32, 8, 1995,348–356.

Lavie, T., and Tractinsky, N. Assessing dimensions of perceived visual aesthetics ofweb sites. International Journal of Human–Computer Studies, 60, 2004, 269–298.

Lindgaard, G. Aesthetics, visual appeal, usability and user satisfaction: what do theuser’s eyes tell the user’s brain? Australian Journal of Emerging Technologies andSociety, 5, 1, 2007, 1–14.

Lindgaard, G., Fernandes, G. J., Dudek, C., and Brown, J. Attention web designers: Youhave 50 milliseconds to make a good first impression! Behavior and InformationTechnology, 25, 2, 2006, 115–126.

182 M.J. Sanchez-Franco, F.J. Rondan-Cataluña / Electronic Commerce Research and Applications 9 (2010) 171–182

Mathwick, C., Malhotra, N., and Rigdon, E. Experiential value: conceptualization,measurement and application in the catalog and Internet shoppingenvironment. Journal of Retailing, 77, 1, 2001, 39–56.

Mayer, R. C., Davis, J. H., and Schoorman, D. F. An integrative model of organizationaltrust. Academy of Management Review, 20, 3, 1995, 709–734.

McAllister, D. J. Affect and cognition-based trust as foundations for interpersonalco-operation in organizations. Academy of Management Journal, 38, 1, 1995, 24–59.

McKnight, D. H., Choudhury, V., and Kacmar, C. Developing and validating trustmeasures for e-commerce: an integrative typology. Information SystemsResearch, 13, 3, 2002, 334–359.

Morgan, R. M., and Hunt, S. D. The commitment–trust theory of relationshipmarketing. Journal of Marketing, 58, 3, 1994, 20–38.

Ostrom, A., and Iacobucci, D. Consumer trade-offs and the evaluation of services.Journal of Marketing, 59, 1, 1995, 17–28.

Pavlou, P. A., Liang, H., and Xue, Y. Understanding and mitigating uncertainty inonline exchange relationships: a principal–agent perspective. MIS Quarterly, 31,1, 2007, 105–136.

Petter, S., Straub, D., and Rai, A. Specifying formative constructs in informationsystems research. MIS Quarterly, 31, 4, 2007, 623–656.

Petty, R. E., and Cacioppo, J. T. Issue involvement as a moderator of the effects onattitude of advertising content and context. Advances in Consumer Research, 8,1981, 20–24.

Petty, R. E., and Cacioppo, J. T. Central and peripheral routes to persuasion:application to advertising. In L. Percy and A. Woodside (eds.), Advertising andConsumer Psychology, Lexington Books, Lexington, MA, 1983, 3–23.

Petty, R. E., and Wegener, D. T. The elaboration likelihood model: current status andcontroversies. In S. Chaiken and Y. Trope (eds.), Dual Process Theories in SocialPsychology, Guilford Press, New York, NY, 1999, 41–72.

Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., and Podsakoff, N. P. Common methodbiases in behavioral research: a critical review of the literature andrecommended remedies. Journal of Applied Psychology, 88, 5, 2003, 879–903.

Pullman, M. E., and Gross, M. A. Ability of experience design elements to elicitemotions and loyalty behaviors. Decision Sciences, 35, 3, 2004, 551–578.

Rafaeli, S., Ravid, G., and Soroka, V. De-lurking in virtual communities: a socialcommunication network approach to measuring the effects of social andcultural capital. In R. Sprague (ed.), Proceedings of the 37th Hawaii InternationalConference on System Sciences, Waikoloa, HI, IEEE Computing Society Press, LosAlamitos, CA, 2004.

Ridings, C. M., Gefen, D., and Arinze, B. Some antecedents and effects of trust invirtual communities. Journal of Strategic Information Systems, 11, 3–4, 2002,271–295.

Ringle, C. M., Wende, S., and Will, A. SmartPLS 2.0 (Beta). University of Hamburg,Hamburg, Germany, 2005. Available at www.smartpls.de. Accessed on June 1,2009.

Sanchez-Franco, M. J. The moderating effects of involvement on the relationshipsbetween satisfaction, trust and commitment in e-banking. Journal of InteractiveMarketing, in press.

Sangwan, S. Virtual community success: a uses and gratifications perspective. InProceedings of the 38th Hawaii International Conference on System Sciences,Waikoloa, HI, IEEE Computing Society Press, Los Alamitos, CA, 2005.

Shang, R.-A., Chen, Y.-Ch, and Liao, H.-J. The value of participation in virtualconsumer communities on brand loyalty. Internet Research, 16, 4, 2006, 398–418.

Shankar, V., Smith, A. K., and Rangaswamy, A. Customer satisfaction and loyalty inonline and offline environments. International Journal of Marketing Research, 20,2, 2003, 153–175.

Stone, M. Cross-validatory choice and assessment of statistical predictions. Journalof the Royal Statistical Society, 36, 1, 1974, 111–147.

Tax, S. S., Brown, S. W., and Chandrashekaran, M. Customer evaluations of servicecomplaint experiences: implications for relationship marketing. Journal ofMarketing, 62, 2, 1998, 60–77.

Tractinsky, N. Aesthetics and apparent usability: empirically assessing cultural andmethodological issues. In Proceedings of the SIGCHI Conference on Human Factorsin Computing Systems, Atlanta, GA, 1997, 115–122.

Tractinsky, N., and Lowengart, O. Web-store aesthetics in e-retailing: a conceptualframework and some theoretical implications. Academy of Marketing ScienceReview, 11, 1, 2007, 1–18.

Tractinsky, N., Cokhavi, A., Kirschenbaum, M., and Sharfi, T. Evaluating theconsistency of immediate aesthetic perceptions of web pages. InternationalJournal of Human–Computer Studies, 64, 2006, 1971–1983.

Tractinsky, N., Shoval-Katz, A., and Ikar, D. What is beautiful is usable. Interactingwith Computers, 13, 2000, 127–145.

Tractinsky, N., Katz, A. S., and Ikar, D. What is beautiful is usable. Interacting withComputers, 13, 2, 2000, 127–145.

Van der Heijden, H. Factors influencing the usage of websites: the case of ageneric portal in the Netherlands. Information and Management, 40, 6, 2003,541–549.

Wang, Y., and Fesenmaier, D. R. Modelling participation in an online travelcommunity. Journal of Travel Research, 42, 3, 2004, 261–270.

Wang, Y., Yu, Q., and Fesenmaier, D. R. Defining the virtual tourist community:implications for tourism marketing. Tourism Management, 23, 4, 2002, 407–417.

Westbrook, R. A. Product/consumption-based affective responses and postpurchaseprocesses. Journal of Marketing Research, 24, 8, 1987, 258–270.

Zhang, P. Toward a positive design theory: principles for designing motivatingInformation and communication technology. In M. Avital, R. J. Boland andD.L. Cooperrider (eds.), Proceedings of the Positive Design WorkingConference: Designing Information and Organizations with a Positive Lens, 2,2007, 1–363.