Beyond likes and tweets: Consumer engagement behavior and movie box office in social media

Accepted Manuscript Title: Beyond Likes and Tweets: Consumer Engagement Behavior and Movie Box Office in Social Media Author: Chong Oh Yaman Roumani Joseph K. Nwankpa Han-Fen Hu PII: DOI: Reference:

S0378-7206(16)30027-1 http://dx.doi.org/doi:10.1016/j.im.2016.03.004 INFMAN 2892

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INFMAN

Received date: Revised date: Accepted date:

25-3-2015 12-1-2016 20-3-2016

Please cite this article as: Chong Oh, Yaman Roumani, Joseph K.Nwankpa, Han-Fen Hu, Beyond Likes and Tweets: Consumer Engagement Behavior and Movie Box Office in Social Media, Information and Management http://dx.doi.org/10.1016/j.im.2016.03.004 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Beyond Likes and Tweets: Consumer Engagement Behavior and Movie Box Office in Social Media

Abstract This study examines the effects of social media, from the perspective of consumer engagement behavior (CEB), to investigate how CEB is associated with economic performance. Based on social media activities surrounding US movies, we used ordinary least square (OLS) regression models and found that CEB on Facebook and YouTube positively correlate with box-office gross revenue; however, the same effect was not observed on Twitter. This study proposed and tested a set of metrics for CEB on social media, and also provided empirical support for associating CEB with economic performance. The results underscore the importance of investing in social media communication across multiple channels. Keywords: social media, social media analytics, consumer engagement behavior, personal engagement, interactive engagement, movie box office

1

Introduction

Edge of Tomorrow, the much anticipated blockbuster film starring Tom Cruise, debuted in US cinemas nationwide on 6 June 2014 and grossed $28.7 million in its opening weekend. It was unexpectedly overshadowed by Fault in Our Stars, a $12 million budget movie with $48 million opening-weekend revenue. While Edge of Tomorrow cost the producer Warner Bros. a whopping $178 million in production budget and a return on investment (ROI) of 16%, Fault in Our Stars exceeded all expectations with its ROI of 400%.

Movie marketing executives are constantly perplexed and dumbfounded by such outcomes. A pertinent question arises: “What are reliable indicators of a movie’s box office performance?” Perhaps social media can help predict future fiscal performances of movie releases. According to Forbes (Mendelson, 2014), Fault’s “strong [social media] buzz would interest people outside of the converted….” The social media measures of both movies on the release day seemed to support this statement. Edge of Tomorrow had 444,000 Facebook (FB) likes and a 195,000 talk count, whereas Fault in Our Stars had 4.6 million FB likes and a 2.4 million talk count. These numbers showed that the wide division between the economic performances of the two movies was also reflected in their social media activities. To a certain degree, a movie’s social media activities may well be a preview of the movie’s future economic performance. With the explosive growth of the Internet as well as Web 2.0 technologies (Shang, Li, Wu, & Hou, 2011), people are turning away from such traditional media as television, radio, magazines, and newspapers toward online social media (Mangold & Faulds, 2009). Such social media sites as FB (http://facebook.com), YouTube (YT: http://youtube.com), and Twitter (TW: http://twitter.com) have become essential communication tools for millions of people – in short, they have become ubiquitous. Recent reports have shown that there are more than one billion monthly active users on FB (FB, 2015) and YT (YT, 2015), and 288 million monthly active users on TW (TW, 2015). Unlike traditional media, online social media have given the public more control over the content, frequency, and timing of generated information, and have provided them with immediate access to information at their own convenience (Vollmer & Precourt, 2008). More importantly, consumers are also using social media to search for information pertaining to purchasing decisions (Vollmer & Precourt, 2008).

The success of businesses depends on consumers’ purchasing decisions and the ability of organizations to influence, encourage, and engage with their consumers in the process of making such decisions. Logically, social media have become an important communication tool for businesses and marketers and a crucial factor for influencing consumers’ attitudes, opinions, and purchasing behaviors. For example, retail stores constantly use social media to advertise new products and services, offer discounts, engage with consumers, and seek feedback (Butler, 2013). In addition, through social media, businesses can build communities of new and existing consumers to collaborate with and to form relationships. As such, firms may potentially gain business value by interacting with their customers through social media (Culnan, McHugh, & Zubillaga, 2010). Given the importance of social media, a number of existing studies have looked at the relationship between consumer engagement (CE) and economic performance in social media (Braojos-Gomez, Benitez-Amado, & Llorens-Montes, 2015a; Chevalier & Mayzlin, 2006; Duan, Gu, & Whinston, 2008; Kaplan, 2012; Kaplan & Haenlein, 2011a; Rui, Liu, & Whinston, 2013). However, research into this relationship lacks consensus among scholars as to the effectiveness of social media as a channel for CE. In fact, scholars have claimed that the undertaking of scholarly CE empirical research in the context of social media is lagging and nebulous (Brodie, Ilic, Juric, & Hollebeek, 2013; de Valck, van Bruggen, & Wierenga, 2009). Moreover, existing research does not examine the effectiveness of CE on economic performance for businesses that engage through multiple social media channels as opposed to a single social media channel. In addition, there is a lack of research in CE metrics in social media perhaps due to social media’s nascent nature. Although prior research has attempted to account for consumer activities in social media with data from multiple social media channels (e.g., Braojos-Gomez et al., 2015a), a

cohesive theoretical framework is needed to define metrics for online CE behaviors (CEBs) to ensure the generalizability of those metrics. The purpose of the present study is to address these gaps in the literature. Drawing on prior research and the theory of CEB, we design a research model to examine the impact of CEB on future box-office economic performance. Specifically, we investigate how personal and interactive CE in social media channels of FB, YT, and TW relate to first-weekend movie box-office revenue. Although personal engagement refers to an individual’s need for stimulation and inspiration from using a website pertaining to one’s self-worth, interactive engagement describes one’s socialization and participation in an online community (Calder, Malthouse, & Schaedel, 2009). Given the distinction between personal and interactive engagement, this study examines the respective associations with movie box-office revenue, in order to achieve a more profound understanding of online CEBs. The article is organized as follows. First, it reviews the background literature on CE and social media. Second, it discusses our research objective in the social media context of FB, YT, and TW. Third, it presents the research model and the research hypotheses. Fourth, it reports on an empirical study based on collected data, followed by a presentation of the methodology and results. Finally, it concludes with a discussion of research findings and implications for theory and practice.

2

Literature Review

2.1

Consumer Engagement

Understanding CE and its utility is an important area of focus for both researchers and practitioners. The extant literature has associated a high level of CE with customer loyalty

(Brodie et al., 2013), satisfaction (Challagalla, Venkatesh, & Kohli, 2009), as well as the tendency of reputation to spread by word of mouth (WOM; Cheung, Lee, & Jin, 2011). CE describes consumers’ interactions and interaction experiences (Brodie et al., 2013). In essence, the concept of engagement relates closely to involvement and interactivity, two factors that reflect consumers’ attention to or interest in a brand, firm, or a product (Abdul-Ghani, Hyde, & Marshall, 2011), and extends beyond the cognitive and emotional aspects (Mollen & Wilson, 2010). CE therefore should be considered with cognitive, emotional, as well as behavioral dimensions (Brodie et al., 2013). Previous studies have distinguished between two types of online CE: social and conventional. Social online CE is enabled by social media websites such as FB and TW, whereas conventional online CE is enabled by web technologies such as a firm’s website (Braojos-Gomez et al., 2015a). Social online CE has been shown to have a greater positive impact in B2C firms, while conventional online CE is more critical for B2B firms (Braojos-Gomez et al., 2015a). We noted that different dimensions of CE in the current literature are behavioral and reflect consumers’ experiences while consuming products and services. To model these constructs accurately, Van Doorn et al. (2010) proposed the concept of CEB to capture how consumers behave in ways that exhibit their connection to a brand or a product, and thus reflect the different levels of CE. CEB goes beyond purchase behavior, and it is defined as consumers’ behavioral manifestation toward an organization or a brand as a result of motivational drivers (Van Doorn et al., 2010). Examples of CEB include WOM activities, reviews and recommendations, blogging, and microblogging. Although CE is a complex construct since the cognitive and emotional states of consumers are not easy to measure, the behavioral focus of CEB makes it an appropriate proxy for users’ level of engagement, and can be used to predict the consequences for firms,

including the financial, reputational, and competitive results (Van Doorn et al., 2010). We adopt the term CEB as a more salient representation of the CE construct for the social media measures in this study. 2.2

CEBs in Social Media

Social media are defined as Internet-based applications that are built on the foundations of Web 2.0 and that allow the creation and exchange of user-generated content (Kaplan & Haenlein, 2010). Social media can transform the business models of firms; as noted by Braojos-Gomez, Benitez-Amada, and Llorens-Montes (2015b), social media not only allow managers to efficiently sense and seize business opportunities and the reconfiguration of business resources but also help manage the relationships between customers and firms. In essence, firms’ capability of using social media for business transformation can vary greatly. Thus, additional research is imperative to understand firms’ social media strategies, management, measures, and business values (Braojos-Gomez et al., 2015b). CEB on social media involves a variety of activities, ranging from consuming content to participating in discussions and interacting with other consumers (Heinonen, 2011). CEB on social media therefore increases the chance of viral messages (Kaplan & Haenlein, 2011b), and creates ambient through constant reception and exchange of information (Kaplan, 2012). Even though research effort in online CEB has focused on identifying the antecedents and consequences of online WOM, empirical studies in this area have mostly used self-reported items to measure CEB in online communities, rather than actual behavioral data. The availability of behavioral data on social media has opened up new research opportunities. Existing studies have explored the relationship between social media channels and CEB, in order to examine their effects on business outcome and user awareness (e.g., Chevalier

& Mayzlin, 2006; Duan et al., 2008; Kaplan, 2012; Kaplan & Haenlein, 2011a; Phang, Zhang, & Sutanto, 2013; Rui et al., 2013). The collective results confirm that CEB has a positive impact on sales performance. For example, scholars found that negative consumer ratings and reviews of books on websites such as Amazon and Barnes & Noble have a greater effect on sales than those of positive reviews (Chevalier & Mayzlin, 2006). Using social network measures extracted from a popular product review website, Phang et al. (2013) found that increased user participation in social media led to favorable consumption intentions. Several studies have investigated the influences of online WOM on movie sales where scholars have found that volume, rate, and the valence of online WOM, such as those from TW, affect movie sales and movie ranking (Duan et al., 2008; Rui et al., 2013). Furthermore, the influences of online WOM appear to be moderated by the source and the content of WOM (Rui et al., 2013). It was not surprising that higher customer participation in brand communities was found to be related to higher product consumption (Rishika, Kumar, Janakiraman, & Bezawada, 2013; Wu, Huang, Zhao, & Hua, 2015). We can categorize CEB on social media by the level of consumers’ input (Heinonen, 2011; Shao, 2009): consumption of information, participation in social interaction and community development, and production of self-expression and self-actualization. For instance, reading the content or watching videos is categorized as CEB of consumption; CEB of participation refers to behavior such as following a profile on TW or liking a profile on FB. Creating content regarding a specific topic is considered as CEB of production. Motivated by the need for entertainment, social connection, or information (Heinonen, 2011), consumers often involve all three activities or a combination of two; thus, it is not always possible to differentiate clearly among such input activities (Shao, 2009).

Clearly, a detailed investigation of the different levels of activities for CE is much needed (Heinonen, 2011). It is also valuable to move beyond just online WOM and further analyze the outcome of different CEB to provide insights into how such behaviors on social media can affect economic performance.

3

Research Context

Movies clearly generate keen interest among the general public; according to BoxOfficeMojo, the US consumers spent over $10.36 billion on movies in 2014 (BoxOfficeMojo, 2015). In days before the web, individuals shared their excitement and anticipation of upcoming movies with their peers via face-to-face interactions. Today, with the emergence of Web 2.0, this behavior has migrated to the web in the form of movie review sites as RottenTomatoes and IMDB. More recently, with the advent of social media, individuals have been able to broadcast their opinions and interact with movie profiles and other consumers on social media channels such as FB, YT, and TW. Following is a brief discussion of these three popular channels. 3.1

Facebook

FB is a social network site featuring >1.39 billion active monthly users (FB, 2015). FB allows users to create their own profile page where they can engage with others by sending messages, sharing content (links, photos, and videos), and participating in groups and events. Moreover, users can comment on shared content and post “likes” (i.e., thumbs up icon). Similarly, organizations use FB to create a profile page as a primary advertising and marketing outlet. FB users can also engage with businesses by commenting on and liking their shared content. For businesses, this implies that engaging with consumers over FB may increase the customer lifetime value. Relevant to the context of this study, a movie marketing company may set up an

FB profile page to recruit individuals via likes, to share content, and to engage with its fan base. Figure 1 shows an FB profile page for the movie Iron Man 3 listing the total number of likes and talk-abouts. FIGURE 1 ABOUT HERE 3.2

YouTube

YT, currently the world’s largest video-sharing social media site1 (Alexa, 2014; ComScore, 2014), allows users to upload, share, and view videos as well as comment on them. Individuals and businesses can create their own profile page and video channels, and viewers can subscribe to certain channels, vote, and write comments. One of the distinctive features of YT is that videos are publically available and accessible to nonsubscribers, while commenting is allowed only to people registered with the platform. The popularity of YT has changed the way consumers interact with brands (Duan et al., 2008; Laroche, Yang, McDougall, & Bergeron, 2005). By posting videos on YT, responding to content via posts, and sharing posted content, consumers have the ability to influence marketing and communication campaigns, as well as the purchasing decisions of consumers across virtually every product category (Duan et al., 2008). Relevant to the context of this study, a movie marketing company may set up a YT channel to share movie trailers and other related videos in promoting its offerings and to engage with its subscribers. 3.3

Twitter

Since its launch in 2006, the popular microblogging channel TW has seen tremendous growth with >288 million monthly users (TW, 2015). TW allows its users to send a short message (a “tweet”), to repost an existing message (“retweet”), or to view existing messages. Based on an 1

According to Alexa.com, YouTube is currently the third-ranked website in terms of average daily visitors and page views after Google and Facebook. ComScore reported GoogleSites (primarily YouTube) as the top-ranked video content provider measured by 150 million unique video viewers in August 2014.

embedded social network, TW allows users to follow each other and view their latest tweets; thus, users have control over how content disseminates among their followers. Existing studies have highlighted the WOM effect of tweets and shown how businesses utilize TW to promote their products and services (Rui et al., 2013). In the context of this study, a movie marketing company may set up a TW profile page to recruit followers, broadcast promotional information via tweets, and to engage with the movie’s fan base.

4

Research Model and Hypotheses Development

Previous studies characterized CEB into different dimensions (Calder et al., 2009; BraojosGomez et al., 2015a; Gambetti, Graffigna, & Biraghi, 2012; Van Doorn et al., 2010; Vivek, Beatty, & Morgan, 2012). Braojos-Gomez et al. (2015a) distinguished between two types of CEB based on information technology (IT) capabilities and customer service performance: social online customer engagement and conventional online customer engagement. While the first type is enabled by social media, the second type is enabled by web technology. In their work, Vivek et al. (2012) conceptualized CEB as having a three-dimensional view: conscious attention, enthused participation, and social connection. Van Doorn et al. (2010) examined CEB based on the ways in which consumers might choose to engage and proposed five dimensions of CEB (valence, form/modality, scope, impact, and consumer goals). However, their study was conceptual in nature, and it has not been empirically supported. Gambetti et al. (2012) extended CEB beyond the traditional dimensions (cognitive, emotional, and conative) and introduced experiential and social dimensions. The experiential dimension comprises corporeal, physical, and multisensory elements of the consumer–brand encounter. However, the social dimension

emphasizes openness of the brand toward consumers, and it consists of interaction, dialogue participation, cocreation, and sharing of brand-related contents. Building on previous conceptualizations, theory, and quantitative research, Calder et al. (2009) examined the relationship between CEB in social websites with advertising effectiveness and proposed two dimensions of engagement: personal and social-interactive.2 According to these authors, personal engagement is affected by the individual qualities of the user and it involves the user’s stimulation and inspiration through interaction with the content. Interactive engagement, however, involves socialization and participation among people, and it is intrinsically and extrinsically motivated (Calder et al., 2009). The authors also concluded that engagement over social websites shares some commonality with other concepts such as consumer’s attention, involvement, interest, and interactivity. Practitioners and academics alike have suggested the use of likes and comments as important metrics for CEB in social media. The number of likes and comments a brand or product receives has been found to be a strong indicator and proxy for customer engagement behavior (Hoffman & Fodor, 2010). Similarly, He, Zha, and Li (2013) in their investigation of social media competitive analysis concluded that a higher number of likes and comments correspond to a higher level of CE. In fact, FB users who click “like” are more engaged, active, and connected than average users. These FB fans tend to have 2.4 times as many friends and 5.3 times more likely to click more links compared with other users (FB, 2010). More importantly, FB users who “like” a product or brand spend up to five times as much money on their product than those who do not “like” these products on social media (Hollis, 2011).

2

In this paper, we used the term interactive engagement instead of social-interactive engagement to reduce confusion between social-interactive engagement and social media engagement in general.

The use of personal and interactive model of CEB is driven by the parsimony and distinction that these dimensions offer in the social media landscape. The degree of personal and social interaction can have varying influence on web content usage behavior (Pagani & Mirabello, 2011). Likes represent self-expressive behavior (Schau & Gilly, 2003), which is consistent with the personal element of CEB. However, comments represent a more interactive posture because such communication is essential in maintaining social connectedness in social community (Lin, Fan, & Chau, 2014). Users in social media are more likely to comment when a posting is action driven, or when it contains validating information or new information (Malhotra, Malhotra, & See, 2013). Thus, comments are consistent with the interactive element of the CEB. The present study adopted the Calder et al. (2009) framework for two reasons. First, the context of this study was a logical extension of their model, which investigated CEB in social websites. In fact, the authors stated that the effect of engagement on communication effectiveness could be extended to other types of media such as mobile and social media (Calder et al., 2009). We thus heeded that suggestion in extending their CEB model to online engagement in the social media context to understand the relationship of CEB with future economic performance. Second, given the empirical support and rigor of the original model, we concurred that the model was a good fit for our research topic. Specifically, their framework allowed us to align well the available social media measures with the two constructs of personal and interactive engagement (concepts we discuss in the next section). Figure 2 illustrates this study’s conceptual model. FIGURE 2 ABOUT HERE The original model of Calder et al. (2009) is based on uses and gratifications (U & G) theory (Huang, Hsieh, & Wu, 2014; McQuail, 1985), which is popular in the area of communications.

In general, the theory touches on four dimensions: information, personal identity, integration and social interaction, and entertainment (McQuail, 1985). “Information” refers to finding out about relevant events, seeking advice and opinions, satisfying curiosity, and learning and gaining a sense of security through knowledge. “Personal identity” refers to finding reinforcement for personal values, identifying with others, and gaining personal insights. “Integration and social interaction” refers to identifying with others, gaining a sense of belonging, social conversing and interacting, and connecting with others. Lastly, “entertainment” refers to escaping from challenges, gaining intrinsic cultural and aesthetic enjoyment, and seeking emotional release. 4.1

Personal Engagement

Personal CEB is intrinsically motivated, and it reflects user experiences. With personal CEB, users seek stimulation and inspiration from using a site as it affirms their self-worth and enables interactions with others (Calder et al., 2009). According to Mangold and Faulds (2009), consumers feel more involved with organizations and their products when they engage in criticism, compliments, and suggestions. Pagani and Mirabello (2011), in their research, concluded that there is a positive effect between personal CEB and passive TV utilitarian value in social websites. CE, in alignment with personal values, will lead to higher enjoyment and personal satisfaction (Zhang, Lu, Wang, & Wu, 2015). Social media sites, such as FB, YT, and TW, allow passive seeking of information. For example, FB allows visitors to read comments, as well as status updates, and to “like” FB pages and their content. By liking the brand, users are able to associate with it and positively impact personal self-esteem and self-worth. YT permits users to subscribe to video channels, view videos, and “like” or “dislike” them. In addition, watching movie trailers, sometimes repeatedly, provides intrinsic enjoyment.

Likewise, TW enables users to follow other profiles and retweet messages. Followers of movie profiles receive all future tweets sent by the movie, which may be of utilitarian value to them. In the context of the present research, personal CEB is formed by subscribing (following) to movie channels, watching movie trailers, reading about the latest movie news, and liking movie-related content. Because personal CEB reflects user experiences and is intrinsically motivated, people who are involved in personal CEB are more likely to passively consume the content than those who are not involved (Pagani & Mirabello, 2011). Similarly, the past literature has stressed the importance of the relationship between CE and a firm’s economic performance. In a dynamic business environment, CEB is regarded as an effective vehicle for improving corporate performance, sales growth (Neff, 2007), superior competitive advantage, and performance (Brodie et al., 2013). FB users who “like” a product or brand spend up to five times as much money on their liked products compared with those users who do not “like” these products (Hollis, 2011). Thus, we argue that movies with high personal CEB will positively correlate with increasing movie box-office revenue. With this discussion, we posit that HYPOTHESIS 1 (H1): Ceteris paribus, there is a positive correlation between personal consumer engagement behaviors in Facebook, YouTube, and Twitter and each movie’s opening-weekend box-office revenue.

In this study, we used the measures of FB likes, YT views, and TW followers as predictor variables for personal CEB. 4.2

Interactive Engagement

Interactive CEB involves socializing and participating with the community. This type of engagement is intrinsically and extrinsically motivated, and it values input from others (Calder et

al., 2009). It is also associated with a larger engagement experience, such as valuing input from community, and a sense of social collaboration with others, rather than focusing on personal and individual activities. Existing research has highlighted the positive effect of interactive CEB on electronic commerce sales and advertising. For example, Calder et al. (2009) concluded that interactive CEB through participation, discussions, and content sharing was positively correlated with advertising effectiveness. Similarly, interactive CEB was found to have a positive effect on active and passive usage of social TV websites (Pagani & Mirabello, 2011). Moreover, Gillin (2009) found that the role of interactive CEB in building engagement through interactions and promoting communication among potential and existing consumers does improve sales. The objective of every social media channel is to encourage its users to socially engage with one another. For example, FB and YT enable users to engage through conversations by posting text-based comments and replies. Via the dimensions of participation and socialization, users share with peers their excitement and anticipation as well as their opinions about brands and other topics. Similarly, TW allows its users to send tweets and to retweet messages. Wohn and Na (2011) performed content analysis of messages posted on TW and concluded that people use TW to selectively seek others who have similar interests. Thus, individuals engage with others by sharing opinions or even their consumption behaviors. In this study, individuals share links to movie trailers with his/her network of followers, writing comments on movie-related posts and news, retweeting movie-related announcements, as well as receiving feedback from the community. Interactive CEB increases brand familiarity, which is defined as brand reflections accumulated by the consumer through advertising exposure, product usage, and purchase

behavior (Alba & Hutchinson, 1987). Such brand experiences further improve consumer knowledge about the brand and should increase consumers’ desire to “own” the brand and thus increase the likelihood of consumption. Moreover, interactive CEB has a positive effect on consumers’ buying decisions and a means of value creation and competitive advantage for digital content providers (Oestreicher-Singer & Zalmanson, 2012). Another important aspect of interactive CEB is that people trust their family and friends and value their opinions when discussing products or brands endorsed by them (Kozinets, de Valck, Wojnicki, & Wilner, 2010). In the context of this study, interactive CEB occurs when users discuss and share their opinions about movies with others. So as users engage with their peers by posting comments on FB and YT or sending tweets about a movie, higher WOM effects are generated that ultimately translate into higher number of moviegoers and higher revenues. Thus, we argue that movies with higher interactive CEB will positively correlate with increasing movie box-office revenue. With this discussion, we posit that HYPOTHESIS 2 (H2): Ceteris paribus, there is a positive correlation between interactive consumer engagement behaviors in Facebook, YouTube, and Twitter and each movie’s openingweekend box-office revenue.

In this study, we used the measures of FB talk-abouts, YT comments, and TW abouttweets as predictor variables for interactive CEB. 5

Data and Variables

We downloaded data for this study using automated scripts via Web API (Application Programming Interface)3 from three popular social media channels: FB, YT, and TW, for sample movies released in the US market between November 2013 and October 2014. We retrieved FB, 3

API is a set of programming instructions and standards for accessing web-based applications.

YT, and TW profiles for each movie 1 day before each movie’s opening-weekend release day. TW tweets about each movie and FB talk counts were downloaded during the 7 days before the release day4. We selected these social media channels based on their popularity and their relevance to the movie industry, in particular, due to their wide reach and high usage. We obtained such movie box-office information as opening-weekend gross revenue, release type, and movie genre from boxOfficeMojo.com (MOJO: http://www.boxofficemojo.com). We removed movies with missing data (e.g., those without box-office gross revenue, FB, YT, or TW profiles) from the final dataset, resulting in a sample of 106 movies. Table 1 shows the list of top 10 movies with key social media variables and other movie information (e.g., distributor, release date, and type) sorted by opening-weekend gross revenue (OPEN-GROSSi). The movie with the highest OPEN-GROSSi in our dataset was Hunger Games: Catching Fire ($158.074 million revenue). The mean OPEN-GROSSi in our sample is $15.764 million. TABLE 1 ABOUT HERE

5.1

Dependent Variables

The main dependent variable for this study was each movie’s (i) opening-weekend box-office gross revenue (OPEN-GROSSi) obtained from boxOfficeMojo.com based on showtime tracking information. Opening-weekend gross revenue (OPEN-GROSSi) was a reliable measure for a movie’s performance that has been used in the previous literature (Duan et al., 2008; Reinstein & Snyder, 2005; Rui et al., 2013). Opening weekend, consisting of the Friday, Saturday, and 4

Most of our variables are metrics that are accumulated over time from when a profile is created or when a video is posted. Examples are the number of Twitter followers and YouTube video views. Such data are collected the day before the release date in order to obtain the most up-to-date information. Another type of data is aggregated over a specific time period much like a snapshot view. In this study, number of tweets about a movie and number of Facebook TALK are metrics collected during the 7 days before the release date.

Sunday of the first week of a movie’s run, has traditionally been the highest earning weekend for most movies (BoxOfficeMojo, 2015). As OPEN-GROSSi is collected immediately after a movie’s release, it is an ideal dependent variable to avoid possible endogeneity issues between the movie’s screenings from the initial weeks, which may influence social media after the movie’s release (Reinstein & Snyder, 2005). An additional dependent variable of first month’s gross (MONTH-GROSSi) revenue is used in the robustness test models. MONTH-GROSSi represents gross revenue generated within the first month after the release date. 5.2

Independent Variables

5.2.1

Social Media Variables

The main predictor variables for this study consisted of social media variables obtained from the social media channels FB, YT, and TW. The motivation for selecting these independent variables was consistent with prior research on social media analytics (Cha, Kwak, Rodriguez, Ahn, & Moon, 2007; Jin, Wang, Luo, Yu, & Han, 2011) and a movie’s box-office performance (e.g., Rui et al., 2013). For example, Cha et al. (2007) examined the number of views and rankings of YT videos in relation to demand. Similarly, Jin et al. (2011) used FB likes in relation to user interest as an effective way to share and promote information in social media. Rui et al. (2013) examined the relationship between the TW numbers of tweets about a movie (i.e., WOM) and each movie’s economic performance. Following are the independent variables from our study. The variable FB_LIKESi represents the count of FB likes for each movie (i) profile, a metric that accumulated as more consumers liked the profile over time. It was downloaded 1 day before the release date. FB_LIKESi is known to be a major social media driver, and it has proven to determine user interest and to be an effective strategy to share and promote information in

social media (Jin et al., 2011). FB’s like button allows users to express approval of companies, organizations, articles, ideas, and others (Kerpen, 2011). Individuals are motivated to like movie profiles as a way to build self-esteem, show support, and a consequence of an existing connection with the movie. Such personal CEB as FB_LIKESi has a high likelihood of binding individuals to the movie. FB_LIKESi ranged from 131 to 31.958 million “likes” with a mean of 1.399 million. The variable FB_TALKi represents the number of unique users who have created what could be called a “story” about the movie (i) profile in a 7-day period before the release date. This included interacting with the movie profile by liking the profile, sharing something from the movie profile, posting on a wall about the profile, mentioning the profile, as well as many other actions5 (Darwell, 2012). Essentially, it is a deeper measure of CEB from each movie profile’s followers in addition to the “like” button. Such interactive engagement as FB_TALKi tends to bind individuals to the movie. FB_TALKi ranged from 9 to 1.579 million counts with a mean of 184,703. The variable YT_VIEWSi represents the YT count of views for a particular video of each movie (i) profile, a metric that accumulated as more consumers view the video over time. It was downloaded 1 day before the release date. This variable is a measure of popularity of the particular video (Susarla et al., 2012). Individuals are motivated to view a particular movie trailer in seeking information for the purpose of entertainment or curiosity. Especially for popular movies, it is common for videos to be viewed multiple times by the same individual. Similar to FB_LIKESi, such personal CEB as YT_VIEWSi has a high likelihood of binding individuals to the movie. YT_VIEWSi ranged from 116 to 30.3 million views with a mean of 2.859 million.

5

Refer to Darwell (2012) for a full list of actions in determining the FB_TALK or “People Talking About This” metric.

The variable YT_COMi represents the YT count of comments from YT viewers for a particular video posted by the movie (i) profile, a metric that accumulated as more consumers post comments about the video over time. It was downloaded 1 day before the release date. Individuals on YT are motivated to comment on videos to show support, share opinions, participate in conversations, and to connect with peers. Similar to the variable FB_TALKi, such interactive engagement as YT_COMi tends to bind individuals to the movie. The variable YT_COMi ranged from 0 to 62,791 comments with a mean of 2,799. The variable TW_FOLLOWi represents the count of TW followers for each movie (i) profile. This metric accumulated as more consumers followed the profile over time and was downloaded 1 day before the release date. This has been termed a measure of popularity in the information systems literature (Hoffman & Fodor, 2010) based on the assumption that individuals are motivated to follow movie TW profiles in seeking more information about the movie and intrinsic enjoyment from new trailers and other related promotional materials. These materials could be interviews with directors, actors, as well as photo shots of the movie. Similar to the variable FB_LIKESi, such personal CEB as TW_FOLLOWi has high likelihood of binding individuals to the movie. TW_FOLLOWi ranged from 3 to 1.99 million followers with a mean of 109,649. The variable TW_ABOUTi represents the count of TW public tweets sent by other TW profiles about the movie (i) profile. We downloaded TW_ABOUTi during the 7-day period leading up to the movie’s release date. TW_ABOUTi represented the public’s WOM (Rui et al., 2013) surrounding each movie, which was related to the movie’s ability to connect with its audience. Individuals are motivated to share content, engage with peers, and participate in conversations about movies in establishing bonds with his or her group of followers. Research

has shown that such interactive engagement in building engagement through interactions and promoting communication among consumers improves sales (Gillin, 2009). Similar to the variable FB_TALKi, an interactive CEB such as TW_ABOUTi has a high likelihood of binding individuals to the movie. TW_ABOUTi ranged from 2 to 289,756 tweets with a mean of 16,645. Table 2 shows descriptive statistics for all the relevant variables. TABLE 2 ABOUT HERE 5.2.2

Control Variables

The control variable RELEASEi represents each movie’s (i) release type (either wide or limited). Wide or major releases are nationwide releases involving a significant amount of marketing and a large promotional spending budget, a well-known cast, and a large number of theaters. Limited releases, however, are those movies with limited marketing budget and are usually confined to a small number of theaters. This control variable accommodated for confounding variables that relate to differences between the two groups of movies affecting box-office revenue. Specifically, wide releases are those associated with higher social media activity as well as higher gross revenue. This is a binary variable where wide = 1 and limited = 0. In our sample, 55% (N=59) of the movies were wide release and 45% (N=47) were limited release. The control variable GENREi represents the nine types of movie genre consisting of action, drama, thriller, animation, comedy, horror, family, documentary, and others. Drama has the highest frequency (35.85%), followed by action (16.04%), thriller (12.26%), and comedy (12.26%). Table 3 shows the genre distribution for all movies while Table 4 gives a bivariate Pearson’s correlation matrix for key explanatory variables. 6

6

Marketing spending for each movie could possibly affect its exposure on social media and thus influence CEB. Nevertheless, the unavailability of marketing budget data limits our ability to include marketing budget in the model. Movie Picture Association of America (MPAA) had ceased disclosing movie production and marketing costs since 2009 (Verrier, 2009). Various sources such as BoxOfficeMojo.com and other movie sites have published

TABLE 3 ABOUT HERE TABLE 4 ABOUT HERE

6

Methodology and Results

The main focus is to examine two groups of robust OLS stepwise regression models regressing opening-weekend movie box-office gross revenue (OPEN-GROSSi) for each movie (i) on CEB social media metrics controlled by movie box-office measures. The two groups presented various models in examining both social media personal and interactive CEB variables with the movie control variables of release type (RELEASEi) and genre (GENREi) in relation to OPEN-GROSSi. These models included a robust option for estimating the standard errors using the Huber-White sandwich estimators (Huber, 1967; White 1980), which account for minor concerns of meeting assumptions underlying multiple regressions such as problems with normality and heteroscedasticity (Chen, Ender, Mitchell, & Wells, 2003). Essentially, this option does not change coefficient estimates but instead provides a more reasonable set of p-values (Allison, 1995). In addition, we also tested models of the first-month (MONTH-GROSSi) box-office gross revenue for robustness to better understand the examined relationships. In order to address the research question, we rely on robust OLS models to examine whether and to what extent personal and interactive CEB relate to a movie’s economic performance. Even though causal relationship was not tested in this study given the limitation of data availability, the testing results of the stepwise regression models do provide insights into whether robust associations exist between CEB and a movie’s economic performance. 6.1

Do personal CEBs in social media relate to a movie’s economic performance?

estimated marketing spending data; however, as such information is estimated, it may differ from one site to another and the reliability of such data is unknown. Nonetheless, the models in this study do control for RELEASE type of each movie, which is related to marketing budget.

We first examined the different personal engagement predictors from each social media channel separately in the following robust OLS stepwise regression models in relation to a movie’s economic performance (OPEN-GROSSi). We started with the baseline model of RELEASEi (Model 1) followed by TW_FOLLOWi (Model 2), FB_LIKESi (Model 3), and YT_VIEWSi (Model 4), respectively. The aggregated models tested all three social media predictors in a single personal CEB model (Model 5) as well as controlled for genre types (Model 6). The models are below. OPEN-GROSSi = β0 + β1*RELEASEi + ei

(1)

OPEN-GROSSi = β0 + β1*RELEASEi + β2*TW_FOLLOWi + ei

(2)

OPEN-GROSSi = β0 + β1*RELEASEi + β2*FB_LIKESi + ei (3) OPEN-GROSSi = β0 + β1*RELEASEi + β2* YT_VIEWSi + ei (4) OPEN-GROSSi = β0 + β1*RELEASEi + β2*TW_FOLLOWi + β3*FB_LIKESi + β4*YT_VIEWSi + ei

(5)

OPEN-GROSSi = β0 + β1*RELEASEi + β2*TW_FOLLOWi + β3*FB_LIKESi + β4*YT_VIEWSi + β5*DRAMAi + β6*THRILLERi + β7*ANIMATIONi + β8*COMEDYi + β9*HORRORi + β10*FAMILYi + β11*DOCUMENTARYi + β12*OTHERSi + ei (6) β0 is the intercept and ei is the error term. The primary objective was to measure β2, β3, and β4, which are robust coefficient values for the personal CEB social media predictor measures. Table 5 presents the results for estimating these models. TABLE 5 ABOUT HERE The baseline model of RELEASEi had a robust R-squared value of 0.284 (Model 1). The addition of TW_FOLLOWi to Model 1 had minimal effect on outcome and generated a slightly

higher R-squared value of 0.292 (Model 2). It was surprising that the TW effect (TW_FOLLOWi) was not significant. Indeed, past studies have found that TW measures may not relate well to box-office performance (Wong, Sen, & Chiang, 2012). However, Models 3 and 4 showed strong personal CEB effects through FB (FB_LIKESi) and YT (YT_VIEWSi). Specifically, the robust Rsquared values were 0.525 (Model 3) and 0.691 (Model 4). Both FB_LIKESi (β2 = 0.003) and YT_VIEWSi (β2 = 0.003) were positively correlated with OPEN-GROSSi. Model 5 with robust Rsquared of 0.735 confirmed the findings of Models 2, 3, and 4 in implying that on average one unit of FB_LIKESi was related to $1.00 (β3 = 0.001), one unit of YT_VIEWSi was related to $2.00 (β4 = 0.002) in gross revenue, while TW_FOLLOWi is not significant. Model 6, with the inclusion of GENREi control variables, generated a robust R-squared of 0.77 and obtained similar results for the predictor variables. The results also showed that the mean of all genre types are less than the mean of the omitted variable of action7. Thus, we conclude that Hypothesis H1 is supported where most personal CEB in social media channels is relevant in explaining gross revenue beyond the effects of RELEASEi and GENREi.8 The mean variance inflation factor (VIF) values for all models were below 10, which is an indication of a lesser concern with multicollinearity (Mason, Gunst, & Hess, 1989; Kennedy, 1992). 6.2

Do interactive CEBs in social media relate to a movie’s economic performance?

This section examines the relationship between different interactive CEB predictors from each social media channel in relation to a movie’s economic performance (OPEN-GROSSi). We referenced the baseline model of only RELEASEi (Model 1) as a benchmark followed by

7

Refer to Acock (2014) for more information on regression with categorical variables. We perform the analysis incorporating all the variables using partial least squares (PLS) method and find the results consistent with the robust OLS stepwise regression models, supporting H1. Specifically, the path coefficients for RELEASEi = 0.299 (p < 0.001), TW_FOLLOWi = 0.017 (not significant), FB_LIKESi = 0.253 (p < 0.001), YT_VIEWSi = 0.543 (p < 0.001). A significant portion of the variances in OPEN-GROSSi can be explained by personal engagement predictors (R2 = 0.735). 8

TW_ABOUTi (Model 7), FB_TALKi (Model 8), and YT_COMi (Model 9), respectively. The aggregated models in this group tested all three social media predictors in a single interactive CEB model (Model 10) as well as controlled for genre types (Model 11). The models are as follows: OPEN-GROSSi = β0 + β1*RELEASEi + β2*TW_ABOUTi + ei

(7)

OPEN-GROSSi = β0 + β1*RELEASEi + β2*FB_TALKi + ei

(8)

OPEN-GROSSi = β0 + β1*RELEASEi + β2* YT_COMi + ei

(9)

OPEN-GROSSi = β0 + β1*RELEASEi + β2*TW_ABOUTi + β3*FB_TALKi + β4*YT_COMi + ei (10) OPEN-GROSSi = β0 + β1*RELEASEi + β2*TW_ABOUTi + β3*FB_TALKi + β4*YT_COMi + β5*DRAMAi + β6*THRILLERi + β7*ANIMATIONi + β8*COMEDYi + β9*HORRORi + β10*FAMILYi + β11*DOCUMENTARYi + β12*OTHERSi+ ei (11) β0 is the intercept and ei is the error term. The primary objective was to measure β2, β3, and β4, which are the robust coefficient values for the various interactive CEB social media predictor measures. Table 6 presents the results for estimating these models.9 TABLE 6 ABOUT HERE The addition of TW_ABOUTi to Model 1 increased robust R-squared to 0.412 (Model 7). Models 8 and 9 showed strong interactive CEB effects through FB (FB_TALKi) and YT (YT_COMi). The robust R-squared values were 0.608 (Model 8) and 0.715 (Model 9). Both FB_TALKi (β2 = 0.051) and YT_COMi (β2 = 2.119) were positively correlated with OPEN-GROSSi. We noted that TW_ABOUTi, which was significant in Model 7, was not significant in Model 10. We thus 9

We perform the analysis incorporating all the variables using PLS method and find the results consistent with the robust OLS stepwise regression models, supporting H2. Specifically, the path coefficients for RELEASEi = 0.265 (p < 0.001), TW_ABOUTi = 0.212 (not significant), FB_TALKi = 0.415 (p < 0.001), YT_COMi = 0.599 (p < 0.001). In addition, a significant portion of the variances in OPEN-GROSSi can be explained by interactive engagement predictors (R2 = 0.847).

observed that social interactive CEB measures of FB_TALKi and YT_COMi may override the effect of TW_ABOUTi in relation to box-office performance. Model 10, the aggregated model, confirmed the findings of Models 7, 8, and 9 in implying that, on average, one unit of FB_TALKi was related to $33.00 (β3 = 0.033) while one unit of YT_COMi was related to $1,871 (β4 = 1.871) in gross revenue for each movie. Model 11, with the inclusion of genre control variables, generated a robust R-squared of 0.846 and produced similar results for the predictor variables. The results also showed that the mean of all genre types is less than the mean of the omitted variable of action. Thus, we concluded that most interactive CEBs in social media channels were relevant in explaining gross revenue beyond the effects of RELEASEi and genre, and these models supported Hypothesis H2. The mean VIF values were below 10, which indicated a lesser concern with multicollinearity (Kennedy, 1992; Mason et al., 1989). 6.3

Robustness Tests

For the robustness test, we examined the first month’s gross revenue (MONTH-GROSSi; Models 12 and 13) for each movie. Table 7 presents the results for estimating these models. MONTHGROSSi is a reliable measure, as on average 84% of movie revenue in our dataset occurred within the first month. As a cautionary note, as MONTH-GROSSi extends beyond the movie release date, this measure may be influenced by social media conversations from consumers who have watched the particular movie after its release date. Models 12 and 13 showed high robust R-squared values of 0.739 (Model 12) and 0.817 (Model 13), respectively. This was consistent with OLS regression results from the first-weekend gross models (Models 1–11) in confirming the saliency of social media in relation to a movie’s performance outcomes. Table 7 presents the results for estimating the MONTH-GROSSi models. TABLE 7 ABOUT HERE

6.4

Addressing Multicollinearity Concerns

The high bivariate correlations (Table 4) among the variables in our dataset may pose a concern with multicollinearity. The key issue was that, while a multiple regression model with correlated independent variables may demonstrate the ability of the bundle to predict the dependent variable, it may not be reliable in explaining how individual predictors relate to the outcome variable or even how they relate to one another. Multicollinearity needs to be examined in the context of this study (O’Brien, 2007). This implies that even though bivariate correlation between variables could have been high, the context of this study may have required it to be so. For example, the correlation between YT_COM and YT_VIEW is high (corr=0.915), but both measures behave differently; YT_COM refers to comments about the movie trailer while YT_VIEW refers to views of a video of the movie trailer. However, in the context of this study, popular movies were expected to have high measures of both YT_COM and YT_VIEW. The same argument is applicable to YT_COM and TW_ABOUT (corr=0.637); although both were similar behaviors of commenting and sharing opinions about movies, both were from different social media channels, and were most likely not have been generated by the same group of individuals. In essence, these measures were from different dimensions of the CEB theoretical framework. VIFs may provide additional insights to explain the high bivariate correlation. As a common rule of thumb, while a VIF value above 5 is a cause for concern (Menard, 1995), a value above 10 is regarded by practitioners to be a sign of serious or severe multicollinearity (Mason et al., 1989; Kennedy, 1992). As shown in the results, the average VIF values for all models were below 5. Another telltale sign of multicollinearity is having high standard errors, but those numbers in our models were not high, as evidenced from our results. From these

discussions, we posited that despite high bivariate correlations among independent variables, the VIF values and standard errors were acceptable, and thus multicollinearity is not a concern in our models.

7

Discussion

This study explored how CEB, in the dimensions of personal and interactive engagement, shapes a movie’s future box-office economic performance in the context of social media. The research model, estimated using data extracted from three popular social media channels (FB, YT, and TW), finds good overall support. The results indicated that CEB, specifically personal and interactive engagement, positively relates to future economic performance. More importantly, we assert that social media do not function in silos as separate channels but as a collective presence in harnessing CEB. Thus, a movie should not be promoted in a single social media channel but should have an active presence in all the popular channels. 7.1

Implications for Research

The findings from this study provide robust empirical support for the impact of CEB on future economic performance in the social media context. Previous research have strongly advocated for businesses to collect, monitor, and analyze consumer opinions in order to extract patterns and intelligence in supporting decision making and enhancing competitive advantage (Fan & Gordon, 2014; He, Wu, Yan, Akula, & Shen, 2015; Zeng, Chen, Lusch, & Li, 2010). In heeding the call, the current study advanced the CEB theory building process by developing and empirically testing a set of metrics from FB, YT, and TW social media channels. Furthermore, the study empirically validated this conceptual model and the results attest to the value of the research model.

As the previous literature has limited such investigations to a single social media channel (e.g., TW, by Rui et al., 2013), this work furthers our understanding of CEB in social media by examining how firms can take advantage of popular social media channels simultaneously. Specifically, our study investigates online WOM from the theoretical perspective of CE, identifies measures for two types of CEBs (i.e., personal and interactive engagements) in the social media context, and examines their respective associations with a movie’s future performance, within a broader range of social media channels (i.e., FB, YT, and TW). Built upon a sound theoretical foundation, this study contributes to the social media research not only by offering further explanations for the well-received phenomenon but also by presenting a generalizable framework for investigating the effects of various social media channels on economic performance. For example, prior studies have found the number of tweets about a movie to be a strong predictor of the movie’s future economic performance (Rui et al., 2013). Consistent with the current literature, our findings reveal that when TW is examined in isolation, TW_ABOUTi (count of TW public tweets) is positively associated with movie performance (Rui et al., 2013); however, this significant association disappears when FB and YT measures are included in the model. We believe that this lack of significance is an important finding, because it reaffirms and addresses an issue noted in previous research (e.g., Wong et al., 2012). Wong et al. (2012) noted that the weak association between the count of TW followers and box-office performance may be due to the prevailing and overriding effects of two other popular social media channels, FB and YT. This finding confirms that more dominant consumer user-generated content channels, such as FB and YT, may weaken the effect of TW. Further research in this area may provide additional insights into this interplay. Another possible explanation for TW’s lack of significance is that, the very nature of TW is designed for broadcasting and news sharing

rather than a channel for user-generated information richness (e.g., FB). Therefore, it may undercut CEB and related effects on movie performance (Malhotra et al., 2013). Current literature has used media richness theory to explain how communication media’s ability to convey information richness can affect purchase expenditures and outcomes (Suh, 1999; Goh, Heng, & Lin, 2012). Thus, understanding how TW compliments richer social media channels, such as FB and YT, may help movie studios that are grappling with finding the optimal social media marketing strategy in an increasingly convoluted social media landscape. We also contribute to the online CEB literature by extending the effort in measuring CEB in social media by prior studies, such as Braojos-Gomez et al. (2015a; 2015b). Although activity data from multiple social media have been used to measure CEB, we further identify and distinguish measures as related to the two dimensions of the CEB framework and examine their respective effects. By doing so, we offer a generalizable approach to account for different social media activities on various channels with proper theoretical support. Activities on other social media channels can therefore be taken into consideration in future research with the expected emergence of new social media channels. With social media being such a pertinent element in today’s business world, having a validated CEB model for social media is critical to enhance research in this nascent area of Web 2.0. We further provided empirical validations of the Calder et al. (2009) framework in presenting it as a valid model in the context of the relevant social media measures, the validation of which should benefit other researchers working in the social media realm. Other research contributions include presenting the importance of a holistic approach in working with social media instead of focusing within a single channel as is the standard practice of mainstream academics.

7.2

Implications for Practice

Our findings should be of practical value to managers and executives alike who are seeking effective communication channels in driving CE and overall economic performance. We provide evidence to support the role of social media as an effective medium fulfilling this objective. More specifically, our study suggests that FB- and YT-based CEBs are key predictors of a movie’s future economic performance. Thus, movie executives and managers need to recognize the business value a firm can extract from FB and YT, respectively. First, given that on average one unit of FB_LIKESi is related to $1.00 gross revenue while one unit of YT_VIEWSi is related to $2.00 gross revenue, movie executives and managers need to allocate more resources and develop innovative social media contents and strategies that will generate personal CEB. Second, given that on average, one unit of FB_TALKi is related to $33.00 in gross revenue while one unit of YT_COMi is related to $1,871.00 in gross revenue for each movie, firms should seek to foster interactive CEB within FB and YT. In fact, it is obviously more effective and beneficial for movie studios and executives alike to aggressively pursue innovative social media strategies and content deliveries that can trigger the transition of personal CEB from FB_LIKESi and YT_VIEWSi to more interactive CEB, such as those of FB_TALKi and YT_COMi. In addition, the results underscore the importance of investing in the development of integrated social media communication channels across multiple platforms instead of treating them as disparate silos that operate independently (Hanna, Rohm, & Crittenden, 2011). For managers and executives, this means that the key to creating and sustaining CE may come from the ability to adequately use cohesive social media strategies across different channels and different online communities. In essence, social media channels differ in their structure, content, and delivery. Thus, we urge managers to design cohesive social media strategies that take advantage of the strengths and yet

able to create synergy among the different channels. For example, movie studios could disseminate periodic news and updates about the actors and the movie via FB and TW with inbound links to movie trailers or related short videos on YT. Conversely, YT videos should have outbound links to encourage more CE on FB and TW. Other social media channels such as Pinterest and Instagram could be incorporated into the strategy as well. Above all, the key to successful CE is to have a proactive presence in all channels. In addition, we strongly urge managers to pay more attention to social media measures as supplementary monitoring aids for their brands. Scholars and practitioners alike have stressed the need to monitor social footprints of consumers in social media settings (Bruce & Solomon, 2013; Zeng et al., 2010). In today’s competitive world, businesses are losing control of the WOM surrounding their brands, and the antidote is to embrace social media technology. Davenport and Harris (2007) have pointed out that social media analytics are vital in today’s competitive business environment and are increasingly transforming marketing from an art to a science. 7.3

Limitations and Future Research

Despite the significant contributions of our study, our findings must be interpreted in the light of several limitations. First, this study adopted a cross-sectional view in measuring the respective constructs. This design may not adequately capture the interactions among constructs and cannot establish causality. However, the theory used in this study suggests that the relationships tested in the research model are causal in nature. For future research, using a longitudinal study may help researchers better understand the temporal relationship among our constructs. Furthermore, future research may extend the scope of the current study by expanding the dimensions of CEB.

Second, to test our hypotheses, we used data extracted from three popular social media platforms and within the context of a movie’s economic performance. Future researchers may consider using different contexts with longitudinal data to validate the current framework.

8

Conclusion

This study examined CEB in relation to future economic performance. Specifically, we investigated two dimensions of CEB, personal and interactive engagements, in the social media context of FB, YT, and TW in relation to movie box-office opening-weekend gross revenue. We found a significant positive correlation between CEB and gross revenue. We concluded that social media CEB does play a pertinent role in relation to future economic performance. Our results show that practitioners and scholars need to pay more attention to social media metrics in monitoring future economic performance. Contributions to both research and practice were discussed.

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Figure Caption

Figure 1 Iron Man 3 Facebook Profile Figure 2 Consumer Engagement Behavior Model with Economic Performance

Figure 1 Iron Man 3 Facebook Profile Note: The figure shows Facebook metrics of total likes and total talk about.

Figure 2 Consumer Engagement Behavior Model with Economic Performance

List of Tables

Table 1 Top Ten Movies Sorted by Opening-Weekend Gross Revenue Movie Title

Openin

Distributor

g-

Release

Releas

Twitter

Faceboo

YouTube

Date

e Type

FOLLO

k LIKES

VIEWS

W (‘000)

(‘000)

(‘000)

965.995

11,621.53 30,300.34

Weeken d Gross Revenu e ($M) The Hunger

158.074

Lionsgate

22

Games:

Novemb

Catching

er 2013

Wide

8

Fire Transformer 100.038

Paramount

s: Age of

27 June

Wide

137.565

2014

31,958.91 17,959.26 2

8

Extinction Godzilla

93.188

Warner Bros.

16 May

Wide

41.031

2014 The

91.608

Amazing

Sony/Columb 2 May ia

2014

Fox

23 May

1,300.766 25,850.89 1

Wide

211.154

9,755.933 4,131.058

Wide

131.965

11,492.76 27,890.94

Spider-Man 2 X-Men:

90.823

Days of

2014

4

6

Future Past Dawn of the

72.611

Fox

11 July

Planet of the

Wide

15.248

2,174.104 7,628.965

Wide

40.447

989.773

Wide

191.670

1,219.521 10,460.27

2014

Apes Teenage

65.575

Paramount

Mutant

8 August

3,907.106

2014

Ninja Turtles Divergent

54.607

Lionsgate

21 March

5

2014 How to

49.451

Fox

13 June

Train Your

Wide

60.700

6,982.645 700.637

Wide

450.515

4,115.064 20,707.09

2014

Dragon 2 The Fault in

48.002

Fox

6 June

our Stars

2014

1

Table 2 Descriptive Statistics of Key Variables Sourc

Data

Variable

e

Type

OPEN-

MOJ

GROSSi

O

Std. Period

Description Min

Max

Mean

Dev.

Numeri First

Opening-

0.000

158.074

15.764

21.968

c

weekend

2

weeken

d

gross revenue (in million)

MONTH-

MOJ

Numeri First

First month

GROSSi

O

c

gross

month

0.002

362.950

40.318

60.954

0

1

0.556

0.499

0.003

1990.025 109.649 343.630

0.002

289.756

0.131

31958.91 1399.21 4094.47

revenue (in million) RELEASEi

MOJ

Binary

NA*

O TW_FOLLO

TW

Wi

Movie release type

Numeri Over

Number of

c

followers

time

for each movie (in thousand) TW_ABOUTi TW

Numeri 7 days

Number of

c

tweets

16.645

37.079

posted about the movie by the public (in thousand) FB_LIKESi

FB

Numeri Over

Number of

c

time

FB likes for

0

2

6

the movie (in thousand) FB_TALKi

FB

Numeri 7 days

Number of

c

other FB

0.009

1579.771 189.290 324.342

0.116

30300.34 2859.74 5558.62

profiles mentioning this movie profile (in thousand) YT_VIEWSi

YT

Numeri Over

Number of

c

YT views

time

0

7

2

62.791

2.799

8.278

of the trailer posted by the movie (in thousand) YT_COMi

YT

Numeri Over

Number of

c

YT

time

comments posted by

0

public (in thousand) GENRE

MOJ

Binary

NA*

O

Movie genre type: action, drama, thriller, animation, comedy, horror, family, documentar y, and others

* Not applicable

Table 3 Distribution of Movie Genre Type GENRE

Frequency Percent

action

17

16.038%

drama

38

35.849%

thriller

13

12.264%

animation

7

6.604%

0

1

NA*

NA*

comedy

13

12.264%

horror

7

6.604%

family

2

1.887%

documentary

5

4.717%

others

4

3.774%

N

106

100

Table 4 Pearson’s Correlation Matrix of Key Variables 1 1 OPEN-

2

3

4

5

6

7

1.000

GROSSi 2 MONTHGROSSi 3 RELEASEi

4 TW_FOLLOW

0.989

1.000

* 0.533

0.558

1.000

*

*

0.1461

0.1382

0.104

1.000

0.522

0.501

0.349

0.091

1.000

*

*

*

0.625

0.606

0.292

0.079

0.209

*

*

*

0.753

0.746

0.472

i

5 TW_ABOUTi

6 FB_LIKESi

7 FB_TALKi

1.000

* 0.050

0.626

0.703

1.000

8

9

8 YT_VIEWSi

9 YT_COMi

*

*

*

0.764

0.721

0.285

*

*

*

0.763

0.723

0.236

*

*

*

*

*

0.645

0.523

0.616

*

*

*

0.202

0.637

0.411

0.574

0.915

1.00

*

*

*

*

*

0

0.146

1.000

* <0.05, ** <0.01 Note: All the social media variables, except TW_FOLLOWi, have high correlations (above 0.5 as highlighted in the table) with OPEN-GROSSi and MONTH-GROSSi signifying a possible strong relationship between social media CEB metrics with future economic performance. TW_FOLLOWi (corr=0.1461 and 0.1382) is the only social media variable with weak and insignificant correlations with OPEN-GROSSi and MONTH-GROSSi.

Table 5 Robust OLS Regression of Box-Office Gross Revenue (OPEN-GROSSi) on Personal Engagement Social Media Variables

RELEASEi

TW_FOLLOWi

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

27.737

27.241

19.934

17.841

15.760

12.854

(3.852)

(3.537)

(2.549)

(2.681)

(2.316)

(1.902)

****

****

****

****

****

****

0.006

0.001

0.002

(0.01)

(0.003)

(0.003)

0.001

0.001

(0.000) **

(0.000) **

0.003 FB_LIKESi

(0.001) ****

YT_VIEWSi

0.003

0.002

0.002

(0.000)

(0.000)

(0.000)

****

****

**** −13.324

Drama (7.491) * −6.245 Thriller (8.491) −1.516 Animation (8.832) −14.823 Comedy (7.447) ** −12.840 Horror (7.667) * −14.474 Family (8.841) −16.401 Documentary (8.805) * −12.089 Others (7.511) 0.326

−3.063 −0.156

_cons

0.116

(0.107) (0.764)

(0.128)

*** Robust R-

0.284

−2.628

9.768

(1.031) **

(7.679)

0.735

0.770

(1.065) *** 0.292

0.525

0.691

squared F

51.83

31.42

41.38

60.61

40.69

18.29

Mean VIF

2.257

1.897

1.981

2.044

1.883

3.152

* <0.1, **<0.05, ***<0.01, ****<0.001, β - Robust Coefficient, Standard error in parenthesis, N = 106 Table 6 Robust OLS Regression of Box-Office Gross Revenue (OPEN-GROSSi) on Interactive Engagement Social Media Variables

RELEASEi

TW_ABOUTi

FB_TALKi

YT_COMi

Model 1

Model 7

Model 8

Model 9

Model 10

Model 11

27.737

20.790

11.855

19.428

14.008

10.670

(3.852)

(3.611)

(2.267)

(2.314)

(2.514)

(2.076)

****

****

****

****

****

****

0.000

−0.000

−0.000

(0.000) **

(0.000)

(0.000)

0.051

0.033

0.029

(0.011)

(0.008)

(0.007)

****

****

****

2.119

1.871

1.637

(0.238)

(0.177)

(0.243)

****

****

**** −17.762

Drama (6.352) *** −9.465 Thriller (7.163)

−10.001 animation (7.042) −15.532 comedy (6.493) ** −15.980 Horror (6.338) ** −13.914 Family (6.228) ** −17.643 Documentary (6.446) *** −15.937 Others (6.385) ** 0.326

−0.265

−0.622

−0.981

−1.111

14.611

(0.107) *** (0.315)

(0.422)

(0.467) **

(0.515) **

(6.379) **

0.284

0.412

0.608

0.715

0.802

0.846

F

51.83

31.61

56.56

78.17

133.73

64.93

Mean VIF

2.257

2.065

2.288

1.943

2.503

3.104

_cons

Robust Rsquared

*<0.1, **<0.05, ***<0.01, ****<0.001, β - Robust Coefficient, Standard error in parenthesis, N=106 Table 7 Robust OLS Regression of First Month (MONTH-GROSSi) Box-Office Gross Revenue on Personal and Interactive Engagement Social Media Variables Personal

Interactive

RELEASEi

TW_FOLLOWi

Engagement

Engagement

Model 12

Model 13

MONTH-

MONTH-

GROSSi

GROSSi

34.882 (5.236)

29.335 (5.463)

****

****

0.0046 (0.008) 0.0029 (0.001)

FB_LIKESi ** YT_VIEWSi

0.005 (0.001) ****

TW_ABOUTi

−0.0003 (0.000) *

FB_TALKi

0.0699 (0.014) **** 3.5507 (0.643)

YT_COMi **** −30.776 (18.073) −39.634 (15.222) Drama *

**

Thriller

−11.696 (21.386) −17.656 (18.599)

Animation

4.462 (22.697)

−14.199 (17.818)

−30.339 (18.144) −30.678 (15.672) Comedy *

*

−32.632 (18.749) −38.652 (15.488) Horror *

**

−33.942 (19.739) −31.3175 Family *

(15.088) **

−37.7441

−39.6976

(20.514) *

(15.305) **

Documentary

−34.2526 Others

−26.693 (18.229) (15.566) ** 33.1669 (15.314)

_cons

23.576 (18.485) **

Robust R0.739

0.817

F

17.46

54.21

Mean VIF

3.153

3.102

squared

*<0.1, **<0.05, ***<0.01, ****<0.001, β - Robust Coefficient, Standard error in parenthesis, N=106