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See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/276378321 Major League Baseball and Twitter Usage: The Economics of Social Media Use Article  in  Journal of Sport Management · December 2015 DOI: 10.1123/jsm.2014-0229 CITATIONS 35 READS 2,690 3 authors: Some of the authors of this publication are also working on these related projects: Economic Discrimination on Sport Social Media View project Incentives in Sports View project Nicholas Masafumi Watanabe University of South Carolina 49 PUBLICATIONS   313 CITATIONS    SEE PROFILE Grace Yan University of South Carolina 30 PUBLICATIONS   432 CITATIONS    SEE PROFILE Brian Soebbing University of Alberta 80 PUBLICATIONS   474 CITATIONS    SEE PROFILE All content following this page was uploaded by Nicholas Masafumi Watanabe on 18 December 2015. The user has requested enhancement of the downloaded file. 619 ARTICLE Journal of Sport Management, 2015, 29, 619 -632 http://dx.doi.org/10.1123/JSM.2014-0229 © 2015 Human Kinetics, Inc. Nicholas Watanabe and Grace Yan are with the Department of Parks, Recreation and Tourism, University of Missouri, Columbia, Missouri. Brian P. Soebbing is with the School of Tourism and Hospitality Management, Temple University, Philadelphia, Pennsylvania. Address author correspondence to Nicholas Watanabe at [email protected] Major League Baseball and Twitter Usage: The Economics of Social Media Use Nicholas Watanabe and Grace Yan University of Missouri Brian P. Soebbing Temple University From the perspective of economic demand theory, this study examines the factors that determine daily changes in Twitter following of Major League Baseball teams as a form of derived demand for a sport product. Spe- cifically, a linear regression model is constructed by taking consideration of factors relevant to fan interest: team performance, market characteristics, scheduling, and so on. The results reveal specific determinants that have significant relationship with Twitter following. From a team management perspective, factors such as the content of social media messages, certain calendar events, and postseason appearances can be used to enhance fan interest on social media. In so doing, it brings together communication inquiries and economic literature by delineating a comprehensive and nuanced account of interpreting sport social media from a consumer demand perspective. The growing role of social media in sport has been an increasingly researched topic (Clavio & Walsh, 2014; Hambrick, Simmons, Greenhalgh, & Greenwell, 2010; Pedersen, 2012; Pegoraro, 2010; Perez, 2013; Sanderson, 2014; Stavros, Meng, Westberg, & Farrelly, 2014). The surge of research interests has, in response, raised the awareness of the pivotal role of social media platforms in the sport industry, applauding the participatory culture (van Dijck, 2009) that enables fans and participants to seek creative self-expressions in digital spaces (Hutchins, 2014; Rowe, 2014). In so doing, the inquiries have certainly made significant contributions to the field, delineating the communicative patterns of social media used by athletes (e.g., Hambrick, Simmons, Greenhalgh, & Greenwell, 2010), the parasocial relationship between audience and athlete (e.g., Sanderson, 2011), sport organi- zation engagement on social media in fostering marketing effects (Lovejoy, Waters, & Saxton, 2012), and so forth. Although many individuals have approached Twitter as a communicative tool that forces new ways of thinking about the interaction between sport and digital media (Hutchins, 2011), there is also a critical awareness that these early efforts may not have fully captured the wide array of possible intersections between social media and sport (e.g., Hutchins, 2014; Pedersen, 2014; Wenner, 2014). It is argued that a limited focus on thematic analy- sis of sport social media content still plays an underpin- ning role in the current research agenda (Billings, 2014; Leonard, 2009; Hardin, 2014; Hutchins, 2014; Wenner, 2014). That is, very little attention has been given to frame the communicative usage of sport social media as a consumption behavior, the demand and meanings of which are engaged in a variety of socioeconomic ramifi- cations (Pedersen, 2012). With this in mind, this research seeks to introduce economic demand theory to examine the usage of social media in relation to Major League Baseball (MLB) franchise performance, scheduling, and other factors, aiming to bridge sport economics research and communication inquiries. Previously, economic demand theory has been widely used to study fan interest in sport products (Bor- land & Macdonald, 2003). The empirical examination of the demand for sport is often traced to the modeling done by Bird (1982) with a demand function to analyze attendance by fans at sporting contests. Based on this approach, the following studies have further developed and refined models to understand determinants of fan interest for sport products in the realm of sport economics (Borland & Macdonald, 2003; Jewell & Molina, 2005). In recent years, with evolving theoretical discussion as well as emergence of new technologies, the methodologi- cal inquiry of attendance models has been extended into television (Tainsky & McEvoy, 2012), pay-per-view 620 Watanabe, Yan, and Soebbing JSM Vol. 29, No. 6, 2015 (Watanabe, 2012), and other digital channels of sport product (Budzinski & Satzer, 2011). That is, the views on sport consumption and demand have been expanded from the literal form of attending sport events to a wider array of consumption in contexts that are mediated by various technological platforms and socioeconomic forces (Borland & Macdonald, 2003; Dwyer & Drayer, 2010; Shoham & Kahle, 1996; Tainsky & McEvoy, 2012). As such, it is recognized that compared with sport attendance, the new forms of sport consumption may be associated with varied levels of efforts and costs, while a commonly identified domain is their serving together as active locations of fan interests and desires to be involved in the sporting scene (Dwyer & Drayer, 2010; Seo & Green, 2008). In particular, social media provides a platform that is convenient to access, and constantly ongoing, as well as with more freedom in choosing degrees of interactivity and personalized involvement (Hutchins, 2014; Wenner, 2014). To some, the experience of engaging sport through social media can be minimal, whereas to others, the convenience of access to social media accounts can be developed into habitual, long- term, and intense involvement (Pegoraro, 2010). Thus, with functions of Following and other communicative features as indicators of fans’ engagement (Jensen, Ervin, & Dittmore, 2014), the usage of sport social media also needs to be incorporated as a form of derived demand for sport products (Perez, 2013), where the employment of an attendance model provides a fitting theoretical framework of examination. With this in mind, situated within the fan attendance literature and methodology, this study seeks to develop a model via regression analysis to analyze determinants of demand that are critical to fan interest in MLB team social media accounts. In so doing, a large panel dataset is employed, aiming to capture daily changes in Twitter followers and usage for every MLB team over a 1-year period. Specifically, the change of Twitter followers in a 24-hr period serves as the dependent variable within the model constructed in this research. The explanatory variables include measures of on-field success for MLB franchises, timing of MLB games, important dates on the MLB calendar, and other crucial variables to provide a comprehensive analysis of consumer interest in MLB Twitter accounts. The findings attempt to contribute to the literature from three aspects. First, by extending the lens of inquiry into the usage of social media as consumption behaviors in relation to a variety of factors of team performance and game scheduling, the current study seeks to bridge the communication and sport economic research. That is, considering the call for communicative investigations of sport social media to be grounded in stronger theoretical agendas with elaborated explorations of methodological approaches (Hardin, 2014; Hutchins, 2014; Rowe, 2011; 2014; Wenner, 2014; Yoo, Smith & Kim, 2013), this study aims to address the concerns through incorporating eco- nomic demand theory and econometric modeling into the inquiry of sport social media. Second, despite the fact that the usage of sport social media has become one dominant way for fans to engage in sport product (Clavio & Walsh, 2014), very little examination has been done to analyze its consumption as a derived form of sport demand from an economic perspective (Perez, 2013). Thus, while this study seeks to enrich the diversified profile of sport attendance studies, the results also shed light on moving toward a further conceptualization of fan engagement with sport organizations through social media. Finally, from the management perspective, this study provides ways to assist sport teams to obtain insights in creating social media management strategies through a better understanding of fan interest. The results pertain to not only team performance, but also scheduling of league events, inclusion of teams in postseason playoffs, as well as a team’s presence and use of Twitter. Literature Review Social Media, Economics, and Sport Social media, or social network sites, operate as virtual communities supported by interactive applications allow- ing users to participate in designing, publishing, editing, and sharing in a dynamic environment where content is primarily equal among users (e.g., Boyd & Ellison, 2007; Kasavana, Nusair, & Teodosic, 2010; Starvos et al., 2014; Williams & Chinn, 2010). Among all the different types of social media websites—including blogs, content com- munities, forums and bulletin boards, and content aggre- gators (Constantinides & Fountain, 2008)—Twitter has been particularly regarded as an intervention to traditional sport broadcasting and communication (Pegoraro, 2010; 2013; Rowe, 2011). With increasingly simple-to-use technological advantages that enable users to self-define the utilization of the platform, it “produces stories about sports, intensifying and proliferating media sports content and information available in the public sphere” (Hutchins, 2011, p. 239). In various ways, Twitter has expanded the sport consumers’ participation in communication by breaking away from the traditional one-to-many, single-medium framework offered by television to the many-to-many possibilities, allowing sport consumers to interact with teams, organizations, and athletes in ways that were previously impossible (Anderson, 2008; Fisher, 2008; Pegoraro, 2013; Rowe, 2011). As framed by Clavio (2011), the extant literature has primarily approached the investigation of sport social media following two lines of inquiry: content-based inquiry and audience-based inquiry, with the exception of a few studies that integrated both (see p. 310–312). Within content-based inquiry, studies have heavily focused on thematic analysis (Sanderson, 2014), aiming to reveal patterns of communication by various sport stakeholders via social media. For example, Hambrick et al. (2010) Major League Baseball and Twitter Usage 621 JSM Vol. 29, No. 6, 2015 examined how athletes used Twitter accounts to connect with fans, identifying a range of major communicative categories: interactivity, sport information, promotion, diversion, fanship, and content. From the audience-based perspective, studies have sought to understand the char- acteristics, demographics, uses, and gratifications that influence fans’ interaction with social media (Clavio, 2011). Importantly, the underlying assumption for these studies is that the individuals’ activities on social media, such as following a team’s or an athletic department’s account, are considered as expressing consumer interest. Following this common thread, there includes multiple studies on the utilization of social media by college sport fans (Clavio, 2011; Clavio & Walsh, 2014), and Clavio and Kian’s (2010) study that investigates followers’ use of the feed and the affinity with an athlete, as well as Kassing and Sanderson’s (2010) examination on athletes’ uses of tweets cultivate insider perspectives for fans. In addition, a qualitative study on fan motivation for interacting with each other on social media has been conducted by Stavros et al. (2013), revealing four major motivational factors: passion, hope, esteem, and camaraderie. There are also a few studies that have attempted to approach social media from an economic perspective (e.g., Feddersen, Humphreys, & Soebbing, 2013; Jensen et al., 2014; Perez, 2013). These studies have focused on the analytical modeling of consumer behavior, with social media use serving as a proxy of fan interest in sport products. That is, Twitter or Facebook Followers/ Likes has been commonly used as an indicator to consider the popularity and consumer interest of a sport figure or organization (Feddersen, et al., 2013; Jensen et al., 2014; Perez, 2013). The study by Feddersen et al. (2013), for example, uses Facebook Likes as a proxy for participants with investor sentiment and analyzes evidence of senti- ment bias in the sport betting market. In another study conducted by Jensen et al. (2014), focus is placed on exploring factors that are significant predictors of Football Bowl Subdivision head football coaches’ popularity on Twitter. Specifically, it assumes the coach’s number of followers on Twitter as an indicator of popularity, where a factor analysis as well as regression model were per- formed by taking the number of the coach’s followers as a dependent variable. Moreover, of particular relevance to this current study is Perez’s (2013) research, which modeled factors in delineating the economic demand for Spanish professional football (soccer) teams on Twitter. That is, by examining the number of followers of teams’ Twitter accounts, the analysis reveals the interconnections between the demand of sport consumption via social media and factors of team performance in addition to other market characteristics. As such, it establishes a framework for social media to be used as a legitimate means in measuring fan interest through the approach of an economic demand study. With this in mind, this current study needs to be considered as carrying inherent connections with the prior researches at various levels. From a theoretical perspec- tive, it shares the foundation in economic demand theory with Perez’s (2013) study. Furthermore, in terms of the methodological approach to analyze social media data, it employs a linear regression model by regarding social media use as a proxy of fan interest in sport products, similar to the previously mentioned economic studies (Feddersen, et al., 2013; Jensen et al., 2014; Perez, 2013). This study must also be considered as a further development in creating a more robust model. It takes into account of a number of multifaceted variables that are important and relevant in engaging fans—market characteristics, team performance and scheduling—in delineating a more comprehensive view of the consump- tion and demand patterns of MLB social media usage. That is, considering the capturing of social media data, the previous studies are conducted with a single value to measure the number of Likes or Followers on social media in a season (Feddersen et al., 2013; Jensen et al., 2014). However, social media use is an ongoing dynamic process, meaning that interests and numbers of followers are constantly submitted to changes (Sanderson, 2011). Thus, the employment of a single metric to capture lengthy intervals of social media usage can be problem- atic, as it provides less control of numeric changes in the metric over time. Further, the size of the dataset is also relatively limited in these studies. The study by Perez (2013), for instance, has examined a sample of 14 teams in top-flight Spanish soccer for 23 weeks, producing a total of 658 observations. Considering the nature of speed and ephemerality of sport social media, the purpose to produce more developed and precise models naturally requests studies to incorporate enlarged dataset as well as more control variables. With this in mind, this study seeks to enhance the prior models by analyzing daily changes in Twitter use for a 13-month period by employing team- day observations for every MLB franchise. Demand Theory The demand for sport products has been one major research inquiry in the field of sports economics, used to theoretically (Neale, 1964; Rottenberg, 1956) as well as empirically (Bird, 1982) control and model factors as to why individuals decide to consume sport. Eco- nomic demand theory has been used as a framework to investigate factors reflecting economic, organizational, demographic, and market factors that lead to increases or decreases in fan interest (Soebbing, 2008). Specifically, the demand for sport comes in a variety of forms, includ- ing live attendance (Borland & MacDonald, 2003), televi- sion viewership (Bruggink & Eaton, 1996; Carmichael, Millington, & Simmons, 1999; Garcia & Rodriguez, 2002; Feddersen & Rott, 2011; Tainsky & McEvoy, 2012), pay-per-view (Tainsky, Salaga, & Santos, 2013; Watanabe, 2012), and so forth. In reviewing the literature of sport demand studies, Borland and MacDonald (2003) 622 Watanabe, Yan, and Soebbing JSM Vol. 29, No. 6, 2015 has noted five major categories in driving sport demand: consumer preference, economic variables, quality of viewing, sporting contest and supply capacity. Moreover, with the revenue shift from traditional gate attendance to broadcast revenues, the need to understand the digital demand for sport has also grown tremendously (Budz- inski & Satzer, 2011). That is, through the formation of online ticket sites and social media, the digital realm has increasingly played a significant role in influencing prices as well as demand for sport products (Shapiro & Drayer, 2012; Watanabe, Soebbing, & Wicker, 2013). Thus, for sport organizations to obtain maximized profits, it is important to understand the interrelationships between different segments of their product, which in turn provides insights as how to manage revenue streams for optimal financial and organizational outcomes (Budzinski & Satzer, 2011; Buraimo & Simmons, 2009; Mongeon & Winfree, 2012). As such, extending the investigation of sport product demand into the realm of social media may be considered as the next logical step for research. Specifically, the modeling of sport demand can be traced back to seminal work by Bird (1982) who noted that demand for attendance at sporting events can be set out Equation 1: Ait = [Pit, Qit, Qit, Mit] (1) In Equation 1, A is the average attendance for the home team in a single season. The subscript t denotes year and i denotes franchise. The ticket price is P, the first Q is home team quality, the second Q is the quality of the visiting team, and M is the market potential of the home team to attract consumers. The term M can be a wide variety of market variables, such as racial demographics, income, population density and other franchises in the same area (Bird, 1982). As such, Bird’s modeling attempts to control for various factors to understand variables that affect attendance. Following a similar modeling method, researchers have also produced a reduced form equation where the ticket price variable is omitted; often specifically denoted as sport attendance studies, due to the absence of price variable (Jewell & Molina, 2005; Soebbing, 2008). Practices of sport attendance study based on economic demand theory have been applied in a number of sporting contexts, including professional soccer (Jewell & Molina, 2005), minor (Gitter & Rhoads, 2010) and major (Soebbing, 2008) league baseball, professional hockey (Coates & Humphreys, 2012), and professional football (Coates & Humphreys, 2010). In consideration of this current study, since there is technically no price variable associated with the usage of Twitter, this investigation should also be positioned as following the approach of an attendance study. Thus, situated within the attendance study literature based on economic demand theory, this research seeks to follow the modeling methodology of Bird (1982) and Jewell and Molina (2005), with the employment of theoretical factors posited by Borland and Macdonald (2003). Specifically, two research questions are formulated: RQ1: From the perspective of economic demand theory, which factors have a significant relationship with changes in Twitter following for MLB teams in a 24-hour period? RQ2: How can results from the model estimated in this research be applied to the management of sport team social media accounts? Methods To provide effective control for the instantaneous changes in Twitter, the NodeXL social media analytical software was employed to obtain reliable data measuring Twitter use. NodeXL is described as a Social Network Analysis tool, which has been employed in a large array of research studies examining content, use and the importance of various forms of social media (Hansen, Shneiderman, & Smith, 2010; Sharma, Khurana, Shneiderman, Schar- renbroich, Locke, 2011). This software allows users to enter commands that interface the program with social media platforms. In the next procedural step, the data can be exported to an Excel sheet, providing temporal consistency in collection. MLB Twitter data were collected every morning at 10 a.m. from July 6, 2013, to July 27, 2014. That is, as noted by Edelman (2012), data scraping on a daily basis is vital in collecting data from large scale samples over time, which carries significance in economic research examining consumer behaviors in relation to daily changes in price and other factors (Cavallo, 2011; Ellison & Ellison, 2009). Afterward, the program of NodeXL was operated to facilitate the data collection process during this 1-year period. Data for the number of Followers, individual followed (Followed), Tweets, and Favorites was collected for each MLB team’s Twitter account. The data were then transferred from the NodeXL spreadsheets to a single comprehensive panel dataset, which was later imported into the STATA12 statistical software package for further analysis. In a detailed account, the unit of observation is a team-day. During the sample collection period, there are 11,259 team-day observations. This sample accounts for a 13-month time period, over which approximately five of the months have no baseball games being played. Although the period does not reflect exactly one perfect calendar year or one complete season, the 13-month period is chosen to include as many observations as pos- sible to better capture the daily changes in social media use over time. Thus, the larger dataset is employed in this model to present results without omitting any data to prevent the potential for bias (Gujarati, 2003). Moreover, following the study by Perez (2013), where the depen- Major League Baseball and Twitter Usage 623 JSM Vol. 29, No. 6, 2015 Table 1 MLB Twitter Follower Change Team July 6, 2013 Followers July 27, 2014 Followers Net Change Percentage Change, % Angels 118,881 190,718 71,837 60 Astros 90,600 152,825 62,225 69 Athletics 121,336 191,042 69,706 57 Blue Jays 297,338 460,188 162,850 55 Braves 357,413 513,734 156,321 44 Brewers 144,701 203,210 58,509 40 Cardinals 308,072 475,063 166,991 54 Cubs 255,892 365,142 109,250 43 Diamondbacks 89,964 140,500 50,536 56 Dodgers 315,440 537,185 221,745 70 Giants 441,137 617,034 175,897 40 Indians 136,266 221,791 85,525 63 Mariners 121,177 203,402 82,225 68 Marlins 88,711 126,570 37,859 43 Mets 192,490 271,599 79,109 41 Nationals 131,795 192,691 60,896 46 Orioles 154,376 229,750 75,374 49 Padres 83,992 127,092 43,100 51 Phillies 766,705 834,755 68,050 9 Pirates 140,443 259,729 119,286 8 Rangers 295,262 401,170 105,908 36 Rays 117,853 176,413 58,560 50 Reds 217,502 313,419 95,917 44 Red Sox 495,768 817,873 322,105 65 Rockies 93,462 141,105 47,643 51 Royals 120,480 193,567 73,087 61 Tigers 276,271 447,433 171,162 62 Twins 161,311 223,188 61,877 38 White Sox 145,965 205,542 59,577 41 Yankees 926,323 1,176,926 250,603 27 dent variable was based on the change in the number of Twitter followers from the previous day, the current study uses the change in the number of Twitter followers from the previous collection period (24 hr in length) as the dependent variable (ΔFollowers). As such, this change measures the new individuals who follow the official account of a MLB franchise. Table 1 presents the change in followers from the beginning of the sample period to the end of the sample period. From Table 1, one notices a large variation in the increase in twitter followers ranging from a 9% increase to an 85% increase. Explanatory Variables To examine the factors affecting fans’ engagement with MLB social media accounts, a variety of control variables are included in the modeling. These factors are based on the theoretical underpinning of determinants of demand presented by Borland and Macdonald (2003), but also include more recent empirical consideration in regards to social media (Perez, 2013) postseason appearance (Tainsky, Xu, & Zhou, 2014), as well as scheduling and timing of events (Tainsky, Salaga, & Santos, 2012, 2013; 624 Watanabe, Yan, and Soebbing JSM Vol. 29, No. 6, 2015 Watanabe, 2012). The model in this research is thus constructed upon variables that have been presented in prior research as being important to take into account when controlling for a variety of factors in the demand for sport products. The first set of explanatory variables look at the teams’ social media use, considering the amount of interaction and interest that these accounts generated. Specifically, Tweets measures the total number of tweets the account made since the past collection period, and represents how active the account of the franchise is in communicating through social media. Favorites denotes instances when the official Twitter account of an MLB team favorites the tweet of an account (either their own tweets or tweets by other accounts) over the past 24 hr. Followed indicates the number of other accounts an MLB franchise followed on Twitter during the past 24 hr. TwitterDays is a variable that controls for the number of days the observed team’s Twitter account has been active on the observed day. In reviewing MLB teams’ initial approach to Twitter, all 30 teams joined Twitter through making official accounts over an 18-month period, indicating that initially there was probably not a general directive from the league for all teams to join the platform. Over time, more strategic governance policies have been formulated, as it is now the case that both the MLB front office and all teams have social media direc- tors/coordinators whose sole job duty is to manage Twit- ter and other social media accounts, including posting content and interacting with other users/fans (Hutchins & Rowe, 2012). As such, the creation date of the team’s Twitter account was captured by NodeXL and included in the model to control for number of days that teams are on Twitter. Finally, this model seeks to control for the total number of followers for a team’s Twitter account in the previous collection period (Followers). This vari- able is used to take into account the preexisting number of Twitter followers for a team. In addition to these specific MLB franchise social media variables, team performance variables as well as performance events with potential to have changed the number of Twitter followers are also taken into con- sideration. Specifically, the team performance data are collected from the Baseball-Reference website, which lists daily and aggregate statistics for MLB team perfor- mance. The first variable attempts to capture if the previ- ous day is either a regular season or a postseason game for the observed team (Gameday). The second variable considers if the observed team is currently participating in the Wild Card, Division, or League Championship playoff series on the observed date (PlayoffPart). It is a dummy variable equal to 1 for participating in a playoff game on the observed date, 0 otherwise. In addition to participating in one of the first three rounds of the MLB playoffs, this model also includes a variable equal to 1 if the observed team is playing in a World Series game on the observed date, 0 otherwise (WSPart). It is anticipated that both the PlayoffPart and WSPart variables will be positive and significant, reflecting an increase in follow- ers, as prior …