Exploring the Roles of DMO’s Social Media Efforts and Information Richness on Customer Engagement: Empirical Analysis on Facebook Event Pages

Abstract

This study examines how destination marketing organization’s (DMO’s) social media posts and comments increase customer engagement and explores the moderating role of information richness in the smart tourism context. To test the proposed hypotheses, the current study employed social media analytics by analyzing unique panel data from 72 DMOs’ Facebook event pages. The research findings indicate that a DMO’s social media efforts (quantitative aspect) are positively associated with customer engagement and DMOs having a higher level of information richness (qualitative aspect) in their messages have greater customer engagement on DMOs’ Facebook event pages. The current study also sheds light on the scant research stream of measuring DMOs’ social media success empirically and presents the possibility of how social media analytics can be applied to hypothesis testing in tourism research instead of traditional research methods.

Keywords

social media destination marketing organizations customer engagement Smart tourism and information richness

Introduction

The continuously growing popularity of social media has prompted organizations to view this particular communication channel as their marketing and strategic decision-making tool for customer engagement (Hoffman and Novak 2012). Information and communication technologies (ICTs) enable consumers to use social media in their daily activities through an “always-on” communication environment by using their mobile devices and home/public networks (Heinonen 2011). Such progress has increased individuals’ online social networking and information sharing behaviors and has simultaneously assisted organizations in building better customer relationships in social media (Hoffman and Novak 2012; Risius and Beck 2015). For example, Facebook is one of the large-scale social media platforms, with an average of 2.2 billion monthly active users and is essential to the success of social media marketing (Statista 2018).

Social media have received considerable attention among scholars in various academic fields, such as marketing, information systems (IS), and tourism disciplines (Kaplan and Haenlein 2010; Heinonen 2011; Khang, Ki, and Ye 2012; S. A. Lee and Lee 2017; Ngai, Tao, and Moon 2015). Past marketing studies identify influential determinants (e.g., content/media characteristics, cultural differences, and posting time) of brand post popularity (De Vries, Gensler, and Leeflang 2012; Lin, Swarna, and Bruning 2017) and consumer engagement (Weinberg and Pehlivan 2011; Cvijikj and Michahelles 2013; Hollebeek, Glynn, and Brodie 2014) in social media. In addition, effective social media activities enhance relational outcomes, such as firm value (S. Chung et al. 2014), parasocial interactions (S. A. Lee and Lee 2017), word of mouth (WOM) and loyalty (Risius and Beck 2015), consumer attachment (VanMeter, Grisaffe, and Chonko 2015), and public relations (Saxton and Waters 2014; Carboni and Maxwell 2015).

In the hospitality and tourism industry, this significant and pervasive trend has also attracted the attention of practitioners and researchers in various ways (Xiang and Gretzel 2010). Tourism organizations (e.g., DMOs) consider social media a global reach marketing tool with a relatively low cost (Hays, Page, and Buhalis 2013; Law, Buhalis, and Cobanoglu 2014; Mariani, Di Felice, and Mura 2016). Accordingly, social media have been actively used by event managers (Leask, Hassanien, and Rothschild 2011), festival organizations (Gyimóthy and Larson 2015), and DMO managers (Mariani, Di Felice, and Mura 2016) to generate tourist visitation (Gretzel et al. 2006). For example, European DMOs use social media to promote and market their destinations in various social media platforms such as Facebook, Twitter, Instagram, and YouTube (Uşaklı, Koç, and Sönmez 2017). Thus, travelers use social media as an essential tool for travel planning (Xiang, Magnini, and Fesenmaier 2015).

Given that emerging ICTs and social media platforms are commonly used by travelers and travel organizations (e.g., DMOs), the tourism industry has changed in various ways (Sigala and Marinidis 2012; Mariani, Di Felice, and Mura 2016), “smart tourism” has become a new buzzword in the tourism field. Smart tourism refers to

tourism supported by integrated efforts as a destination to collect and aggregate/harness data derived from physical infrastructure, social connections, government/organizational sources and human bodies/minds, in combination with the use of advanced technologies, to transform such data into on-site experiences and business value-propositions, with a clear focus on efficiency, sustainability and experience enrichment. (Gretzel et al. 2015, p. 181)

Although social media and ICTs play a critical role in developing technology-mediated co-creation and establishing public-private–consumer collaborations in smart tourism (Gretzel et al. 2015; Gyimóthy and Larson 2015), a critical issue that has yet to be extensively studied in the tourism field is whether social media effort geared toward travelers assist DMOs or event managers to achieve these goals. There is a paucity of research in examining how travel organizations (e.g., DMOs) actually perform and manage social media activities. In particular, we have a limited understanding of what the key factors are of such customer engagement in DMOs’ social media. Therefore, we focus on customer engagement as the outcome of DMOs’ social media efforts and determine the efficient strategies to increase customer engagement.

Despite the increasing scholarly and practical interest in the use of social media in tourism, we lack a better understanding of how DMOs’ social media efforts and information richness influence traveler behavior and event performance. By doing so, we attempt to capture both the quantity of social media efforts (i.e., the number of postings and comments) and the quality of those efforts (i.e., information richness of messages). Moreover, the degree to which DMOs measure social media success based on the degree of engagement with consumers is relatively limited in the tourism literature (Hays, Page, and Buhalis 2013). To fill in this research gap, the current study aims to explore DMOs’ efforts and customer engagement in social media. In particular, this study (1) examines the impact of DMOs’ social media efforts on customer engagement, (2) explores the moderating role of information richness on their Facebook event pages, and (3) demonstrates effective DMOs’ social media marketing strategies. To answer these research questions, the current study built and tested empirical models by employing social media analytics. Detailed information was extracted from the Facebook event pages and analyzed to test the research hypotheses. Research findings indicate that the impact of DMOs’ social media efforts on customer engagement is positive and significant. Moreover, our empirical analysis suggests that information richness moderates the relationship between DMOs’ social media efforts and customer engagement. Therefore, research findings shed light on the impact of DMOs’ quantitative and qualitative social media actions on customer engagement in the context of smart tourism and destination marketing.

Research Background: The Roles of Social Media in Smart Tourism and Destination Marketing

The tourism industry is information-intensive and substantially relies on ICTs (Benckendorff, Sheldon, and Fesenmaier 2014). Such information-intensive industry characteristics and emerging ICTs have led to the emergence of smart tourism (Gretzel et al. 2015; Jovicic 2019). By introducing a smart tourism ecosystem, recent seminal work of Gretzel et al. (2015) described and conceptualized smart technologies, smart cities, and smart tourism. Figure 1 shows that smart tourism involves multiple components and layers, including smart destinations, smart experience, and smart business ecosystem (Gretzel et al. 2015). Although smart destinations are related to the integration of ICTs with physical infrastructure (Buhalis and Amaranggana 2015), smart experience and smart business focus more on travelers’ technology-mediated experiences and the complex business ecosystems resulting from co-created tourism experiences.

Figure 1.

Components and layers of smart tourism.

In a smart tourism ecosystem, social media are extremely critical because consumers are likely to produce, share, and consume social contents (Hunter et al. 2015; Xiang and Gretzel 2010; Ye, Ye, and Law 2020) and those contents could influence travelers’ and service providers’ behavior (Jovicic 2019). Local tourism organizations and governments utilize social media for tourism development and the network for smart tourism ecosystem (J. H. Park et al. 2016). Because of the importance of social media in smart tourism, social media research is the third most common research topic in smart tourism research context (Ye, Ye, and Law 2020). For instance, both residential and touristic consumers generate and consume social media data through their mobile devices (Gretzel et al. 2015). At the same time, service providers or marketing organizations such as DMOs, local governments and event organizers actively utilize social media to enhance user engagement (Ge and Gretzel 2018; Mariani, Di Felice, and Mura 2016; Mariani, Mura, and Di Felice 2018) and to promote and share events, social ads, and destination news (J. H. Park et al. 2016; Jovicic 2019). Furthermore, social media and other online-based tourism applications enable travelers to have a memorable experience at smart tourism destinations by enhancing accessibility, informativeness, and interactivity in the smart tourism context (Jeong and Shin 2019). Therefore, this study is along the lines of the integration of social media and smart tourism because we focus on the significant role of the organization’s social media content and activities in the context of smart tourism and destination marketing.

The recent tourism and hospitality literature has suggested that social media and emerging ICTs have fundamentally changed the methods travelers plan for and book trips, evaluate and share travel experiences (Sigala, Christou, and Gretzel 2012; Xiang, Magnini, and Fesenmaier 2015). The research on the role of social media and ICT in tourism (generally referred to as e-Tourism research) has evolved for the last three decades (Mariani, Di Felice, and Mura 2016; Buhalis and Law 2008). Although previous consumer-focused studies in the hospitality and tourism literature have concentrated on the use and impact of social media in the travelers’ travel planning phase, supplier-focused studies have generally focused on promotion, management, research functions, and product distribution (Leung et al. 2013). M. Lee, Lowry, and Delconte (2015) identified that the majority of social media–related tourism studies deal with consumer/traveler behavior, such as travelers’ online/offline behavior and social media use, commitment, loyalty, and information search behavior. For example, N. Chung and Koo (2015) demonstrated that the users of new social media for their travel information searches are substantially affected by benefits (i.e., information reliability, enjoyment) and sacrifices (i.e., complexity, perceived effort). However, only a few tourism studies have explored the service provider’s perspectives including promotion (Michaelidou et al. 2013; Mistilis, Buhalis, and Gretzel 2014; Stepchenkova and Zhan 2013) and product distribution (Duverger 2013; Månsson 2011; Yang, Pan, and Song 2014). Recently, Ge and Gretzel (2018) emphasize the importance of firm-initiated social media conversations by demonstrating the positive impact of humorous messages on customer engagement in social media. In addition, DMOs’ social media usage behaviors (e.g., time, seasonality, content, and information type) and outcomes (e.g., website success and social media KPIs) have been explored through content analysis and survey methods (Stankov et al. 2018; Uşaklı, Koç, and Sönmez 2017; Villamediana, Küster, and Vila 2019; Wozniak et al. 2017).

At the same time, social media play a critical role in destination marketing and management, event sponsoring and promotion, and innovative communication strategy in smart tourism ecosystems (Brandt, Bendler, and Neumann 2017; Choe, Stienmetz, and Fesenmaier 2017; Jovicic 2019). In particular, destination/event organizations tend to consider and use social media as a new destination marketing tool (Ge and Gretzel 2018; Gretzel and Fesenmaier 2016; Mariani, Di Felice, and Mura 2016; Mariani, Mura, and Di Felice 2018). For instance, travel organizations adopt social media to communicate and interact with travelers as a new destination marketing tool by building social media blogs or fan pages (Minazzi 2015; J. H. Park et al. 2016). Thus, social media are considered a significant destination and event-marketing tool (Gyimóthy and Larson 2015; Leask, Hassanien, and Rothschild 2011; Mariani, Di Felice, and Mura 2016; Bogaert, Ballings, and Van den Poel 2016). Diverse social media platforms provide novel and effective interaction with consumers and organizations (Kaplan and Haenlein 2010). Thus, DMOs and event organizations strategically employ Facebook to promote their destinations and enhance marketing communications (Gyimóthy and Larson 2015; Mariani, Di Felice, and Mura 2016). Social media enable marketers to stimulate conversation and encourage interaction, thereby enhancing consumer engagement, sharing, collaboration, and positive WOM (Tuten 2008; Weinberg and Pehlivan 2011). Table 1 summarizes how the current study is related to and differs from the extant literature.

Table 1.

Summary of Related Studies and Comparison with Our Study.

Studies	Organization’s Efforts as IV	Consumer Behavior Source as IV	Considering Message Richness	Organizational Value as DV	Research Context	Key Findings
Dewan, Freimer, and Zhang (2002)	No	Web traffic	No	Yes (stock market performance, monthly)	Advertisement-supported Web site	A forward-looking measure of Web traffic better predicts market capitalization than page views
Luo (2009)	No	Consumer complaints	No	Yes (stock market performance, monthly)	Negative WOM in the airline industry	Negative WOM’s long-term financial harm becomes more destructive in magnitude, kicks in more quickly, and haunts investors longer
Trusov, Bucklin, and Pauwels (2009)	Yes	Traditional marketing activities	No	No, only new sign-ups	Only a single Internet social networking site	WOM referrals have substantially longer carryover effects than traditional marketing actions and produce substantially higher response elasticities
Da et al. (2011)	No	Search frequency	No	Yes (stock market performance, weekly)	Russell 3000 stocks	Search volume index is correlated with but different from existing proxies of investor attention
Aggarwal et al. (2012)	No	Blogs	No	Amounts and valuations of venture financing	Venture financing across industries	Negative eWOM (blogs) has a greater impact on venture financing than positive eWOM
Albuquerque et al. (2012)	Yes (only focal firm’s price promotions and public relations)	Firm’s online community	No	No, purchase and create user-generated content	Only Hewlett-Packard’s user-created magazines	Firm’s price promotions have strong effects but are limited to the purchase decisions, whereas content creator referrals and public relation efforts have broader effects that impact all consumer decision at the platform
K. Lee and Lee (2012)	No	Facebook likes	No	No, only sales	Groupon sales and Facebook Likes	Facebook likes of a Groupon deal has a positive impact on the deal’s sales
Tirunillai and Tellis (2012)	No	Consumer reviews	No (only text)	Yes (stock market performance, daily)	UGC (consumer reviews) across 6 markets (15 firms)	UGC metrics has a strong relationship with stock market performance
Goh, Heng, and Lin (2013)	Yes	Facebook posts, comments	Yes (but only from textual contents)	No, only sales	Only a single apparels retailer	Engagement in social media brand communities leads to a positive increase in purchase expenditure, and there are different roles between user-generated content and marketer-generated content
Lee et al. (2018)	Yes	Facebook posts, comments, and Likes	No (but content analysis)	No, only consumer engagement	Facebook pages of 782 firms	Persuasive social media content increases more consumer engagement than informative content, and the combination of persuasive and informative content increases consumer engagement
Luo, Zhang, and Duan (2013)	No	Blogs, ratings, Web traffic, and search	No	Yes (stock market performance, daily)	Independent, 9 IT firms	Social media metrics (blogs and ratings) have a stronger predictive relationship with firm value than traffic and search metrics
Luo and Zhang (2013)	No	Consumer reviews and Web traffic	No	Yes (stock market performance, daily)	Independent, 9 IT firms	Consumer reviews and Web traffic has a positive relationship with firm stock performance
Miller and Tucker (2013)	Yes	Facebook posts	No	No, only user-generated content	Cross-sectional data for 5,035 hospitals’ Facebook pages	Active social media management drives more user-generated content. Only social media content focused around consumer needs is more effective to increase consumers engagement
Rishika et al. (2013)	No	Facebook posts, sales transaction	No	No, only customer visit	Only a single wine retailer’s Facebook	Customers’ participation in a firm’s social media increases customer visit frequency and profitability
Yu, Duan, and Cao (2013)	No	Blogs, forums, and Twitter	No	Yes (stock market performance, daily)	824 firms across industries	Social media has a stronger relationship with firm stock performance than conventional media while social and conventional media have a strong interaction effect on stock performance
Cabiddu, De Carlo, and Biccoli (2014)	No	Likes and responses on Facebook and Twitter	No	No. only customer engagement	Hotels’ social media presence (fans, likes, responses) on Facebook and Twitter	Three social media affordances for customer engagement (i.e., persistent engagement, customized engagement, triggered engagement) were identified
Mariani, Di Felice, and Mura (2016)	Yes	Users’ social media activities on the DMOs’ Facebook pages	No	No, but various customer engagement activity measures	8 DMOs’ Facebook pages	Photo-based and moderately long posts have a positive impact on DMOs’ Facebook engagement although high frequency of or early posts have a negative impact on engagement
Harrigan et al. (2017)	No	Social media users	No	No, only customer engagement and behavioral intention of loyalty	Customer survey data	Customer involvement is positively associated with customer engagement, leading to behavioral intention of loyalty in the context of social media
Ge and Gretzel (2018)	Yes	Customer engagement	No	No, but this study considered DMOs’ communication styles as IVs	Case study using Chinese DMO pages on Sina Weibo	Humorous, non-product related and simple contents increase customer engagement
Mariani, Mura, and Di Felice (2018)	Yes	Social media user responses to DMO’s posts	No	No, but three specific user engagement	15 DMOs’ Facebook pages	Users are likely to engage in photos and posts made during the weekends while they are less likely to engage in evening posting
This study	Yes	Facebook posts, comments, Likes, and Web traffic	Yes	Yes, both the number of actual visitor and customer engagement	DMOs’ Facebook official pages (72 organizations)	A DMO’s social media efforts (quantity aspect—number of Facebook posts and comments) are significantly associated with an increase in customer engagement and actual visitors.

Although there has been the extensive research interest in engagement and social media within various academic fields, relatively limited attention has been provided to travelers’ engagement with service organization’s social media activities in the smart tourism context. In particular, few empirical studies have directly tested the relationship between DMOs’ social media efforts and customer engagement. Unlike prior research that examined the effect of consumer actions on consumer behavior and organizational value in the context of consumer/third party–owned platforms or the impact of organizations’ actions on consumer behavior, we focus on the impact of organization’s actions on an organization-owned social media platform (i.e., a DMO’s Facebook event page) in terms of the customer engagement. In addition to extending the prior research by examining the value of organizations’ actions in social media, the present study identifies and examines the impacts of organization’s efforts (i.e., quantity aspect such as posts and comments) and information richness (i.e., quality aspect) on customer engagement in social media.

Hypothesis Development

DMOs’ Social Media Efforts and Customer Engagement

The impressive progress of ICTs and interactive features of Web 2.0 have resulted in an explosion of interest in customer engagement with respect to social media (Sashi 2012). Such an interactivity element is suggested to enhance the interest and engagement of social media users (Sigala, Christou, and Gretzel 2012). The reason why customer engagement is a central issue in social media research is that customer engagement may lead to a positive corporate reputation and loyalty (Bowden 2009; Van Doorn et al. 2010; Hollebeek 2011; Dijkmans, Kerkhof, and Beukeboom 2015), relational outcomes (Risius and Beck 2015), and value co-creation (Jaakkola and Alexander 2014).

Although many different definitions and conceptualizations of customer engagement have been developed, customer engagement in social media generally involves four aspects, namely, behavioral (e.g., participation in a company’s activities), emotional (e.g., feeling positive with a company’s activities), cognitive (e.g., being interested in a company’s activities), and social (e.g., sharing a company’s activities with friends and potential travelers) aspects (Dijkmans, Kerkhof, and Beukeboom 2015; Vivek, Beatty, and Morgan 2012). In line with this view, the current study defines customer engagement as the intensity of a traveler’s participation in and connection with a DMO’s offerings, event promotions, or activities that either the traveler or DMO initiates (Vivek, Beatty, and Morgan 2012).

Sashi (2012) conducted seminal research on customer engagement in social media and suggested that customer engagement concentrates on satisfying travelers by offering superior value and providing more frequent and richer interactions. The process of building customer engagement follows a customer engagement cycle in social media: (1) connection, (2) interaction, (3) satisfaction, (4) retention, (5) commitment, (6) advocacy, and (7) engagement (Sashi 2012). This customer engagement cycle can be applied to the case of DMOs’ Facebook pages. For example, a potential traveler joins a DMO’s Facebook event page to establish an initial relationship (i.e., connection stage). Once connected, he or she can interact with the particular DMO and also other members through DMO-initiated contents or user contents (i.e., interaction stage). If such interactions are satisfactory to a focal member, then he or she will remain connected and continue to interact with the DMO’s Facebook event page and other members (i.e., satisfaction stage), leading to customer retention, commitment, and advocacy on the DMO’s Facebook event page (Sashi 2012). Thereafter, this customer is likely to be connected with and participate in DMO’s offerings, promotions, and other activities (i.e., engagement stage).

Consistent with this view, DMO’s social media efforts can play a pivotal role in enhancing customer engagement on Facebook. In this study, social media efforts refer to the volume of a travel organization’s actions (i.e., posts, comments) to communicate with its current and potential customers through an organization’s social media site (S. Chung et al. 2019). A greater volume of social media posts and comments may enable DMOs to create and disseminate more information for their current and potential customers. Previous studies have suggested that a firm’s active online activities are positively associated with this firm’s social presence (Godes and Mayzlin 2009), thereby leading to more user activities in social media (Miller and Tucker 2013). In other words, a DMO’s more frequent posts and comments to actively manage its social media site and interact with its customers increase the possibility that customers participate in the DMO’s social media event page through their postings, comments, and “Likes.”

In the smart tourism context, smart business as one of the main components of smart tourism “creates and supports the exchange of touristic resources and the co-creation of the tourism experience” (Gretzel et al. 2015). Service exchange and value co-creation can happen when they are embedded within social systems (Edvardsson, Tronvoll, and Gruber 2011; Jeong and Shin 2019) and customers react to the offerings of a firm or the other stakeholders within the multi-stakeholder service system. Moreover, social media platforms (e.g., Facebook) enable DMOs to provide and disseminate voluminous and various information regarding new services, regional information, promotions, events, festivals, and customer relationships (Mariani, Di Felice, and Mura 2016, Hays, Page, and Buhalis 2013; Jovicic 2019). Such information can be more accessed by DMO’s Facebook page members, their friends, and other potential travelers (Algesheimer, Dholakia, and Herrmann 2005; Thompson and Sinha 2008) and enhance their participation in information related to brands, products, and services posted in virtual communities (Brodie et al. 2013). Therefore, the volume of posts, comments, content, and information offered by DMOs in social media (i.e., DMOs’ social media efforts) leads to positive customer engagement in smart tourism because more social media efforts can create more opportunity to frequently interact with current and potential travelers. Satisfaction with such interactions results in customer engagement (Sashi 2012). Past social media research and anecdotal evidence show that social media posts significantly enhance customer engagement on brand pages as one of the effective social media marketing and communications strategies (De Vries, Gensler, and Leeflang 2012; Cvijikj and Michahelles 2014; Lin, Swarna, and Bruning 2017). Therefore, we propose the following hypothesis:

Hypothesis 1: DMOs’ social media efforts are positively associated with customer engagement in social media.

Moderating Role of Information Richness in DMOs’ Social Media Efforts

In this study, the information richness refers to the richness of the messages posted by the DMO. In the social media context, posting messages serves as an important medium to convey information. Information delivered through different message types, such as texts, pictures, or videos, has varying abilities to deliver information; accordingly, people classify messages and determine the richness of those messages based on uncertainty (lack of information) and equivocality (ambiguity) (Daft and Lengel 1986; Daft, Lengel, and Trevino 1987; Dennis and Kinney 1998). In other words, communication performance and interpersonal relationships can differ in the information richness to facilitate understanding. For instance, a rich information (e.g., video clip) facilitates insight and rapid understanding more than a less-rich information (e.g., writing, text-based posting) (Daft, Lengel, and Trevino 1987; Dennis and Kinney 1998). Therefore, we argue that the volume of DMOs’ messages is positively associated with customer engagement, but such a positive relationship can be strengthened when those messages are richer.

For example, richer social media messages are more likely to be noticed and propagated (i.e., through message sharing) by travelers because they are more engaging and informative. Messages delivered using pictures are suggested to be richer than text because they require minimum processing effort (Larkin and Simon 1987). Similarly, video is superior to static pictures because the former is more explicit and easier to understand (O. C. Park and Hopkins 1992). Arguably, more information cues can be communicated as one moves from text to pictures and from pictures to video. Such additional information cues lead to engaging and valuable content that can enhance the social engagement of prospects, thereby differentiating the organization from competitors and boosting the customer awareness and loyalty (Emerson 2012). By actively investing efforts in richer messages (e.g., photos and videos), a DMO can enhance the chance for current and potential customers to participate more in the DMO’s social media efforts through their postings, comments, and “Likes,” because those efforts can be considered as loyal and trustful (Algesheimer, Dholakia, and Herrmann 2005; Thompson and Sinha 2008) and increase the exposure and reach of the DMO’s messages via social contagion (Aral and Walker 2011; Bond et al. 2012; Ugander et al. 2012). This situation implies that increasing the level of information richness in a DMO’s social media efforts can strengthen the positive association between such efforts and customer engagement. Therefore, we expect that the richer the DMO-initiated contents are, the more customer engagement occurs.

Hypothesis 2: Information richness positively moderates the association between DMOs’ social media efforts and customer engagement.

Research Methodology

To test the proposed hypotheses, the present study employed social media analytics techniques. Previous literature defines the social media analytics as the process of gathering and analyzing data from social media such as Facebook, Instagram, LinkedIn, and Twitter and call for the future research (Zeng et al. 2010; Fan and Gordon 2014). In this study, we collected and analyzed data from DMOs’ Facebook event pages.

Facebook Event Pages

Facebook is an online medium that allows users to interact with one another or with a DMO by sharing a variety of information. As of July 2018, Facebook is the largest online social media platform, with an average of 2.2 billion active monthly users. Facebook introduced the “Page” application in November 2007, and users share their information by “liking” DMOs (or becoming a “fan”) and allowing DMOs to access their profiles. When someone “likes” a page, they will see updates from that page through the “newsfeed” feature.

A DMO’s Facebook page is set up by the DMO, thereby allowing users to share information with the DMO and other users. Facebook pages also can connect individuals who are not “fans” of a DMO but share common interests. In this sense, Facebook pages are an official DMO-initiated online forum for communication and interaction between a DMO and its current/potential visitors and also among current/potential visitors. In particular, a DMO typically delivers announcements, venue information, and new events and schedules by posting status updates, photos, or video clips, as well as shares external content by using the links function. Travelers may post questions through status updates or comment threads, as well as share their experiences with photos and video clips. The fans of a DMO’s page can “like” or comment on a post by the DMO or other fans (travelers) to express their opinions and attitudes. Thereafter, the action is broadcast to the fans’ Facebook friends through the newsfeed, thereby increasing the page’s exposure and reach.

Data Collection and Sample

We chose South Korea’s top 100 DMOs in terms of their total visitors and popularity as the sample for two reasons. First, focusing on large DMOs can ensure active interactions between DMOs and travelers because the larger DMOs’ Facebook pages are likely to be updated and accessed more frequently. Second, the top 100 DMOs are likely to invest substantially in their social media compared with smaller DMOs because of the relatively higher levels of advertising expenditure. To select the sample, we used Nielsen Korean Click’s web traffic information. Among such data, we used the reach rate indicating the rate of visitors per 100 Internet users because it is the most reliable information across organizations (Luo, Zhang, and Duan 2013). Specifically, the reach rate is gauged by the number of visitors who browse a given website (by the rate of visitors per 100 Internet users tracked by Nielsen). A large number of visitors may reflect a greater pool of potential customers for the firm. Compared with another commonly used metric of “unique visitors” that also measures audience size, reach rate is typically calculated as a percentage in a relative sense and, thus, more comparable across firms.

A customized Apache Hadoop Hive-based crawler was developed and used for data collection on a distributed computing platform. This crawler was run in parallel on Thirty Linux x86 servers and collected all the posts, comments, and other relevant information on each DMO’s Facebook page from January 2010 to December 2012 through the Facebook Graph application programming interface (API). However, out of the 100 DMOs, the Facebook Graph API was unavailable for 28 DMOs because of their private settings. Consequently, the actual sample contained 72 DMOs.

Note that we could obtain the timestamp of the posts/comments and the anonymized unique user identification numbers. Similar to Da, Engelberg, and Gao (2011), we aggregated the data at the weekly level for each DMO and constructed a panel data set containing each DMO’s weekly social media activities and travelers’ weekly responses. Moreover, note that we do not use daily data because the variation of DMOs’ social media activities at the daily level was relatively small (e.g., average number of postings was 1.23, and standard deviation of postings was 2.19). Our final sample comprises 1,236 DMO-week observations from 72 events throughout 2010–2012 (i.e., spanning 147 weeks).¹ The selected DMOs manage different events as shown in Table 2) and have an average of 14,762 fans who “like” their Facebook pages. Note that we operationalize DMO as an organizer of a certain event—each DMO runs each event.

Table 2.

Sample Distribution by Event Characteristics.

	Number of Event	Number of Observations	Average Number of Facebook Fans
Event type
Arts and entertainment event	36	547	20,941
Community and organization event	29	594	11,159
Sports and travel event	7	95	1,712
Event size
Small/medium event	26	459	7,833
Big event	46	777	18,855
Event scope
Domestic event	29	332	4,747
International event	43	904	18,440
Total	72	1,236	14,762

Note: Arts and entertainment event covers concert tour and venue, movie and music award, etc. Community and organization event comprises government event, local business events, and community events.

Variables and Measures

The current study focuses on the DMO efforts and their information richness as the main independent variables. To determine the DMO efforts, we measured the number of postings and comments made by a focal DMO in week t. As a moderator, information richness was measured as the ratio of the number of the DMO’s enriched postings (e.g., flash, videos, photos, and music) to the total number of postings in week t. A higher value of this measure reflects the DMO’s greater efforts spent on social media in terms of the richness of information provided to travelers. Table 3 presents the definitions of the variables and descriptive statistics used in our research. On average, DMO posts and comments approximately 25 times per week, 49% of which contain enriched information.

Table 3.

Descriptive Statistics of Key Variables.

Variable	Definition/Operationalization	Mean	Standard Deviation	Minimum	Maximum
Customer engagement	A number of Facebook fans’ postings and comments in week t	91.733	295.413	0	4,281
Visitors	The total number of visitors in a focal event in year t	433,985	882,129	3,000	5,486,313
DMO efforts	A number of DMO’s postings and comments in week t	25.310	42.844	1	509
Information richness	Ratio of the number of a DMO’s enriched postings (flash, video, photo, music) to the total number of a DMO’s postings in week t	0.496	0.342	0	1
Fan size	The unique number of travelers who have Liked on a DMO’s Facebook event page	14,762	36,840	35	186,178
Post length	Average post length of a DMO in week t (bytes)	95.240	54.957	0	584
Comment length	Average comment length of a DMO in week t (bytes)	38.469	22.270	3	300
Event category	Dummy: 1 = art and entertainment, 2 = community and organization, 3 = sport and travel	1.634	0.621	1	3
Event scope	Dummy: 1 = domestic, 2 = international	1.731	0.443	1	2
Event size	Dummy: 1 = small/medium, 2 = big	1.371	0.483	1	2

Note: Variables in bold are key variables; weekly observations = 1,236 and yearly observations = 123.

We use customer engagement as our main dependent variable. To determine the customer engagement, we use the number of postings and comments by DMO’s Facebook fans in week t (S. Chung et al. 2019; Lee, Hosanagar, and Nair 2018). The mean value of weekly customer engagement is 91.73 (see Table 3). Furthermore, we use the number of visitors as an alternative measure of customer engagement. To determine the visitors, we measured the total number of visitors in a focal event in year t. We discuss the results using this alternative dependent variable in the succeeding section.

Thereafter, a few control variables were included to control for customer engagement on a DMO’s Facebook page. To control for the fan size, we include the unique number of travelers who have “Liked” a focal DMO’s Facebook page at the time of data collection. This measure is DMO-specific because Facebook does not provide the time trend of fan size. We also include the average Post Length and Comment Length of a DMO in week t (unit: bytes) to control for the amount of information. Table 3 shows that a DMO has an average of approximately 14,700 Facebook fans, while the length of a posting (comment) is approximately 95 (38) bytes. Lastly, event category (1 = arts and entertainment, 2 = community and organization, 3 = sports and travel), event scope (1 = domestic, 2 = international), and event size (1 = small/medium, 2 = big) dummies are included to control for time-invariant event-specific effects. Week dummies are included in our model to control for week-specific effects. Table 4 provides the correlations among the variables.

Table 4.

Correlation Coefficients among Key Variables.

	1	2	3	4	5	6	7
1. Customer engagement	1.000
2. Visitors	0.034	1.000
2. Visitors	(0.787)
3. DMO efforts	0.747	0.017	1.000
3. DMO efforts	(0.000)	(0.890)
4. Information richness	0.211	0.249	0.203	1.000
4. Information richness	(0.000)	(0.045)	(0.000)
5. Fan size	0.432	–0.105	0.289	0.162	1.000
5. Fan size	(0.000)	(0.402)	(0.000)	(0.000)
6. Post length	0.161	–0.201	0.119	0.099	0.400	1.000
6. Post length	(0.000)	(0.110)	(0.000)	(0.001)	(0.000)
7. Comment length	–0.056	–0.047	–0.077	–0.129	–0.001	0.060	1.000
7. Comment length	(0.046)	(0.709)	(0.007)	(0.000)	(0.947)	(0.034)

Note: The table presents pairwise correlation coefficients among the key variables used in our empirical analysis. Significance levels (p value) are displayed in parentheses. Correlation coefficients with visitors were based on yearly observations.

Empirical Models

We use the following empirical model to examine whether a DMO’s efforts are associated with customer engagement (hypothesis 1):

\begin{array}{l} C u s t o m e r E n g a g e m e n t_{i, t} \\ = α_{0} + α_{1} D M O' s S o c i a l M e d i a E f f o r t s_{i, t} \\ + α_{2} F a n s i z e_{i, t} + α_{3} l n {(P o s t L e n t h)}_{i, t} \\ + α_{4} l n {(C o m m e n t L e n t h)}_{i, t} + α_{5} E v e n t s c o p e_{i} \\ + α_{6} E v e n t s i z e_{i} + \sum_{γ j} E v e n t c a t e g o r y_{j} \\ + \sum_{γ s} T i m e D u m m i e s_{s} + δ_{i} + φ_{i, t} \end{array}

(1)

where the subscript represents DMO i in week t and δ_i indicates a DMO fixed effect. In addition, we use the following specification with the interaction term to test the moderating effect of Information Richness (hypothesis 2):

\begin{array}{l} C u s t o m e r E n g a g e m e n t_{i, t} \\ = β_{0} + β_{1} D M O' s S o c i a l M e d i a E f f o r t s_{i, t} \\ + β_{2} I n f o r m a t i o n R i c h n e s s_{i, t} \\ + β_{3} D M O' s S o c i a l M e d i a E f f o r t s_{i, t} \\ \times I n f o r m a t i o n R i c h n e s s_{i, t} + β_{4} F a n s i z e_{i, t} \\ + β_{5} l n {(P o s t L e n t h)}_{i, t} + β_{6} l n {(C o m m e n t L e n t h)}_{i, t} \\ + β_{7} E v e n t s c o p e_{i} + β_{8} E v e n t s i z e_{i} + \sum_{γ j} E v e n t c a t e g o r y_{j} \\ + \sum_{γ s} T i m e D u m m i e s_{s} + θ_{i} + ζ_{i, t} \end{array}

(2)

In equation (1), the main terms are the “unconditional” effects although the main effects in equation (2) are conditional on the value of the moderating variable. In particular, the main effect of the DMO’s efforts is the effect when the information richness is at its mean which is zero due to centering. We use the first model to test hypothesis 1 by examining the overall (unconditional) effect of the DMO’s social media efforts. Then, we use the results from estimating equation (2) to test the hypotheses on the interaction effect (hypothesis 2). We center the variables comprising the interaction terms by calculating the deviations from their respective mean values to reduce the multicollinearity between the main and interaction terms.

We consider the fixed and random effects models to account for the unobserved heterogeneity across DMOs. To choose between fixed and random effects models, we conducted a Hausman test and could reject the null hypothesis that errors are not correlated with the explanatory variables (Prob > chi² = 0.000). Therefore, we use the fixed effects model as our main specification. However, the Breusch–Pagan Lagrange multiplier (LM) test shows the presence of random effects (Prob > chi² = 0.124). Thus, we also estimate the random effects model and use it as a robustness check along with other alternative specifications. To improve the robustness of our analysis, we further conduct a generalized method of moments (GMM)–based dynamic panel data estimation (Arellano and Bover 1995; Blundell and Bond 1998).

Research Results

Main Analysis Results

We estimated our fixed effects model in a hierarchical manner. We first estimated our model with the control variables only (see column 1 of Table 5). Subsequently, we estimated equation (1) including the main independent variables and control variables (see column 2 of Table 5). Basing on the results of equation (1), we find that both the DMO’s social media efforts and its information richness show a significant positive association with customer engagement (α₁ = 4.936, p < 0.01; α₂ = 24.102, p < 0.01, respectively). This finding suggests that the DMO’s social media efforts increase customer engagement and also underscore the importance of social media communication as an effective strategy to achieve the success of the DMO’s event. Thus, this result provides support for hypothesis 1.

Table 5.

Model Estimation Results.

DV: Customer Engagement	(1)	(2)	(3)
Specification	FE	FE	FE
Specification	Control	Main IVs	Full
DMO marketing
DMO’s Social Media Efforts		4.936***	3.266***
DMO’s Social Media Efforts		(0.180)	(0.340)
DMO’s Information Richness		24.102***	59.166***
DMO’s Information Richness		(22.210)	(22.726)
DMO’s Social Media Efforts × DMO’s Information Richness			3.559***
DMO’s Social Media Efforts × DMO’s Information Richness			(0.625)
Control variables
Fan size	–0.137	–0.054	–0.015
Fan size	(0.366)	(0.278)	(0.274)
Post length	–0.323*	–0.241*	–0.261**
Post length	(0.177)	(0.135)	(0.133)
Comment length	–0.038	0.091	0.128
Comment length	(0.400)	(0.303)	(0.299)
Event scope	8.217***	9.233***	9.264**
Event scope	(11.034)	(11.032)	(11.496)
Event size	0.160	0.165	0.157
Event size	(13.019)	(14.020)	(15.157)
Event category and Time Dummies	Included	Included	Included
Observations	1,236	1,236	1,236
R ²	0.184	0.371	0.499

Note: Standard errors are in parentheses. Significance: ***p < 0.01; **p < 0.05; *p < 0.10.

The results of several control variables are noteworthy. First, Fan size and Comment Length are not statistically associated with customer engagement. Second, Post Length is negatively associated with customer engagement. A possible explanation for this result is that travelers are likely to prefer photos and videos over simple text-based messages. Further, regarding event scope, international events have more customer engagement than domestic events.

To test our hypothesis 2 (interaction between the DMO’s efforts and the information richness of its messages), we estimated equation (2). As shown in column 3 of Table 5, the coefficient on the interaction term is positive and statistically significant (β = 3.559, p < 0.01). This result indicates the complementarity between the DMO’s efforts and its information richness, thus supporting hypothesis 2—an increase in the information richness of messages strengthens the positive association between the DMO’s social media efforts and customer engagement. Comparing the two nested models (i.e., columns 2 and 3), we find a significant difference in R² between the two models: ∆R² = 0.128, F(1, 72) = 17.373, p < 0.01. This finding suggests that the DMO’s social media strategy, focusing on the volume of messages and the information richness of messages, is efficient in terms of its contribution to customer engagement.

Robustness Checks

To ensure the robustness of our findings, we conducted a series of additional analyses. First, we reestimated equation (2) with several alternative specifications. For ease of reference, we included the main results from the fixed effects (FE) model from Table 5 in column 1 of Table 6. We estimated a random effects model (RE) and an RE model through maximum likelihood, and we reported the results in columns 2 and 3, respectively. Then, we estimated a population-averaged (PA) model allowing for an exchangeable correlation structure of a generalized linear model (see column 4). The parameter estimates from these specifications are qualitatively similar to those of the model in column 1, indicating the robustness of our results.

Table 6.

Results of Alternative Specifications and Dependent Variable.

DV: Customer Engagement	(1)	(2)	(3)	(4)	(5)
Specification	FE	RE	RE-ML	PA	FE
Dependent variable	Customer engagement	Customer engagement	Customer engagement	Customer engagement	ln(Event Visitors)
DMO Marketing
DMO’s Social Media Efforts	3.266***	2.929***	2.929***	2.849***	0.014**
DMO’s Social Media Efforts	(0.340)	(0.308)	(0.285)	(0.282)	(0.004)
DMO’s Information Richness	59.166***	72.684***	72.684***	73.928***	0.045
DMO’s Information Richness	(22.726)	(19.617)	(18.158)	(17.708)	(0.475)
DMO’s Social Media Efforts × DMO’s Information Richness	3.559***	4.011***	4.011***	4.106***	0.012**
DMO’s Social Media Efforts × DMO’s Information Richness	(0.625)	(0.581)	(0.538)	(0.536)	(0.060)
Control variables
Fan size	–0.015	0.002***	0.002***	0.002***	2.105***
Fan size	(0.274)	(0.001)	(0.001)	(0.001)	(0.324)
Post length	–0.261**	–0.276**	–0.276**	–0.282**	–0.002
Post length	(0.133)	(0.121)	(0.112)	(0.111)	(0.002)
Comment length	0.128	0.045	0.045	0.030	0.031***
Comment length	(0.299)	(0.274)	(0.254)	(0.285)	(0.006)
Event scope	9.264**	34.260**	34.260**	34.556**	1.399**
Event scope	(11.496)	(16.782)	(15.534)	(13.222)	(0.284)
Event size	0.157	0.756	0.756	–0.227	2.105***
Event size	(15.157)	(20.157)	(18.658)	(16.877)	(0.324)
Event category and Time Dummies	Included	Included	Included	Included	Included
Observations	1,236	1,236	1,236	1,236	123
R ²	0.499	0.666	—	—	—
Wald χ²	—	—	1,355.46	29,719.40	145.87

Note: Standard errors are in parentheses. Significance: ^***p < 0.01; ^**p < 0.05; ^*p < 0.10.

Further, we considered the number of visitors in a focal event in year t as an alternative measure of customer engagement. Considering that a high level of social media engagement and usage is likely to attract more people and increase the number of visitors to a focal event (W. Lee and Paris 2013; Frankosky 2014), we tracked the number of visitors in each event on a yearly basis during our sample period: 2010–2012. Two authors and three independent tourism researchers directly contacted all regional DMOs and the Korea Tourism Organization (KTO), which officially supervises and/or manages events and the DMOs’ Facebook event pages in order to collect data. In case the DMOs and KTO did not provide the number of visitors for the event, the authors and the three independent researchers alternatively searched for this information through the news media and industry reports. Only when all five investigators arrived in an agreement were the searched data included in the analysis. Then, we computed the yearly average of all independent variables, resulting in 123 DMO-year observations over 3 years in total. We used the natural logarithm of visitors because of its skewness and the different absolute frequency. Column 5 of Table 6 presents the results of estimating an FE model with the number of visitors. The results of this analysis confirm our main findings based on the number of visitors in a focal event as an alternative measure of customer engagement.

Discussion and Conclusion

This study primarily investigated the relationship between a DMO’s social media efforts and customer engagement while examining the moderating role of information richness in a DMO’s social media efforts. Basing from a sample of 72 DMO’s Facebook event pages during 2010–2012, we found that the volume of a DMO’s social media efforts (i.e., quantity aspect) is significantly associated with an increase in customer engagement on a DMO’s Facebook event page. Interestingly, a DMO’s social media efforts and its information richness are complementary in increasing customer engagement. These findings provide important implications for theory and practice regarding smart tourism by using social media.

Theoretical Implications

The current study offers substantial implications for tourism research by addressing calls for effective social media management strategies in smart tourism (Gretzel et al. 2015) and by measuring DMOs’ social media success on the basis of the degree of customer engagement (Hays, Page, and Buhalis 2013). This study sheds light on the currently scant research stream of examining how travel organizations actually perform and manage social media activities in the context of smart tourism (Gretzel et al. 2015; Hays, Page, and Buhalis 2013). In particular, a majority of prior studies examined the DMOs’ social media success based on customer’s perspectives or user-generated content in the tourism literature. To fill this gap, our study proposed the significant role of DMOs’ social media efforts (i.e., supplier’s perspectives) and information richness of social media messages posted by DMOs in the smart tourism context. We developed hypotheses and empirically tested them by measuring both quantitative (i.e., number of postings and comments) and qualitative (i.e., information richness of messages) aspects of DMOs’ social media activities through social media analytics. Social media analytics is one of the research areas called for the future research (Zeng et al. 2010; Fan and Gordon 2014). More importantly, there is a recent tendency that travel experiences can be enhanced or cocreated through heavy use of ICTs and smart technologies in the smart tourism context (Boes, Buhalis, and Inversini 2016; Jovicic 2019). Therefore, the current study provides a valid rationale and improved understanding of how tourism organizations can actually construct and continuously manage social media to increase customer engagement and attract more people to destinations and events theoretically and methodologically.

The current study complements previous social media studies in tourism marketing and management by integrating both consumer-focused perspectives (i.e., customer social media engagement) and supplier-focused perspectives (i.e., DMOs’ social media efforts and their richness) (Leung et al. 2013; M. Lee, Lowry, and Delconte 2015). For instance, past studies have mainly focused on travelers’ information search behavior in social media and their social media use (e.g., sharing photos or travel experiences) (Xiang, Magnini, and Fesenmaier 2015; Xiang and Gretzel 2010; Law, Buhalis, and Cobanoglu 2014; M. Lee, Lowry, and Delconte 2015; N. Chung and Koo 2015) as the consumer-focused perspectives. The current study introduces another important topic (i.e., customer engagement in DMOs’ Facebook event pages) in the social media and tourism literature and presents an integrated view including consumers and suppliers. In specific, the present research is to hypothesize the relationships between suppliers’ social media efforts and consumers’ reactions toward them in the context of smart tourism. Therefore, our study expands the body of knowledge in the tourism literature not only by introducing another important topic in the social media and tourism literature but also by integrating two perspectives, including travelers and suppliers, in the smart tourism economy.

Extending the concept of customer engagement in the marketing (Leckie, Nyadzayo, and Johnson 2016; Sashi 2012; Brodie et al. 2013) and IS literature (Risius and Beck 2015; Laroche et al. 2012; Habibi, Laroche, and Richard 2014), we examined customers’ social media engagement in the context of smart tourism. While achieving a high level of customer engagement is considered ideal because of its positive outcomes (Dijkmans, Kerkhof, and Beukeboom 2015; Weinberg and Pehlivan 2011; Sashi 2012; Hollebeek, Glynn, and Brodie 2014; Brodie et al. 2013), few studies (e.g., Mariani, Mura, and Di Felice 2018; Dijkmans, Kerkhof, and Beukeboom 2015; Yoo and Lee 2017) have examined this important aspect in the tourism literature. Accordingly, the current study reconfirms and unveils the important role of DMOs’ social media efforts on customer engagement in the context of smart tourism.

Finally, our study shows the possibility of how social media analytics can be used for testing research hypotheses in tourism research rather than finding hidden patterns or predicting future outcomes. The current study employed social media analytics techniques to test the proposed hypotheses (Gandomi and Haider 2015). Additionally, we addressed how social media analytics can be integrated and tested with other performance data (i.e., the number of visitors in a focal event) in empirical models.

Managerial Implications

The findings of this study provide practical implications for managing social media in the tourism industry and for achieving higher customer engagement and actual visitors. First, our findings can help managers in DMOs gauge the impact of their social media efforts in terms of their contribution to customer engagement and even actual visitors. Our results provide an explanation as to why many DMOs are investing heavily in social media management (Semple 2016) and are interacting with existing and potential travelers through the DMO’s social media sites. The recent tourism industry faces challenges in the valuation of its intangible assets (Gretzel et al. 2006). Our findings can help tourism industries by providing evidence about the positive effect of investment in social media on both customer engagement and actual visitors. Thus, DMO’s social media efforts can play an important role in delivering informative messages about the destination/event and communicating with its travelers more efficiently and effectively.

Second, our study findings can help managers in DMOs formulate social media strategies that will affect customer engagement in social media. In general, our results suggest that DMOs’ social media efforts focusing on the quantity of messages and efforts focusing on the quality of messages are complements in increasing both customer engagement and actual visitors. Thus, DMOs should invest efforts in both the quantity and quality of their messages to maximize customer engagement.

Finally, our study provides a useful methodology based on social media analytics that DMOs can use to collect and analyze data from their own and their competitors’ social media sites, especially with respect to the quantity and quality dimensions of social media efforts. By collecting and analyzing data based on the social media analytics used in our study, DMOs can benchmark against their competitors, which in turn can enable them to formulate and revise their social media strategies. By doing so, DMOs can achieve a higher level of customer engagement than when they blindly invest in social media.

Limitations and Future Research

This research has limitations and offers directions for future research. First, our data were collected from DMOs’ Facebook event pages within a specific geographical context (South Korea), where tourism promotion and management are mainly organized and handled by a specific level of government (KTO and regional DMOs). Therefore, our findings can only be generalized to countries or travel organizations with similar conditions. In future work, several countries or regions having different management structures should be compared to provide a broader and deeper understanding. Second, although we empirically examined the impact of DMOs’ social media efforts in terms of quality (i.e., information richness) and quality (i.e., number of travelers’ posts and comments) on customer engagement (i.e., number of travelers’ postings and comments), other influential and measurable factors, such as message valence, message informativeness, DMOs’ responsiveness to travelers’ postings and comments, and the purpose or target of posted information, may be present (S. Chung et al. 2014). Third, while the current study empirically demonstrated the positive impact of DMOs’ social media efforts on customer engagement, we cannot ignore its potential negative effect. According to past research (Gretzel and Dinhopl 2014), travelers may terminate their relationships with travel-related organizations and destination on Facebook due to demographic and psychographic differences and also different travel planning and internet usage behaviors. Thus, the future studies may consider other contextual factors as moderators or examine the curvilinear impact of DMOs’ social media efforts on customer engagement. In addition, while the current study adopted operationalization of customer engagement from recent IS studies (S. Chung et al. 2019; D. Lee, Hosanagar, and Nair 2018), weighted customer engagement can be used for measuring customer engagement better in the tourism context as suggested by a recent study (Ge and Gretzel 2018). Thus, future studies may include other variables in social media for analysis. Fourth, our robust analyses suggest that DMOs’ social media efforts positively affect the number of visitors in a focal event. However, the number of observations for these analyses was significantly reduced because of missing values. Therefore, further studies should collect additional data or consider other performance variables as a longitudinal study.

Footnotes

Declaration of Conflicting Interests

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

Funding

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

ORCID iDs

Minwoo Lee

Jung Hwa Hong

Sunghun Chung

Notes

Author Biographies

Minwoo Lee is an Assistant Professor at Conrad N. Hilton College of Hotel and Restaurant Management, University of Houston. His research focuses on various roles of information technology, business analytics, machine learning, service innovation, and persuasion in the context of hospitality and tourism management.

Jung Hwa Hong is a Professor in the area of hotel management at Dongseo University in South Korea since 2004. Her research focuses on international hotel development and hospitality marketing.

Sunghun Chung is an Assistant Professor of Information Systems and Analytics in the Leavey School of Business at Santa Clara University. His primary research interests focus on the FinTech analytics, business analytical issues of social media, and the business value and economic impact of information technology.

Ki-Joon Back is Eric Hilton Distinguished Chair Professor at Conrad N. Hilton College of Hotel and Restaurant Management, University of Houston. His focused research areas are consumer behaviors involving branding, satisfaction, loyalty, consumption emotion, and decision-making process in various hospitality and tourism industries.

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