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From virtual travelers to real friends: Relationship-building insights from an online travel community
Journal of Business Research 68 (2015) 1822–1828
Contents lists available at ScienceDirect
Journal of Business Research
From virtual travelers to real friends: Relationship-building insights from an online travel community Werner Kunz ⁎, Sukanya Seshadri 1 College of Management, University of Massachusetts, 100 Morrissey Boulevard, Boston, MA 02125, USA
a r t i c l e
i n f o
Available online 12 February 2015 Keywords: Online travel community Tourism Social networks Social media Couch surfing
a b s t r a c t The growing trend of online travel communities connects travelers worldwide. This study addresses whether or not these relationships lead to offline interactions. The theoretical framework reflects cue utilization theory, social balance theory, and uncertainty reduction theory. A field experiment examines responses from 293 travel community members. Results show individual reputation, online communication, and perceived similarity among travelers play significant roles in offline relationships. Trust and sympathy among community members mediate this decision process. Study results offer several managerial implications and highlight the importance of vivid and complete participation profile in social media. Moreover, finding the right tone for effective communication in online communities is critical. Published by Elsevier Inc.
1. Introduction The Internet substantially changed the travel industry over the last two decades. Customers easily access information and build new relationships using social media. Previously, companies typically employed traditional marketing channels to build customer relationships (Hennig-Thurau et al., 2010). Today, companies also use social media to build these relationships. Online communities offer a wide variety of possibilities to establish, maintain, and develop relationships between individuals and businesses. For tourists, online travel communities represent a growing trend (Bialski & Batorski, 2007). For example, the CouchSurfing.com web platform is an Internet service that connects travelers worldwide. Online encounters between travelers often lead to offline relationships (e.g., visiting each other's home city). In contrast with online matchmaking sites, a travel online community primarily shares trip experiences, not romantic matches (Whitty, Baker, & Inman, 2007). Prior research examines relationships among online community participants; however, a paucity of research exists regarding how online relationships might lead to offline relationships (Boyd & Ellison, 2008; Foster, Francescucci, & West, 2010; Jahn & Kunz, 2012; Raacke & Bonds-Raacke, 2008). Typically, these communities rely on preexisting, offline relationships (e.g., Facebook, LinkedIn). Online communities likely help users build offline relationships with strangers as well. This study identifies and investigates key conditions necessary for online travel community members to engage in an offline relationships. ⁎ Corresponding author. Tel.: +1 617 291 8736. E-mail addresses: [email protected] (W. Kunz), [email protected] (S. Seshadri). 1 Tel.: +1 617 291 8736.
http://dx.doi.org/10.1016/j.jbusres.2015.01.009 0148-2963/Published by Elsevier Inc.
The proposed framework builds on various theoretical approaches. According to the Uncertainty Reduction Theory (Berger & Calabrese, 1975), individuals follow risk-reducing steps in uncertain situations. Arguably, meeting a stranger online is such an uncertain situation. According to cue utilization theory, specific information serves as a proxy for other attributes (Olsen, 1977). Accessing online communities, individuals look for informational cues to make their decisions. Finally, Heider's (1946) Balance theory implies that individuals try to avoid imbalanced situations. Two people's views differing significantly cause imbalance and create anxiety. A field experiment with 293 CouchSurfing members tests the hypotheses. Results show that the community reputation, online communication behavior, and perceived similarities among travelers play significant roles building potential offline relationships. Trust and sympathy between members also mediate this process. The present study contributes to online community research by explaining the transformation process, from online to offline relationships, in a global travel community. This study provides insights into how to build relationships through online communities that can lead to offline interactions (e.g., location, or event visit). These insights help practitioners to use global online communities more effectively. 2. Research background 2.1. Online community relationships Prior research identifies several motives for community engagement. Findings suggest that social connections (i.e., keeping in touch with friends) and information sharing (e.g., events or gossip) are central reasons for online community engagement (Foster et al., 2010; Jahn & Kunz, 2012; Raacke & Bonds-Raacke, 2008; Ridings & Gefen, 2006;
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Sheldon, 2008). Thus, relationship building and information exchange serve as primary motives for online community participation. The relationship partner or source of information need not be a close friend. Strangers (i.e., no preexisting relationship, or “latent ties”) also serve as valuable information providers. Prior research notes how people perceive information from strangers in general and in online environments (Brown & Reingen, 1987; Constant & Sproull, 1996; Weiss, Lurie, & MacInnis, 2008). For example, Brown and Reingen (1987) find that active information seeking (e.g., initiating an online conversation to obtain product information) likely occurs among weak rather than strong tie sources (close friends). Constant and Sproull (1996) show weak ties give useful advice; such usefulness largely stems from the expertise and experience of the weak tie source. Weiss et al. (2008) suggest past behavior guides information seekers' judgments of information value. A fast response may be more valuable— clarifying information seekers' problems sooner. Thus, strangers become valuable information sources and suggest a need to investigate requirements for building these relationships. 2.2. Online travel communities with strangers Most online communities build on preexisting social relations (Boyd & Ellison, 2008). Ellison, Steinfield, and Lampe (2007) show that Facebook users prefer to search for people with whom they have offline relationships. In contrast, online travel community members are strangers; the community focuses on shared travel experiences. Wang, Yu, and Fesenmaier (2002) describe online travel communities as platforms for travelers to obtain trip information, find travel companions, provide travel tips, or simply share interesting experiences (e.g., CouchSurfing, TripAdvisor, WAYN). Members usually do not know one another in-person. On the other hand, online travel community members often share high levels of personal information (SanchezFranco & Rondan-Cataluña, 2010). In contrast to online matchmaking sites, a travel online community focuses primarily on shared travel experiences rather than romantic relationships (Whitty et al., 2007). Bialski and Batorski (2007) show that “intense” exchange activities on CouchSurfing helps transform online to offline relationships. Their study does not consider exchange activity types, information cues community members use, or the time when they meet new people (i.e., strangers) on the platform and just start the relationship. Rosen, Lafontaine, and Hendrickson (2011) reveal that community members
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who lack face-to-face meetings with other members suffer a lower sense of belongingness. On the flip side, a sense of community belongingness relates positively to greater attendance at offline gatherings. Finally, Wu and Chang (2005) find interactivity and trust are the key drivers of community flow experiences.
3. Framework of the hypotheses This section develops a theoretical framework for achieving offline relationships. To establish offline relationships, travel community members must be willing to interact offline (e.g., meet at events, visit each other in their home towns). Such openness to offline interactions is to form an offline relationship and represents the dependent construct (see Fig. 1). Due to the lack of face-to-face contact, an online community increases perceived relationship risk (Ridings, Gefen, & Arinze, 2002). Uncertainty and equivocation reduction serve as primary goals for online community members (Weiss et al., 2008). Community members obtain information about other member from community profiles and the member's past online behavior. As Ellison et al. (2007) note, online communities allow users to view one another's personal information easily and “identify those who might be useful in some capacity (such as the math major in a required calculus class), thus providing the motivation to activate a latent tie” (Ellison et al., 2007, p. 1162). Missing profile information raises a red flag. Either the person does not care about the community profile, or he is hiding important information. Cue utilization theory posits specific information about a person serves as a proxy for other attributes (Olsen, 1977). People make inferences based on known attributes if specific information is not available (Brown & Dacin, 1997; Gurhan-Canli & Batra, 2004). For example, a person's reputation comes from collective indicators based on a community's value system (Kunz, Schmitt, & Meyer, 2011). Thus, some profile information reflects the person's particular status and esteem in the community (e.g., number of friends or references, and membership years). A person's community reputation affects interactions with other community members. If the focal person feels uncertain about the potential partner's reputation, the interaction's perception is riskier. H1: Willingness to participate in an offline relationship is stronger when the counterpart's community reputation is good.
Fig. 1. Theoretical framework.
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According to the Uncertainty Reduction Theory (Berger & Calabrese, 1975), people who interact go through certain steps and checkpoints to reduce uncertainty and to decide whether they might like each other. Weiss et al. (2008) find information seekers make inferences based on an information provider's past communication style (e.g., response speed, response extent), and the information value that person can offer in the future. In this sense, past communication behavior reduces uncertainty about the counterpart. Communication behavior clues include writing style, message usefulness, response speed, communication frequency, and language barriers. How a person communicates online likely influences uncertainty perceptions. Greater frequency and high-quality conversations arguably cause members to perceive lower risk interacting with another member and critical for developing relationships. H2: Good online communication behavior positively affects a community member's willingness to participate in an offline relationship. In addition to the separate importance of reputation and communication, each variable likely affects the other. For example, good communication influences an ongoing relationship. Reputation may influence the community member's openness to an offline relationship, but poor communication manners likely diminish initial positive impressions. H3: Good online community reputation coupled with good online communication behavior lead to higher travel community member's willingness to participate in an offline relationship. Balance theory implies that individuals have perceptions about themselves and others (Heider, 1946). When two people differ or disagree about a topic, discomfort and imbalance arise. People generally try to avoid this imbalance and make an effort to restore balance. Similarity generally generates a positive feeling of balance. Online travel communities grant users the opportunities to find others with similar interests and characteristics, according to their profile information (Andrews, 2002). When members find common ground, feelings of discomfort and uncertainty should diminish, leading to stronger relationships. Prior research shows that people prefer to rely on advice from others who are physically proximate (Allen, 1977; Forman, Ghose, & Wiesenfeld, 2008; Monge, Rothman, Eisenberg, Miller, & Kirste, 1985) and socially similar (Wagner, Pfeffer, & O'Reilly, 1984; Zenger & Lawrence, 1989). In online contexts, similar interests help predict the advice's usefulness (Andrews, 2002; Ridings & Gefen, 2006). H4: High perceived similarity with an online counterpart increases willingness to participate in an offline relationship. Although community reputation, online communication, and perceived similarity likely exert central influences on potential offline relationships, their influence may be mediated by other constructs in a succeeding evaluation phase. The literature suggests trust and sympathy as two constructs central to relationships. Moorman, Zaltman, and Deshpande (1992, p. 314) define trust as “willingness to rely on an exchange partner in whom one has confidence,” and prior literature derives two dimensions of such trust: competence and benevolence (Casaló, Flavián, & Guinalíu, 2008; Kantsperger & Kunz, 2010). Competence relates to individual perceptions of another party's task-related knowledge and skills and whether the other party can deliver consistent good quality. Benevolence prompts faith in the integrity, goodwill, and honesty of the partner. Trust is important for virtual communities, helping to overcome problems associated with opportunistic behaviors (Gefen, Karahanna, & Straub, 2003; Ridings et al., 2002). This role's importance increases when personal information comes at risk (Bart, Shankar, Sultan, & Urban, 2005; Sanchez-Franco & Rondan-Cataluña, 2010). When a trusting relationship exists, people share and listen to others' knowledge, in addition to being open to exchanges with other parties (Levin & Cross, 2004; Pavlou & Gefen, 2004). Foundations for trusting relationships vary. Examples include cases where good communication rapport exists between parties, the person initiating the contact perceives that the other person maintains a positive community reputation (i.e., a source of credible information), or
the other member possesses similar interests or views. In such cases, the focal person should experience a higher trust in their counterpart due to the positive experience and feel more comfortable initiating contact with the other member. Pleasant, useful, and enriching online interactions between members increase trust and reduce uncertainty and risk perception (Ridings et al., 2002). According to Nahapiet and Ghoshal (1998), parties in trusting relationships tend to engage in more cooperative interactions. Thus, these parties might be motivated and comfortable with an offline interaction. H5: Counterpart trust mediates the effects of online community reputation, online communication behavior, and perceived similarity on community members' willingness to participate in an offline relationship. Perceived sympathy offers a second potential mediator. Perceived sympathy implies in this study a positive emotional response by the community member to the relationship partner in the form that he can relate to a particular person, see her as a likable person and share a common understanding. Shared language and vision often are relevant, moving beyond the written words. This notion also addresses “the acronyms, subtleties, and underlying assumptions that are the staples of day-to-day interactions” (Chiu, Hsu, & Wang, 2006, p. 1878). According to Nahapiet and Ghoshal (1998), a shared language provides an avenue for participants to understand one another and to build a common vocabulary in specific domains. A shared vision “embodies the collective goals and aspirations of the members of an organization” (Tsai & Ghoshal, 1998, p. 497). Because online communities form around common interests, a shared vision should help members become partners. The information that a community member reads from the member's profile and the online communication behavior helps to form a judgment about his or her sympathy for this person. Sympathy as a positive emotional experience leads to a higher tendency to approach the relationship rather than a missing sympathy or negative feelings. H6: Perceived sympathy mediates the effect of online community reputation, online communication behavior, and perceived similarity on the community members' willingness to participate in an offline relationship. 4. Methods To examine the proposed relationships, the present study applied various qualitative and quantitative methods, in the context of the global online travel community CouchSurfing.org. The countries representing the largest numbers of couch surfers are USA, Germany, France, Canada, Britain, Italy, Brazil, and Australia. The genders are distributed almost evenly (53% male, 47% female) and the average age is 28 years (Couchsurfing, 2013). To participate in CouchSurfing, new members register on the website by creating a profile, including one or more photos, and completing personal descriptions (i.e., age, gender, hometown, travel experiences, and willingness to host a person). If open to hosting, a member describes the available living situation and accommodations for guests, along with any further necessary information. The member's profile also lists friends and any references received from others' offline, real-life CouchSurfing experiences. Profiles may display other pertinent information. For example, members may display their level of community integration. A vouching system serves as a testimonial representing a symbol of trust. To be “vouched,” the member must receive recommendations by other members who have received at least three vouches. Usually people vouch for others only after an exceptionally positive offline interaction. A voluntary verification process offered by CouchSurfing.com also requires the member to pay $25 USD to the organization, provide a complete address to verify his or her identity (via verification), and location (via a postcard). Some members organize social events in their cities, to which they invite other members. A respected participant of a local CouchSurfing community may be elected Ambassador for that local city or town.
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4.1. Qualitative pre-study A first, qualitative study served to gauge travelers' thoughts on the CouchSurfing community and their typical usage behavior. Ten members were interviewed using an open-ended questionnaire. Questions related their past experiences with CouchSurfing and offline meetings with other members. Interview results reveal that viewing a member's profile is central to the choice of potential hosts and people to meet. Respondents report that the profile helped them decide whether a person was “trustworthy,” using the reputation symbols in the profile. Members also use the profile to identify common elements with other members. The participants mention trusting people within the CouchSurfing community more than people from other virtual communities. Qualitative study results support the development of adequate manipulations and scales for the main experiments. 4.2. Quantitative main study A field experiment on the CouchSurfing community website helped test the proposed hypotheses. The 293 travel community members worldwide participated in the study. Participants were entered a sweepstakes to win a variety of small prizes. The sample's gender distribution is slightly male (44% female versus 56% male), and community member's reported age approximately represents the CouchSurfing age structure (18–20, 6.8%; 21–25, 38.9%; 26–30, 32.8%; 31–35, 13.3%; 36 and older, 8.2%). The respondent's age averages 27.5 years, and this variable's distribution is near-normal. The most common nationalities in the sample include the United States (15.0%), Egypt (5.8%), Sweden (5.8%), India (5.1%), Britain (4.1%), Poland (4.1%), Italy (3.8%), Canada (3.4%), and Germany (3.4%). Generally, the sample represents the general socio-demographic structure of CouchSurfing, only the Egyptian participation level is atypical (Couchsurfing, 2013). To investigate member community reputation, online communication behavior, and perceived similarity as influencing the tendency to interact with their counterparts offline, this experimental study manipulates three factors, using a 2 (reputation) × 2 (communication) × 2 (similarity) between-subject design. Before the respondents viewed the manipulated scenario stimulus though, they were provided information about socio-demographics, hobbies and interests, usage behavior, experience with offline relationships on CouchSurfing, and travel experiences. Next, participants reviewed a scenario describing a typical CouchSurfing session. During this session, they came in contact with another member who might be someone to visit or host through CouchSurfing. The scenario person's fictitious name is well known in all major cultural contexts. Care was taken to prevent priming by country of origin because names could influence perceived similarity. Comparing the most popular names of 40 countries and consultations with five students from three different continents suggested “Nina” and “Paul” were appropriate names for the scenarios. The person's CouchSurfing profile summary contained manipulated elements for community reputation (high vs. low) and perceived similarity (very similar vs. very distinct). The high reputation CouchSurfing scenario was characterized by verified long-time membership, considerable experience as host and guest, many listed friends, a fully completed profile, several great references and vouchers by others, and demonstrates active involvement in a local CS group. Opposite attributes were developed for the low reputation scenario. The perceived similarity manipulation instead used the questions from the first part of the survey and constructed a pertinent scenario member. The high perceived similarity manipulation featured the same or similar (and low perceived similarity the opposite) gender, age (±1 year), countries visited, and hobbies and interests. Next, participants read the manipulated information related to the typical communication behavior of the scenario person (good versus bad communication). The good online communication behavior condition highlighted (cf. bad online communication behavior) a good
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response rate and response speed to messages, high English language proficiency, strong level of communication organization, and communication clarity of own and counterparts' expectations. Finally, several questions measured tendencies to interact with the scenario person offline, the person's trust level, and degree to which this person was sympathetic. The scale to measure willingness to participate in an offline relationship comes from insights gain through the qualitative pre-study. For example, items such as “I would consider this person as a potential guest at my home” and “It would be interesting to meet this person in real life” measure willingness to participate. For trusting the counterpart, the measures are expertise and credibility subscales from Ohanian (1990). The sympathy measure also feature items adapted from Ohanian, including being “sympathetic,” “interesting,” and “funny.” The manipulation check measured the constructs that reflected attributes associated with the scenario person. Community reputation used “reputable” and “high esteem,” and online communication behavior relied on “communicative,” “polite,” and “respectful.” For perceived similarity, the scale by Obal, Burtch, and Kunz (2011) includes items such as, “We have something in common” and “We are very similar.” To check the manipulation's validity, a mean comparison of the average scale ratings was applied across the manipulated groups. All manipulations show significant differences in the associated construct scale (community reputation: t = 9.03, p b 0.001; online communication behavior: t = 11.24, p b 0.001; perceived similarity: t = 2.17, p b 0.05). All constructs are multi-item scales (seven-point Likert scales; anchored by 1 = “I fully disagree” and 7 = “I fully agree”), adapting previous measures or developed from the qualitative pre-study (see the Appendix A). Constructs were aggregated by the average rating across the items. The constructs reliability results indicate acceptable psychometric properties for all measures and discriminant validity based on the Fornell–Larcker criteria (Fornell & Larcker, 1981).
5. Results Multivariate inference statistical methods test the hypotheses (e.g., mean comparisons, ANOVA, multiple regressions). H1 compares the mean willingness to participate in an offline relationship for participants in the high (Mhigh = 5.13, SD = 1.37) versus low (Mlow = 4.77, SD = 1.16) reputation scenarios. The results show the mean difference is significant according to the unpaired sample t-test (t = 2.38, p b 0.01). Further, participants presented with a scenario with good online communication behavior show significantly higher tendencies to start offline relationships (Mgood = 5.10, SD = 1.31) than participants in the bad communication scenario (Mbad = 4.80, SD = 1.24). The differences between the scenarios is not as large as in the other cases, but the results still are significant and support H2 (t = 1.96, p b 0.05). The test for H3 involves a 2 (high vs. low reputation) × 2 (good vs. bad communication) analysis of variance on willingness to participate in an offline relationship. The results indicate significant main effects (reputation F(1,292) = 5.49, p b 0.05; communication F(1,292) = 4.02, p b 0.05) and significance in the interaction effect between reputation and communication (F(1,292) = 6.27, p b 0.05).The evidence does not support reputation's influence on willingness to participate in an offline relationship if the online communication behavior is not good as well. To test H4, a mean comparison featured willingness to participate in an offline relationship with participants whose profiles were similar (Msimilar = 5.11, SD = 1.29) versus distinct to the scenario person (Mdistinct = 4.79, SD = 1.26). An unpaired sample t-test finds the difference significant (t = 2.15, p b 0.05). To test H4, a mean comparison between the willingness to start an offline relationship of participants with similar profiles (Msimilar = 5.11, SD = 1.29) versus distinct profiles (Mdistinct = 4.79, SD = 1.26).
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Again, the difference show significant differences according to the unpaired sample t-test results (t = 2.15, p b 0.05). Finally, linear regression models (i.e., ordinary least squares) test the proposed influence constructs on willingness to participate in an online relationship (see Table 1). Considering the main effects regression model (see Table 1, Model 1), online communication is the weakest factor among all the significant effects (p b 0.05). This result shows that communication behavior alone does not built up enough trust to engage in an offline relationship. The second model also integrates an interaction effect between reputation and communication (Table 1, Model 2). In this case, all effects are significant (p b 0.05), although a difference in the effect size between reputation and communication on the one side and similarity on the other is visible. Thus perceived similarity seems to be less important than the other factors. The interaction effect between reputation and communication was significant (p b 0.05) as H3 proposes. To test H5 and H6, a mediation analysis features counterpart trust and perceived sympathy (Baron & Kenny, 1986). Regressing the two potential mediator constructs on the independent variables of Model 2 (see Table 1, Models 3 and 4) indicates highly significant effects (p b 0.01), except for similarity. Thus, similar profiles do not necessarily lead to more counterpart trust or sympathy. Next, model 2 depicts trust and perceived sympathy integrated as independent covariates (see model 5 in Table 1). Trust and sympathy might relate strongly, so potentially multi-colinearity could affect results. The variance inflation factor result suggests but no severe problems emerged (VIF b 3). Covariate parameters also are significant (p b 0.01), and the main effect of reputation and communication, as well as their interaction effect, became non-significant (p N 0.05). The main effect of similarity remained significant (p b 0.05) and the effect size of similarity remains almost constant across regression models. Trust and similarity both fully mediate the effects of reputation and communication; similarity does not experience mediation by the two constructs. In addition, the simultaneous procedure that Hayes and Preacher (2011) propose to test for mediation in H5 and H6 supports both hypotheses. The regression models (Models 3–5) exhibit larger r2 values. These results suggest trust and sympathy serve as central constructs for evaluating a counterpart. Finally, controlling for age and gender effects by integrating these factors into the regression, all the estimated effects remain significant on the same level. 6. Discussion Study results show that community member reputation, online communication behavior, and perceived similarity are critical determinants of whether or not travelers engage in offline relationships during their travels. Trust and sympathy are central mediators to relationship development. Although the results are based on peer-to-peer relationships, they offer implications for relationship marketing and management in general.
First, reputation drives consumers' interactions. In an environment lacking face-to-face signals, members rely on alternative proxies to evaluate interaction counterparts. The user profile plays an important role. Companies offering a travel community service must provide a vivid community life that includes a broad range profile attributes. Online users depend on profile information just as people depend on nonverbal communication (e.g., smiles) in the real world. Beyond common attributes (e.g., number of friends, number of recommendations), the provider should suggest special or “exotic” attributes to give travel community members a chance to express and describe themselves more accurately. These special attributes likely prompt the recognition of similarities and thus closer interpersonal relations. People participating in online communities for private or professional reasons cannot underestimate the importance of vivid profiles. Second, this study emphasizes the central role of communication behavior and manners. Internet-based communication demands “netiquette” for effective communication, especially for relationship building with a stranger. The high uncertainty and perceived risk negatively affects relationship development. A stranger's social mistake is difficult to forgive, regardless of how impressive the person's profile might be. This social psychology view on the individual consumer level also has implications for businesses. The right tone and communication manners are critical for a successful social media strategy. Every channel has essential rules to follow. Companies engaging actively with online communities must learn the expected manners and behaviors firsthand. Third, turning an online into an offline relationship requires building trust and gaining some sympathy. These two constructs are very different measures. Trust pertains to an image of being competent and reliable. Arguably, trust is more objective because the evidence is quantifiable. On the other hand, sympathy is a more subjective concept. Quantifying sympathy is more difficult and the final assessment depends on the eye of the beholder. Gaining trust from the community is harder and takes time. Using online communities for short-term sales activities potentially jeopardizes a newly started relationship. Longterm strategies that integrate the nature of communities and slowly build trust provide tremendous opportunities for participating companies. This study's limitations provide opportunities for future research. The present study only manipulates consumer-to-consumer relationships. Researchers also might compare relationship differences between users and toward businesses within an online community environment. Also, data only come from one social networking travel site. CouchSurfing is an interesting platform with many advantages for these research purposes; however, some implications may be valid only on this platform. To generalize the results, data from other (travel) online communities need to be collected. Further research should replicate the current study on other online (community) platforms (e.g., Flickr, TripAdvisor, Facebook, or Google+) to validate the findings. Moreover, this study's cross-sectional data provide both advantages and limitations. Additional
Table 1 Regression models. Model
Model 1
Model 2
Model 3
Model 4
Model 5
Dependent Variable
Willingness for offline relationship
Willingness for offline relationship
Trust
Sympathy
Willingness for offline relationship
Par
t
p
Par
t
p
Par
t
p
Par
t
p
Par
t
p
5.45 .36 .30 .34
36.8 2.5 2.0 2.3
.000 .015 .041 .024
5.62 .71 .66 .33 .72
34.6 3.5 3.2 2.2 2.5
.000 .001 .002 .026 .014
5.60 1.40 1.12 −.10 .58
48.1 9.6 7.5 1.0 2.8
.000 .000 .000 .333 .006
5.23 .98 .84 .10 .65
40.1 6.0 5.1 .9 2.8
.000 .000 .000 .377 .006
1.35 −.19 −.09 .30 .23 .29 .51 28.26 37.2%
3.3 1.0 .5 2.5 .9 3.1 6.0
.001 .331 .643 .013 .359 .002 .000
Intercept High reputation Good communication High similarity High reputation × good communication Trust Sympathy F R2
4.99 4.9%
5.33 6.9%
46.07 39.0%
14.79 17.0%
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Global online travel communities provide a way to transform virtual relationships with strangers into real relationships with friends. Using online communities to enter into relationships with new people (and consumers) around the world is a fascinating endeavor that enriches consumers' lives and opens new horizons for the business world.
research might build a database over a longitudinal time frame and use a consumer panel to observe relationship dynamics over time. Finally, the present study investigates the relationship-building process on a global scope. Future studies should focus on local differences in the relationship-building processes. Appendix A. Measurement items and reliability
1
2
3
4
5
6
Community reputation - Reputable–not reputable - High esteem–low esteem Online communication -Communicative–not communicative -Polite–impolite -Respectful–disrespectful Similarity (Obal et al., 2011) -We have something in common -I can relate to this person -I am very different from this person -We are very similar Trust (Ohanian, 1990) -Trustworthy–untrustworthy -Honest–dishonest -Reliable–unreliable -Experienced–inexperienced -Qualified–unqualified -Knowledgeable–not knowledgeable Sympathy (Ohanian, 1990) -Sympathetic–not sympathetic -Interesting–uninteresting -Funny–not funny Willingness for an offline relationship -I would consider this person as a potential guest at my home -I would consider this person as a potential host on a trip -It would be fun to meet this person at an offline event -It would interesting to meet this person in real life -I can imagine showing this person around my town -I can imagine touring a place with this person on a trip
EFA
CFA
alpha
AVE
CR
1
2
3
4
5
.89 .89
-
.74
.79
-
.89
.86 .91 .91
.75 .87 .87
.85
.83
.82
.72
.91
.82 .81 .78 .90
.73 .72 .70 .92
.85
.77
.78
.48
.49
.87
.87 .80 .84 .79 .83 .83
.86 .75 .83 .73 .78 .78
.90
.79
.86
.85
.80
.51
.89
.85 .85 .86
.77 .76 .79
.82
.77
.73
.70
.68
.55
.75
.88
.88 .85 .85 .87 .82 .81
.85 .82 .83 .86 .77 .76
.92
.81
.89
.48
.39
.57
.49
.58
6
.90
Note: All measures were rated on seven-point Likert scales, anchored by “I strongly disagree” and “I strongly agree.” EFA = exploratory
factor analysis, CFA = confirmatory factor analysis, α = Cronbach's alpha, AVE = average variance extracted, CR = composite reliability. Bold numbers on the diagonal are the square root of the AVE, whereas the numbers below the diagonal are the construct correlation values.
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Contents lists available at ScienceDirect
Journal of Business Research
From virtual travelers to real friends: Relationship-building insights from an online travel community Werner Kunz ⁎, Sukanya Seshadri 1 College of Management, University of Massachusetts, 100 Morrissey Boulevard, Boston, MA 02125, USA
a r t i c l e
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Available online 12 February 2015 Keywords: Online travel community Tourism Social networks Social media Couch surfing
a b s t r a c t The growing trend of online travel communities connects travelers worldwide. This study addresses whether or not these relationships lead to offline interactions. The theoretical framework reflects cue utilization theory, social balance theory, and uncertainty reduction theory. A field experiment examines responses from 293 travel community members. Results show individual reputation, online communication, and perceived similarity among travelers play significant roles in offline relationships. Trust and sympathy among community members mediate this decision process. Study results offer several managerial implications and highlight the importance of vivid and complete participation profile in social media. Moreover, finding the right tone for effective communication in online communities is critical. Published by Elsevier Inc.
1. Introduction The Internet substantially changed the travel industry over the last two decades. Customers easily access information and build new relationships using social media. Previously, companies typically employed traditional marketing channels to build customer relationships (Hennig-Thurau et al., 2010). Today, companies also use social media to build these relationships. Online communities offer a wide variety of possibilities to establish, maintain, and develop relationships between individuals and businesses. For tourists, online travel communities represent a growing trend (Bialski & Batorski, 2007). For example, the CouchSurfing.com web platform is an Internet service that connects travelers worldwide. Online encounters between travelers often lead to offline relationships (e.g., visiting each other's home city). In contrast with online matchmaking sites, a travel online community primarily shares trip experiences, not romantic matches (Whitty, Baker, & Inman, 2007). Prior research examines relationships among online community participants; however, a paucity of research exists regarding how online relationships might lead to offline relationships (Boyd & Ellison, 2008; Foster, Francescucci, & West, 2010; Jahn & Kunz, 2012; Raacke & Bonds-Raacke, 2008). Typically, these communities rely on preexisting, offline relationships (e.g., Facebook, LinkedIn). Online communities likely help users build offline relationships with strangers as well. This study identifies and investigates key conditions necessary for online travel community members to engage in an offline relationships. ⁎ Corresponding author. Tel.: +1 617 291 8736. E-mail addresses: [email protected] (W. Kunz), [email protected] (S. Seshadri). 1 Tel.: +1 617 291 8736.
http://dx.doi.org/10.1016/j.jbusres.2015.01.009 0148-2963/Published by Elsevier Inc.
The proposed framework builds on various theoretical approaches. According to the Uncertainty Reduction Theory (Berger & Calabrese, 1975), individuals follow risk-reducing steps in uncertain situations. Arguably, meeting a stranger online is such an uncertain situation. According to cue utilization theory, specific information serves as a proxy for other attributes (Olsen, 1977). Accessing online communities, individuals look for informational cues to make their decisions. Finally, Heider's (1946) Balance theory implies that individuals try to avoid imbalanced situations. Two people's views differing significantly cause imbalance and create anxiety. A field experiment with 293 CouchSurfing members tests the hypotheses. Results show that the community reputation, online communication behavior, and perceived similarities among travelers play significant roles building potential offline relationships. Trust and sympathy between members also mediate this process. The present study contributes to online community research by explaining the transformation process, from online to offline relationships, in a global travel community. This study provides insights into how to build relationships through online communities that can lead to offline interactions (e.g., location, or event visit). These insights help practitioners to use global online communities more effectively. 2. Research background 2.1. Online community relationships Prior research identifies several motives for community engagement. Findings suggest that social connections (i.e., keeping in touch with friends) and information sharing (e.g., events or gossip) are central reasons for online community engagement (Foster et al., 2010; Jahn & Kunz, 2012; Raacke & Bonds-Raacke, 2008; Ridings & Gefen, 2006;
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Sheldon, 2008). Thus, relationship building and information exchange serve as primary motives for online community participation. The relationship partner or source of information need not be a close friend. Strangers (i.e., no preexisting relationship, or “latent ties”) also serve as valuable information providers. Prior research notes how people perceive information from strangers in general and in online environments (Brown & Reingen, 1987; Constant & Sproull, 1996; Weiss, Lurie, & MacInnis, 2008). For example, Brown and Reingen (1987) find that active information seeking (e.g., initiating an online conversation to obtain product information) likely occurs among weak rather than strong tie sources (close friends). Constant and Sproull (1996) show weak ties give useful advice; such usefulness largely stems from the expertise and experience of the weak tie source. Weiss et al. (2008) suggest past behavior guides information seekers' judgments of information value. A fast response may be more valuable— clarifying information seekers' problems sooner. Thus, strangers become valuable information sources and suggest a need to investigate requirements for building these relationships. 2.2. Online travel communities with strangers Most online communities build on preexisting social relations (Boyd & Ellison, 2008). Ellison, Steinfield, and Lampe (2007) show that Facebook users prefer to search for people with whom they have offline relationships. In contrast, online travel community members are strangers; the community focuses on shared travel experiences. Wang, Yu, and Fesenmaier (2002) describe online travel communities as platforms for travelers to obtain trip information, find travel companions, provide travel tips, or simply share interesting experiences (e.g., CouchSurfing, TripAdvisor, WAYN). Members usually do not know one another in-person. On the other hand, online travel community members often share high levels of personal information (SanchezFranco & Rondan-Cataluña, 2010). In contrast to online matchmaking sites, a travel online community focuses primarily on shared travel experiences rather than romantic relationships (Whitty et al., 2007). Bialski and Batorski (2007) show that “intense” exchange activities on CouchSurfing helps transform online to offline relationships. Their study does not consider exchange activity types, information cues community members use, or the time when they meet new people (i.e., strangers) on the platform and just start the relationship. Rosen, Lafontaine, and Hendrickson (2011) reveal that community members
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who lack face-to-face meetings with other members suffer a lower sense of belongingness. On the flip side, a sense of community belongingness relates positively to greater attendance at offline gatherings. Finally, Wu and Chang (2005) find interactivity and trust are the key drivers of community flow experiences.
3. Framework of the hypotheses This section develops a theoretical framework for achieving offline relationships. To establish offline relationships, travel community members must be willing to interact offline (e.g., meet at events, visit each other in their home towns). Such openness to offline interactions is to form an offline relationship and represents the dependent construct (see Fig. 1). Due to the lack of face-to-face contact, an online community increases perceived relationship risk (Ridings, Gefen, & Arinze, 2002). Uncertainty and equivocation reduction serve as primary goals for online community members (Weiss et al., 2008). Community members obtain information about other member from community profiles and the member's past online behavior. As Ellison et al. (2007) note, online communities allow users to view one another's personal information easily and “identify those who might be useful in some capacity (such as the math major in a required calculus class), thus providing the motivation to activate a latent tie” (Ellison et al., 2007, p. 1162). Missing profile information raises a red flag. Either the person does not care about the community profile, or he is hiding important information. Cue utilization theory posits specific information about a person serves as a proxy for other attributes (Olsen, 1977). People make inferences based on known attributes if specific information is not available (Brown & Dacin, 1997; Gurhan-Canli & Batra, 2004). For example, a person's reputation comes from collective indicators based on a community's value system (Kunz, Schmitt, & Meyer, 2011). Thus, some profile information reflects the person's particular status and esteem in the community (e.g., number of friends or references, and membership years). A person's community reputation affects interactions with other community members. If the focal person feels uncertain about the potential partner's reputation, the interaction's perception is riskier. H1: Willingness to participate in an offline relationship is stronger when the counterpart's community reputation is good.
Fig. 1. Theoretical framework.
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According to the Uncertainty Reduction Theory (Berger & Calabrese, 1975), people who interact go through certain steps and checkpoints to reduce uncertainty and to decide whether they might like each other. Weiss et al. (2008) find information seekers make inferences based on an information provider's past communication style (e.g., response speed, response extent), and the information value that person can offer in the future. In this sense, past communication behavior reduces uncertainty about the counterpart. Communication behavior clues include writing style, message usefulness, response speed, communication frequency, and language barriers. How a person communicates online likely influences uncertainty perceptions. Greater frequency and high-quality conversations arguably cause members to perceive lower risk interacting with another member and critical for developing relationships. H2: Good online communication behavior positively affects a community member's willingness to participate in an offline relationship. In addition to the separate importance of reputation and communication, each variable likely affects the other. For example, good communication influences an ongoing relationship. Reputation may influence the community member's openness to an offline relationship, but poor communication manners likely diminish initial positive impressions. H3: Good online community reputation coupled with good online communication behavior lead to higher travel community member's willingness to participate in an offline relationship. Balance theory implies that individuals have perceptions about themselves and others (Heider, 1946). When two people differ or disagree about a topic, discomfort and imbalance arise. People generally try to avoid this imbalance and make an effort to restore balance. Similarity generally generates a positive feeling of balance. Online travel communities grant users the opportunities to find others with similar interests and characteristics, according to their profile information (Andrews, 2002). When members find common ground, feelings of discomfort and uncertainty should diminish, leading to stronger relationships. Prior research shows that people prefer to rely on advice from others who are physically proximate (Allen, 1977; Forman, Ghose, & Wiesenfeld, 2008; Monge, Rothman, Eisenberg, Miller, & Kirste, 1985) and socially similar (Wagner, Pfeffer, & O'Reilly, 1984; Zenger & Lawrence, 1989). In online contexts, similar interests help predict the advice's usefulness (Andrews, 2002; Ridings & Gefen, 2006). H4: High perceived similarity with an online counterpart increases willingness to participate in an offline relationship. Although community reputation, online communication, and perceived similarity likely exert central influences on potential offline relationships, their influence may be mediated by other constructs in a succeeding evaluation phase. The literature suggests trust and sympathy as two constructs central to relationships. Moorman, Zaltman, and Deshpande (1992, p. 314) define trust as “willingness to rely on an exchange partner in whom one has confidence,” and prior literature derives two dimensions of such trust: competence and benevolence (Casaló, Flavián, & Guinalíu, 2008; Kantsperger & Kunz, 2010). Competence relates to individual perceptions of another party's task-related knowledge and skills and whether the other party can deliver consistent good quality. Benevolence prompts faith in the integrity, goodwill, and honesty of the partner. Trust is important for virtual communities, helping to overcome problems associated with opportunistic behaviors (Gefen, Karahanna, & Straub, 2003; Ridings et al., 2002). This role's importance increases when personal information comes at risk (Bart, Shankar, Sultan, & Urban, 2005; Sanchez-Franco & Rondan-Cataluña, 2010). When a trusting relationship exists, people share and listen to others' knowledge, in addition to being open to exchanges with other parties (Levin & Cross, 2004; Pavlou & Gefen, 2004). Foundations for trusting relationships vary. Examples include cases where good communication rapport exists between parties, the person initiating the contact perceives that the other person maintains a positive community reputation (i.e., a source of credible information), or
the other member possesses similar interests or views. In such cases, the focal person should experience a higher trust in their counterpart due to the positive experience and feel more comfortable initiating contact with the other member. Pleasant, useful, and enriching online interactions between members increase trust and reduce uncertainty and risk perception (Ridings et al., 2002). According to Nahapiet and Ghoshal (1998), parties in trusting relationships tend to engage in more cooperative interactions. Thus, these parties might be motivated and comfortable with an offline interaction. H5: Counterpart trust mediates the effects of online community reputation, online communication behavior, and perceived similarity on community members' willingness to participate in an offline relationship. Perceived sympathy offers a second potential mediator. Perceived sympathy implies in this study a positive emotional response by the community member to the relationship partner in the form that he can relate to a particular person, see her as a likable person and share a common understanding. Shared language and vision often are relevant, moving beyond the written words. This notion also addresses “the acronyms, subtleties, and underlying assumptions that are the staples of day-to-day interactions” (Chiu, Hsu, & Wang, 2006, p. 1878). According to Nahapiet and Ghoshal (1998), a shared language provides an avenue for participants to understand one another and to build a common vocabulary in specific domains. A shared vision “embodies the collective goals and aspirations of the members of an organization” (Tsai & Ghoshal, 1998, p. 497). Because online communities form around common interests, a shared vision should help members become partners. The information that a community member reads from the member's profile and the online communication behavior helps to form a judgment about his or her sympathy for this person. Sympathy as a positive emotional experience leads to a higher tendency to approach the relationship rather than a missing sympathy or negative feelings. H6: Perceived sympathy mediates the effect of online community reputation, online communication behavior, and perceived similarity on the community members' willingness to participate in an offline relationship. 4. Methods To examine the proposed relationships, the present study applied various qualitative and quantitative methods, in the context of the global online travel community CouchSurfing.org. The countries representing the largest numbers of couch surfers are USA, Germany, France, Canada, Britain, Italy, Brazil, and Australia. The genders are distributed almost evenly (53% male, 47% female) and the average age is 28 years (Couchsurfing, 2013). To participate in CouchSurfing, new members register on the website by creating a profile, including one or more photos, and completing personal descriptions (i.e., age, gender, hometown, travel experiences, and willingness to host a person). If open to hosting, a member describes the available living situation and accommodations for guests, along with any further necessary information. The member's profile also lists friends and any references received from others' offline, real-life CouchSurfing experiences. Profiles may display other pertinent information. For example, members may display their level of community integration. A vouching system serves as a testimonial representing a symbol of trust. To be “vouched,” the member must receive recommendations by other members who have received at least three vouches. Usually people vouch for others only after an exceptionally positive offline interaction. A voluntary verification process offered by CouchSurfing.com also requires the member to pay $25 USD to the organization, provide a complete address to verify his or her identity (via verification), and location (via a postcard). Some members organize social events in their cities, to which they invite other members. A respected participant of a local CouchSurfing community may be elected Ambassador for that local city or town.
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4.1. Qualitative pre-study A first, qualitative study served to gauge travelers' thoughts on the CouchSurfing community and their typical usage behavior. Ten members were interviewed using an open-ended questionnaire. Questions related their past experiences with CouchSurfing and offline meetings with other members. Interview results reveal that viewing a member's profile is central to the choice of potential hosts and people to meet. Respondents report that the profile helped them decide whether a person was “trustworthy,” using the reputation symbols in the profile. Members also use the profile to identify common elements with other members. The participants mention trusting people within the CouchSurfing community more than people from other virtual communities. Qualitative study results support the development of adequate manipulations and scales for the main experiments. 4.2. Quantitative main study A field experiment on the CouchSurfing community website helped test the proposed hypotheses. The 293 travel community members worldwide participated in the study. Participants were entered a sweepstakes to win a variety of small prizes. The sample's gender distribution is slightly male (44% female versus 56% male), and community member's reported age approximately represents the CouchSurfing age structure (18–20, 6.8%; 21–25, 38.9%; 26–30, 32.8%; 31–35, 13.3%; 36 and older, 8.2%). The respondent's age averages 27.5 years, and this variable's distribution is near-normal. The most common nationalities in the sample include the United States (15.0%), Egypt (5.8%), Sweden (5.8%), India (5.1%), Britain (4.1%), Poland (4.1%), Italy (3.8%), Canada (3.4%), and Germany (3.4%). Generally, the sample represents the general socio-demographic structure of CouchSurfing, only the Egyptian participation level is atypical (Couchsurfing, 2013). To investigate member community reputation, online communication behavior, and perceived similarity as influencing the tendency to interact with their counterparts offline, this experimental study manipulates three factors, using a 2 (reputation) × 2 (communication) × 2 (similarity) between-subject design. Before the respondents viewed the manipulated scenario stimulus though, they were provided information about socio-demographics, hobbies and interests, usage behavior, experience with offline relationships on CouchSurfing, and travel experiences. Next, participants reviewed a scenario describing a typical CouchSurfing session. During this session, they came in contact with another member who might be someone to visit or host through CouchSurfing. The scenario person's fictitious name is well known in all major cultural contexts. Care was taken to prevent priming by country of origin because names could influence perceived similarity. Comparing the most popular names of 40 countries and consultations with five students from three different continents suggested “Nina” and “Paul” were appropriate names for the scenarios. The person's CouchSurfing profile summary contained manipulated elements for community reputation (high vs. low) and perceived similarity (very similar vs. very distinct). The high reputation CouchSurfing scenario was characterized by verified long-time membership, considerable experience as host and guest, many listed friends, a fully completed profile, several great references and vouchers by others, and demonstrates active involvement in a local CS group. Opposite attributes were developed for the low reputation scenario. The perceived similarity manipulation instead used the questions from the first part of the survey and constructed a pertinent scenario member. The high perceived similarity manipulation featured the same or similar (and low perceived similarity the opposite) gender, age (±1 year), countries visited, and hobbies and interests. Next, participants read the manipulated information related to the typical communication behavior of the scenario person (good versus bad communication). The good online communication behavior condition highlighted (cf. bad online communication behavior) a good
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response rate and response speed to messages, high English language proficiency, strong level of communication organization, and communication clarity of own and counterparts' expectations. Finally, several questions measured tendencies to interact with the scenario person offline, the person's trust level, and degree to which this person was sympathetic. The scale to measure willingness to participate in an offline relationship comes from insights gain through the qualitative pre-study. For example, items such as “I would consider this person as a potential guest at my home” and “It would be interesting to meet this person in real life” measure willingness to participate. For trusting the counterpart, the measures are expertise and credibility subscales from Ohanian (1990). The sympathy measure also feature items adapted from Ohanian, including being “sympathetic,” “interesting,” and “funny.” The manipulation check measured the constructs that reflected attributes associated with the scenario person. Community reputation used “reputable” and “high esteem,” and online communication behavior relied on “communicative,” “polite,” and “respectful.” For perceived similarity, the scale by Obal, Burtch, and Kunz (2011) includes items such as, “We have something in common” and “We are very similar.” To check the manipulation's validity, a mean comparison of the average scale ratings was applied across the manipulated groups. All manipulations show significant differences in the associated construct scale (community reputation: t = 9.03, p b 0.001; online communication behavior: t = 11.24, p b 0.001; perceived similarity: t = 2.17, p b 0.05). All constructs are multi-item scales (seven-point Likert scales; anchored by 1 = “I fully disagree” and 7 = “I fully agree”), adapting previous measures or developed from the qualitative pre-study (see the Appendix A). Constructs were aggregated by the average rating across the items. The constructs reliability results indicate acceptable psychometric properties for all measures and discriminant validity based on the Fornell–Larcker criteria (Fornell & Larcker, 1981).
5. Results Multivariate inference statistical methods test the hypotheses (e.g., mean comparisons, ANOVA, multiple regressions). H1 compares the mean willingness to participate in an offline relationship for participants in the high (Mhigh = 5.13, SD = 1.37) versus low (Mlow = 4.77, SD = 1.16) reputation scenarios. The results show the mean difference is significant according to the unpaired sample t-test (t = 2.38, p b 0.01). Further, participants presented with a scenario with good online communication behavior show significantly higher tendencies to start offline relationships (Mgood = 5.10, SD = 1.31) than participants in the bad communication scenario (Mbad = 4.80, SD = 1.24). The differences between the scenarios is not as large as in the other cases, but the results still are significant and support H2 (t = 1.96, p b 0.05). The test for H3 involves a 2 (high vs. low reputation) × 2 (good vs. bad communication) analysis of variance on willingness to participate in an offline relationship. The results indicate significant main effects (reputation F(1,292) = 5.49, p b 0.05; communication F(1,292) = 4.02, p b 0.05) and significance in the interaction effect between reputation and communication (F(1,292) = 6.27, p b 0.05).The evidence does not support reputation's influence on willingness to participate in an offline relationship if the online communication behavior is not good as well. To test H4, a mean comparison featured willingness to participate in an offline relationship with participants whose profiles were similar (Msimilar = 5.11, SD = 1.29) versus distinct to the scenario person (Mdistinct = 4.79, SD = 1.26). An unpaired sample t-test finds the difference significant (t = 2.15, p b 0.05). To test H4, a mean comparison between the willingness to start an offline relationship of participants with similar profiles (Msimilar = 5.11, SD = 1.29) versus distinct profiles (Mdistinct = 4.79, SD = 1.26).
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Again, the difference show significant differences according to the unpaired sample t-test results (t = 2.15, p b 0.05). Finally, linear regression models (i.e., ordinary least squares) test the proposed influence constructs on willingness to participate in an online relationship (see Table 1). Considering the main effects regression model (see Table 1, Model 1), online communication is the weakest factor among all the significant effects (p b 0.05). This result shows that communication behavior alone does not built up enough trust to engage in an offline relationship. The second model also integrates an interaction effect between reputation and communication (Table 1, Model 2). In this case, all effects are significant (p b 0.05), although a difference in the effect size between reputation and communication on the one side and similarity on the other is visible. Thus perceived similarity seems to be less important than the other factors. The interaction effect between reputation and communication was significant (p b 0.05) as H3 proposes. To test H5 and H6, a mediation analysis features counterpart trust and perceived sympathy (Baron & Kenny, 1986). Regressing the two potential mediator constructs on the independent variables of Model 2 (see Table 1, Models 3 and 4) indicates highly significant effects (p b 0.01), except for similarity. Thus, similar profiles do not necessarily lead to more counterpart trust or sympathy. Next, model 2 depicts trust and perceived sympathy integrated as independent covariates (see model 5 in Table 1). Trust and sympathy might relate strongly, so potentially multi-colinearity could affect results. The variance inflation factor result suggests but no severe problems emerged (VIF b 3). Covariate parameters also are significant (p b 0.01), and the main effect of reputation and communication, as well as their interaction effect, became non-significant (p N 0.05). The main effect of similarity remained significant (p b 0.05) and the effect size of similarity remains almost constant across regression models. Trust and similarity both fully mediate the effects of reputation and communication; similarity does not experience mediation by the two constructs. In addition, the simultaneous procedure that Hayes and Preacher (2011) propose to test for mediation in H5 and H6 supports both hypotheses. The regression models (Models 3–5) exhibit larger r2 values. These results suggest trust and sympathy serve as central constructs for evaluating a counterpart. Finally, controlling for age and gender effects by integrating these factors into the regression, all the estimated effects remain significant on the same level. 6. Discussion Study results show that community member reputation, online communication behavior, and perceived similarity are critical determinants of whether or not travelers engage in offline relationships during their travels. Trust and sympathy are central mediators to relationship development. Although the results are based on peer-to-peer relationships, they offer implications for relationship marketing and management in general.
First, reputation drives consumers' interactions. In an environment lacking face-to-face signals, members rely on alternative proxies to evaluate interaction counterparts. The user profile plays an important role. Companies offering a travel community service must provide a vivid community life that includes a broad range profile attributes. Online users depend on profile information just as people depend on nonverbal communication (e.g., smiles) in the real world. Beyond common attributes (e.g., number of friends, number of recommendations), the provider should suggest special or “exotic” attributes to give travel community members a chance to express and describe themselves more accurately. These special attributes likely prompt the recognition of similarities and thus closer interpersonal relations. People participating in online communities for private or professional reasons cannot underestimate the importance of vivid profiles. Second, this study emphasizes the central role of communication behavior and manners. Internet-based communication demands “netiquette” for effective communication, especially for relationship building with a stranger. The high uncertainty and perceived risk negatively affects relationship development. A stranger's social mistake is difficult to forgive, regardless of how impressive the person's profile might be. This social psychology view on the individual consumer level also has implications for businesses. The right tone and communication manners are critical for a successful social media strategy. Every channel has essential rules to follow. Companies engaging actively with online communities must learn the expected manners and behaviors firsthand. Third, turning an online into an offline relationship requires building trust and gaining some sympathy. These two constructs are very different measures. Trust pertains to an image of being competent and reliable. Arguably, trust is more objective because the evidence is quantifiable. On the other hand, sympathy is a more subjective concept. Quantifying sympathy is more difficult and the final assessment depends on the eye of the beholder. Gaining trust from the community is harder and takes time. Using online communities for short-term sales activities potentially jeopardizes a newly started relationship. Longterm strategies that integrate the nature of communities and slowly build trust provide tremendous opportunities for participating companies. This study's limitations provide opportunities for future research. The present study only manipulates consumer-to-consumer relationships. Researchers also might compare relationship differences between users and toward businesses within an online community environment. Also, data only come from one social networking travel site. CouchSurfing is an interesting platform with many advantages for these research purposes; however, some implications may be valid only on this platform. To generalize the results, data from other (travel) online communities need to be collected. Further research should replicate the current study on other online (community) platforms (e.g., Flickr, TripAdvisor, Facebook, or Google+) to validate the findings. Moreover, this study's cross-sectional data provide both advantages and limitations. Additional
Table 1 Regression models. Model
Model 1
Model 2
Model 3
Model 4
Model 5
Dependent Variable
Willingness for offline relationship
Willingness for offline relationship
Trust
Sympathy
Willingness for offline relationship
Par
t
p
Par
t
p
Par
t
p
Par
t
p
Par
t
p
5.45 .36 .30 .34
36.8 2.5 2.0 2.3
.000 .015 .041 .024
5.62 .71 .66 .33 .72
34.6 3.5 3.2 2.2 2.5
.000 .001 .002 .026 .014
5.60 1.40 1.12 −.10 .58
48.1 9.6 7.5 1.0 2.8
.000 .000 .000 .333 .006
5.23 .98 .84 .10 .65
40.1 6.0 5.1 .9 2.8
.000 .000 .000 .377 .006
1.35 −.19 −.09 .30 .23 .29 .51 28.26 37.2%
3.3 1.0 .5 2.5 .9 3.1 6.0
.001 .331 .643 .013 .359 .002 .000
Intercept High reputation Good communication High similarity High reputation × good communication Trust Sympathy F R2
4.99 4.9%
5.33 6.9%
46.07 39.0%
14.79 17.0%
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Global online travel communities provide a way to transform virtual relationships with strangers into real relationships with friends. Using online communities to enter into relationships with new people (and consumers) around the world is a fascinating endeavor that enriches consumers' lives and opens new horizons for the business world.
research might build a database over a longitudinal time frame and use a consumer panel to observe relationship dynamics over time. Finally, the present study investigates the relationship-building process on a global scope. Future studies should focus on local differences in the relationship-building processes. Appendix A. Measurement items and reliability
1
2
3
4
5
6
Community reputation - Reputable–not reputable - High esteem–low esteem Online communication -Communicative–not communicative -Polite–impolite -Respectful–disrespectful Similarity (Obal et al., 2011) -We have something in common -I can relate to this person -I am very different from this person -We are very similar Trust (Ohanian, 1990) -Trustworthy–untrustworthy -Honest–dishonest -Reliable–unreliable -Experienced–inexperienced -Qualified–unqualified -Knowledgeable–not knowledgeable Sympathy (Ohanian, 1990) -Sympathetic–not sympathetic -Interesting–uninteresting -Funny–not funny Willingness for an offline relationship -I would consider this person as a potential guest at my home -I would consider this person as a potential host on a trip -It would be fun to meet this person at an offline event -It would interesting to meet this person in real life -I can imagine showing this person around my town -I can imagine touring a place with this person on a trip
EFA
CFA
alpha
AVE
CR
1
2
3
4
5
.89 .89
-
.74
.79
-
.89
.86 .91 .91
.75 .87 .87
.85
.83
.82
.72
.91
.82 .81 .78 .90
.73 .72 .70 .92
.85
.77
.78
.48
.49
.87
.87 .80 .84 .79 .83 .83
.86 .75 .83 .73 .78 .78
.90
.79
.86
.85
.80
.51
.89
.85 .85 .86
.77 .76 .79
.82
.77
.73
.70
.68
.55
.75
.88
.88 .85 .85 .87 .82 .81
.85 .82 .83 .86 .77 .76
.92
.81
.89
.48
.39
.57
.49
.58
6
.90
Note: All measures were rated on seven-point Likert scales, anchored by “I strongly disagree” and “I strongly agree.” EFA = exploratory
factor analysis, CFA = confirmatory factor analysis, α = Cronbach's alpha, AVE = average variance extracted, CR = composite reliability. Bold numbers on the diagonal are the square root of the AVE, whereas the numbers below the diagonal are the construct correlation values.
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