Understanding communication types on travel information sharing in social media: A transactive memory systems perspective

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Contents lists available at ScienceDirect

Telematics and Informatics journal homepage: www.elsevier.com/locate/tele 6 7

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Understanding communication types on travel information sharing in social media: A transactive memory systems perspective

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Namho Chung 1, SeungJae Lee ⇑, Heejeong Han 1

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College of Hotel and Tourism Management, Kyung Hee University, Republic of Korea

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a r t i c l e

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Article history: Received 5 September 2014 Received in revised form 30 January 2015 Accepted 4 February 2015 Available online xxxx Keywords: Transactive memory systems Formal communication Informal communication Social media Travel information sharing

a b s t r a c t Transactive memory systems (TMS) is now an important factor in information sharing. This study adopted TMS to understand the intentions of tourists who share travel information in social media. Tourist’s information is shared either by the formal or informal types of communication. To understand their intentions in using social media, this paper concerns the three measurements employed by TMS; that is, specialization, credibility, and coordination; and the potential tourists’ perception of communication types and TMS are measured in this research model. This research employed empirical analysis using structural equation modeling upon 309 Korean users who shared travel information on social media to attain three findings. First, that the users’ perception of formal communication has a positive influence on the specialization, credibility, and coordination of social media. Second, that the perception of informal communication has a positive effect on the credibility and coordination of social media. Third, that three sub-dimensions of TMS affect intentions to share travel information, which provides us with significant theoretical and practical implications. Ó 2015 Published by Elsevier Ltd.

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1. Introduction

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In Korea, municipal governments, tourism stakeholders, and the Korea National Tourism Organization, have adopted social media such as Facebook and Twitter for real-time communication with tourists. Kaplan and Haenlein (2010) define social media as ‘‘Internet-based applications complex that build on the ideological and technological foundations of the Web 2.0,’’ which allows its users to generate contents such as articles, pictures, drawings, videos, etc. and build relationships via exchange material and ideas. Social media can be categorized into microblogs (e.g. personal blogs or Twitter), online communities or social networking sites (SNS) (e.g. Facebook or Tripadvisor.com), pictures or video sharing applications (e.g. Flickr, YouTube), and dictionary-type applications like Wikipedia (Leung et al., 2013; Parra-López et al., 2011). The inherent characteristics of tourism products, being invisible and experience-oriented, make travel information essential in reducing the risk of purchasing tourism products (Kim et al., 2007; Tan and Chen, 2012). For potential tourists, travel information is crucial to decide where to go, how to go, where to stay, what to eat and what to do at the destination (Gursoy and McCleary, 2004). Furthermore, these potential tourists, when they acquire travel information, become self-advisors by

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⇑ Corresponding author. Tel.: +82 2 964 9387; fax: +82 2 964 2537. 1

E-mail addresses: [email protected] (N. Chung), [email protected] (S. Lee), [email protected] (H. Han). Tel.: +82 2 964 2353; fax: +82 2 964 2537.

http://dx.doi.org/10.1016/j.tele.2015.02.002 0736-5853/Ó 2015 Published by Elsevier Ltd.

Please cite this article in press as: Chung, N., et al. Understanding communication types on travel information sharing in social media: A transactive memory systems perspective. Telemat. Informat. (2015), http://dx.doi.org/10.1016/j.tele.2015.02.002

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referencing their own experience and knowledge, but when the internal source does not provide enough information, they use external sources of information such as the Internet, books, travel agencies, or acquaintances (Gursoy and McCleary, 2004; Kim et al., 2007; Xiang et al., 2008). The various social media, in particular, enables users to publically exchange information, opinions, and experiences, and is actively considered by potential tourists as external information sources (Kaplan and Haenlein, 2010; Xiang and Gretzel, 2010). Social media harbors travel information provided by tourism-related government organizations, companies, and individuals, and it allows the users to search for or share travel information. In terms of travel information, social media becomes an external source of information where users combine what they know with what they have acquired. Therefore, social media is an information repository combining one’s own knowledge and information shared online, and acts as a transactive memory system (TMS) in communication (Wegner, 1987). TMS refers to a meta-memory that develops collectively within a group, with a process of encoding, storage, and retrieval, and updates knowledge via communicative process (Wegner, 1987). Through TMS, members know who knows what and how to access expertise (Jarvenpaa and Majchrzak, 2008). There are two forms of communication in social media: formal and informal (Dittrich and Giuffrida, 2011; Kim and Benbasat, 2012). Both forms affect constructing TMS (Akgün et al., 2005; Choi et al., 2010; Kanawattanachai and Yoo, 2007), and are key factors of information transfer in travel information sharing (Szulanski, 1996). Despite growing recognition of TMS in social media, that is, the member’s strong intention for knowledge-sharing and their increasing reliance on social media, prior studies are limited on the role of communication types and the development of TMS. Moreover, the precise roles of TMS and information sharing in social media have not been fully explored in the academic literature. Thus further empirical research on communication types amongst users in social media is essential for understanding TMS in travel information. Generally, potential tourists are now interacting by using both formal and informal type of communication and developing group-knowledge in virtual worlds. The various social media are both information sharing channels and platforms for the ever-growing shared store of knowledge. In searching for or sharing travel information, potential tourists utilize both communication types in their endeavor to strengthen TMS. Understanding the intent of potential tourists’ travel-informationsharing is necessary to understand their communication types, and to view social media from the TMS perspective; the recognition of communication types (the formal or the informal communication in social media) will affect TMS recognition, which will further affect travel-information-sharing behavior. This research identifies the user’s intention to share travel information on social media in terms of TMS. The objectives of this paper are as follows. First, to identify how formal communication or informal communication affects TMS sub-dimensions (specialization, credibility, and coordination). Second, to confirm the effect of TMS sub-dimensions (specialization, credibility, and coordination) on travel information sharing. This research will improve the understanding of social media’s role in travel information sharing via identifying the relationship between formal or informal communication and TMS (i.e., specialization, credibility, and coordination), and travelinformation-sharing. It is further expected the research will have theoretical and practical implications.

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2. Theoretical background

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2.1. Transactive memory systems

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TMS is a set of memory system that occurs in combination with communication between individuals (Wegner et al., 1985). In other words, TMS is a collection of individuals, their memory systems, and the communication occurring among them (Wegner, 1987), and was introduced to explain intimate couples’ ability to negotiate knowledge when faced with disruptions in their group memory (Wegner, 1987; Zhong et al., 2012). Transactive memory exists as a collective system of knowledge, and it entails each individual’s memory system as well as the details of the interactions during communication (Wegner, 1987). Therefore, individual’s memory systems join to form the collective information processing systems, ultimately giving members access to a knowledge base more complex and potentially more effective than each individual constituent possesses (Wegner, 1987). Previous researchers have chosen specialization, credibility, and coordination as criteria in gauging TMS (Li and Huang, 2013). Specialization is the degree of the users’ expertise; credibility is the accuracy of the information and trust between users; coordination describes users’ ability to cooperate in building their shared understanding (Akgün et al., 2005; Li and Huang, 2013). In short, TMS requires members’ expertise, accurate information, and the users’ collaborative efforts on the subject. TMS reinforces members’ performance, and is built up by communication, feedback, and learning (Akgün et al., 2005; Choi et al., 2010; Kanawattanachai and Yoo, 2007). TMS has drawn attention from researchers because it provides understanding of knowledge utilization and coordination among groups of people. TMS is also as an important antecedent variable for knowledge sharing (Choi et al., 2010). Individuals can strengthen their memory not only with their own knowledge but also with the knowledge of others (Li and Huang, 2013). In particular, researchers have emphasized the role of TMS from the perspective of team performance (Akgün et al., 2005; Choi et al., 2010; Li and Huang, 2013; Zhong et al., 2012). Akgün et al. (2005) suggested that TMS is suitable for the

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understanding of task-specific expertise and knowledge for team-projects in the context of new product development. Choi et al. (2010) explained that TMS provides meta-knowledge and formed the basis of effective knowledge sharing, which lead to knowledge sharing and application within a team. They insisted that team performance could be enhanced through knowledge sharing and application. The results of Li and Huang (2013) suggested that TMS reinforces exploitative and exploratory learning in the context of team learning. According to Zhong et al. (2012), TMS is an effective knowledgeprocessing technique and can lead to improved team outcomes. They examined the relationship between TMS and team outcomes and the results showed that TMS had an impact on team outcomes through team efficacy. In sum, a review of previous studies suggests that TMS strengthens performance through knowledge sharing, team learning, and knowledge processing within a team. In acquiring travel information, potential tourists use their personal experience and knowledge, but when this proves insufficient, they reach out to external sources such as the Internet, travel agencies, books, and acquaintances (Gursoy and McCleary, 2004; Kim et al., 2007; Xiang et al., 2008). For example, users of social media can link or forward articles that other people have written on Facebook or Twitter in order to share knowledge. Furthermore, social media is an external source of travel information (Kaplan and Haenlein, 2010; Xiang and Gretzel, 2010). Thus, social media is deemed as a kind of team for sharing tourism information and can be provisioned as TMS.

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2.2. Types of communication in social media

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Smooth communication is considered a success factor of knowledge transfer (Szulanski, 1996), and communication is considered as being important in facilitating the sharing of travel information. In social media, which connects one to another, users generate or share knowledge-based formal or informal communication (Kim and Benbasat, 2012). Travel information can be shared via articles, personal messages, or threads; these can be categorized as formal communication and informal communication. Dittrich and Giuffrida (2011) define formal communication as formatted and controlled; informal communication is defined as free and mercurial. According to Kim and Benbasat (2012), formal communication has utilitarian values and structured contents, whereas informal communication provides hedonic values and unstructured contents. Table 1 summarizes communication type (formal vs. informal), as defined in this paper. In social media, messages on bulletin boards with word limits and regular formats can be seen as formal communication, while replies or instant messages can be seen as informal communication. Figs. 1 and 2 show sample screen shots of formal and informal communication in social media. Since effective communication is associated with knowledge management in a community, some studies on TMS-related communication have been given attention (e.g., Akgün et al., 2005; Kanawattanachai and Yoo, 2007). For example, since communication among members enables both them and us to know who has which knowledge, Akgün et al. (2005) found that effective communication is a critical factor of forming TMS within a community. Kanawattanachai and Yoo (2007) also insisted that task-oriented communications formed both expertise location and cognition-based trust of TMS, because effective and cognitive communication leads to learning about other members’ knowledge and to forming member’s belief about the other’s expertise within a virtual team. Tourism products, when compared with other merchandise products, are high-involvement and can be only consumed by experience. As they are immaterial or intangible, they are accompanied by higher risk at purchase (Tan and Chen, 2012). As a consequence, potential tourists tend to search and collect travel information to minimize the risk. Through social media, potential tourists can express their opinions, share information about tourism destinations, attractions, and activities, and make decisions based on this knowledge exchange (Ku, 2012). Social media users can share and post travel information via bulletin boards, messages, instant messages, and replies. Until now, little research has been conducted on social media users’ intention to share travel information followed by types of communication. However, as potential tourists now share travel information via both formal and informal communication, there is a need to focus on this phenomenon.

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2.3. Sharing travel information via social media

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When potential tourists are planning a travel destination, accommodation, and means of travel, they require accurate information (Gursoy and McCleary, 2004). Naturally, potential tourists consult internal sources of information such as their own experiences and accumulated knowledge, and then turn to external sources if the internal sources prove insufficient (Gursoy and McCleary, 2004). Advanced Internet technology provides potential tourists online places in the virtual community such as social media, where they search for information or ask other users’ opinion. For potential tourists, social media is an external source of information, and plays the role of collective intelligence (Kang and Schuett, 2013; Leung et al., 2013; Litvin et al., 2008; Xiang and Gretzel, 2010). Thus potential tourists in the pre-consumption stage use social media by sharing travel information and re-sharing what others have already posted; they choose destinations or receive feedback and then assess tourism products. During the trip stage, social media is used to describe the tourists’ impressions or to search for more specific information or activities at the destination. After they have consumed the tourism product, tourists use social media to post their experiences, to educate others or to reminisce (Parra-López et al., 2012). In other words, social media is directly related to tourist behaviors such as planning in the pre-travel stage, sharing experience in the on-travel stage, and recommending tourism products in the after-travel stage (Chung and Buhalis, 2008; Parra-López et al., 2011).

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Table 1 Summary of two type of communication on social media. Classification

Formal communication

Informal communication

Ties to information source Information sharing behavior Personal Goal Feature of Contents

Formal relationship One-way need-to-know Approach Utilitarian value by leaning from expert Structured contents

Informal relationship Two-way need-to-share Approach Hedonic value through friendship/sharing interest Unstructured contents

Modified from Kim and Benbasat (2012).

Fig. 1. Sample screenshot of formal communication in social media.

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In the field of tourism, the social media on information sharing (Huang et al., 2010; Kang and Schuett, 2013; Lee et al., 2012), information searching (Xiang and Gretzel, 2010), intention of using (Parra-López et al., 2011; Yoo and Gretzel, 2011), and marketing (Chan and Guillet, 2011) have been extensively researched. While the research on using social media as marketing constitutes one pillar of the literature on tourism, the research on social media as a source of information with the only purpose of sharing information is another. Huang et al. (2010) studied the factors that encourage and discourage travel information sharing on social networking sites such as Facebook. Kang and Schuett (2013) investigated the decisive factors in sharing travel experiences on travel websites, delving into how social factors affect perceived enjoyment, travel planning and travel experiences through the use of social media. Lee et al. (2012) explored the effect of community identification on information-sharing in online communities. Previous researchers have studied travel information sharing as a personal or social phenomenon (e.g. Huang et al., 2010; Kang and Schuett, 2013; Lee et al., 2012). Although social media functions as a collective intelligence, studying it as types of communication or as TMS formed by the communication to understand travel information sharing was rather rare. This paper applies the characteristics of TMS and the types of communication to understanding how an individual uses social media to share travel information. Moreover, this study has defined the intention to travel information sharing as the willingness to share travel information via social media, following the definition of information sharing intention (Bock et al., 2005), and argues that the intention to travel information sharing can be generated by recognizing the types of communication in social media as a factor that builds TMS.

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3. Research model and hypothesis development

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The objective of this study is to understand the intention to share travel information in social media through recognition of communication types and TMS. Previous studies have shown that communication in social media can be categorized as

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Fig. 2. Sample screenshot of informal communication in social media.

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formal and informal, and the recognition of communication types allows prospective travelers to identify social media as a TMS. TMS sub-dimensions such as specialization, credibility, and coordination can affect the intention to share travel information. Thus, this study will focus on the formal and informal communication in terms of travel information sharing, and the intermediary role of TMS. The configured research model is depicted in Fig. 3.

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3.1. Communication types and TMS

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Following the classification by Dittrich and Giuffrida (2011) and Kim and Benbasat (2012), this study will treat formal communication in social media as structured communication such as a formatted article, and informal communication as unformatted communication used, for example, in instant messages or replies. The three sub-dimensions of TMS – specialization, credibility, coordination – can be defined as follows (Akgün et al., 2005; Wegner, 1987; Li and Huang, 2013). Specialization is the degree of distinction and professionalism of the travel information provided by the users; credibility is the level of confidence that the users have in the provided travel information; coordination is the shared understanding between the users of the social media. Communication can be an important factor in the intention to share travel information because smooth communication is crucial in knowledge transfer (Szulanski, 1996). Moreover, communication among the members of a community is a critical element in establishing TMS (Akgün et al., 2005; Kanawattanachai and Yoo, 2007). Akgün et al.’s (2005) study of the knowledge networks for product-developing projects claimed that formal and/or informal communication among members is essential for TMS development. They have stated that formal communication facilitates the establishment of TMS on social media, because documented and formatted communication can be easily understood and spread. TMS allows users to understand ‘‘who knows what’’ in their community (Wegner, 1987). In social media, formal communication on the bulletin board (e.g. Facebook wall) is readily available to users because they can see who posted and where to find travel information. Thus prospective tourists can evaluate and modify travel information in social media through formal communications. In other words, effective and fluent formal communication will enhance for social media users to know others’ expertise or to trust their information or to facilitate a shared interest in social media. Thus the following hypotheses can be made:

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H1a. Formal communication will have a positive effect on specialization of TMS.

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H1b. Formal communication will have a positive effect on credibility of TMS.

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H1c. Formal communication will have a positive effect on coordination of TMS. Please cite this article in press as: Chung, N., et al. Understanding communication types on travel information sharing in social media: A transactive memory systems perspective. Telemat. Informat. (2015), http://dx.doi.org/10.1016/j.tele.2015.02.002

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Formal Communication

H1a

Specialization

H1b

H3

H1c

Credibility

H4

Travel information sharing

H2a

H2b H5

Informal Communication

H2c

Type of communication

Coordination

Transactive Memory System

Outcome

Fig. 3. Research model. 220

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While formal communication, as it is open to the public, may contain only formatted contents, informal communication based on rapport may compensate for its informal appearance by a more professional and deeper information sharing. Social media is characterized by effective and quick communication via replies or instant messages (Mayfield, 2008). This means that social media users share travel information through both formal and informal communication. Kanawattanachai and Yoo (2007) have proved that business communication affects TMS established by the level of professional knowledge, the credibility of the recognition basis, and coordination. Similarly, it is expected that informal communication will have a similar effect on TMS. Active informal communication such as replies or instant messages allows users to communicate, share, update and adjust travel information in real time. It strengthens the development of TMS as a source of external knowledge. Thus the informal communication fosters the establishment of TMS in social media. The following hypotheses can therefore be made:

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H2a. Informal communication will have a positive effect on specialization of TMS.

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H2b. Informal communication will have a positive effect on credibility of TMS.

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H2c. Informal communication will have a positive effect on coordination of TMS.

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3.2. TMS and intention to share travel information

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Travel information sharing is defined by the confidence of the users who are willing to share travel information (Bock et al., 2005). Travel information sharing can take place when users know who has the needed information (Alavi and Leidner, 2001; Choi et al., 2010). TMS, which has a great influence on individual participants, is a memory system that is a collection of memories of individual participants and provides meta-knowledge that allows users to use the memory (Choi et al., 2010; Wegner, 1987). Therefore, TMS seeks needed information and motivates the participants to share their knowledge (Choi et al., 2010). As TMS provides a venue for knowledge sharing, it affects participants’ knowledge sharing. Previous studies of TMS have confirmed the relationship between TMS and information sharing (e.g. Choi et al., 2010; Huang, 2009). Huang (2009) has claimed and proven that in Taiwan’s RandD groups, TMS precipitates knowledge sharing. Choi et al. (2010) have stated that TMS strengthens the results of group effort and have proven the TMS affects knowledge sharing. Therefore, when social media establishes the TMS factors of specialization, credibility, and coordination, it will have a positive effect on travel information sharing. The more information there is available on whether the information is credible, on whether the information is professional, on whether there are users with similar interest, the more likely users of social media will harbor travel information sharing intent. In contrast, if social media is not professional and credible, and in the absence of communication or solidarity among participants, then potential tourists will gather travel information in other ways. Several hypotheses can be made based on this claim:

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H3. Specialization of TMS will have a positive effect on travel information sharing.

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H4. Credibility of TMS will have a positive effect on travel information sharing.

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H5. Coordination of TMS will have a positive effect on travel information sharing.

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4. Research methodology

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4.1. Instrument development

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In this study, the measurements were derived from previous studies of the constructs of formal communication, informal communication, specialization, credibility, coordination and the intention to share travel information within social media. Three formal and three informal communication items were adopted from the previous study (Akgün et al., 2005; Choi and Lee, 2003). In addition, two specialization items related to TMS were adopted from previous research (Akgün et al., 2005; Choi et al., 2010; Ha and Ahn, 2011). Three credibility items related to TMS were drawn from the work of Akgün et al. (2005), Choi et al. (2010), and Kim et al. (2012). Four coordination items related TMS were adopted from the work of Akgün et al. (2005), Choi et al. (2010), Wang and Fesenmaier (2004), and Casaló et al. (2011). Finally, four travel information sharing intent items were drawn from previous research (Bock et al., 2005; Ha and Ahn, 2011). All of these items were measured on a 7-point Likert scale ranging from strongly disagree (1) to strongly agree (7). In order to verify the content validity of the items, three academic experts on social media were also asked to indicate whether these measurement items needed to be deleted or reworded and, if necessary, to suggest items that should be added. A pretest was also administered to 20 people who had participated in social media within the past year. As a result of these three procedures, we deleted three of the 21 items that had been generated from the literature. Furthermore, some ambiguous items associated with source credibility and perceived usefulness were reworded to ensure clarity.

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4.2. Data collection

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To validate the proposed research model, we collected data from a top-ranking Korean Internet survey firm. This Internet survey firm has a nationwide panel of 995,406 online respondents from whom representative samples are selected. Our study targeted a sampling group that focused on the acquisition of travel information rather than socialization in social media. The Internet survey was collected during the two weeks in April 2012. Online questionnaires were sent to potential respondents who had been randomly chosen from the panel of the Internet survey firm, Macromillembrain (www.embrain. com). For screening, the survey firm selected those who had joined at least one social media such as SNS (e.g. Facebook) and microblogs (e.g. Twitter) for sharing or seeking travel information during the past year. Based on the screening question, 309 out of the 700 respondents were selected. Table 2 summarizes the characteristics of respondents. The percentage of males (56.0%) was slightly higher than that of females (44.0%). Overall, 102 respondents (33.0%) were between 30 and 39 years old, 97 respondents (31.4%) were under 29 years old, 58 respondents (18.8%) were between 40 and 49, and 52 respondents (16.8%) were over 50 years old. The majority had a university degree (57.3%). The following percentages indicate the proportions of device types in the sample: smart phone (56.3%), desktop or laptop (41.1%) and tablet PC (2.6%). The amount of time spent per travel information searching using social media is 30 min to less than 1 h (52.8%), followed by less than 30 min (24.3%). The largest proportion of respondents (27.2%) reported twice-weekly usage, followed by more than five times (26.2%). Finally, the types of social media are SNS (66.0%) and microblogs (34.0%).

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5. Data analysis and results

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5.1. Confirmatory factor analysis

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This study used structural equation modeling (SEM) approach to test the hypotheses that were presented in Fig. 3. SEM is designed to evaluate how well a proposed model or hypothetical construct explains collected data (Hair et al., 2006). The collected data were analyzed using SEM software, such as Analysis of Moment Structures (AMOS 18.0) (Arbuckle, 2009). The SEM approach employed a two-step hybrid method by specifying a measurement model in a confirmatory factor analysis and testing a latent structural model developed from the measurement model (Kline, 2011). In addition, our research model was tested with the maximum likelihood estimation (MLE). MLE was more efficient and unbiased when the assumption of multivariate normality is met. Moreover, MLE is a flexible approach to parameter estimation where the ‘‘most likely’’ parameter values to achieve the best model fit is found (Hair et al., 2010). We assessed the constructs for convergent validity and discriminant validity via confirmatory factor analysis using AMOS 18.0. In confirmatory factor analysis, the measurement model is revised by dropping items that share a high degree of residual variance with other items and have high modification indices as well. The modification indices (MIs) indicate the decrease in the chi-square value when a specific parameter that had been constrained is relaxed. Therefore, we dropped a total of three such items (i.e., FC2, IC1, and CO1) which MIs ranging from 10.964 to 53.813 for increasing model fit. As shown in Table 3, the statistics showed a v2 fit of 127.750 (p = 0.000) with 75 degrees of freedom (v2/df = 1.677). The goodness-of-fit index was 0.948, the adjusted goodness-of-fit index was 0.917, the normed fit index was 0.951, the comparative fit index was 0.979, and the root mean square error of approximation was 0.047. All of these statistics supported the overall measurement quality based on the number of indicators (Anderson and Gerbing, 1992). Convergent validity was confirmed using three other criteria (Bagozzi and Yi, 1988; Hair et al., 2010). First, the standardized path loading of each item had to be statistically significant and greater than 0.6. Second, the composite reliability and the Cronbach’s a for each con-

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Table 2 Demographic characteristics of respondents. Characteristics Gender

Male Female 19–29 30–39 40–49 50–59 High school 2 years of college University Graduate school Desktop/laptop Smartphone Tablet PC Less than 30 min 30 min–1 h

Age

Education

Type of using device

Amount of time spent per searching

Frequency

%

Characteristics

173 136 97 102 58 52 59 48

56.0 44.0 31.4 33.0 18.8 16.8 19.1 15.5

Occupation

177 25 127 174 8 75 163

57.3 8.1 41.1 56.3 2.6 24.3 52.8

52 19

16.8 6.1

1–2 h More than 2 h

311 312 313 314 315 316 317 318 319 320 321

Using frequency per week

Type of social media

Frequency

%

Student Office worker Services Technician Professional Self-employed Civil servant Homemaker

49 121 15 22 35 21 5 28

15.9 39.2 4.9 7.1 11.3 6.8 1.6 9.1

Other Less than 1 time 2 times 3 times 4 times More than 5 times Social networking site Microblogs

13 52 70 84 22 81 204

4.2 16.8 22.7 27.2 7.1 26.2 66.0

105 309

34.0 100.0

Total

struct had to be greater than 0.7. Third, the average variance extracted (AVE) for each construct had to exceed 0.5. As shown in Table 3, all of the standardized path loadings were significant and greater than 0.6. Additionally, the minimum value of the composite reliability and Cronbach’s a of all constructs were 0.747 and 0.743 respectively. Finally, the minimum AVE for each construct was 0.596. Therefore, the convergent validity of the constructs was supported. The discriminant validity of the measurement model was verified by comparing the square root of the AVE for each construct with the correlations among the constructs. If the square root of the AVE was greater than the correlations among the constructs, then this outcome would indicate the discriminant validity of the model (Fornell and Larcker, 1981). As shown in Table 4, the square root of the AVE for each construct exceeded the correlations among the constructs. Therefore, discriminant validity was established. Additionally, Table 4 shows that skewness ranged from 0.42 to 0.25, and kurtosis ranged from 0.21 to 0.79. According to by Ghiselli et al. (1981), these results indicate that the scales are normally distributed (skewness < 2.0, and kurtosis < 5.0). Table 3 Results of convergent validity testing.a Constructs

Variables and items

Loadings

CRb

AVEc

Formal communication

FC1 FC2

Travel information well codified in social media Travel information can be acquired easily through formal format in social mediad Travel information is shared through codified forms in social media

0.783 –

0.804

0.672

0.801

Cronbach’s

a

FC3

0.855

Informal communication

IC1 IC2 IC3

It is easy to get advices directly from friends in social mediad Informal dialogues are used for travel information sharing in social media Travel information is acquired by instant messages and comments in social media

– 0.737 0.837

0.766

0.622

0.759

Specialization

SP1

0.814

0.747

0.596

0.743

SP2

The person who wrote the travel information in social media is knowledgeable Travel information in social media was written by an expert

Credibility

CR1 CR2 CR3

Travel information in social media keeps its promise and commitments Travel information in social media cares about its users Travel information in social media is trustworthy

0.792 0.812 0.791

0.841

0.637

0.841

Coordination

CO1 CO2 CO3 CO4

– 0.669 0.803 0.809

0.806

0.582

0.794

Intention to share travel information

SH1 SH2 SH3

I communicate with other members in social mediad I get involved with other members in social media Other social media members and I behave in a similar way When we are using social media in order to seek or share travel information, other social media members and I share the same objectives I will try to share travel information with other members of social media I will share travel information with other members of social media I will share travel information using social media

0.892 0.899 0.828

0.906

0.763

0.904

0.728

a

v2 = 127.750, df = 75, v2/df = 1.677, p = 0.000, GFI = 0.948, AGFI = 0.917, NFI = 0.951, CFI = 0.979, RMSEA = 0.047.

b

Composite reliability. Average variance extracted. The item was deleted after confirmatory factor analysis.

c d

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N. Chung et al. / Telematics and Informatics xxx (2015) xxx–xxx Table 4 Correlation and descriptive statistics. Construct

(1) (2) (3) (4) (5) (6)

Mean

Formal communication Informal communication Specialization Credibility Coordination Travel information sharing

4.40 4.25 4.17 4.82 4.65 4.78

S.D.

1.05 1.19 1.08 0.89 0.95 0.99

Skewness

0.20 0.34 0.12 0.25 0.42 0.01

Kurtosis

0.22 0.03 0.19 0.21 0.79 0.31

Correlation of constructs (1)

(2)

(3)

(4)

(5)

(6)

0.820 0.464** 0.536** 0.519** 0.534** 0.527**

0.789 0.387** 0.397** 0.565** 0.395**

0.772 0.396** 0.441** 0.451**

0.798 0.597** 0.491**

0.763 0.565**

0.874

Note. The diagonal elements in boldface in the ‘‘correlation of constructs’’ matrix are the square root of the average variance extracted (AVE). For adequate discriminant validity, the diagonal elements should be greater than the corresponding off-diagonal elements. ** p < 0.01.

Formal Communication

0.607***

Specialization R2=0.516

0.545***

0.243**

0.452***

Credibility R2=0.493

0.192**

Travel information sharing

R2=0.480

0.165

0.387***

0.223**

Informal Communication

0.445***

Coordination R2=0.646

* p<0.05, ** p<0.01, *** p<0.001

Fig. 4. Results of the structural equation modeling analysis.

322

5.2. Hypothesis testing

323

Prior to hypothesis testing, several underlying assumptions for SEM were checked. The underlying assumptions for the SEM analysis were similar to the factor analysis: an adequate variable-to-sample ratio, normality, linearity, no extreme multicollinearity, and sampling adequacy (Hair et al., 2010). The variable-to-sample ratio was 1 to 17.2 and satisfied the criteria suggested by Nunnally (1978) and Hair et al. (2010). Kaiser–Meyer–Olkin’s measure of sampling adequacy was 0.897, and Bartlett’s test of sphericity index showed significant p-value at the 0.000 significance level (Kaiser, 1974). Extracted communalities were 0.629–0.868 across all measurement items, demonstrating that there was no extreme multicollinearity or strong linear combinations among the fifteen measurement items. Nonredundant residuals with absolute values over 0.05 were 16 (15.0%). The model demonstrates a good model fit between observed correlation and assumed correlation since the nonredundant residuals with absolute values over 0.05 are below 50%. Fig. 4 presents the results of the SEM. The v2 statistic fit was 159.923 with 80 degrees of freedom (v2/df = 1.999, p = 0.000). The goodness-of-fit index (GFI) was 0.936, the adjusted goodness-of-fit index (AGFI) was 0.904, the normed fit index (NFI) was 0.938, the comparative fit index (CFI) was 0.968, the root mean square error of approximation (RMSEA) was 0.057, the root mean square residual (RMR) was 0.058, and the standardized RMR was 0.046. These multiple indicators suggested that the model demonstrated a good fit and thus merited further interpretation. Considering that all the fit indices are successfully met, we can judge that the estimated structural equation model is statistically proper and valid for hypothesis test (Gefen et al., 2000; Hair et al., 2010). The squared multiple correlations (R2: coefficient of determinant) for the structural equations for specialization, credibility, coordination, and travel information sharing intent are shown in Fig. 4 and Table 5. For specialization, 51.6% of the variance was explained by the direct effects of formal and informal communication, and 49.3% of the variance in credibility was explained by the direct effects of formal and informal communication. Additionally, 64.6% of the variance in the coordination was explained by the direct effects of formal and informal communication. Finally, for travel information sharing intent (R2 = 48.0%), the variance was explained by the effects of specialization, credibility, and coordination. Table 5 presents the standardized parameter estimates. H1a–H1c posit the structural relationships among formal communication, specialization, credibility, and coordination. Formal communication had a positive effect on specialization (b = 0.607, t-value = 6.453) with statistical significance at the p < 0.001 level; thus, this result supports H1a. The significant positive effect of formal communication on credibility sup-

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Table 5 Standardized structural estimates and tests of the main hypotheses. Hypothesis

Path

Estimates

t-value

Results

H1a H1b H1c H2a H2b H2c H3 H4 H5

Formal communication ? Specialization Formal communication ? Credibility Formal communication ? Coordination Informal communication ? Specialization Informal communication ? Credibility Informal communication ? Coordination Specialization ? Travel information sharing Credibility ? Travel information sharing Coordination ? Travel information sharing

0.607 0.545 0.452 0.165 0.223 0.445 0.243 0.192 0.387

6.453 6.243 5.429 1.851 2.669 5.099 3.181 2.766 4.879

Supported Supported Supported Not supported Supported Supported Supported Supported Supported

360

ports H1b (b = 0.545, t-value = 6.243, p < 0.001). In addition, formal communication also positively affects coordination (b = 0.452, t-value = 5.429, p < 0.001), supporting H1c. Moreover, the set of hypotheses H2a–H2c addressed whether informal communication influence specialization, credibility, and coordination. Informal communication had a positive effect on credibility (b = 0.223, t-value = 2.669, p < 0.01), and coordination (b = 0.445, t-value = 5.099, p < 0.001). Therefore, H2b and H2c are supported. However, informal communication did not have a positive effect on specialization (b = 0.165, t-value = 1.851, n.s.), thus invalidating H2b. Finally, H3–H5 state that specialization, credibility, and coordination are associated with the travel information sharing intent. Specialization had a positive effect on travel information sharing intent (b = 0.243, t-value = 3.181) and was statistically significant at the p < 0.01 level; thus, H3 is supported. H4 is supported by the significantly positive effect of credibility on the travel information sharing intent (b = 0.192, t-value = 2.766, p < 0.01). Additionally, coordination (H5) had a significant positive effect on the travel information sharing intent (b = 0.387, t-value = 4.879, p < 0.001). Therefore, H3–H5 are supported.

361

6. Discussion

362

379

The objective of this study is to explore the effects of potential tourists’ recognition of the various communication types in social media, on the travel-information-sharing via TMS factors. According to this analysis, formal communication leads to specialization, credibility, and coordination; informal communication leads to credibility, and coordination. Informal communication does not affect specialization. Compared to the each path’s coefficients, formal communication is the more powerful antecedent to the TMS than informal communication. Potential tourists using social media believe that it is an external source of travel information and they share travel information either forms of communication, that is, the formal or informal. Thus social media is to be conceived of as a TMS, with active interaction with members. This result is consistent with previous studies claiming that communication is an effective factor in establishing TMS (e.g. Akgün et al., 2005; Kanawattanachai and Yoo, 2007). Social media develops specializations in travel information sharing or seeking. The cognizance of informal communication via instant messages or replies did not affect potential tourists’ thought, whereas formal postings are believed to be professional and convincing. This suggests that formal communication tends to be more professional than informal instant messages or replies. Therefore, formal communication strengthens the professional image of a social media serving as a TMS. To restate the study, the function of social media, when the communication is formal, consists of meta-memory, and is subject to specialization, credibility, and coordination, as an external source of travel information, and users become voluntary participants in sharing travel information. On the contrary, social media relying on informal communication only have better credibility and coordination, so long as the users recognize the function of social media and have willingness to share travel information.

380

7. Conclusion

381

Social media have become external sources of travel information via various communication channels such as posting, instant messages, and replies. From the perspective of social media as TMS, this study identified the effects of formal and informal communication on TMS by using three measurements (specialization, credibility, and coordination) and examined how this affects the intention to travel-information-sharing. The theoretical implication of this research is as follows. Most of the previous studies on the sharing of travel information focused on the individual traits or social characteristics of communities, and analyzed social media from the perspective of travel information sharing behavior (Huang et al., 2010; Kang and Schuett, 2013; Lee et al., 2012). Neither did those researches travel information sharing in terms of the communication types, nor view social media as TMS. Communication being as a basis of social media can function as an external source of information (Kaplan and Haenlein, 2010; Xiang and Gretzel, 2010).

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This study empirically demonstrates that prospective tourists consider social media to facilitate the development of TMS through two types of communication, which are likely to share travel information within them. Therefore, our study contributed to providing a bridging of the concept of TMS to sharing of travel information among users based on communications in social media. Moreover, this research has the following practical implications. First, the recognition of formal communication affects specialization, credibility, and coordination of TMS, and subsequently affects travel-information-sharing. The fact is that the more formal communication is used in social media, the more willing users are to share travel information; this is because they perceive the social media as a TMS with three traits of specialization, credibility, and coordination. It suggests firstly that tourism-related government organizations or travel companies actively employ formal communication to entice potential tourists who use social media. Second, potential tourists using informal communication such as replies or instant messages tend to share travel information, in so far as social media’s credibility and coordination allows. This indicates that both formal and informal communication can trigger participation and knowledge-sharing among potential tourists. Therefore, to elicit the users’ proactive participation and to solidify the credibility and coordination of social media, tourism marketers should also take advantage of instant messages or replies. Third, our study manifested the results that informal communication had no effect on specialization of TMS. Regarding the selection of communication types, it is advised that for its own specialization of travel information, tourism marketers and social media users should pay attention to such formal communications as posting on bulletin boards, more than replies or instant messages. Lastly, this study has the following limitations and needs for improvement. First, the subjects of this study are limited to SNS and microblogs. Other forms of social media and their functional differences should be checked. Furthermore, this study focused on communication in social media as a main factor that affects TMS, but for better understanding of social media as TMS, additional factors such as the depth and types of the relationship, and the characteristics of information technology, should be considered, as seen in previous studies (e.g. Choi et al., 2010; Zhong et al., 2012).

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Acknowledgments

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This work was supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF2013S1A3A2043345).

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