Max Sjöblom /

Posted in category: eSport, MSSC, SEM, sport research

Why do people watch eSports?

eSports is a fast growing form of digital entertainment, but why exactly is it that viewers tune in to see the best compete in games such as League of Legends and CS:GO? To answer this question, we set forth to investigate motivations for consumption using the Motivation Scale for Sports Consumption (MSSC) as our guiding framework for an online survey [1].

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League of Legends. Image courtesy of artubr (https://www.flickr.com/photos/48006568@N05/8095444017/)

Survey on eSports

Before administering the survey, we piloted (n=20) the survey in order to acquire both qualitative feedback as well as to explore the validity of measurement. No major problems were observed in the pilot study concerning the measurement of sports consumption motivations. The online survey used self-selection of participants and was distributed primarily through the social news and social network sites Reddit (approx. 70-75% of total), Twitter (10-15%) and Facebook (5-10%), along with some dedicated gaming forums (5-10%). As a participatory incentive, we raffled six games (worth $50) from the Steam store. The eSports survey gathered a total of 888 valid responses.

Measurement

We measured the results using eight constructs from the original MSSC (vicarious achievements, aesthetics, drama, escape, knowledge, skills, social interaction, physical attractiveness) and added two new scales (novelty and enjoyment of aggression), as recommended by Trail [2]. The dependent variable of watching frequency had five options: “never”, “once a year”, “once a month”, “once a week” and “daily”.

We conducted model-testing using the component-based Partial Least Squared Structural Equation Modeling (PLS-SEM). Convergent validity was met as AVE, CR and Alpha measures were all over their respective thresholds. Discriminant validity was also met, as the square root of the AVE of each construct was larger than its correlation to any other construct, and each measurement item had the highest loading with its corresponding construct.

Results

First, let’s take a look at the means and standard deviations of the responses. We combined the items of constructs, and calculated the unweighted means and standard deviation of the aggregated responses. These are displayed in the table below

If the table is cut off scroll to the right to see more.

GratificationMSD
Vicarious achievement4.4831.764
Aesthetics5.0451.458
Drama6.2030.948
Escape4.4541.770
Acquisition of knowledge6.0481.042
Skills of the players/athletes6.3970.870
Social interaction4.0671.920
Physical attractiveness1.9151.209
Novelty5.0581.468
Enjoyment of aggression3.3751.607

Note: as the scale is 1-7, 4 is the average neutral value.

Perhaps not unexpectedly, the highest means are reported for drama, acquisition of knowledge and skills of the players. Much as in traditional sports, dramatic turn of events are important for keeping spectators glued to their seats in anticipation of that next big play. Often the games that are most well remembered after they are finished are the ones featuring some amount of drama and excitement. One could argue that eSports enable a buildup of drama better than traditional sports, as there is often a certain amount of hidden information that can be used to the broadcasters advantage when choosing what to show to the public. For example in Starcraft many directors have mastered the art of concealing potential ambushes until the very last minute, thus creating a spike of drama for the spectators. Compare this to traditional sports such as football, where most information (outside of team strategies) are visible in the sport environment.

As many spectators also play games themselves, appreciating the skills of players and learning from these players are strong gratifications for spectating. While we do not have absolute data of the relation between playing a certain game and watching eSports events for that game, based on personal observations we can speculate that the correlation between these two is positive. The fact that several games offer spectating from within the game client is a fact that further should go to show the connection between playing and spectating.

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Image couresy of artubr (https://www.flickr.com/photos/48006568@N05/12033862545/).

Low means are reported for physical attractiveness and enjoyment of aggression. While some games can be portrayed as aggressive due to the in-game violence, the actual player behavior is quite far from that, as physical violence and general overtly aggressive behavior is almost unheard of in the eSports scene. To some extent these results show that spectators do not experience the in-game violence present in games such as CS:GO as inherently aggressive in the traditional sense. Within traditional sports, boxing and ice hockey are traditionally perceived as highly aggressive sports, with the aggressive nature being a strong gratification for spectating. As with aggression, as majority of screen time is dedicated to activity within the game, the actual eSport athletes are not given as much screen time as in traditional sports, thus lowering the potential impact physical attractiveness can have on spectator gratifications. This is naturally nothing very surprising, as separating the sporting performance from the physical realm means the emphasis is on something completely different.

Secondly, let’s take a look at which factors would predict the frequency of watching eSports. To do this, we use the aforementioned PLS-SEM regression analysis. The results are presented in the table below.

How to read these results: For people driven by a particular type of motivation (for example drama), an increase of drama by X will increase the predicted behavior (consumption frequency) in relation to X. So for example, a β-value of 0.50 would mean that for each 1 unit drama increases, spectating increases by 0.50 units. Effect sizes are commonly considered small when around 0.1, medium when around 0.3 and large when over 0.5.

Note: * indicates a results with p < 0.001

If the table is cut off scroll to the right to see more.

GratificationBCI99 lowCI99 hip
Vicarious achievement0.068-0.0470.1730.114
Aesthetics-0.157*-0.240-0.0000.000
Physical attractiveness-0.076-0.1670.0940.161
Drama-0.004-0.1360.1370.938
Escape0.131*0.0340.2170.000
Acquisition of knowledge0.165*0.0300.2980.001
Skills of the players/athletes0.096-0.0670.2610.125
Social interaction-0.021-0.1110.0740.567
Novelty0.076-0.0420.1790.079
Enjoyment of aggression0.117*0.0220.2070.001

These results show us that four gratifications show statistically significant correlations with the frequency of watching eSports: aesthetics (-0.157), escape (0.131), acquisition of knowledge (0.165) and enjoyment of aggression (0.177). While the previously presented results related to means were fairly straight forward, some of these results present more of a puzzle.

Especially interesting is the positive predictive power of aggression, compared to the fairly low means that were reported by respondents. One way to interpret this is that yes, while a majority of spectators rate aggression low, there is a certain number of spectators actively driven by the need to see aggression. Perhaps these spectators have a way of interpreting aggression that is different from many other viewers. For example, the spectators that appreciate aggression might consider an all-out attack on the enemy base in a MOBA as a highly aggressive maneuver while other spectators may view this as just another strategy. In any case, it seems that spectators that receive gratification from aggression also watch eSports more frequently.

Escape and acquisition of knowledge are both gratifications that one might assume lead to a higher frequency of eSports consumption. Gratifications related to escape from everyday life are commonly associated with increased levels of consumption when it comes to entertainment products. Likewise, a player wanting to learn more about a game or particular strategy is incentivized to keep watch more and more eSports to learn from the very best. While it is hard to say how effective this transition of knowledge is, it is however quite clear that it is very important for many spectators, both based on the reported means and the positive predictive power.

The negative effect that appreciation of aesthetics has on spectating frequency is quite interesting, as it is not immediately apparent why this is. One might assume that aesthetics play a large role, as most eSport games are very complex audiovisual spectacles with lasers, explosions and fast moving characters. While aesthetic appreciation has been shown to positively impact spectating frequency in some traditional sports (think figure skating), we argue that the level of complexity present in many eSports games serves as a hindrance for appreciating the aesthetic aspects present. The fact that many eSports require a somewhat comprehensive understanding of the game rule is something that more casual observers might be put off by. Perhaps the awesome explosions are not enough to keep spectators interested in the game if they feel they do not have an understanding of what is going on with the rest of the game.

Limitations

The survey was aimed at consumers of online videogame streaming services, namely Twitch. This means the results of this study are slightly biased towards that particular form of consumption, rather than for example viewing eSports events live, a growing trend. It would be interesting to perform the same survey for live spectators in order to study how gratifications change depending on the form of consumption.

 

For the full research paper, please visit the Social Sciences Research Network: http://ssrn.com/abstract=2686182.

[1] Trail, G. T., & James, J. D. (2001). The Motivation Scale for Sport Consumption: A Comparison of Psychometric Properties with other Sport Motivation Scales. Journal of Sport Behavior, 24(1), 108-127.

[2] Trail, G. T. (2012). Manual for the MSSC. Retrieved from http://sportconsumerresearchconsultants.yolasite.com/resources/MSSC%20Manual%20-%202012.pdf