Abstract
This study puts both esports gameplay and spectatorship into consideration and pinpoints how individual and structural factors explain why people play and watch esports to better understand the complexities and intricacies of esports consumption. Results indicate that both measures commonly associated with active audience, and structural theories played a significant role in explaining esports consumption. Specifically, esports gameplay was explained relatively more by structural factors than by individual factors. Different from esports gameplay, esports spectatorship was driven significantly more by individual factors. Preferences, motivations, availability, and access significantly predicted both esports gameplay and spectatorship. Sports fandom and use of interactive features, on the other hand, only predicted esports spectatorship but did not influence gameplay. By employing an integrative approach, this study aids in the development of conceptual framework that will serve to predict esports consumer behavior.
Keywords
Esports (i.e., electronic sports) has become a blooming global phenomenon with millions of consumers and significant revenue-generating promise (Adams et al., 2019). Tournament viewership and prize pools now compete with the traditional sporting mega-events. For the first time in history, esports will be included as a sport in the 2022 Asian Games (Graham, 2017). In addition, the first esports forum was hosted by the International Olympic Committee (IOC) and Global Association of International Sports Federation in July 2018, demonstrating the growing importance of the esports industry (IOC, 2018).
While various stakeholders from both traditional sports and esports communities have extensively debated whether esports could be recognized as a sport, they generally agree that esports shared a lot of similarities with traditional sports, including competitions, mental and physical skill training and development, governance, and global institutionalization (Adams et al., 2019; Pizzo et al., 2018). In addition, previous research indicated that esports and traditional sports consumption are driven by similar motivations such as social opportunities, skill development, escapism, competition excitement, and entertainment (Hamari & Sjöblom, 2017; Lee & Schoenstedt, 2011; Pizzo et al., 2018; Qian, Wang, et al., 2019; Weiss & Schiele, 2013). Yet compared to traditional sports consumers, esports consumers are more immersive, engaging, and committed (Brown et al., 2018). Moreover, industry practitioners and scholars suggest that esports consumption is a twofold phenomenon. Esports gameplayers are often considered to be potential viewers of esports events. In turn, most esports spectators are active gamers who seek to improve their gameplay skills and performance by watching others play (Jang & Byon, 2020; Murray, 2018; Qian, Wang, et al., 2019; Seo & Jung, 2016). With the increasing populace of the gaming-centric streaming sites like Twitch and YouTube Gaming, watching becomes playing and vice versa. Therefore, it is important to put both esports gameplay and spectatorship into consideration to better understand esports consumer behavior.
While the topic of esports consumption has generated increasing academic attention (Adams et al., 2019; Brown et al., 2018; Jang & Byon, 2020; Pizzo et al., 2018; Qian, Wang, et al., 2019; Rogers, 2019; Xiao, 2020), most of the existing esports research tends to treat esports consumers as either players or spectators. They have examined esports gameplay and spectatorship in isolation, overlooking direct comparisons to identify the shared (or different) antecedents of esports gameplay and spectating. In addition, much of the literature studying esports consumers is guided by active-audience theories (e.g., uses and gratifications) and has heavily relied on individual characteristics and people’s sociopsychological needs (e.g., motivations) to explain esports gameplay and spectatorship (Qian, Zhang, et al., 2019). Yet esports consumption is not free of constraints of time, access, and cost. Indeed, recent trade publications have pointed out the importance of gaming equipment and time availability to esports players (see Buckle & Mander, 2018). There is a need for more systematic efforts that put both individual and structural factors into consideration when examining esports consumption.
This study, thus, integrates active-audience and structure theories in audience research and the literature of sports communication and esports to examine individual and structural factors that predict esports gameplay and spectatorship. Specifically, it makes theoretical and practical contributions in the following ways.
First, this research examines both esports gameplay and spectatorship in one study and directly compares the antecedents of these two integral components of esports consumption (Jang & Byon, 2020; Pizzo et al., 2018). In this way, it provides the theoretical reasoning underlying the extent to which esports gameplay is driven similarly (or differently) to esports spectatorship. Second, this study integrates active-audience and structural theories in audience research to examine esports consumption. It contributes to audience research, sports consumption literature in particular, by responding to the enduring theoretical debate on active versus passive audiences and unpacking the interrelated influence of individual agents, structures, and sociopsychological schemas on esports gameplay and spectatorship. Third, by pinpointing how and why people play and watch esports, this study addresses the critical practical question in terms of the motivators of esports consumption, thus offering sports and esports communities the needed information for further engaging diverse audience populations, determining resource allocations, and developing strategies for collaboration.
Ultimately, we believe that esports consumption is not just a matter of seeking “affective arousal before the screens” (Frandsen, 2007, p. 76) but has a prolonged impact on meaning building and culture sharing (Seo & Jung, 2016). Only when we understand the complexities and intricacies of esports consumption, can we identify how, what, and when the effects of such experience will occur.
Theoretical Conceptualization and Related Studies
Esports
Electronic sports, or esports, has been broadly defined as “an area of sport activities in which people develop and train mental or physical abilities in the use of information and communication technologies” (Wagner, 2007, p. 182). Warr (2014) streamlined this definition, suggesting that esports is a form of sports, where electronic systems facilitate as the primary function of competitive gaming for either professionals or amateurs. Thus, the focus is on the “e” in esports, as the outcomes of the real physical world competitions take place in the electronic virtual world (Adam et al., 2019; Hamari & Sjöblom, 2017). Adam et al. (2019) also noted that esports gameplay takes place at various levels, including casual to professional and team or individual play.
While a universal definition of esports has not been determined, as the esports industry progressed, the connection between traditional sports and esports became clearer. Similar to traditional sports, esports contain comparative measures to access a player’s performance level within the game (Seo, 2013). Researchers suggest that the competitive structure of esports, such as refined leagues, live broadcasts, and global institutionalization, is derived from traditional professional sports (Funk et al., 2018; Hallmann & Giel, 2018; Karhulahti, 2017). It is also believed that similar to traditional sports, esports can naturally transcend many boundaries by surpassing self-contained digital play with shared experience (Seo & Jung, 2016). Esports and traditional sports also share a united passion for competition and similar values in promoting ethics and good governance (IOC, 2018; Xiao, 2020).
Moreover, there have been serious efforts in the esports industry in developing structures similar to traditional sports competitions. For example, Twitch paid US$90 million to purchase the distribution rights for the Overwatch League, one of the esports competitions that restructures itself around traditional sports leagues (Wolf, 2018). Teams compete against one another, announcers commentate, and millions of viewers watch the events. Such restructuring efforts further parallel esports with traditional sports; thus, we see increasing collaborations between the sports and esports communities. For example, the French government officially recognizes esports as a national sport and expressed interest in possibly adding esports to the 2024 Paris Olympic Games (BBC News, 2018).
Similar to traditional mega-sporting events, esports spectators mainly experience esports tournaments via mediated experience. According to Nielsen, while 66% of esports consumers have watched live streams of esports competitions, only 37% have attended live esports events (Takahashi, 2017). In addition, researchers found that seeking knowledge, friend bonding, Schwabism, competition excitement, and entertainment are shared motivations for both esports and traditional sports consumers (Brown et al., 2018; Hamari & Sjöblom, 2017; Qian, Wang, et al., 2019). Yet the magnitude of motives sets esports consumers apart from traditional sports fans. Brown et al. (2018) suggested that esports fans showed significantly more desire to engage with esports content than in any traditional sporting context. According to Nielsen, an average esports player follows 5.7 different games and 2.6 genres (Takahashi, 2017). They are connected to their teams or games of interest almost on a 24-7 basis and can use synchronous new technology to chat, ask questions, provide strategies, or even change the course of play.
As esports allows for real-time interaction between players and spectators, researchers suggest that esports consumers “adopt multiple roles moving beyond being considered merely as ‘players’, by performing multifaceted practical activities that actualize and sustain esports as an amalgamated cultural phenomenon” (Seo & Jung, 2016, p. 637). Gommesen (2012) suggests that players and viewers cocreate the esports experience and culture. Esports gameplayers are considered to be potential viewers of esports events. In turn, esports spectators are believed to be active gameplayers who watch others play to improve their own gameplay performance (Jang & Byon, 2020; Murray, 2018; Qian, Wang, et al., 2019; Seo & Jung, 2016). There is considerable evidence regarding the significant role of both esports gameplay and esports spectatorship in understanding esports consumption (Jang & Byon, 2020; Pizzo et al., 2018). As the esports industry leads to a more committed and unique fan base, increased media coverage, and investment (Buckle & Mander, 2018), it is important for scholars and practitioners to put both esports gameplay and spectatorship into consideration and pinpoint how and why people play and watch esports to better understand the complexities and intricacies of the esports marketplace.
The Integration of Audience Theories
Within scholarship in audience research, there is an enduring debate on whether audiences are active or passive in their media consumption decision (Cooper & Tang, 2009). Active-audience theories (e.g., uses and gratifications, theory of reasoned action) believe that audiences are active and goal-directed and have emphasized the role of individual reasons (e.g., motivations, preferences, fandom, demographics) for media use (Brown et al., 2018; Sjöblom & Hamari, 2016; Xiao, 2020). On the other hand, scholars in the structural theoretical school traditionally see audiences as more passive when consuming media (Webster et al., 2006) and have focused on how structural factors (e.g., availability, access, cost) influence the size and composition of the audience.
The relationship between individuals and structures is illustrated by Giddens (1984), a British sociologist, who introduced “duality of structure” in his structuration theory, suggesting that individuals and structures interact with each other; individual agents act within the social system, while the repetition of their acts reproduces the structure. Cooper and Tang (2009) advocate Giddens’ structuration theory and further conceptualize media users as “active within structures,” proposing that individuals actively seek media content within internal and external structures. Individual characteristics (e.g., demographics), sociopsychological needs (e.g., motivations, preferences, fandom), and structures (e.g., availability, access to technologies, cost) interact with each other and influence media use.
While most explanations of sports and esports consumption have heavily relied on individual characteristics and sociopsychological needs to explain consumer behavior (Brown et al., 2018; Pizzo et al., 2018; Sjöblom & Hamari, 2016; Xiao, 2020), media use is not completely free of constraints of time, access, and cost. Indeed, contrary to the prevailing perception that media consumption has relied on preference, recent research suggested that people often choose a medium because of convenience, availability, and access (Phalen & Ducey, 2012). Moreover, while a number of researchers have conceptualized general TV viewing as largely passive and gaming as more active (Webster et al., 2006), most esports consumers are both gameplayers and spectators (Fragen, 2018; Jang & Byon, 2020; Murray, 2018). They can be both active and passive. Thus, it is necessary to integrate active-audience theories with structural theory by putting both individual and structural factors into consideration when examining esports consumption.
Factors Predicting Esports Gameplay and Spectatorship
Individual factors
Active-audience theories suggest that individual factors such as motivations, preferences, fandom, and demographics predict sports and esports consumption (Brown et al., 2018; Jang & Byon, 2020; Pizzo et al., 2018; Rubin, 1984; Sloan, 1989). Uses and gratifications research identified two orientations toward media use—instrumental media use and ritualistic use (Rubin, 1984). Instrumental use, such as information and entertainment seeking, is generally linked to active activities like playing games, while ritualistic use reflects a habitual use with a medium, such as general TV viewing (Cooper & Tang, 2009). Specifically, entertainment, positive arousal, escape, economic, self-esteem, group affiliation, aesthetic, and family needs are major reasons for people to watch sports (Gantz et al., 2006; Sloan, 1989; Wann, 2002). In addition, researchers suggested that agency, escapism, habit, moral self-reaction, narrative, pastime, performance, and social motivations had a positive relationship with playing digital games (De Grove et al., 2016).
Largely guided by the uses and gratifications approach, esports researchers have examined the relationships between motivations and esports consumption (Brown et al., 2018; Pizzo et al., 2018; Sjöblom & Hamari, 2016). Jang and Byon (2020) found that hedonic motivation and habit were antecedents of esports gameplay across genres. Kim and Ross (2006) suggested that knowledge application, identification with sport, fantasy, competition, entertainment, social interaction, and diversion are major motivations of playing sports video games. In terms of esports spectating, Hamari and Sjöblom (2017) found that knowledge acquiring, escapism, interest in players and teams, and aggression predicted esports spectating frequency. Hilvert-Bruce et al. (2018) suggested that social and community motivations were particularly associated with esports viewing via live streams.
In comparing esports and traditional sports consumption, Pizzo et al. (2018) found that group dynamics, drama, interest in player, entertainment, knowledge acquisition, athlete skill, and aggression are shared motivations between esports and sports spectatorship. Brown et al. (2018) also indicated that people consumed both sports and esports for social support, fanship, and Schwabism. Yet researchers noted that while sports and esports fans share similar motivations for their media consumption, the magnitude of their motives is different. Compared to sports viewers, esports consumers are more committed, engaged, interactive, and immersive (Brown et al., 2018; Pizzo et al., 2018). Specifically, esports consumers demonstrated unique motives of skill improvement and vicarious sensation, while aesthetics, a motivation of sports viewers, negatively predicted esports consumption (Hamari & Sjöblom, 2017; Pizzo et al., 2018; Qian, Wang, et al., 2019; Sloan, 1989).
In addition to motivations, preference is long believed to predict audience behavior. For example, sports communication scholars suggested that preference for various types of sports positively predicted mega-sporting event consumption for both sports fans and nonfans (Cooper & Tang, 2012). In the context of esports, researchers suggest that esports consumers are divided by game genres. The most popular esports game genres include multiplayer online battle arena (MOBA) games, first-person shooters (FPS) games, real-time strategy (RTS) games, battle royale games, and sports games (Adams et al., 2019; Buckle & Mander, 2018; Jang & Byon, 2020). Jang and Byon (2020) further categorized these game genres into three types—imagination, physical enactment, and sport simulation games.
Researchers suggested that it is necessary to put game genres into consideration when understanding esports consumption (Brown et al., 2018; Jang & Byon, 2020). Fans of different esports genres spent a different amount of time playing the games (Takahashi, 2017). For example, Brown et al. (2018) found that playing FPS, massively multiplayer online role-playing game (MMORPG), fighting, sports, and MOBA games positively predicted consuming esports content across media. Jang and Byon (2020) demonstrated that compared to the gameplay of physical enactment and sport simulation games, playing imaginative games was predicted by different factors.
The relationship between sports fandom and media use has attracted considerable attention within sports communication scholarship. The term “fan” generally applies to those who follow sports, are motivated to consume sports across media, are more emotionally involved while viewing, and care about the outcome (Cooper & Tang, 2012; Gantz et al., 2006). At a minimum, fans are generally referred to those active audiences with more knowledge, experience, and affective attachments to their favorite personalities/players/teams compared to nonfans (Cooper & Tang, 2012). Researchers also suggested that the operational definitions of sports fandom should look beyond self-identification, carving up the concepts to include the amount of sports consumed on TV and via new media (Cooper & Tang, 2012; Gantz et al., 2006; Osborne & Coombs, 2013).
Compared to traditional sports fans, researchers suggest that esports fans are more dedicated to consuming esports content in-and-out of the game, as they can connect to their teams and/or games of interest on a 24/7 basis (Brown et al., 2018). Hamari and Sjöblom (2017) found that giving esports players more screen time may increase fandom. Kim and Ross (2006) suggested that sports gameplayers are likely to be sports fans, though sports fans are not necessarily gamers. While ample large-scale interaction data points to esports as a social phenomenon at its core (Pobiedina et al., 2013), what remains unknown is the extent to which esports gameplay and spectatorship are driven by sports fandom.
Structural factors
While many perceive esports consumers as active media users, esports consumption is not free of structural constraints. Researchers working within the structural theory found audience availability to be a powerful determinant of media use, even in today’s convergent environments (Cooper & Tang, 2009; Webster et al., 2006). Audience availability has been operationally defined as whether someone “could” consume the media (Webster et al., 2006). It is believed that having time available to consume a particular medium increases the likelihood of choosing to consume the medium (Cooper & Tang, 2009). In terms of sports and esports consumption, Tang and Cooper (2013) found that audience availability positively predicted Olympics viewing on TV. Qian, Zhang, et al. (2019) suggested that schedule convenience should be considered as a factor influencing esports spectatorship.
In addition to availability, the kinds of technologies owned by individuals can influence their media use (Cooper & Tang, 2009; Webster et al., 2006). Cost and resources also played a role. For example, sports communication researchers found that compared to cord-cutters, people who paid and had access to cable/satellite TV spent significantly more time watching the Rio Olympics on TV (Tang & Cooper, 2017). These findings are also true for esports. Esports players need to have access to ultra-responsive gaming equipment in order to make quick decisions in seconds during their gameplay (Stubbs, 2017). In addition, while gameplay is generally free, players tend to spend money on game enhancements and virtual goodies (e.g., characters, skins for the characters, etc.; Jang & Byon, 2020). For example, according to an industry report (Segal, 2014), League of Legends players spend more than US$100 million every month on the game.
Similar to the cost spent on gaming equipment and in-game purchase, the use of interactive features, such as donate, bit, and chat, may also link to esports consumption. These structures differentiate esports spectatorship from traditional sports viewing by enabling a two-way synchronous experience where players can have exchanges with spectators in real time. For example, researchers found that use of chat positively related to time spent watching esports games (Qian, Zhang, et al., 2019). Similar to watching a basketball game in a sports bar or in the stadium, chats/chat rooms allow esports consumers to be “socially connected” and create a sense of community (Hamilton et al., 2012). Bits, on the other hand, are used to cheer players by sending a message that includes animated emotes to “amplify spectators’ voice” and show support (Twitch, 2020). Through rewards and active loyalty, such as donation and subscriber icons, these interactive features provide social connections, ultimately resulting in not only entertainment but also progression and engagement (Qian, Zhang, et al., 2019; Sjöblom et al., 2019).
As such, it is logical to conclude that both individual and structural factors can have an impact on esports consumption. This study, thus, aims to empirically combine factors commonly associated with both active-audience and structural theories to explain to what extent their collective and relative influences on esports gameplay and esports spectatorship are similar. This attempt leads to the following research questions:
Method
Sample and Procedure
This study examined individual and structural factors that predict esports gameplay and spectatorship using an online survey. Adult esports consumers in the United States (18 years old or older) were recruited via several esports-related online message boards and mobile apps, such as Reddit, Discord, and so on, facilitated by Qualtrics, an online survey service provider (see Brown et al., 2018; Qian, Zhang, et al., 2019). Screening questions (i.e., Do you play esports games every week? Which of the following game is an esports game?) were used to determine whether potential participants had esports experience. The esports games listed in the screening question were chosen based on the popularity of the games (e.g., number of players, prize money, number of tournaments held) and were used to filter out casual video game players (Jang & Byon, 2020), as previous research demonstrated significant differences between esports consumers and casual gamers, particularly in their different levels of engagement (Huang et al., 2019; Korotin et al., 2018). In addition, attention-check questions (i.e., While playing an esports game, you had a fatal heart attack. Please select “strongly disagree”; see Rouse, 2015) were used in this study to ensure data quality. Those who did not pass the screening or attention-check questions were automatically guided to the end of the survey, and their responses were not saved. Participation in this study was completely voluntary. Participants did not receive direct compensation from the researchers.
Overall, 526 participants successfully completed the survey. Among the participants, 63.1% (n = 332) were male and 36.9% (n =194) were female. Their ages ranged from 18 to 77, with a mean age of 30.4 (SD = 14.9). More than half of the sample (54.1%) had average household incomes of more than US$50,000. Approximately 76% of the participants were Caucasian, 6.5% were African American, 6.1% were Asian, and 5.8% were Hispanic. In addition, 95.2% of the participants had access to a personal computer, 82.5% had access to a gaming console, 96.6% had a smartphone, and 53.6% had a tablet. On average, they spent US$1,108 on gaming equipment, US$329 on in-game purchase (e.g., season pass, in-game skins, cosmetics), and US$64 on esports merchandise (e.g., jersey, t-shirts).
Measures
This study measured esports gameplay and esports spectatorship as well as individual (i.e., preferences, motivations, sports fandom) and structural factors (i.e., availability, access and cost, use of interactive features) that may predict esports consumption. Each of the items was presented randomly within categories. The pretest observed no question-order effects.
Individual factors
To measure preferences, on a 7-point scale (1 = do not enjoy it at all, 7 = enjoy it a great deal), participants rated how much they enjoy consuming (i.e., playing/watching) each of the following genres of esports games, including fighting games (e.g., Street Fighter), FPS games (e.g., Overwatch), RTS games (e.g., Warcraft III), MOBA games (e.g., League of Legends, Dota 2), sports games (e.g., NBA 2K), and battle royale games (e.g., Fortnite, PUBG). All the genres were drawn from previous esports research (Adams et al., 2019; Buckle & Mander, 2018; Jang & Byon, 2020).
To measure motivations for esports consumption, participants were asked to rate each of the 22 statements 1 on a 7-point Likert-type scale. All the statements were drawn from previous esports studies (Lee et al., 2012; Lee & Schoenstedt, 2011; Sjöblom & Hamari, 2016). A principal component factor analysis of these items with varimax rotation was conducted. The analysis generated six factors/categories—escape/fantasy (α = .872; M = 4.62, SD = 1.54), group affiliation (α = .849; M = 4.56, SD = 1.37), entertainment (α = .800; M = 5.98, SD = 1.00), performance (α = .808; M = 5.03, SD = 1.24), knowledge acquisition (α = .799; M = 5.58, SD = 1.17), and pastime (α = .794; M = 4.51, SD = 1.35). Indexes of escape/fantasy motive, group affiliation motive, entertainment motive, performance motive, knowledge acquisition motive, and pastime motive were created respectively by averaging the scores from the items in each factor/category 1 and used in the subsequent analyses.
To measure sports fandom, we borrowed the existing scale measuring sports fandom in previous sports communication research (e.g., Gantz et al., 2006; Tang & Cooper, 2017). Participants were first asked to indicate how much they consider themselves to be a sports fan on a 7-point scale (1 = not a fan at all, 7 = very much a fan). In addition, participants were asked to report the estimated time they spend consuming sports on TV, online, and via mobile on a typical day, respectively. Based on previous research, sports fans would score higher on the “self-identification” scale and spend more time watching and following sports across different platforms (Cooper & Tang, 2012; Gantz et al., 2006).
Structural factors
To measure audience availability, participants were asked to select the time periods they are available for esports consumption on a typical day (Cooper & Tang, 2009). The periods listed for the selection included: 6–8 a.m., 8–10 a.m., 10 a.m.–12 p.m., 12–2 p.m., 2–4 p.m., 4–6 p.m., 6–8 p.m., 8–10 p.m., 10 p.m.–12 a.m., 12–2 a.m., 2–4 a.m., and 4–6 a.m. Participant responses were summed. The sum (ranging from 0 to 24) operationally defined audience availability and was used in the subsequent analyses.
To measure access to media technologies and cost, participants reported the number of devices they typically use for esports consumption. In addition, they were asked to report the estimated cost that they spent on gaming equipment, in-game purchase (e.g., season pass, in-game skins, cosmetics, etc.), and esports merchandise (e.g., jersey, accessories, etc.), respectively.
Furthermore, frequency of interactive feature use was measured with a single question for each of the three interactive features on a 7-point Likert-type scale (1 = never, 7 = always), including chat (i.e., a feature that allows esports spectators have a virtual conversation with other esports viewers and players), donate (i.e., a feature that allows esports spectators to donate money to esports streamers), and bit (i.e., a feature used to cheer esports players by sending a message that includes animated emotes to amplify spectators’ voice and show support; see Twitch, 2020). This single-question frequency measure was drawn from previous information and communication technologies (ICTs) research (see Gibbs et al., 2014; Jackson et al., 2010; Scott & Timmerman, 2005; Tang & Cooper, 2017).
Esports gameplay and esports spectatorship
To measure esports gameplay, participants reported the estimated number of hours they spend playing esports games on a typical week. Similarly, participants reported, respectively, the estimated number of hours they spend watching competitive esports tournaments/competitions, and the estimated number of hours they spend watching esports streamers on a typical week (Pizzo et al., 2018; Wohn & Freeman, 2020). The sum of the scores from these two items was created to measure esports spectatorship.
Control variables
Participants provided responses to demographic questions, including age, gender, education, and income. Research has pointed out that esports consumers tend to be male, young, and affluent (Brown et al., 2018; Buckle & Mander, 2018). Past studies also demonstrated the effect of demographics on media use (Tang & Cooper, 2017). Thus, to eliminate the possible influence of demographics variables, participants’ gender, age, education, and income were included in the regression model as controls.
Data Screening and Analysis
To answer the research questions, stepwise multiple regression analyses were conducted to examine the explanatory power of individual and structural factors on esports gameplay and esports spectatorship, respectively. The predictor variables entered into the regressions in the following order/steps: preferences, motivations, sports fandom, availability and access, and use of interactive features. Variance inflation factor statistics were examined, and no evidence of multicollinearity was found between the variables studied in this research.
Results
Overall, participants reported spending an average of 18 hr 13 min on esports gameplay on a typical week (M = 18.21, SD = 13.93) and 9 hr 26 min a week on esports spectating (M = 9.44, SD = 8.87). Almost 85% of the esports gameplayers were also esports spectators. Respondents self-reported using an average of 2.31 devices for esports consumption. The Pearson correlation result indicated a significant positive relationship between time spent on esports gameplay and spectating (r = .231; p < .0001).
To answer RQ1, a stepwise regression analysis was conducted to examine the relative and collective influences of the individual and structural variables on predicting esports gameplay. Table 1 provides a summary of the stepwise regression results with standardized regression coefficients. As shown in Table 1, six factors significantly predicted esports gameplay. Availability was the strongest predictor, followed by performance motivation, in-game cost, use of chat, preference for sports games (which was a negative predictor), and escape motive. Together, these variables explained 26% of the variance in esports gameplay. Structural factors provided a larger explanation to esports gameplay (by offering a unique contribution of 9.7%) than did individual factors (which explained 8.8% of the variance in esports gameplay). Specifically, availability and access provided a unique contribution of 8.8% when all other variables were controlled. Motivations explained 4.8% of the variance, and preferences added 3.6%. Sports fandom and use of interactive features were not statistically significant, and each added less than 1% of the variance, respectively.
Individual and Structural Factors That Predict Esports Gameplay and Spectatorship.
*p ≤ .05. **p ≤ .01. ***p ≤ .001.
To answer RQ2, regression results indicated that seven factors were significant predictors of esports spectatorship, including time spent consuming sports online, via mobile, use of donate, chat, number of devices used for esports consumption, preference for MOBA games, and knowledge acquisition motivation (see Table 1). Together, these variables explained 36.7% of the variance in esports spectatorship. Different from esports gameplay, individual factors provided a significant larger explanation (23.4% of the variance) of esports spectatorship than did structural factors (9.2% of the variance). Specifically, sports fandom provided the largest contribution by explaining 13.3% of the variance when all other variables were controlled. Preferences explained 7% of the variance, use of interactive features added 5%, availability and access 4.2%, and motivations added 3% of the variance when all other variables were controlled.
Discussion
This study puts both esports gameplay and esports spectatorship into consideration and pinpoints how individual and structural factors explain why people play and watch esports to better understand the complexities and intricacies of esports consumption. Findings suggest that esports consumers are both participants and audiences. Both measures commonly associated with active-audience, and structural theories played a significant role in explaining esports consumption. Specifically, esports gameplay was explained relatively more by structural factors (unique explanation of 9.7%) than by individual factors (8.8%). Different from esports gameplay, esports spectatorship was driven significantly more by individual factors (unique explanation of 23.4%) than by structural factors (9.2%). Preferences, motivations, availability, and access significantly predicted both esports gameplay and spectatorship. Sports fandom and use of interactive features, on the other hand, only predicted esports spectatorship but did not influence gameplay.
Motivations provided a larger explanation for esports gameplay compared to esports spectatorship. Both performance motivation and escape motive significantly predicted esports gameplay, while knowledge acquisition predicted esports spectating. Similar to what has been found in previous research (Lee & Schoenstedt, 2011; Pizzo et al., 2018; Weiss & Schiele, 2013), esports gameplay is driven by both competition and escapism. On the other hand, esports spectating is motivated by learning tactics and strategies to improve spectators’ skills and gameplay performance. Results indicate that the knowledge acquisition motive differentiates esports spectators from casual sports viewers and highlights the need to put both esports spectating and gameplay into consideration to better understand esports consumer behavior.
Sports fandom provided the largest explanation for esports spectating, yet it did not predict esports gameplay. Consistent with what Kim and Ross (2006) suggested that sports fans are not necessarily gamers, this study found that none of the sports fandom measures stood out as a significant predictor of esports gameplay. Nonetheless, this study found that esports spectators shared similarities with traditional sports viewers. Time spent consuming sports online, a literal manifestation of performative sports fandom as elucidated by Osborne and Coombs (2013), was the strongest predictor of esports spectatorship. Results lend some credence to the idea that consumers’ traditional media use routine would likely influence their new media use, as suggested by Chan-Olmsted et al. (2012). As such, esports organizations should consider integrating new media marketing strategies used by traditional sports communication practitioners into their practices (Jang & Byon, 2020; Pizzo et al., 2018).
Preferences provided a larger explanation to esports spectatorship than gameplay. Preference for MOBA games positively predicted esports spectatorship, while preference for sports games was a negative predictor of esports gameplay. We speculate that people enjoy watching organized teams play strategy-based MOBA games as a way of improving their own gameplay. Practitioners should consider how to use such games to build committed esports consumers and encourage long-term loyalty. At the very least, this might include strategic placement of games in the proper media outlets and in relevant stream genres. The industry might also want to include updates and patches that solidify the MOBA genre identification for consumers. Surprisingly, fans who preferred sports games spent less time on esports gameplay. Probably as what Brown et al. (2018) noted, esports consumers “do not seek out any other sport-based media product with nearly the avidity” (p. 431) observed in esports. Future research is encouraged to further examine this unexpected result and to explore how sports organizations can effectively integrate esports into their practices to draw audiences. In addition, future research should pinpoint the effects of consuming specific genres of esports games, such as differences in healthy versus unhealthy attachment to different games (Puerta-Cortés et al., 2017).
Structural factors played a critical role in explaining both esports gameplay and spectatorship. Availability and access provided the largest explanation to esports gameplay. Audience availability, a “theoretical linchpin of the passive audience” (Cooper & Tang, 2009, p. 521), was the strongest predictor of esports gameplay (i.e., an active activity). This result suggests that schedule constraints determine media use regardless of content and indicates the problem when conceptualizing media consumers as either active or passive. Interestingly, availability did not predict esports spectatorship, further demonstrating that esports consumers actively watch esports tournaments/streamers to learn from others in order to improve their own gameplay performance (Qian, Wang, et al., 2019). Audience activity is not an absolute concept. An individual media user is likely to be both active and passive at different points choosing the same medium (Cooper & Tang, 2009; Rubin, 1984).
Use of interactive features (i.e., both chat and donate feature) significantly predicted esports spectatorship, highlighting that esports consumers actively sought the “fixed” structures to interact with other esports players and viewers and to obtain a sense of community. In addition, cost spent on in-game purchases (e.g., in-game skins, cosmetics, characters, etc.) and use of chat were positive predictors of esports gameplay. Similar to how Olympics audiences integrate social media into their mediated mega-event experience (Tang & Cooper, 2017), these embedded interactive features uniquely link players with spectators and make esports consumers more involved in the game. Furthermore, this study found that the number of devices used for esports consumption positively predicted esports spectatorship. Results give a hint on the possible media multitasking behavior in esports consumption. Nonetheless, this study did not examine simultaneous media uses in esports consumption. Future research that examines gameplay multitasking is encouraged. Ultimately, these results point out the need for academicians and industry practitioners to give more attention to the essential, though less “visible” role of structural factors in media use. Esports, sports, and media companies should provide structural flexibility in their “products” to build loyal consumers and maximize their return on investment. On the other hand, findings indicate a need to understand how the structures embedded by the industry influence esports consumers and their understandings of meanings, identities, and cultures.
Limitations and Conclusion
While this study provided important insights into understanding how and why people consume esports, the results should be viewed in context. Due to the cross-sectional design of this research, we did not claim any causal inferences. In addition, this research was based on self-report using an online survey with a convenient sample; thus, findings may not be generalizable to a wider population. Future research is encouraged to integrate survey with technology-enhanced data to better capture real-time behavior and the contextual and structural cues in which the individual choice is embedded. Moreover, this study employed several single-item measures and only conducted multiple regression analyses. While all the measures were drawn from previous sports communication/ICTs research, future studies are encouraged to employ multidimensional scales (e.g., Sport Spectator Identification Scale, Sports Fandom Questionnaire; Wann, 2002; Wann & Branscombe, 1993) and advanced statistics (e.g., path analysis, structural equation modeling, mediation/moderation tests) to better capture the interactions between and among individual factors, structures, and esports consumption. Additionally, the convergent and discriminant validity of the individual factors should also be put into consideration when examining esports consumption (Xi & Hamari, 2019). Furthermore, while esports is a global phenomenon that naturally transcends cross-cultural boundaries, this research only studied esports gameplay and spectatorship in the U.S. context. Future comparative studies are needed to make cross-nation and cross-continent comparisons regarding the uses and effects of esports.
Despite the limitations, this study makes a unique contribution to understand esports consumer behavior by directly comparing predictors of esports gameplay to predictors of esports spectatorship. As esports practitioners aim to transform esports gameplayers into viewers (Murray, 2018), identifying shared drivers between the two is key to develop appropriate esports products and marketing strategies. In addition, this research demonstrates the connection between esports and traditional sports consumption. Results suggest that esports spectatorship shares similarities with mediated mega-sporting event viewing and can draw audiences who are less likely to be attracted by traditional televised sports, and esports gameplay shares similarities with traditional sports in promoting the positive spirit of competition, community building, empowerment, and activism. As such, future mega-sporting events may want to put esports into consideration.
Importantly, this study responds to the enduring theoretical debate on active versus passive audiences and contributes to the extant body of esports and sports communication work by examining how the integration of both individual and structural factors predicts esports consumption. We suggest that esports consumers are both active and passive. No single theoretical construct can explain the complexities that determine esports gameplay and spectatorship. Esports represents a new area in studying sports consumer behavior (Funk, 2017; Pizzo et al., 2018). By calling for abandoning active/passive dichotomy altogether, this study aids in the development of conceptual framework that will serve to predict esports consumer behavior and steps towards an integrative research agenda to yield more coherence, nuance, and richness to esports, sports communication, and audience research.
Footnotes
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
