Towards understandingmembers’ interactivity, trust, andflow in online travel community

Jyh-Jeng Wu and Yong-Sheng ChangDepartment of Business Administration, Providence University,

Taichung Hsien, Taiwan, Republic of China

Abstract

Purpose – This study targeted members of online travel communities to explore the factors thataffect the experience of flow and how flow affects the transaction intentions of these members.

Design/methodology/approach – In order to test this model, data were collected via an onlinequestionnaire, with a total of 286 survey instruments available. The data were analyzed usingstructural equations modeling with AMOS.

Findings – The empirical findings showed that, first, as far as the online travel communitiesmembers are concerned, both interactivity and trust do affect each other. Second, interactivity is thekey factor for the members to have flow experience in online travel communities. Third, the experienceof flow can enhance the transaction intentions of members while they are in the online travelcommunities.

Practical implications – Web site administrators should improve the interactivity with themembers, so that they can have flow experience, and further strengthen their transaction intentions.

Originality/value – This paper provides a model to understand online travel communities memberswho place their trust in the online travel agencies and, interacting with the web sites in ways thatresult in a flow experience, ultimately intensify their transaction intentions.

Keywords Modelling, Trust, Travel, Electronic commerce

Paper type Research paper

IntroductionOnline tourism is an information intensive industry. Through the internet consumerscan easily access information that is both convenient and efficient, and receive moreflexible rates, save time, and reduce cumbersome negotiations involved in processessuch as booking tickets and accommodations. These features have contributed to thepopularity of online travel portals. Thus, online travel community has been regardedas central to models of internet marketing and electronic commerce in travel industry(Wang and Fesenmaier, 2004).

According to a report put out by MIC (Market Intelligence Center, Taiwan’s leading ITindustry analysis and consulting service provider) in June 2004, of Taiwan’s 23 millioninhabitants, roughly 8.88 million people (39 percent) surf the internet. The report also statesthat in 2003, the overall value of the business to consumer (B2C) market transactions reachedUSD 0.66 billion. Online travel agencies account for 48 percent of the B2C e-commercemarket, or USD 0.32 billion, making them the leading industry in B2C e-commerce.

The exponential development of world wide web (WWW) has created clusters ofonline communities, enabling interactivity among cohorts to satisfy communication,information and entertainment needs. These online communities are also communitiesof trust and belonging. Early flow theory was initially applied to living, working,

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Industrial Management & DataSystems

Vol. 105 No. 7, 2005pp. 937-954

q Emerald Group Publishing Limited0263-5577

DOI 10.1108/02635570510616120

leisure activities, sports and readings (Ellis et al., 1994; Moneta and Csikszentmihalyi,1996; Csikszentmihalyi, 1997), and later employed by scholars in the field of computersoftware learning and network browsing, leading to the discovery that networking canentice the users into a flow state (Hoffman and Novak, 1996; Chen et al., 2000; Novaket al., 2000; Koufaris, 2002; Hsu and Lu, 2004). However, few empirical studies havebeen conducted on the flow experience of online travel community members. Thisstudy targets members with significant involvement in online travel communities todemonstrate the high involvement. These members, who place their trust in the onlinetravel agencies and interacting with the web sites in ways that result in a flowexperience, ultimately intensify their transaction intentions.

Literature reviewOnline communityIn the last few years, thousands of computer users worldwide have begun to engage incommercial online activities (Chou et al., 2005; Kuo et al., 2004; Liang et al., 2004). Manyhave joined one or more of the online communities that have begun to serve consumerneeds for information, communication, and entertainment (Wang et al., 2002). The notionof online community (also known as virtual community, computer-mediated community,or simply e-community) has been discussed among academia (Rheingold, 1994;Armstrong and Hagel, 1996; Hagel and Armstrong, 1997; Werry, 1999; Wang et al., 2002;Wang and Fesenmaier, 2004). Rheingold (1994, pp. 57-8) defined virtual community as“social aggregations that emerge from the Net when enough people carry on those publicdiscussions long enough, with sufficient human feelings, to form webs of personalrelationships in cyberspace. A virtual community is a group of people who may or maynot meet one another face to face, and who exchange words and ideas through themediation of computer bulletin boards and networks.” Armstrong and Hagel (1996)regarded it as the congregating of those who share the same interest that forms aninterest community, and proposed that the formation of a virtual community arises fromthe four basic needs of human beings: interest, social relationship, fantasy andtransaction. In addition, they also argued that conventional business functions such asmarketing and sales will be significantly transformed in a community environment.

InteractivityPrevious studies have established interactivity as a critical feature of modern media.Consumers no longer interact with salespeople or have a direct physical experiencewith the store and its products. Instead, their experience is mediated through the web,using a graphical display without any face-to-face interaction with the e-vendor(Koufaris and Hampton-Sosa, 2004). Nelson (1990) suggested that human-computeractivities exemplify the human impulse to create interactive representation.

From an interpersonal communication perspective, interactivity has been defined asthe extent to which messages in a sequence relate to each other, and especially theextent to which later messages describe the relatedness of earlier messages (Rafaeliand Sudweeks, 1997). Interactivity is also defined as the extent to which thecommunicator and the audience respond to each other’s communication need (Ha andJames, 1998; Rafaeli, 1988; Fox, 2000; Levine et al., 2000; Newman et al., 2004).

In a business setting, interactivity tends to be seen as the “combination of richcontent, active intelligence, and collaborative communications to create a compellingconsumer experience” (Robb et al., 1997) or a person-to-person or person-to-technology

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exchange designed to effect change in the knowledge or relevant behavior of at leastone person (Haeckel, 1998). Hoffman and Novak (1996) argued that the method ofmedia communication in a hypermedia computer-mediated environment (CME) differsfrom the traditional, and hence propose a CME marketing model. They believed thatCME infuses network software and hardware, and hence enables consumers andcorporations to attain machine-interactivity and person-interactivity.

Machine-interactivity is the extent to which users can participate in modifying theform and content of a mediated environment in real time. While person-interactivity, isdefined as interactivity between people that occurs through a medium or unmediated,as in the case of face-to-face communication (Steuer, 1992; Hoffman and Novak, 1996).

TrustLack of trust in online companies is a primary reason why many consumers do notshop online. Despite general scholarly agreement that trust is essential to socialrelationships, the role of trust in commercial transactions remains a matter of debate(Saparito et al., 2004). Trust is also a central element in many commercial activities(Dwyer et al., 1987; Moorman et al., 1993; Fukuyama, 1995; Reichheld and Schefter,2000; Smagt, 2000; Sahay, 2003). Trust in the e-vendor is one of the critical factors ofsuccess in e-commerce (Torkzadeh and Dhillon, 2002). Therefore, it is imperative, forcompanies and researchers alike, to study how online consumer trust is promoted anddeveloped (Koufaris and Hampton-Sosa, 2004).

The perspective of trust has been studied in diverse contexts, by researchers fromvarious disciplines and backgrounds, and as a result, there are various definitions oftrust (Mill, 1909; Rotter, 1967; Morgan and Hunt, 1994; So and Sculli, 2002; Kim et al.,2004; Lander et al., 2004). Mill (1909, p. 131) observed, “The advantage of humankind ofbeing able to trust one another, penetrates into every crevice and cranny of human life:the economical is perhaps the smallest part of it, yet even this is incalculable.” An earlytrust Theorist, Rotter (1967) defined interpersonal trust as an expectancy held by anindividual or a group that the word, promise, verbal or written statement of anotherindividual or group can be relied upon. Morgan and Hunt (1994, p. 23) looking atinter-organizational trust, defined trust as: “when one party has confidence in anexchange partner’s reliability and integrity.” Trust is also developed through consistentand predictable acts of the different parties over an extended period (So and Sculli, 2002).Kim et al. (2004, p. 198), addressing trust between businesses and consumers, opined“Trust is a dynamic process. Trust can only be built over a certain period of time, and itusually contributes to customer satisfaction over and beyond the effects of the economicoutcomes of the relationships.” Trust exists because the parties effectively understandand appreciate the other’s wants, and this mutual understanding is developed to thepoint that each can effectively act for the other (Lander et al., 2004).

Some empirical studies have also developed measurement variables for onlineconsumer trust (Cheung and Lee, 2001; Lee and Turban, 2001; Bhattacherjee, 2002;McKnight et al., 2002). As a rule, trust is measured through consumer beliefs in theability, benevolence, integrity, and predictability of a given company, although inseveral studies those beliefs are tested as antecedents of consumer trust. Trust is alsoan interpersonal determinant of behavior that deals with beliefs about the ability,benevolence, integrity and predictability of other people (Mayer et al., 1995; McKnightet al., 1998; Lee and Turban, 2001). However, in contrast to face-to-face trade, there areapparently no interpersonal interactions in e-commerce, neither direct nor implied.

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Such interactions, or even cues relating to them, are apparently missing frome-commerce web sites (Reichheld and Schefter, 2000).

FlowCsikszentmihalyi (1975) introduced the original concept of flow. It is defined as the holisticexperience that people feel when they act with total involvement. Hoffman and Novak(1996, p. 57) defined it as “the state occurring during network navigation which is:characterized by a seamless sequence of responses facilitated by machine-interactivity;intrinsically enjoyable; accompanied by a loss of self-consciousness; and self-reinforcing.”Flow is also regarded as a psychological condition in which the person feelssimultaneously cognitively efficient, motivated, and happy (Moneta and Csikszentmihalyi,1996). Hsu and Lu (2004, p. 857) considered it as an “extremely enjoyable experience,where an individual engages in an online game activity with total involvement, enjoyment,control, concentration and intrinsic interest.”

The concept of flow has been proposed recently by several researchers as importantfor understanding consumer behavior on the internet. For example, Hoffman andNovak (1996) conceptualize flow on the internet as a cognitive state experienced duringnavigation that is determined by:

. high levels of skill and control;

. high level of challenge and arousal;

. focused attention; and

. enhanced by interactivity and tele-presence.

Furthermore, other studies have related the characteristics of flow to informationtechnology, as presented in Table I. For instance, Hsu and Lu (2004) argue that flow isan extremely enjoyable experience, where an individual engages in an online gameactivity with total involvement, enjoyment, control, concentration and intrinsicinterest. They verified the effect of social norms, perceived critical mass, and flow onthe behavior of online game users. Hoffman and Novak (1996) demonstrated that skilland control, challenge and arousal, focused attention, and interactivity andtele-presence (Steuer, 1992) are cognitive state experienced during online navigation.

In Csikszentmihalyi’s (1975) original model, flow was thought to occur when theparticipant perceived an equal match between challenge and skill. When bothchallenge and skill are perceived to be low, the participant experiences apathy and theoverall quality of subjective experience is the lowest. If challenge is perceived to begreater than skill, the participant experiences anxiety. If skill is perceived to be greaterthan challenge, the participant experiences boredom (Moneta and Csikszentmihalyi,1996). Therefore, here, flow is defined as a temporarily unaware experience, wheremembers of online travel communities engage in the travel web sites process withenjoyment and time distortion.

In summary, flow is regarded as a multi-dimensional construct with characteristicsthat include skill, enjoyment, challenge, time distortion, etc. We directly measured flowin the present study with enjoyment and time distortion. Further research (Ghani andDeshpande, 1994; Hoffman and Novak, 1996; Chen et al., 2000; Novak et al., 2000) hassuccessfully used a similar approach in eliciting examples of experiences of flowamong online consumers.

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Table I.Characteristics of flow

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Research framework and hypothesesResearch frameworkFigure 1 shows the research framework examined here. It asserts that the flow, whichcontains enjoyment and time distortion, is a function of: interactivity(machine-interactivity and person-interactivity) and trust (ability, benevolence,integrity and predictability). Machine-interactivity is the extent which online travelcommunity members can operate computer and browse web pages. Whileperson-interactivity is defined as interactivity between member and web siteadministrator occurs through a medium. Ability is described as the web site has agood rate. Benevolence refers to the extent to which a seller is believed to want to do goodto the member. Integrity is defined as seller adheres to a set of transaction rules thatmember finds acceptable. Predictability means if member can predict that a seller has apositive performance, member will be interested in transacting with the seller. Flow isalso defined as a temporarily unaware experience, where members of online travelcommunities engage in the travel web sites process with enjoyment and time distortion.

The model further indicated that flow was positively related to transactionintentions. Transaction intentions are defined as the extent which member willpurchase product that travel web site offered.

Research hypothesesLagace et al. (1991) believed interactivity was the foundation of buildiing trust. In thecourse of sustained interaction with the consumer, the foundation of trust is gradually

Figure 1.Research framework

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built, and subsequently progresses to the possibility of long-term cooperation. Thefrequency of interaction and the extent of consumer trust are positively related. Thegreater the frequency of interaction, the easier it is to build trust. Gefen and Straub (2004)pointed out that to deter others from harboring illicit motives, we restrain or forbid othersfrom performing certain unpredictable actions on us. At such times we are unable toengage in sustained and lasting interaction with others, since both parties will be trappedin the dilemma of distrust. Only when mutual trust exists can a desirable interaction takeplace. Luhmann (1988) argued that trust is the foundation of interpersonal interactivity; itis also the most effective means to lessen its complexity. McKnight and Chervany (2002)also pointed out if an e-vendor interacts online with its consumers, it should be able toconvey to them that it is benevolent, competent, honest, and predictable. The interactivityprovides the consumer with assurances that support willingness to rely on the web siteadministrator. Therefore, the online community members are more likely to engage intrust-related internet behaviors like purchasing, cooperating and sharing information. Onthe preceding reasoning this study proposed H1.

H1. Interactivity and trust have a positive correlation.

Novak et al. (2000), Koufaris (2002) and Hsu and Lu (2004) believed that flow isfundamentally a subjective experience of human-machine interactivity, during thecourse of which the individual will subjectively feel enjoyment, involvement and timedistortion. Hoffman and Novak (1996) pointed out that in the state of the internetbrowsing, once an individual has a series of seamless interaction with the machine,enjoyment, loss of self awareness and a heightened sense of the self will ensue, that isto say the higher intensity the interactivity, the more likely flow experience is to ensue.Based on the preceding argument this study proposes H2.

H2. Interactivity is positively related to flow.

Given the significance of trust in preventing geographical distance from becoming apsychological barrier, coupled with its importance in building loyalty in the e-commerceenvironment, one would expect trust that will have a prominent influence on onlinetransaction (Davision et al., 1996). Luhmann (1988) believed that in e-commerce, trust is apsychological shortcut and mechanism to abate uncertainty and complexity. Gefen (2000)proposed that both user familiarity with and trust in a web site and the tradingenvironment of e-commerce (e.g. product inquiry, product purchase) have positive effects.McKnight and Chervany (2002) also argued that the consistent willingness and ability ofe-vendors to provide services of interest to the buyer, and honoring benevolence, integrityand predictability throughout, will induce trust in the buyer as it spares the buyer fromsuch concerns as privacy or online trading security. Thus it enables the buyer toconcentrate and focus on the undertaking. Csikszentmihalyi (1975) regarded flow as whenthe users enter flow state; they will be fully absorbed in the undertaking, filtering out allirrelevant consciousness. Based on the preceding argument this study proposes H3.

H3. Trust is positively related to flow.

According to the definition of flow that Csikszentmihalyi (1975) proposed, the greaterintensity of the individual involved in the undertaking, the more likely it is he or she toenter flow state. Furthermore, Hoffman and Novak (1996) distinguished thecharacteristics of internet browsing users as goal-directed and experiential behavior:the former is less likely to experience flow whereas the latter is more likely for more

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lasting involvement. Varied motivation to browse would affect the degree ofconcentration of the user, affecting the flow experience, and in turn influencingtransaction intentions (Holbrook et al., 1984; Ellis et al., 1994; Holbrook, 1994; Monetaand Csikszentmihalyi, 1996, 1997; Koufaris, 2002). Based on the preceding argumentthis study proposes H4.

H4. Flow is positively related to transaction intentions.

Measurement of variablesThe survey instrument was developed from the literature; the list of the items isdisplayed in the Appendix. The interactivity construct based on Hoffman and Novak(1996) was primarily divided into machine-interactivity and person-interactivity.Drawing upon measures in Table II, a total of six questions were developed. The trustconstruct was primarily predicated on the four sub-constructs proposed by Mayer et al.(1995): ability, benevolence, integrity and predictability. Based on the four variables, atotal of 12 questions were developed. The flow construct, enjoyment and timedistortion were primarily based on Ellis et al. (1994), Novak et al. (2000), Koufaris (2002)and Hsu and Lu (2004), and addressed the two variables of enjoyment and timedistortion with a total of five questions. The transaction intentions construct primarilyfollowed McKnight and Chervany (2002), and a total of three questions were developed.

Data analysisPilot-testThis study consisted of four major constructs: interactivity, trust, flow and transactionintentions. Variables comprised independent variables (machine-interactivity,person-interactivity, ability, benevolence, integrity and predictability), mediatevariables (enjoyment and time distortion), and dependent variable (transactionintentions). All questions were constructed using a Likert five-point scale. These Likertscale questions ranged from 1 ¼ strongly disagree to 5 ¼ strongly agree. In order toavoid ambiguous sentence phrasing, the survey instrument was reviewed ininterviews with five webmasters of the online travel community web sites and twoexperts in the field of tourism. The final survey consisted of 26 questions. Findingsshowed that Cronbach’s a for all constructs ranged from 0.7069 to 0.9331; all variableswere higher than 0.7, evidencing relatively high reliability (Jomes and James, 1979).

Constructs Variables References

Interactivity Machine-interactivity Hoffman and Novak (1996)Person-interactivity

Trust AbilityBenevolence Mayer et al. (1995)IntegrityPredictability

Flow Enjoyment Ellis et al. (1994), Novak et al. (2000), Koufaris (2002)and Hsu and Lu (2004)

Time distortionTransaction intentions Transaction intentions McKnight and Chervany (2002)

Table II.Measure variables

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Research designThis study employed survey instrument survey over the internet at Yahoo, PChome, Yam and CityFamily, the three most popular portals for in Taiwan.According to a report of Business Weekly (Taiwan’s leading business magazine) inNovember of 2003, CityFamily was the largest local online community in Taiwan,with 3.10 million members in 230,000 online communities. The reason for postingthe survey instrument at such web sites was to maximize exposure in gatheringlarger samples.

The manner of distributing the survey instrument was via selecting the most activetravel web sites (by the criteria of the greatest memberships and most threads indiscussion forum) for posting the announcement of the survey. The survey instrumentwas sent via e-mail to the web site administrators to fill out as well as to redirect thesurvey instrument to their community members.

Based on the rule of experience, for an infinite population to attain 90-95 percentsampling accuracy, 100-400 samples are called for (Ellis et al., 1994). Based on this, andprevious internet survey experience, the collection period was set for two months, for aduration of time from 1 March 2004 until 30 April 2004. In the end, a total of 302 surveyinstruments were returned, with 16 invalid survey instruments and 286 valid ones.Among the returned samples, representing 286 online travel community members, theproportion of male and female was 50.3 and 49.7 percent, respectively. The majoritiesof respondents were 21-30 years old (63 percent). Above half (52.5 percent) membersspent more than 31 hours per week online. Table III summarizes the profiles ofrespondents.

Coefficient of correlation analysisWe employ the concept of structural equation modeling (SEM) in our analysis, andused Amos 4.0 and SPSS 8.0 as our analytical tools. Pearson product-momentcorrelation coefficient analysis was undertaken to prove the H1 of this study, i.e. toexamine if a correlation exists between interactivity and trust. The result showed thatbetween the online travel community members and web site administrators,interactivity and trust were significantly correlated; hence H1 of the study wassupported. The findings are shown in Table IV.

Structural modelA structural model expresses the relationships among independent and dependentvariables, even when a dependent variable becomes an independent variable in otherrelationships. The fit criteria employed and indicators are presented in Table V.Moreover, SEM analysis yielded a x 2/df to evaluate fit extent. The smaller chi-squaredegree of freedom indicates greater model fit and vice versa. Generally speaking, whenx 2/df is smaller than 2, it means the model has an ideal fit (Bentler, 1988). In theanalysis of the model fit, the following results were obtained: GFI ¼ 0:949; AGFI ¼0:908; PGFI ¼ 0:516; NFI ¼ 0:922; IFI ¼ 0:982; CFI ¼ 0:976; RMSEA ¼ 0:027;RMR ¼ 0:033; all reaching acceptable levels, suggesting the model fits the datawell. Additionally, path analysis was applied to demonstrate H2, H3 and H4 of thisstudy. The research framework proposed by this study describes those members withsignificant involvement in a online travel community to demonstrate that highinvolvement internet users who place their trust in a given web site, through

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interactions with the web site that result in a flow experience, ultimately intensify theirtransaction intentions. Figure 2 shows the structural model where values are pathcoefficients, solid lines indicate a significant path, and dotted lines show a path thatwas not significant.

Measure Sample size Percentage

SexMale 144 50.3Female 142 49.7

AgeUnder 20 34 11.921-25 100 35.026-30 80 28.031-35 48 16.8Above 35 24 8.3

EducationElementary school 2 0.7Junior high school 2 0.7Senior high school 36 12.6College degree 178 62.2Graduate degree 68 23.8

OccupationScience and technology industry 22 7.7Manufacturing industry 20 6.9Finance industry 4 1.4Service industry 34 11.9Building industry 6 2.1Traffic industry 6 2.1Soldier, servant and teacher 42 14.7Student 98 34.3Other 54 18.9

Times of surf the internet (hour/week)Under 20 22 7.621-25 64 22.426-30 50 17.531-35 68 23.8Above 36 82 28.7

Table III.Demographic andbehavioral characteristicprofile of respondents

VariablesMachine

interactivityPerson

interactivity Ability Benevolence Integrity Predictability

Machine interactivity 1.00Person interactivity 0.569 * * 1.00Ability 0.497 * * 0.512 * * 1.00Benevolence 0.641 * * 0.658 * * 0.577 * * 1.00Integrity 0.504 * * 0.491 * * 0.394 * * 0.578 * * 1.00Predictability 0.315 * * 0.474 * * 0.545 * * 0.411 * * 0.431 * * 1.00

Table IV.Coefficient of correlationmatrix

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Criteria Indicators

x 2 testx 2 test p . 0.05 p ¼ 0:176x 2/df p , 2 153:23=136 ¼ 1:127

Fit indicesGFI p . 0.9 0.949AGFI p . 0.9 0.908PGFI p . 0.5 0.516NFI p . 0.9 0.922IFI p . 0.9 0.982

Alternative indicesCFI p . 0.95 0.976RMSEA p , 0.05 0.027

Residual analysis indicesRMR p , 0.05 0.033

Table V.Model of fit indices

Figure 2.Structural model

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Research findingsThis study employed Amos to calculate path coefficients for understanding the directcausal effect between the constructs. The parameter estimates for Figure 2 arepresented in Table VI. Looking at the interactivity construct, path coefficients ofmachine-interactivity to enjoyment and time distortion were 0.33 ð p , 0:001Þ and 0.22ð p , 0:001Þ; respectively, while path coefficient of person-interactivity to timedistortion was 0.23 ð p , 0:01Þ; all significant. The effect of person-interactivity onenjoyment was not supported by the data. Hence H2 was partially supported. Suchresult is consistent with relevant research theoretical claims made in the past (Websteret al., 1993; Hoffman and Novak, 1996). The more intense the interactivity, the greaterthe probability of experiencing flow entered.

In the trust construct, path coefficients of ability and integrity to enjoyment were0.14 ð p , 0:05Þ and 0.25 ð p , 0:001Þ; respectively, indicating they are positivelyrelated to enjoyment. However, as the level of significance of H3 fell short of half, H3was not supported, which differed from the presumption. Namely, most members ofonline travel communities generate flow experience through interactivity, not trust. Inthe flow construct, path coefficient of enjoyment to transaction intentions was 0.26 atp , 0:001: The effect of time distortion on transaction intentions was not supported bythe data; hence H4 was partially supported. Those members enter flow experience withenjoyment and will display enhanced transaction intentions (i.e. book tickets, reserveaccommodations, purchase package tours). This finding was also consistent with pastresearch; varied motivation to browse will affect user concentration, which in turninfluences flow experience, and ultimately affects transaction intentions (Ellis et al.,1994; Moneta and Csikszentmihalyi, 1996; Csikszentmihalyi, 1997; Koufaris, 2002).

Conclusion and discussionThe purpose of this study was to examine the factors that influence the flow andtransaction intentions of online travel community members. Interactivity and trustwere used as the variables that affected member flow experience, while flow affectedmember transaction intentions. The empirical findings were: first, interactivity is acritical factor in whether a member enters the flow state, while trust is not. Hence travelweb site administrators should increase potential interactivities with members in order

Dependent variables

Enjoyment Time distortionTransactionintentions

Predictor b p b p b p

Machine interactivity 0.33 0.000 * * * 0.22 0.004 * *

Person interactivity 0.06 0.424 0.23 0.004 * *

Ability 0.14 0.046 * 0.00 0.973Benevolence 0.07 0.360 20.11 0.203Integrity 0.25 0.000 * * * 0.00 0.980Predictability 0.02 0.798 0.05 0.468Enjoyment 0.26 0.000 * * *

Time distortion 20.02 0.716

Notes: b ¼ standardized coefficient; p = significance level; *p , 0:05; * *p , 0:01; * * *p , 0:001

Table VI.Parameter estimates forFigure 2

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to generate flow experience. Second, in order to increase member transactionintentions, emphasis should be directed at promoting their flow experience withenjoyment. Hoffman and Novak (1996) conceptualize flow on the internet as a cognitivestate experienced during navigation that is determined principally by thecharacteristic of interactivity.

Based on this, we recommend that travel web site administrators strive to engenderonline community member flow experience in order to increase their transactionintentions. Therefore, web sites should incorporate audio and video effects to createuser pleasure and involvement during the course of browsing to promote transactionintentions. At the same time, it is essential to learn member desires, satisfy their needs,and respond to their questions in a timely way. Nelson (1990) argued that a successfulweb site must combine both entertainment and information to add value in the eyes ofthe consumers. Thus facilitated, online community members will be able to deriveenjoyment from browsing the web site due to the heightened interactivity, andtherefore enter flow state that ultimately promotes transaction intentions.

The B2C aspects of the internet remain important to business practitioners(Newman et al., 2004). As online travel community members generally want to accessinformation on a web page as soon as possible, the most important design principle isto keep it simple but the travel information is detailed and up to date. In other words,information must be as current as possible with regard to product specifications, pricesand relevant links. This is a method to encourage community members to come back tovisit the web site. Addition of banner advertising to a company’s web site is also a wayto enhance interactivity (Fox, 2000). Indeed, the consumer has come to expectinteractive links with banner advertisements and web site links that provide access tothe depth of information desired with the ability to purchase when they feel the time isright (Levine et al., 2000; Newman et al., 2004). Similarly, easy-to-use web site is anotherdesign criterion that has been found to affect B2C success (Eid and Trueman, 2004).

This study investigated the transaction intentions of online travel communitymembers. Further research might be directed at other online community members tounderstand if different types of internet users present varied characteristics. Thesurvey instrument design was based on literature review and expert opinions, and thenmodified via pilot-test. Although most concepts adopted in the survey instrument werereliable, in-depth interviews of internet users might be undertaken in the future formore precise profiling of travel population behavior.

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(Jyh-Jeng Wu is a professor of Business Administration at Providence University inTaiwan. He received his PhD degree in Business Administration from Taiwan’sNational Cheng Kung University in 1997. His research focuses on issues related tomarketing strategy and e-commerce. His research has been published in Tourism

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Management, Issues & Studies, International Service Technology and Management andAsia Pacific Management Review.

(Yong-Sheng Chang received an MBA degree in 2004 from Providence University,Taiwan. His current interests include electronic commerce, marketing and trust issuesrelated to tourism.)

Appendix. Scale items and constructsPlease indicate the extent to which you agree or disagree with the following statements.(Anchored by 1 – strongly disagree and 5 – strongly agree.)

Machine-interactivity

I can easily search the content of product and relevant information at travel web sites.

I can easily filter the content of information at travel web sites.

I can easily connect the information what I wanted.

Person-interactivity

It is easy to contact the call center at travel web sites.

The call center has great pleasure to answer questions.

The speed of response to questions is very fast.

Ability

I feel that the travel web site administrator will update the content of web page any time.

I feel that the travel web site call center will conduct transactions for me.

I feel that all of the transactions will be conducted promptly.

Benevolence

I feel that the travel web site will post hot information actively.

I feel that the travel web site call center is willing to answer questions.

I feel that the travel web site call center will quickly reply to questions.

Integrity

I feel that the travel web site has integrity.

I feel that the travel web site is reliable.

I feel that the travel web site is trustworthy.

Predictability

I can comprehend all of the purchasing procedures in the travel web site.

I feel service quality that the travel web site offered is consistent.

I can perceive an equal match between pre-purchase and post-purchase alternative evaluation.

Enjoyment

I felt content when I was browsing the travel web site.

I felt enjoyable when I was browsing the travel web site.

I felt fulfilled when I was browsing the travel web site.

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Time distortion

I felt that time certainly flies when I was browsing the travel web site.

I felt time distortion when I was browsing the travel web site.

Purchase intention

I have a high intention to purchase at travel web site.

The probability of purchasing product was enhanced when I was browsing the travel website.

I would like to purchase product at travel web site.

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