The Importance of Attribution

Abstract

Using attribution theory, this study examines 207 travel consumers’ views of five online travel communities (namely, Lonelyplanet, Travellerspoint, Tripadvisor, VirtualTourist, and Wayn) and their intentions to patronize one of the websites’ affiliates, Expedia. As a starting point, customers form an opinion of the community website itself based on communication quality and service quality. A high-quality experience, which includes interaction with other community members, engenders loyalty to the community website. With that base, the study tested three aspects of attributional responsiveness (evaluative satisfaction, cognitive loyalty, and affective belonging) and found that they could act as mediating factors to convey favorable attribution to the online communities’ affiliates, as represented by Expedia. Thus, a high standard of information and service will receive a favorable response from the online travel community, and that response can then be transferred to the community’s affiliates, in the form of purchases and return visits.

Keywords

online travel communities attribution theory Expedia evaluative satisfaction cognitive loyalty affective belonging

Online travel sites have rapidly developed into an informative and communication-intense industry. These sites have attracted attention from researchers, who aim to predict changes in tourists’ travel intentions based on their online comments and click stream (Xiang and Gretzel 2010). Site users view the information on these sites as being valuable and credible since the information comes from social communities and peer reference groups. Consequently, such information is thought to have a more powerful influence on consumer intentions than that received through commercial sources (e.g., advertising and traditional print sources; Laczniak, DeCarlo, and Ramaswami 2001). Researchers have estimated that information from reviews in an online travel community and other affiliated sites may influence half of all travelers’ hotel purchase decisions (Gretzel and Yoo 2008; Levy, Duan, and Boo 2013; Vermeulen and Seegers 2009). In this study, we take a step back and examine the information and service quality of online travel communities’ websites, since research indicates that these have a significant effect on travelers’ behaviors. Our study focuses on how an online site’s quality factors influence users’ attributional responsiveness, in the form of evaluative satisfaction, cognitive loyalty, and affective belonging, and we examine the extent to which these factors are in turn attributed to online travel communities’ affiliates, in terms of consumers’ site use and purchase intentions.

Online travel communities, online travel community affiliates, and other travel-related websites are important in providing relevant travel information and facilitating the sale of travel products and hotel services (C.-C. Chen and Schwartz 2006; Pantelidis 2010; Toh, Raven, and Dekay 2011). In the hospitality industry, online sales account for 57 percent of all sales, about one-third of them on third-party websites. HotelSCORES reports that online travel sales have grown from $93 billion five years ago to over $160 billion, with hotel reservations accounting for almost 40 percent of this volume (HotelSCORES 2013). The 2013 data from Statisticbrain indicates that 81 percent of travelers consult online reviews and search for travel information online before making their purchase, and about half of them would not book a hotel without a review. The average number of reviews per hotel in 2013 was 238.

Despite the obvious importance of virtual communities for travel purchasers (W. G. Kim, Lee, and Hiemstra 2004; Lin 2007; Y. Wang and Fesenmaier 2004), few works have discussed the connection of online travel communities to their affiliated sites, where consumers actually make their purchases. We believe that a systematic and empirical study of the antecedents of online travel communities’ influence on consumers’ intentions to use and purchase travel products and services would be useful. However, research on online travel community affiliates has been limited for a number of reasons. First, researchers have implicitly considered the quality of information presented in an online travel community as a precondition of the community’s success (Farquhar and Rowley 2006; D. J. Kim, Kim, and Han 2007; Lin 2008), but the influences of information and service quality have not been concurrently explored as they are internally attributed in an online travel community and externally attributed to the community’s affiliates. Second, although previous works focused on the factors affecting buying intentions, such as whether trust can be transferred from Yahoo to its affiliates (Sia et al. 2009), these studies omitted such other important attributions as evaluative satisfaction, cognitive loyalty, and affective belonging. These have not been examined as alternative mediators to explain attribution of quality factors imputed to an affiliated site from an online travel community website. Third, though internet technologies allow an online travel community to facilitate relationships, reviews, feedback, and consultations (Buhalis and Law 2008; S.-C. Wang, Sy, and Fang 2010), simply allowing commentary on a website does not guarantee a sustainable community. Developments in communities and their multirelationship affiliations have already had a significant effect on communication-focused web strategies (Sia et al. 2009), which thus require a better understanding of the relationship between online travel communities and their affiliates.

This study fills these gaps by examining (1) evaluative satisfaction, with regard to members’ evaluations of the online travel community experience; (2) cognitive loyalty, with regard to the members’ opinions of one online travel community as compared with other communities; and (3) affective belonging or cohesiveness, with regard to the members’ experiences with the online travel community. With that foundation, we investigate the external attribution process that explains the relationship between participants’ attribution of online travel community characteristics to the affiliates and consumers’ intent to purchase via those affiliates. Finally, by investigating the relationship between online travel communities and their affiliates, this study considers communication-intense online strategies to provide a better understanding of the emerging alliance between online communities and their affiliates.

Theoretical Background and Hypotheses

Online Travel Community Affiliation

Online travel communities and other virtual communities are websites that promote social relationships by permitting participants to interact extensively with one another, as they post queries and share ideas on specific travel-related topics (Armstrong and Hagel 1996; H.-C. Wang and Chang 2007). These sites provide many travel-related links to online travel resources, including the communities’ affiliates, links which can be taken as indicators of their reputation. Participation in an online travel community can often lead to intense and in-depth personal connections as people build relationships based on their travel experiences, and these relationships often facilitate travel-related transactions on affiliated sites (Frias, Rodriguez, and Castaneda 2008; W. G. Kim, Lee, and Hiemstra 2004). As consumers are motivated to provide marketing feedback and ideas using online tools (E. E. K. Kim, Mattila, and Baloglu 2011), an online travel community can transfer both marketing feedback and new ideas to its affiliates. Moreover, the reviews posted online are seen as one of the most important sources of information during the travel decision-making process. The site’s affiliates benefit from favorable reviews, as these can increase the probability that a user will make a booking with them (Vermeulen and Seegers 2009; Wilson, Murphy, and Fierro 2012; Xiang and Gretzel 2010).

A chief indicator of the relationship between an online travel community and its affiliates is hyperlinks on the community’s website (Sia et al. 2009; Stewart 2003). Being a widely recognized and reputable online travel community carries a certain amount of assurance about the quality of the information that is presented on the site. These sites, which include Tripadvisor, Virtualtourist, and Lonelyplanet, host affiliates’ logos and links, such as those for Expedia, Cheapoair, and Hotelbooking.com, and these links represent a sophisticated network of relationships between the online travel community and its affiliates (Stewart 2003). With these alliances, the affiliates gain potential customers, creating good value and mutually beneficial relationships for all the firms involved (Farquhar and Rowley 2006; Lemmetyinen and Go 2009).

Attribution Building Process

The reputation effects of these alliances rest in part on classical attribution theory, which involves assessing causal attribution outcomes that people generate in response to information. As users process a website’s information and service quality, they make evaluative and cognitive attributions (Abramson, Seligman, and Teas 1978; Chacko and McElroy 1983; Hanusa 1984; Weiner 2010) that affect their behavioral intention, attitudes, and expectations (Teas and McElroy 1986). With regard to online travel communities, individuals spontaneously make attributions, known as internal attribution, to an online travel community site, including evaluative satisfaction, cognitive loyalty, and affective belonging (Foxall and Yani-De-Soriano 2011). The phenomenon of attributional responses being transferred from an online travel community to the community’s affiliates is known as external attribution.

Thus, internal and external attributions are subject to the influence of the website quality provided by online communities. Quality attributions depend in part on whether the information is conveyed in a direct (i.e., proximal) or indirect (i.e., distal) manner. To explore this attributional perspective, this study examines whether attributional responsiveness to information processing within the online travel community and outside the community (i.e., its affiliates) is a determining factor in an individual’s behavioral intentions toward the community’s affiliates (Martinko, Harvey, and Dasborough 2011; McElroy and Shrader 1986). In this model, internal attributions are postulated to occur as a result of individuals’ active and high involvement with the information contained within an online travel community, whereas it is proposed that external attributions occur as a result of individuals’ passive and limited involvement with information when attributional responsiveness is transferred from an online travel community to its affiliates (Calder and Burnkrant 1977; Lord and Maher 1990; Lord and Smith 1983). In this work, internal attribution is seen as a conscious process in which individuals evaluate the online travel community according to how well the direct, proximal information helps them to understand certain situations, whereas external attribution is seen as a conscious process in which individuals seek explanations for behavior in relation to the community’s affiliates, which involves the use of indirect or distal information.

Internal Attribution: Information Quality and Attributional Responsiveness of the Online Travel Community

Travel communities and their affiliates are one of many conduits of online information (Toh, Raven, and Dekay 2011), which plays a key role in psychological processes of evaluation, cognition, and affection. These processes also could be influenced by the way people process information to make attributions (Lord and Maher 1990). In this view, behavioral intentions are one consequence of information processing and attributions (Bourne, Dominowski, and Loftus 1979).

The quality of information that is provided and shared by an online community has a strong effect on users’ attitudes toward that community (C.-C. Chen and Schwartz 2006; Frias, Rodriguez, and Castaneda 2008). When members search a site for product information, the results are presented as users’ aggregated opinions and subjective evaluations (Granovetter 1985). High-quality information satisfies the needs of network members with regard to knowledge seeking and relationship building (W. G. Kim, Lee, and Hiemstra 2004), and thus can contribute to evaluative satisfaction and help build cognitive loyalty.

A high level of information quality, which is based on accuracy, completeness, timeliness, and presentation format (Nelson, Todd, and Wixom 2005), can promote such customer reactions as satisfaction and cognitive evaluations (DeLone and McLean 2003; Olsen 2007; Y.-S. Wang and Liao 2008). Moreover, one of most powerful implications of attribution theory is that individuals utilize information processing as a primary cue to evaluate and ascribe characteristics to an organization (Chacko and McElroy 1983). Thus, positive information quality can foster evaluative satisfaction and cognitive loyalty (Tsiros, Mittal, and Ross 2004).

Internal Attribution: Service Quality and Attributional Responsiveness

Service quality is another critical success factor for attracting website traffic and increasing sales (Lohse and Spiller 1998), based on a combination of characteristics such as responsiveness to specific customer inquiries, interactivity among members, easy customization, and fast and convenient delivery processes (Ba and Johansson 2008). The improvement, maintenance, and assurance of an online community’s service quality can increase the internal attributional reaction in the form of satisfaction and loyalty to that quality, as well as enable the hotel or travel firm to better meet customer needs and wants (Tse and Ho 2009). The concept of attribution within an online community centers around elements such as the convenience of the website architecture, design, and navigation (Lohse and Spiller 1998; Palmer 2002). Churchill and Surprenant (1982) and Gronroos (1984) suggest that service quality is a useful predictor of cumulative satisfaction, because it increases the cognitive usability of an online community and creates loyal consumers through the process of evaluative satisfaction. Therefore, this study hypothesizes that service quality has a positive effect on the attributions of satisfaction, loyalty, and belonging associated with an online travel community (DeLone and McLean 2004; Lin 2007; Y.-S. Wang and Liao 2008). In the most widely used behavioral model (Ajzen and Fishbein 1973), evaluative, cognitive, and affective attributions are all influenced by cognitions of service quality.

External Attribution: Attributional Responsiveness and Behavioral Intentions to Online Travel Community Affiliates

All attributional perspectives focus on the conditions which determine whether behavior is attributed to internal, personal causes or to external causes (Hamilton 1980; Jones and Davis 1966; Kelley and Michela 1980). Although individuals are biased toward internal attributions, in the absence of direct information they can also make an external attribution, that is, transfer loyalty or satisfaction to a third party through the associated socialized entity.

The information quality needed to make causal assessments can be broken down into the following three stages: (1) evaluative satisfaction with the first causal explanation that comes to mind (i.e., personal dispositions from the consumer’s own behavior, which is an underlying interpersonal influence; Calder and Burnkrant 1977); (2) commitment to buy or patronize a preferred product or service consistently in the future (i.e., cognitive loyalty; Jones and Davis 1966; Oliver 1999); and (3) an affective sense of belonging toward the online travel community (i.e., attributional sensitivity; Calder and Burnkrant 1977).

Evaluative satisfaction

Evaluative satisfaction as we have been discussing it is an overall feeling about the net value of the services received (Churchill and Surprenant 1982; Gronroos 1984; Woodruff 1997; Yang and Peterson 2004), or a pleasurable level of consumption-related fulfillment (Oliver 1997). At least one study has determined that satisfaction has an effect on people’s sense of belonging to a community (Lin 2008). Satisfaction with the online travel community is experienced through such characteristics as information sharing, relationship building, and website-quality factors, which may increase both the sense of belonging and loyalty. The overall satisfaction perspective views customer satisfaction as a cumulative evaluation (Oliver 1997) that requires summing the satisfaction associated with a firm’s specific products and aspects. Evaluative satisfaction may serve as a critical predictor of customer cognitive loyalty, and satisfaction may drive affective belonging through cognitive loyalty (Yang and Peterson 2004).

Research has also provided a theoretical justification for viewing satisfaction as an important antecedent to attachment and loyalty, and has empirically shown significantly positive relationships among these elements (W. G. Kim, Lee, and Hiemstra 2004; Lin 2008; Lin and Lee 2006). Theory predicts that highly satisfied online travel consumers will ascribe stronger and more positive attributes to their online travel community (C.-F. Chen and Tsai 2008; S. S. Kim and Son 2009; Lin 2008; Mathwick 2002; Yang and Peterson 2004).

Cognitive loyalty

Most early studies conceptualized loyalty as being vital to repeat consumption of a particular product or service (Olsen 2007), and so loyalty can be defined as a deeply held commitment to consistently repeat a purchase of a preferred product or service (Oliver 1999). In this context, cognitive loyalty is based on product evaluations, is generated from the consumer’s evaluative response to an experience, and represents a preference for a brand over its competitors (Olsen 2007; Yuksel, Yuksel, and Bilim 2010). This study defines loyalty to an online travel community as similar to the attribute of continued preferred usage, emphasizing the importance of a cognitive process (Yuksel, Yuksel, and Bilim 2010).

Affiliating with a popular and reputable online travel community allows travel companies to obtain valuable attribution and thus generate sales. If attribution can be transferred from the travel community to its affiliates, they should see increased member usage and purchase intentions. A travel company should thus use its affiliated online travel community to increase its sustainable and long-term value, as well as reduce marketing costs and increase usage patterns (Allmendinger and Lombreglia 2005; Stewart 2003).

Affective belonging

The sense of belonging to a community has been defined as a feeling of attachment and similarity to others, acknowledged interdependence with others, and willingness to maintain this condition (Sarason 1974), as well as the feeling of being an integral part of a group (Hagerty et al. 1992), being accepted by the group, and being willing to make sacrifices for it (Burroughs and Eby 1998). Just as some researchers consider the sense of belonging to be a crucial and appropriate indicator of involvement and participation in an online travel community (Lin 2008), this study defines belonging as an affective belonging attribution. This attribution addresses the commitment component, the extent to which individuals in a group enjoy being involved with each other, and the spirit of a group that makes members want it to succeed. Some studies have found that a sense of belonging is an important factor to foster online community members’ loyalty (C.-F. Chen and Tsai 2008; W. G. Kim, Lee, and Hiemstra 2004). The connections between quality evaluations and loyalty (Gracia, Bakker, and Grau 2011), affective commitment and emotional bonds (Mattila 2006), satisfaction and loyalty (Hallowell 1996), and a strong cognitive and affective sense of belonging are expected to generate a strong intention to be further involved in the online travel community’s affiliates.

Behavioral Intentions

Behavioral intentions are key factors for relationship marketing, including the link between the online travel community and its affiliates (W. G. Kim, Lee, and Hiemstra 2004; S. S. Kim and Son 2009). If a community’s affiliate is widely recognized and reviewed within that community, there is a certain amount of assurance about quality carried over from the community to the affiliate, which encourages community members to make more purchases. In the absence of direct information and service quality evaluations for the affiliate, as attribution receiver, the affiliate can acquire positive attributes through an attribution giver, in this instance the community, so that users form behavioral intentions with regard to the attribution receiver. For the purposes of our study, we tested transfer of positive feelings about Tripadvisor and other such sites to Expedia.

Based on previous research, we are particularly interested in the direct and indirect links to behavioral intentions that result from the stages and nature of attributional responsiveness to quality factors. Based on the review of existing research summarized above, the following hypotheses are proposed:

Hypothesis 1 (H1): Information quality directly and positively influences service quality.

Hypothesis 2a (H2a): Information quality directly and positively influences customer evaluative satisfaction.

Hypothesis 2b (H2b): Service quality directly and positively influences customer evaluative satisfaction.

Hypothesis 2c (H2c): Service quality mediates the relationship between information quality and evaluative satisfaction.

Hypothesis 3a (H3a): Information quality indirectly influences cognitive loyalty through evaluative satisfaction.

Hypothesis 3b (H3b): Evaluative satisfaction mediates the relationship between service quality and cognitive loyalty.

Hypothesis 4a (H4a): Evaluative satisfaction directly and positively influences cognitive loyalty.

Hypothesis 4b (H4b): Cognitive loyalty directly and positively influences affective belonging.

Hypothesis 4c (H4c): Cognitive loyalty mediates the relationship between evaluative satisfaction and affective belonging.

Hypothesis 5a (H5a): Cognitive loyalty mediates the relationship between evaluative satisfaction and intention to use the online travel community affiliates.

Hypothesis 5b (H5b): Cognitive loyalty directly and positively influences intention to use the online travel community affiliates

Hypothesis 5c (H5c): Affective belonging directly and positively influences intention to use the online travel community affiliates.

Hypothesis 5d (H5d): Affective belonging mediates the relationship between cognitive loyalty and intention to use the online travel community affiliates.

Hypothesis 6a (H6a): Intention to use the online travel community affiliates mediates the relationship between cognitive loyalty and intention to purchase.

Hypothesis 6b (H6b): Affective belonging directly and positively influences intention to purchase.

Hypothesis 6c (H6c): Intention to use the online travel community affiliates mediates the relationship between affective belonging and intention to purchase.

Hypothesis 7 (H7): Intention to use the online travel community affiliates directly and positively influences the intention to purchase from the online travel community affiliates.

The conceptual model used in this study is shown in Exhibit 1.

Exhibit 1:

The Research Model.

Research Design and Method

Survey Instruments

To measure the study constructs, we adapted measurement scales validated in previous studies, using four items with five-point Likert-type scales. Website quality includes information quality and service quality (DeLone and McLean 2003). Responsiveness to quality factors is the psychological process and sensibility which can reflect the information and service characteristics and attributes. The sense of belonging was measured based on the method proposed in Teo et al. (2003), whereas the satisfaction and loyalty items were adopted from Back and Parks (2003), and the behavioral intentions measurement items were drawn from Zabkac, Brencic, and Dmitrovic (2010).

We pre-tested the questionnaire with fifty consumers who all had been using any of our study’s five online travel communities for more than two years. They were drawn from the sample used for the main survey and were asked to complete paper-based questionnaires. We used these respondents’ comments to revise and refine the survey’s instructions and questions.

Sample and Data Collection

The five online travel communities that we studied met the following criteria: (1) The community is successful and among the most widely visited such sites; (2) the community is perceived as a place to find travel information and share travel experiences; and (3) the community provides forums that host discussions about travel-related products and services. These criteria enable us to test the service quality, sense of belonging, loyalty, and intention to use and purchase products reviewed on the online travel community. Based on these criteria, we selected the following five websites from transitionsabroad.com (2009): Lonelyplanet, Travellerspoint, Tripadvisor, VirtualTourist, and Wayn. Since all five communities are affiliated with Expedia, that became the affiliate in our study.

The authors registered as community members to distribute this web-based online survey in the websites’ forums, and we introduced Expedia as the affiliated business at the beginning of the survey. We made ourselves available online to answer any questions that the respondents had about the survey, which was available on the websites from August to November 2009. Using this approach, we obtained a convenience sample of 207 online travel community members who also used Expedia. Although 300 members of the five communities showed interest in taking part in the survey when contacted, a total of 256 community members responded. We had to eliminate 49 of these due to incomplete responses, resulting in a usable sample of 207 responses. This high response rate (256 of 300) indicates a relatively low possibility of sample bias (Fowler 1984).

As shown in Exhibit 2, over 50 percent of the respondents were women, and nearly all had a college education, including nearly one-third at the graduate-level. Over 40 percent of the respondents were 20 to 25 years old. Finally, more than 70 percent used at least one of the online travel communities for more than eight hours a week.

Exhibit 2:

The Characteristics of the Sample.

Demographic Characteristics	Frequency	Percentage
Gender
Female	110	53.1
Male	97	46.9
Education
High School	2	1
College	137	66.2
Postgraduate	68	32.8
Age
<=20 years	41	19.8
21-25 years	84	40.6
26-30 years	62	30
>30 years	20	9.6
Hours of OTC use per week
<4 hours	19	9.2
4-8 hours	33	15.9
>8 hours	55	74.9

Note. OTC = online travel community.

Our testing found an acceptable level of reliability for the questionnaire, since all Cronbach’s alpha coefficients were higher than .70. The correlation patterns (i.e., the within and between construct correlations) using the correlation matrix provided general evidence for convergent and discriminant validity (Exhibit 3). Therefore, given their acceptable validity and reliability, the measures used in this work are considered to be appropriate for tests of the causal model and the research hypotheses.

Exhibit 3:

Factor Correlations and Assessment of Discriminant Validity.

	IQ	SQ	AB	ES	CL	IU	IP
IQ	1
SQ	.501	1
AB	.265	.362	1
ES	.458	.506	.586	1
CL	.388	.355	.320	.591	1
IU	.366	.376	.475	.542	.607	1
IP	.350	.353	.511	.527	.549	.767	1
M	3.317	3.259	3.533	3.387	3.291	3.427	3.324
SD	0.912	0.878	0.859	0.792	0.910	0.907	0.950

Note. IQ = information quality; SQ = service quality; AB = affective belonging; ES = evaluative satisfaction; CL = cognitive loyalty; IU = intention to use; IP = intention to purchase; SD = standard deviation.

Correlation is significant at the .01 level (two-tailed).

Data Analysis and Results

The respondents were asked to recall their most recent online experience related to one of the travel communities, such as visiting a blog, social networking, or sharing media. The collected data were analyzed using structural equation modeling (SEM) on SPSS for Windows and AMOS 5. Confirmatory factor analysis (CFA) is first used to estimate a measurement model, and then we applied SEM for the model evaluations, modeling comparisons, and hypothesis testing.

Measurement Model

The results of the measurement model showed a good fit to the data, with χ² = 369.309, df = 304, χ²/df = 1.215, p = .006, root mean square error of approximation (RMSEA) = .032, Goodness-of-Fit Index (GFI) = .890, root mean square residual (RMR) = .038, Comparative Fit Index (CFI) = .984, and the Normed Fit Index (NFI) = .918.

As seen in Exhibit 4, the reliability of the scale measures (i.e., composite reliability [CR] > 0.85, and average variance extracted [AVE] > 0.60) exceeds the recommended values by significant amounts (Bagozzi and Dholakia 2002; Zabkac, Brencic, and Dmitrovic 2010), indicating that the convergent validity of these is acceptable. For each construct, the AVE was much higher than its highest shared variance (HSV) between all possible pairs of constructs, providing support for discriminant validity (Fornell and Larker 1981). Finally, correlations were below the problematic level of .80 (Hair et al. 1998). These results suggest that the convergent validity, reliability, and discriminant validity of all the measures are satisfactory.

Exhibit 4:

The Results of the Factor Analyses and Reliability Tests.

Constructs	Research Items	SFL
Information Quality	InfQ _ Information Quality CR = 0.88, AVE = 0.70, HSV = 0.32
	InfQ2 This OTC provides me with a complete set of information.	.862
	InfQ4 The information provided by this OTC is well formatted.	.778
	InfQ3 The information from this OTC is always up-to-date.	.766
	InfQ1 The information provided by this OTC is accurate.	.819
Service Quality	SerQ _ Service Quality CR = 0.87, AVE = 0.63, HSV = 0.35
	SerQ4 The OTC shows a sincere interest in solving members’ problems.	.836
	SerQ3 The OTC has personalized service.	.815
	SerQ2 The OTC provides quick service.	.798
	SerQ1 I feel very confident about the OTC service.	.747
Attributional Responsiveness
Evaluative Satisfaction	Sat _ Satisfaction with the OTC CR = 0.82, AVE = 0.53, HSV = 0.48
	Sat2 I am satisfied with the OTC I am using now.	.766
	Sat3 My choice of using the OTC was a wise one.	.702
	Sat4 I truly enjoy using the OTC.	.760
	Sat1 The OTC I am using now gives me exactly what I need.	.694
Cognitive Loyalty	Loy _ Loyalty to the OTC CR = 0.85, AVE = 0.53, HSV = 0.49
	Loy3 The OTC I am using is superior to others in its class.	.867
	Loy2 When I have a need for services, I prefer those of the OTC I am using.	.876
	Loy1 The OTC I am using has more benefits than others in its class.	.741
	Loy4 I am using the OTC because I like its famous name more than other comparable OTCs	.803
Affective Belonging	SOB _ Sense of Belonging to the OTC CR = 0.89, AVE = 0.69, HSV = 0.48
	SOB3 I am very committed to the OTC.	.875
	SOB2 I enjoy being a member of the OTC.	.827
	SOB1 I feel a strong sense of belonging to the OTC.	.808
	SOB4 Overall, the OTC has a high level of morale.	.813
Behavioral Intentions
Intention to Use the OTCA (Expedia)	IU _ Intentions to use the OTCA CR = 0.86, AVE = 0.60, HSV = 0.49
Intention to Use the OTCA (Expedia)	IU3 I believe it is worthwhile for me to return to use the OTCA.	.786
	IU2 I intend to use this OTCA frequently whenever it is available.	.768
	IU4 I am very likely to return to use the OTCA in the future.	.789
	IU1 I intend to be a heavy user of this OTCA whenever it is available.	.779
Intentions to Purchase Product from the OTCA (Expedia)	IP _ Intentions to Purchase Products CR = 0.90, AVE = 0.69, HSV = 0.49
	IP2 I plan to buy products from this OTCA more often.	.848
	IP4 I am very likely to purchase on this website.	.863
	IP3 I expect to purchase from this OTCA.	.809
	IP1 I intend to buy products from this OTCA frequently.	.815

Note. SFL = standardized factor loading, CR = composite reliability; AVE = average variance extracted; HSV = highest shared variance with other constructs; OTC = online travel community; OTCA = online travel community affiliate.

Following Podsakoff et al.’s (2003) guidelines, the Harman single-factor test was performed on all seven core construct items in the model. We did this with a principal components analysis using SPSS 13. The highest variance explained by one factor only was more than 40 percent, and more than one factor emerged from the analysis. Despite efforts to reduce the issue of common method bias, it may still be a limitation of this study.

SEM

Since the model has a promising overall GFI, we further identified the magnitudes and significance of its structural path coefficients. Exhibit 5 shows the significant relationships among the research variables, as well as the full results of the SEM analysis, including the structural path estimates and explained variances. The partially mediated model (i.e., the model when the moderators were controlled; χ²/df = 1.315, p = .000, RMSEA = .039, RMR = .077, GFI = .882, NFI = .908, CFI = .976), produced a marginally better fit to the data (Parsimony Normed Fit Index [PNFI] = .750) in comparison with the other models.

Exhibit 5:

SEM Path Diagram.

Direct Effects

Based on the suggestion by Kelley and Michela (1980), we analyzed the relationship between information quality and behavioral intention and the influence of attributions on this relationship. Multivariate regression analyses were used to assess the path structure of the regression models, and to reduce the risk of correlated errors the regressions were conducted in a stepwise fashion.

Our hypotheses related to information quality received empirical support, given that information quality significantly influences satisfaction, supporting H2a, as shown in Exhibit 5 and Exhibit 6, (β = .458, R² = .210, p = .000). The path loadings from service quality to satisfaction with the online travel community were also significant (β = .506, R² = .256, p = .000), providing support for H2b.

Exhibit 6:

Results of the Mediated Regression Analysis.

Paths (a→b→c)Notes: Direct Effect a→b, b→c: yes	Mediating Effects (a→c)	Full Mediation	Sobel t Test (2-Tailed)
H3a: Information Quality→ Evaluative Satisfaction→ Cognitive Loyalty	p = .000	Yes	p = .000
H2c: Information Quality→ Service Quality→ Evaluative Satisfaction	p = .000	Yes	p= .000
H3b: Service Quality→ Evaluative Satisfaction→ Cognitive Loyalty	p = .000	Yes	p= .000
H4c: Evaluative Satisfaction→ Cognitive Loyalty→ Affective Belonging	p = .000	Yes	p= .000
H5a: Evaluative Satisfaction→ Cognitive Loyalty→ Intentions to Use	p = .000	Yes	p= .000
H5d: Cognitive Loyalty→ Affective Belonging→ Intentions to Use	p = .000	Yes	p= .000
H6a: Cognitive Loyalty→ Intentions to Use → Intentions to Purchase	p = .000	Yes	p= .000
H6c: Affective Belonging→ Intentions to Use→ Intentions to Purchase	p = .000	Yes	p= .000

Note. H = Hypothesis.

Regarding the outcome variables, consistent with our theoretical expectation, evaluative satisfaction had a significant influence on loyalty, supporting H4a (β = .591, R² = .349, p = .000). As indicated by the path loadings, there was a significant relationship between affective belonging and cognitive loyalty (β = .320, R² = .102, p = .000), providing support for H4b.

Affective belonging to an online travel community significantly influences respondents’ intention to use the online travel community affiliate, supporting H5c (β = .475, R² = .225, p = .000), and intention to purchase the product (β = .511, R² = .261, p = .000), providing support for H6b. Furthermore, cognitive loyalty significantly influences intention to use the community’s affiliate (β = .607, R² = .369, p = .000), providing support for H5b. The effect of intention to use the online travel community affiliate on intention to purchase the affiliate’s product is significant (β = .767, R² = .588, p = .000), and thus H7 is supported.

Mediating Effects

Given the results of the direct effects, we explored the mediating effects along the eight significant pathways (see Exhibit 6), since that is the starting point for Baron and Kenny’s (1986) mediation analysis. When we controlled for the mediating variables, we found that they indeed mediated the forecasted relationships (see Exhibit 6 for the Sobel test scores, Z values, and probability values).

We estimated the indirect effects of the mediators both with a Sobel test and a bootstrapping method using the SPSS-macro provided by Preacher and Hayes (2004). The Sobel test presents the level of significance of the indirect effect of the independent variable on the dependent variable through a mediator. The SPSS-macro provides an estimate of the true indirect effect and its bias-corrected 95-percent confidence interval. Both the Sobel test and bootstrapping method reveal that satisfaction significantly mediates the effect of information quality on cognitive loyalty (the point estimate for the indirect effect is 0.279, p = .00), providing support for H3a, and the effect of service quality on cognitive loyalty (0.212, p = .00), providing support for H3b. Furthermore, service quality significantly mediates the effect of information quality on evaluative satisfaction with the online travel community (0.162, p = .00), providing support for H2c.

As also indicated by the Sobel test results, cognitive loyalty significantly mediates the effect of evaluative satisfaction on affective belonging (0.255, p = .00), providing support for H4c, and the effect of evaluative satisfaction on intention to use (0.342, p = .00), providing support for H5a. Intention to use significantly mediates the effect of affective belonging on intention to purchase (0.19, p = .00), providing support for H6c, and the effect of cognitive loyalty on intention to purchase (0.296, p = .00), providing support for H6a. Finally, affective belonging significantly mediates the effect of cognitive loyalty on intention to use, supporting H5d.

Conclusion

Our results suggest that the existing attribution perspective needs to be revisited. Previous attributional analyses used trust as a mediating factor in the attribution of system characteristics (Sia et al. 2009; Stewart 2003), but our results suggest that aspects of attributional responsiveness, namely, evaluative satisfaction, cognitive loyalty, and affective belonging, could act as mediating factors to convey the attribution. Both information and service quality factors generated attributional responsiveness in online travel communities, and this was then attributed to the communities’ affiliate. This finding implies that a high standard of information and service will receive a favorable response from the online travel community, and that response is then transferred to the community’s affiliates. In this regard, our research suggests that the experience of high-quality information and service is enhanced through evaluative satisfaction, cognitive loyalty, and affective belonging in an online travel community. Through the mediating effects that it has uncovered, this study extends previous research with the finding that online travel community members who have higher levels of cognitive loyalty and affective belonging are more likely to become potential users of and purchasers from the online travel community’s affiliates. Cognitive loyalty is based on positive judgments that arise from pleasant attributions (Yuksel, Yuksel, and Bilim 2010), and we found that cognitive loyalty is a significant predictor of the community members’ use of the affiliate in question. Beyond that, we found that affective belonging, which is the community spirit associated with voluntary and extra-role behavior (Burroughs and Eby 1998), led to purchases from the travel communities’ affiliate.

From an attribution theory perspective, consumer attributional responsiveness factors are observed to benefit online travel firms when they are internally increased by direct and proximal information and service quality and externally increased by indirect and distal information and service quality. Our findings support this idea by illustrating the significant influence that consumer attributions have on five online communities and their affiliate. Attribution theory traditionally puts an emphasis on personality traits to conceptualize internal attribution and on environmental factors to conceptualize external attribution (Chacko and McElroy 1983; McElroy and Shrader 1986). However, this study’s focus on the concept of internal versus external attribution at the individual level indicates that attributional responsiveness could serve as a conduit to convey quality factors both internally and externally, and thus help to develop a strategic alliance between a travel community and its commercial affiliates.

Managerial Implications

Our study underscores the importance of monitoring and managing the quality of electronic communications in an online travel community (Pantelidis 2010), with a goal of making the online firms more competitive by generating customer evaluative satisfaction, cognitive loyalty, and affective belonging. An online travel community site can create value by having high information and service quality, so that online travel community affiliates can collect important information about individuals as potential customers. The affiliates need attributional responsiveness from the online community to attract travelers to the affiliates’ websites thereby expanding the affiliates’ sales. Online travel community affiliates, such as Expedia and Hotelbooking, should extend their social networking channels with additional affiliation partners to avoid overreliance on one or just a few online travel communities. By taking advantage of the community’s interactive communication, community members can be encouraged to get involved in the process of co-producing and co-creating their experience, thus improving evaluative satisfaction, cognitive loyalty, and affective belonging. Customers’ participation in creating the affiliated brand fosters their use of and purchase from the site. A key finding of this work is that affective belonging with regard to an online travel community has a decisive influence on participants’ purchase intentions from affiliates.

Applying Luk and Layton’s (2002) gap theory to tourism and hospitality management, the results of this study imply that internal attribution plays a role in narrowing the gap between the community members’ communication expectations and web providers’ performance, and that external attribution plays a role in narrowing the communication gap between the expectations of potential affiliates’ customers and the service providers themselves (in this case, Expedia). The empirical evidence suggests that evaluation, cognition, and affection are essential for the mechanism of attributing indirect quality factors to community affiliates. By ensuring service and communication quality, online tourism and hospitality communities can build vibrant communication (Gracia, Bakker, and Grau 2011) to generate purchasers for their affiliates. Community websites that enable customer feedback, reviews, and consulting are powerful tools that increase the effectiveness of information searches, enhance users’ overall responsive attitude, and increase their intention to patronize the community’s affiliates.

Limitations and Future Research

Although this study achieved some favorable results, several improvements can be made that would benefit both academics and practitioners. This study investigated online travel community quality factors, members’ attributional responsiveness, and their behavioral intention with regard to Expedia, the affiliate of five online travel communities. The small number of websites may be subject to common method bias and limit the generalizability of these findings. Moreover, almost all respondents had a college or graduate education and thus may not represent the general population. Nevertheless, the amount of time these respondents reported spending on an online travel community was high, and the results of this work can provide useful evidence with regard to the influence of satisfaction on the participants’ sense of belonging and loyalty. Further research may shed light on how the different levels of belonging, satisfaction, and loyalty toward a community, vary among different contexts, cultures, and individuals with different demographic characteristics. The integrated model used in our study should thus be expanded to include a variety of community members’ characteristics, and these should be treated appropriately. Finally, the sample was small and not randomly selected. However, the findings of this work still represent a significant contribution to the work of management academics and practitioners wishing to evaluate satisfaction, sense of belonging, loyalty, and behavioral intentions in the context of online travel websites.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, or publication of this article.

Author Biographies

My-Trinh Bui received her Ph.D., from National Cheng Kung University (buimytrinh@gmail.com).

Don Jyh-Fu Jeng, Ph.D., is an associate professor at the Graduate Institute of Technology, Innovation and Intellectual Property Management, College of Commerce, National Chengchi University (jeng@nccu.edu.tw).

Chinho Lin, Ph.D., is a professor at the Department of Industrial and Information Management and Institute of International Management, College of Management, National Cheng Kung University (linn@mail.ncku.edu.tw).

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