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BSc BA Thesis:

#PlayApartTogether: Online gaming and its effects on social presence in students studying from home during the COVID-19 pandemic.

By, Kees Arwert

11671874

June, 2021

University of Amsterdam Supervisor: Dr. D.M. Dekker

Course: Bachelor’s Thesis Management in the Digital Age

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Statement of originality

This document is written by Student Kees Arwert, who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of the completion of the work, not for the contents.

Special thanks to Dr. D.M. Dekker for the kind and guiding supervision of this Bachelor’s Thesis, as well as to Drs. R. van Hemert for the coordination of the Bachelor’s Thesis course.

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Abstract

The COVID-19 pandemic influenced online gaming, as well as the online education

environment. Students were forced to study from home as conventional ways of interaction became obsolete, while online gaming is used to saturate social interaction needs. Statistical analysis was used to explore the relationship between the amount of online gaming and social presence, a variable positively related to various learning outcomes. It is hypothesized that the amount of online gaming positively influences social presence scores in students and that the type of online game played moderates this relationship in such a way that students who play Multiplayer Online Battle Arena games more over other types of online games have a

strengthened relationship. Both hypotheses were not supported. A mediating effect was found, where gaming more increases the chance of being male, resulting in higher social presence scores. The results suggest that male student gamers have been better prepared for the sudden shift towards online education. Students who are forced to study from home are recommended to play Battle Royale games in their leisure time, as this type of game was shown to have a positive effect on social presence scores. Suggestions for further research and implications have been given.

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Introduction & Theoretical Framework

Online gaming has seen a rise in popularity since the start of the global COVID-19 pandemic (Javad, 2020). Recent research on the impact of COVID-19 on the video game industry shows that Steam, the largest digital distribution platform for PC games, saw its number of users increase by more than 20% during the COVID-19 pandemic (Şener, Yalçın &

Gulseven, 2021). The video game industry as a whole has an estimated worth of $159.3 Billion at the end of 2020, a 9.3% increase from the year before, and is expected to keep growing at this rate in the coming years (Field level media, 2020). It is clear that the COVID-19 pandemic is influencing the landscape of gaming. As Kriz (2020) argues, games are not only a way of entertainment, but also a way to relieve stress by providing a temporary escape from the real world. In the current time, online gaming is used as a way to interact with strangers and friends virtually while curfews, stay-at-home mandates and quarantine are the new norms. The games industry even launched a campaign called #PlayApartTogether in cooperation with the World Health Organization to promote compliance for health guidelines and social distancing (Bloomberg, 2020).

Research on gaming has mostly been about the prevalence and effects of gaming disorder within the social psychology and medical field, or within studies examining game- based learning experiences (Fam, 2018; Vogel, Vogel, Cannon-Bowers, Muse & Wright, 2006).

With the rise of multiplayer and online gaming experiences, gaming has also recently been used as a proxy for research about virtual teams, team performance, and communication (Eaton &

Mendonça, 2019; Leavit, Keegan & Clark, 2016; Robinson, 2016). What research on (online) gaming is lacking, is the effect that the participation of online gaming itself poses on specific variables related to perceived learning and performance, in particular the notion of social presence. By researching the effects of online gaming on students studying from home since the COVID-19 pandemic, this paper tries to add to a different aspect of gaming research and

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contribute to recent research on the effects of the COVID-19 pandemic, specifically in the field of education and it’s forced online setting. The paper will discuss the various relevant variables used in this research in the following section and will then work towards an overview of the research question.

Social presence Social presence and online learning

The definition of social presence has been offered in various social psychology research and has developed over the years to take into account the development of computer-mediated communication (CMC), defined by previous literature as human-to-human communication through or with help of computer technology (Carr, 2020). Social presence is about the degree to which people perceive and feel connected to others in a virtual environment (Richardson, Maeda, Lv & Caskurlu, 2017). Salience, connectedness, co-presence, and a feeling of realness of the interaction are notions that have all been linked to the term social presence (Richardson et al., 2017). Social presence is one of the three components of the Community of Inquiry (Col) framework, developed by Garrison & Vaughan (2008). The other components being cognitive presence & teaching presence. This framework illustrates items that describe a high-level learning experience and is widely used in research between these constructs and learning, whether online or in another environment (Garrison & Akyol, 2017). The paper from Garrison

& Akyol (2017) contributes to the definition of social presence, also connecting it to the development of an engaging environment that supports expression and contribution in a group.

In this way, the distinction of the “social” and “presence” side can be seen, where the former puts more emphasis on social outcomes, such as engagement, whereas the latter is more about a sense of connectedness.

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The emergence and evolution of computer-mediated communication (CMC) seems crucial in the development of research on social presence in the online learning environment (Carr, 2020). In the early nineties, CMC was predominantly text-based communication in an online form, which developed towards more rich formats that increased social prompts in an online setting (Carr, 2020). From as early as 1995, research concluded that CMC can be an interactive, interesting and stimulating way of conferencing within the online learning environment (Gunawardena, 1995).

Research tells us that social presence positively correlates to students’ perceived learning and satisfaction in an online learning environment (Richardson et al., 2017; Richardson

& Swan, 2003; Sawn & Shih, 2005; Lowenthal, 2010). Other findings of social presence on online learning include the positive effect on student participation and motivation to participate (Swan & Shih, 2005), as well as satisfaction with the instructor and the course (Richardson &

Swan, 2003; Akyol & Garrison, 2008). Hostetter & Busch (2006) found that social presence scores rose with students taking more online courses. They suggest that students having more experience in an online environment may have developed specialized learning skills and proficiency in computer-mediated communication, resulting in higher social presence and study satisfaction.

The measurement of social presence is often done by self-reporting questionnaires (Richardson et al., 2017; Hudson & Cairns, 2014). A questionnaire that was useful for this research is the Social Presence Scale (SPS) questionnaire developed by Gunawardena & Zittle (1997) and a similar scale by Richardson & Swan (2003), which is based on the SPS. These are widely used and adopted for research on social presence in online learning, as showed by Richardson et al. (2017). The SPS for measuring social presence in a learning environment has shown high reliability (Richardson et al., 2017). This could be credited to the implementation

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of both the aspect of CMC, as well as the aspect of interactivity in the SPS. In the methods section, more time will be spent on the choice of methodology for social presence in this study.

Online gaming Social presence and online gaming

Hudson & Cairns (2014) provide us with a link between online gaming and social presence in their paper, which states that the social aspect of gaming is increasingly important to be able to understand the full experience. Furthermore, de Kort et al (2007) argued that digital gaming technology is useful as a social presence technology for its many social interaction opportunities. In online games, a player can interact and communicate with others. The way of interaction however can differ from game to game. The paper will discuss this further when we talk about the different types of online games later on in this section.

Jansz and Tanis (2007) interpreted that the amount of time spend playing First Person Shooter (FPS) games was strongly predicted by the need for social interaction. Frostling- Henningsson’s (2009) paper also confirms this, stating that motivation for online gaming is led by social reasons of cooperation and communication. An interesting perspective that this paper gives is that communication was a motivator because gamers playing together had the opportunity to talk about subjects outside of the game, like real-life struggles and problems.

The paper by Tyack et al. (2016) on Multiplayer Online Battle Arena games also supports the social reason of playing together as a motivator to start playing, both in terms of cooperation (playing together) as communication (connecting to new people). Research from 2008 found that for both digital as non-digital games, social interaction is a motivator to start playing (Shamos, 2008). This research also states that women are more likely to be motivated by social interaction in games than men. An explanation for this was that women are always more driven by relational aspects, as theorized by previous literature (Tannen, 1990).

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Coming back to de Kort et al. (2007), who suggest that digital games should be seen as a technology that stimulates social presence because it provides people with a medium for interaction at a distance.

A concern about the developments of gaming during the COVID-19 pandemic is highlighted by an article from King et al. (2020), stating that while gaming can be advantageous to tackle pandemic restraints, vulnerable individuals have a risk of developing unhealthy patterns while trying to use gaming as an escape from current real-life problems. They suggest that types of games that promote physical activity or social interaction and collaboration should be advocated.

Type of online game played

A different aspect of social presence in online games is given by a study from Hudson and Cairns (2016), which concluded that losing in a team vs team game has a negative impact on social presence and team cohesion within teams, while winning increased the within team social presence. In this study, big differences were found between different games, suggesting that some games have elements that amplify the negative effect of losing on so-called cooperative social presence.

Subsequently, while online games in general promote social interaction and collaboration, the type of online game can make a difference in how they achieve and reward this. This paper will shortly go over the different types of online games that are relevant to this paper’s research objective. In short, the types of online games discussed in this study are; MMO (Massively Multiplayer Online), MOBA (Multiplayer Online Battle Arena), First Person Shooter (FPS), Real-Time Strategy (RTS), Battle Royale (BR), sports games, and miscellaneous (other) types of online games.

In an MMO (Massively Multiplayer Online) game, like World of Warcraft, the player finds itself in a virtual world with thousands of other players playing in that same world, all

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questing, leveling up their avatar and exploring the world. The player can choose to collaborate with other players, but can also play alone. In a MOBA (Multiplayer Online Battle Arena) game, like League of Legends, players are teamed up temporarily for the duration of a match, usually in a 5v5 setting. These games are fast-paced and communicatively intensive, often resulting in the development of a very different and highly effective communication framework (Chambers, 2019). The objective is often to destroy the enemy team’s base before your team’s base is taken down. Working together as a team to achieve objectives is necessary, as winning on your own is almost impossible. First Person Shooters (FPS), like Call of Duty or Counter-strike, are games that focus on weapon combat from a first-person perspective. In an online setting, these games are often played in temporary teams to play matches in a deathmatch (team vs. team, most kills win) or free-for-all (all against all, most kills win) setting, or to play for a team objective (i.e. capture the flag). Collaboration and communication are not always necessary but can be a component of online FPS games. Real-Time Strategy (RTS) games, like Starcraft, are strategy games that are not turn-based, meaning that players compete against each other in real- time. A typical RTS game match is about resource-gathering, base-building, unit-building and controlling. While these games are most often played 1v1, there are also options for 2v2 or other settings. The goal in RTS is to out-strategize your opponent by destroying their base or by having them forfeit. RTS games thus seem very individualistic, with less focus on social interaction. Next up are the Battle Royale (BR) games, like Fortnite or Apex Legends. Choi &

Kim (2018) suggest that these games, which have seen a huge rise in popularity in the last years, can be classified as their own new genre within online gaming. While they have elements in common with FPS games, the objective for Battle Royale games is different. Players are put on a shrinking map and fight each other until there is a last-man-standing. A clear inspiration for these games is taken from Fukasaku’s action-thriller Battle Royale from 2000 and Collins’ 2008 novel The Hunger Games (Greszes, 2019). BR games are played between individual players,

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through pairs of two players or in squads (3-5 players). So, depending on how the game is played, social interaction and collaboration can be a factor in these games. The last specific genre discussed are online Sports games, like FIFA21 or Rocket League. These games are also mostly played online between individuals, like a 1v1 soccer or basketball match, but also have options to team up and play with each other in a team. Other types of online games may include online Casino games, online Arcade and Puzzle games, or online Board games.

Overview and research question

The question that rises from the summary of the different types of online games is to what extent playing a specific type of online game more over another type of online game influences the sense of social presence among people, due to the different ways of social interaction through communication and play. We will now move on to an overview of the research question of this study, followed by the hypotheses.

This research wants to further explore the association between experience with different types of online games as a way of online interaction, and social presence among students who were forced to study from home during the COVID-19 pandemic. The research question is formulated as follows: “To what extent is the amount of online gaming related to social presence in students studying from home during the COVID-19 pandemic and how is this relationship moderated by the type of online game played?”. The subsequent moderation model is as follows:

X = Amount of online gaming

Y = Social presence Mod = Amount

played type of online game

Figure 1 The conceptual model

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The first hypothesis stems from the de Kort et al. (2007) statement of games as a social presence technology, as well as the suggestion made by Hostetter & Busch (2006) on the effect of experience within a virtual environment on an increased social presence. As the motivation for playing online games relies heavily on social reasons, the question being asked is whether people that are often participating in online gaming may have a higher sense of social presence within the forced online learning environment. Specifically for this study, do students who were forced to shift towards an online setting of education have a higher feeling of social presence when they are already experienced with an online environment that promotes social engagement or interaction in the form of online gaming? The hypothesis is as follows:

H1: Students who participate in online gaming more often have a higher sense of social presence in the forced online learning environment.

The moderation variable in this paper looks at the different types of online games and their effect on the relationship stated earlier. We established different types of games that handle social interaction and collaboration differently. Overall, MOBA (Multiplayer Online Battle Arena) games seem to have a higher focus on collaboration than other, more individualistic online games, like RTS (Real-Time Strategy) games. This is due to the nature of the game objective, play and requirement of forming small, temporary teams in a match. They always require social interaction and collaboration during play. The other type of games (MMO, FPS, BR, and Sports) can have a big focus on collaboration when teaming up, but do not require players to play in a team and have to interact with each other. The hypothesis for the moderation effect is thus formulated as follows:

H2: The type of online game played moderates the relationship between the amount of online gaming and social presence in students studying in the forced online learning environment in such a way that the relationship is stronger in students that put the most time in playing MOBA games compared to all the other described types of games.

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Now that the complete background for this study has been established and the research objective is clear, it is time to discuss the methods for this study.

Methods Participants

The research type is quantitative, collecting original data on the moderation model shown in the previous section. Data was collected using a Qualitrics (2020) questionnaire measuring the specific variables. The survey questionnaire can be found at the end of the Appendix. The participants for this questionnaire included 120 students in total. Of the 120 questionnaire responses, 69 respondents stated they participated in online gaming. 51 responses stated that they do not participate in online gaming and thus lack a measurement of the moderation variable. Therefore, these responses were excluded for analysis to improve the quality and power of the analysis between the main effect and the moderation effect. Of the 69 respondents, 3 did not fully complete the questionnaire and were excluded. Another 3 responses were deleted as outliers, using the standardized residuals method with a cut-off value of 2. This leaves a research sample of 63 participants. The demographics of this final sample is as follows.

Of the 63 student gamers, 79.4% are male, 19% female and 1.6% stated other, with an age range between 18 and 30 years old (M = 22.38, SD = 2.29). Most of the sample is of Dutch nationality (79.4%). 95.2% followed a full-time study program. The most common study backgrounds are Business & Economics (44.4%) and Computer science & IT (14.3%).

The study was advertised for approximately 1 month through social media posts on Facebook, Instagram, and LinkedIn, as well as through various student gaming communities on Discord, a university WhatsApp chat, and through posting the link on the Canvas learning environment. The sample was a voluntary response sample. Participants gathered through Discord had the chance to win a €20 prepaid gaming card as a reward for their participation. 14

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participants entered the raffle through a direct message on Discord. This was to preserve anonymity, so no other contact details were needed to enter. The winner was selected by a random selection program.

Measures

Social presence. The questions to measure social presence were based upon scales

developed by previous research. Specifically, the Social Presence Scale (SPS) developed by Gunawardena & Zittle (1997), as used by Reio & Crim (2013), which is a 12 item, 5-point Likert scale to measure Social Presence. This scale can also be seen in the paper by Strong et al. (2013) and Cobb (2008). Another variation of this scale is developed by Richardson & Swan (2003), which is also used in Swan & Shih (2005), Hostetter (2013), and Hostetter & Busch (2006). As mentioned before, the SPS implements both the aspect of CMC, as well as the aspect of interactivity. Questions can easily be altered to be able to fit this research. An example question of this scale is, “I feel comfortable participating in the GlobalEd discussions.”. Since this research won’t be focused on participation in the GlobalEd university conference, the question was altered to, “I feel comfortable participating in the online course discussions.”.

Another example question is, “Discussions using the medium of online education tend to be more impersonal than face–to–face discussions”. Reliability analysis for this study showed a Cronbach’s alpha of α = .75, which is acceptable, but lower than the reliability reported by Reio

& Crim (2013) (α = .92) and Gunawardena & Zittle (1997) (α = .88). Deleting items would not significantly improve the internal consistency of the scale. Two items in the scale were reverse- coded to make sure respondents give consistent answers.

Amount of online gaming. The amount of online gaming in general was measured by

asking a multiple choice question (“How many hours, on average, do you spend playing online games in general?”). This variable has 7 categories, with 1 = “less than 1 hour a week”, 2 =

“between 1-2 hours a week”, 3 = “between 2-4 hours a week”, 4 = “between 4-7 hours a week”,

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5 = “between 7-12 hours a week”, 6 = “between 12-20 hours a week”, 7 = “more than 20 hours a week”. Since respondents stated that they participate in online gaming in the previous control question, 0 hours was not an option. After all, this study also wants to record data on the moderating variable Amount played a type of online game. Asking for the amount of online gaming in categories might make answers more reliable, as respondents may have had a hard time stating exactly how many hours per week they spend on online gaming.

Type of online game played. The amount of time a type of online game is played was

measured the same way as the amount of online gaming in general to stay consistent (“How many hours, on average, do you spend playing the type of online game?”). So, in total, 7 variables have been made with 7 categories on the amount of time played a type of online game, being MOBA, FPS, RTS, MMO, BR, Sports & Other.

Control variables. Possible variables to control results are gender, age, nationality, the

number of online courses followed, and the number of EC (college credits) earned from online courses. Richardson & Swan (2003) also controlled for gender, age, and college credits earned while measuring social presence. In their study, gender showed a significant correlation to the perception of social presence. Their sample showed that women had a higher perception of social presence than men. This could arguably be related to other existing literature that shows the effect that gender has on overall educational experiences (Cobb, 2008). However, the study from Cobb (2008), as well as the study from Swan & Shih (2005) did not show a significant relationship between gender and social presence scores. Lastly, a literature review from Romrell (2014) shows that most video game players are (stereotypically) male. Gender differences were also shown for different types of games, with men preferring more competitive or action games while females preferred games with little violence and a more instructive nature. Therefore, gender could indeed cause alternative explanations of the results. The number of courses followed is a desirable control variable when taking into account the research from Hostetter &

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Busch (2006), where students following more online courses showed a higher social presence score. Most of the students in the sample are studying at a Dutch institution, so in this study, there was also a preference to control for nationality, as educational experiences might differ for international students. Additional control variables are whether a student studies full-time or part-time (study program), what their study background is and lastly, what their preferred gaming device is (i.e. “PC”, “Console” or “Mobile/Tablet”).

Procedure

Statistical data was analyzed using the program SPSS, where the correlation between variables was established. To test hypothesis 1, the relationship between the amount of online gaming and perceived social presence of students in online education, a linear regression analysis was used, with social presence score as dependent variable and the general amount of online gaming as independent variable. To test hypothesis 2, the interaction effect between the amount of online gaming and the type of online game played on perceived social presence of students in online education, the PROCESS macro version 3.5 Model 1 developed by Hayes (2017) was utilized. The amount of online gaming is the independent variable, playing MOBA games more often over other types of games is the moderating variable, and social presence the dependent variable. The significance threshold was set at p-value .05. Later on in the results section, PROCESS macro Model 4 by Hayes (2017) was also utilized to explore a mediation effect between the amount of online gaming, gender and social presence. Another linear regression analysis was used with social presence score as dependent variable and the amount of time playing Battle Royale games as independent variable.

The variable for the general amount of online gaming was recomputed using the 7 variables for the amount of time the various types of online games are played. This was done so the general amount of playtime would fit the moderating variable for the type of online game correctly. To give an example, some respondents answered that their general amount of time

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playing online games is “between 12-20 hours a week”, and then proceeded to fill in that they played a certain type of game for “more than 20 hours a week”. To recompute this variable, the mean for every category of amount of playtime was taken. With all the categories then calculated on a continuous scale, all playtimes for types of online games will be summed up for every participant. Utilizing this new value, it was possible to recategorize the general hours of playtime on the same categorial scale. To be able to recompute the variable, “less than 1 hour a week” for the type of online game played was assumed to mean 0 hours of playtime, since it is not possible to determine exactly how much hours was put into every type of game.

Lastly, before data analysis can begin, the moderating variable, as well as the independent variable had to be made dichotomous by creating dummy variables. By doing this, the PROCESS macro Model 1 by Hayes (2017) could be used correctly to test the second hypothesis (Kenny, 2018). Because our second hypothesis wants to explore what effect of playing MOBA games most over all other types of online games has on the established relationship, the dichotomous variable returns 1 when a respondent fills in a higher category of playtime for MOBA games over every other type of game. Else, this variable returns 0. In this way, the variable also takes into account the playtime of all other games. The general hours of playtime could be made dichotomous by coding 1 = “above average amount of weekly playtime”

and 0 = “below average amount of weekly playtime”. To determine what values are above average (high) and below average (low), this study looked at the recent State of Online Gaming reports from Limelight (2020). The reports state that gamers spent 7.11 hours each week playing online games in 2019 and 6.33 hours each week in 2020. Therefore, participants who stated that they play online games “between 7-12 hours a week”, “between 12-20 hours a week” or “more than 20 hours a week” are given a value of 1. Else, this variable is returned as 0. Gender was made into a dummy variable (1 = male, 0 = not male) to make sure that the variable can be included in the correlation table and to make analysis easier, as only one participant stated

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“other” as their gender. Nevertheless, to make the analysis more reliable, an Eta statistic will also be given to establish association between variables and compare correlational results to.

Lastly, Nationality has been made a dummy with 1 = Dutch and 0 = not Dutch.

Results Descriptive statistics and correlations

Table 1 contains the means, standard deviations, and correlations of the different variables for this sample. The scores for students’ social presence are between 1.83 and 4.08 (M = 2.88, SD = 0.51). Of the 63 participants, 6.3% play online games for less than 1 hour a week in general. 15.9% of participants play online games between 1-2 hours a week. 12.7%

play between 2-4 hours a week. 22.2% play online games between 4-7 hours a week. 7.9% play between 7-12 hours a week. 14.3% between 12-20 hours a week and the remaining 20.6% of participants play online games for more than 20 hours a week.

Forty (63.5%) participants play MOBA games for less than 1 hour a week or not at all.

Thirty-six (57.1%) participants play FPS games for less than 1 hour a week or not at all. This number is 51 (81%) for RTS games, 52 (82.5%) for MMO games, 44 (69.8%) for Battle Royale games, 41 (65.1%) for Sports games and 38 (60.3%) for other types of online games. FPS games have the highest frequency of participants playing more than 20 hours a week, being 4 (6.3%).

Three (4.8%) participants play Battle Royale games for more than 20 hours a week. Of all men in the sample, 25 (50%) have a below average amount of weekly playtime. Of the females, 10 (83.3%) have a below average amount of weekly playtime. One participant was neither male nor female and has a below average amount of weekly playtime. Forty-two (84%) males were Dutch, 8 (66.6%) females were Dutch. Of these 50 Dutch participants, 28 (56%) have a below average amount of weekly playtime.

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Table 1 shows that the general amount of online gaming has a weak association with social presence (r = .27), we see the same for the dummy variable for general amount of online gaming (r = .26). These results suggest that our hypothesis and further analysis show some support from the table. The correlation matrix also shows that the playtime for Battle Royale games has a moderate association with social presence (r = .39). Lastly, the dummy for gender also has a moderate correlation with social presence (r = .41). The Eta statistic for gender and social presence scores is .44 (see Appendix). Eta squared is .19 meaning 19.2% of variance in social presence is accounted for by gender. Subsequently, gender was taken as control variable for further analysis.

Gender shows a weak to moderate association with the general amount of online gaming (r = .36). The association between gender and the dummy variable for general amount of online gaming is weak (r = .28). Gender also seems to have a weak to moderate association with the playtime of MOBA games (r = .33). A weak association is also shown between gender and playtime of Battle Royale games (r = .27), as well as the playtime of sports games (r = .28).

The dummy variable for playing MOBA games most of the time has a weak negative association with the amount of playtime for FPS games (r = -.26). Additionally, we see a weak negative association between the dummy variable for playing MOBA games most of the time and the variable preferred gaming device (r = -.27). All respondents who play MOBA games most of the time prefer to game on their personal computers. Preferred gaming device has several other negative associations with playtime for different types of online games. These are;

a weak to moderate negative association with FPS games (r = -.32), weak to moderate negative association with MOBA games (r = -.33) and a weak negative association with other types of online games (r = -.26). For the hypotheses testing, preferred gaming device will also be controlled for, as it correlates with different types of online games and most of the participants in the sample (66.7%) prefer to play on their personal computer. 23.9% prefer a gaming console,

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while 9.5% prefer a mobile or tablet for online gaming. The rest of the control variables originally stated in the methods section will be discarded to increase the overall statistical power of the model.

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Table 1

Descriptive statistics and correlations

Variable M SD 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19

1. Social presence 2.88 0.51 (0.75)

2. General amount of online

gamingᵃ 4.35 1.94 0.27*

3. Type of game MOBAᵃ 2.05 1.65 0.21 0.56**

4. Type of game FPSᵃ 2.08 1.72 0.22 0.53** 0.004

5. Type of game RTSᵃ 1.41 1.04 0.164 0.33** 0.34** 0.04

6. Type of game MMOᵃ 1.49 1.39 0.01 0.40** 0.22 0.20 0.20

7. Type of game BRᵃ 1.86 1.62 0.39** 0.53** 0.28* 0.68* 0.10 0.12

8. Type of game sportsᵃ 1.84 1.45 0.10 0.29* 0.13 -0.12 -0.5 -0.13 -0.003

9. Type of game otherᵃ 1.78 1.29 0.13 0.47** 0.24 0.58** 0.12 0.14 0.48** -0.11

10. Preferred gaming deviceᵇ 1.43 0.67 -0.04 -0.32* -0.33** -0.16 -0.07 -0.11 -0.03 0.12 -0.26*

11. Genderᶜ 0.79 0.41 0.41** 0.36** 0.33** 0.05 0.05 0.10 0.27* 0.28* -0.03 -0.15

12. Ageᵈ 22.38 2.29 0.01 -0.11 0.15 -0.21 0.08 0.24 -0.13 -0.15 -0.05 -0.01 -0.001

13. Nationalityᵉ 0.79 0.41 0.004 0.09 0.04 0.07 -0.33** 0.07 0.20 0.11 0.03 0.15 0.22 -0.31*

14. Study backgroundᶠ 4.35 3.92 -0.12 -0.03 0.03 -0.11 0.01 0.24 -0.29* -0.17 0.02 -0.19 -0.15 0.09 -0.12

15. Study programᵍ 1.05 0.21 0.05 0.15 0.22 -0.01 -0.09 0.24 -0.03 0.18 0.16 -0.03 -0.07 0.09 0.11 0.13

16. Number of online courses 8.22 3.99 -0.10 -0.05 -0.01 0.10 -0.08 0.06 -0.11 0.03 0.03 0.02 -0.23 -0.35** -0.01 -0.06 0.16

17. Number of EC (college credits) 43.36 23.86 0.005 -0.15 0.02 0.07 -0.03 -0.03 -0.10 -0.04 -0.02 -0.09 -0.15 -0.29* 0.07 -0.03 0.18 0.60**

18. Dummy MOBAʰ 0.14 0.35 -0.05 0.18 0.65** -0.26* 0.14 -0.15 -0.13 -0.02 -0.11 -0.27* 0.21 0.09 -0.02 -0.002 -0.09 -0.03 -0.016

19. Dummy general playtimeᶦ 0.43 0.50 0.26* 0.87** 0.50** 0.49** 0.31* 0.32* 0.50** 0.25* 0.38* -0.22 0.28* -0.12 0.05 -0.11 0.11 -0.01 -0.12 0.20

Notes. N = 63. Cronbach's Alphas are in parentheses on the diagonal. ᵃ1 = less than 1 hour a week, 2 = between 1-2 hours a week, 3 = between 2-4 hours a week, 4 = between 4-7 hours a week, 5 = between 7-12 hours a week, 6 = between 12-20 hours a week, 7 = more than 20 hours a week. ᵇ1 = computer (PC), 2 = gaming console, 3 = mobile or tablet. ᶜ1 = male, 0 = not male. ᵈAge was measured in years. ᵉ1 = Dutch, 0 = not Dutch. ᶠ1 = Business and economics, 2 = Life sciences, 3 = Arts and humanities, 4 = Engineering, 5 = Physical sciences, 6 = Social sciences, 7 = Computer science & IT, 8 = Education, 9

= Law, 10 = Clinical and health, 11 = Psychology, 12 = other. ᵍ1 = full-time, 2 = part-time. ʰ1 = Plays MOBA games most of the time, 0 = Does not play MOBA games most of the time. ᶦ1 = Above average amount of online gaming, 0 = Below average amount of online gaming.

* p < .05

** p < .01

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Hypothesis 1: Linear regression

To test hypothesis 1: “Students who participate in more online gaming have a higher sense of social presence in the forced online learning environment.”, a linear regression analysis was used in SPSS with the control variables gender and preferred gaming device in block 1 and the general amount of online gaming in block 2. I first checked if the data meets the different assumptions for linear regression. All outputs can be found in the Appendix. The first assumption check is for linearity. For this check, I plotted the main variables in a scatterplot.

The plot shows that the independent and dependent variables have a (weak) curve linear relationship. For the independence of residuals assumption, no further checks were needed, as the data was collected via a cross-sectional survey. The normality of residuals was checked via a P-P plot, showing that the residuals are approximately normally distributed. Homoscedasticity was examined via another scatterplot, which shows that the residuals are equally variable.

Lastly, it was checked if there is no multicollinearity in the data. Results show that multicollinearity is not present, as none of the VIF values exceed 1.25.

The linear regression results show that Model 2 has an R-squared of 19.2%. Therefore, 19.2% of the total variance is explained by this model. R-squared change of Model 2 is 2.1%

(p < .01), showing that Model 2 is significant, and 2.1% of the total variance is explained by the general amount of online gaming. The coefficients table shows that the general amount of online gaming has a non-significant, positive relationship with social presence (β = 0.04, SE = 0.03, t = 1.25, p > .05, CI [-0.03, 0.11]). Thus, these results do not support hypothesis 1. In this model, the control variable gender is the only variables that shows a significant (positive) relationship with students’ social presence score (β = 0.45, SE = 0.16, t = 2.91, p < .01, CI [0.14, 0.77]).

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Hypothesis 2: PROCESS macro Model 1

For testing the second hypothesis, “The type of online game played moderates the relationship between the amount of online gaming and social presence in students studying in the forced online learning environment in such a way that the relationship is stronger in students that put the most time in playing MOBA games compared to all the other described types of games.”, the SPSS PROCESS macro Model 1 was utilized (Hayes, 2017). Table 2 shows the results of the analysis. The interaction effect is not significant, so hypothesis 2 is not supported (β = -0.23, S.E. = 0.36, t = -0.64, p = > .05, CI = [-0.93, 0.48]).

Table 2

Results for the interaction effect between the amount of online gaming and playing MOBA games most on social presence.

Variable β S.E. t p

Constant 2.41 0.20 11.82 <.001***

Amount of online gamingᵃ 0.22 0.14 1.57 .123

Type of game MOBAᵇ -0.08 0.29 -0.28 .779

Gender 0.49 0.15 3.14 .003**

Preferred gaming device 0.02 0.09 0.20 .841

Interaction effect (X*M) -0.23 0.36 -0.64 .524 Notes. R-squared = .22. ᵃ1 = Above average amount of online gaming, 0 = Below average amount of online gaming. ᵇ1 = Plays MOBA games most of the time, 0 = Does not play MOBA games most of the time.

* p < .05

** p < .01

*** p < .001

Further analysis: PROCESS macro Model 4

To further explore the results for this sample, I will test if there is in fact a mediation effect occurring between the amount of online gaming, gender, and social presence because gender shows a significant relationship with our dependent variable during previous hypothesis testing. For this, the SPSS PROCESS macro Model 4 will be used (Hayes, 2017). The model shows there is a significant positive indirect effect of amount of online gaming on social presence through gender (β = 0.11, S.E. = 0.06, p = <.05 , CI = [0.02, 0.24]).

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a = -0.26, p < .05 b = -0.44, p < .01

Direct effect c’ = 0.14, ns

Indirect effect: a*b = 0.11, 95% CI [0.02, 0.24]

Further analysis: Linear regression

Lastly, since the Battle Royale type of online game is the only other variable that has a significant correlation with the dependent variable social presence, linear regression was used to determine if students who put more time into playing Battle Royale games have a higher sense of social presence. The same control variables used for the previous linear regression analysis were used in this analysis. The assumptions for linear regression were successfully checked, the output for these assumption checks can be found in the Appendix.

The results for linear regression show that Model 2 has an R-squared of 25.3%. So, 25.3% of the total variance is explained by Model 2. R-squared change in Model 2 is 8.2% (p

< .01). Thus, Model 2 is significant, and 8.2% of the total variance is explained by the amount of time playing Battle Royale games. The coefficients table shows that the amount of time playing Battle Royale games has a significant positive relationship with social presence in students (β = 0.09, SE = 0.04, t = 2.55, p < .05, CI [0.02, 0.17]). The results give support to the extra statement that students who put more time into playing Battle Royale games have a higher sense of social presence.

X = Amount of online gaming

Y = Social presence Med = Gender

Figure 2 The indirect effects of the amount of online gaming on social presence through gender

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Discussion

The goal of this study was to explore the relationship between the amount of online gaming and social presence in students studying from home since the COVID-19 pandemic.

The results did not support the first hypothesis: “Students who participate in more online gaming have a higher sense of social presence in the forced online learning environment.”.

The results of the study also did not support the second (moderation) hypothesis: “The type of online game played moderates the relationship between the amount of online gaming and social presence in students studying in the forced online learning environment in such a way that the relationship is stronger in students that put the most time in playing MOBA games compared to all the other described types of games.”. The extra analysis did show that the Battle Royale type of online game has a significant effect on social presence, suggesting that students who play Battle Royale games more often in their leisure time also have a higher sense of social presence in the forced online learning environment. Lastly, the results indicated that gender also influences social presence scores, with men having higher scores. This finding evolved into the establishment of an indirect (mediating) effect on the original conceptual model, where playing online games more often results in a higher chance that someone in this sample is a male. This, in turn, positively influences social presence.

Looking back at previous research, we see that some of the findings contradict these results. Richardson & Swan (2003) established in their sample that women scored higher for social presence than men, while research from Cobb (2008), as well as the study from Swan &

Shih (2005) indicated that there was no significant relationship between gender and social presence. A later study on gender differences in the online learning environment showed that women do perceive a greater sense of social presence (Johnson, 2011). All in all, the fact that this study shows that men score higher on social presence indicates that this result is either very specific to this sample or that another factor influences the perception of men on social presence

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in this online education environment. Of course, this sample consists of solely students gamers.

Therefore, being a gamer or being a student might influence the gender differences in social presence scores as well. Research by Shamos (2008) did suggest that women were more often motivated by social interaction in games than men. This statement was also supported by research from Romrell (2014), which states that women are less likely to prefer violent games and have a higher preferability for communication and interaction in games. One might argue that female gamers thus do also perceive a higher sense of social presence in this sample. The gender differences in social presence scores found in this study consequently show that the women in this sample might be the ones that do not prefer non-violent games and have less preference for social interaction in games. Also, another explanation might be that the findings from Shamos (2008) and Romrell (2014) do not influence women’s higher general perception of social presence as shown in previous works. Taking all into account, the results of this sample implicate that male student gamers have been a better match for the pandemic’s forced online learning setting on a social level. Further research might want to explore the gender differences for perceived social presence further, especially in the environment of online gaming. New empirical studies should also collect an objective sample where gender is more equally distributed, as this research’s distribution of men and women was a limitation due to the high percentage of men. This limitation does however support the statement that men are the ones who stereotypically play video games more often (Romrell, 2014).

While the main hypothesized effect was not found in this study, the results did indicate that Battle Royale type of online games showed a positive relationship with social presence.

Therefore, it is implied that universities or university gaming communities might want to promote playing Battle Royale games as a (shared) leisure activity to boost learning outcomes in the forced online learning environment. We established that MOBA games would have an effect on social presence as this type of game always requires social interaction and

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collaboration. However, in this sample, this has not been the case. Of course, Battle Royale games can also be played in collaboration with others and thus could influence social presence in the same sense. To come back to the definition of play in a Battle Royale game, 100 to 150 players (individually, or as a team of two, three, four, or five) are placed on a map with various locations where they can collect loot like weapons and supplies. The goal is to become the last person (or team) standing by wiping out other players or by simply surviving until the end of the game. It could be the case in this type of game that players are hyper-aware of other people on the small map. Players can be constantly looking out for contact that can determine their outcome of the game, certainly when the map keeps shrinking and tension rises. Players listen to the in-game sound (like gunshots and footsteps) and keep a close eye on the map to determine what is the right course of action. It could then be argued that because of this, the effect of Battle Royale games on perceived social presence seems logical.

The lack of findings for the Multiplayer Online Battle Arena type of online game is surprising because of the determined social influences that this type of game has. When we look back at the paper on the effect of communication frameworks of online multiplayer games we see some interesting contrasts (Chambers, 2019). The paper suggests that the MOBA “League of Legends” has a proven effective communication framework that allows players to improve team performance and notions like trust. Chambers (2019) also suggests that businesses should be inspired by the way these games develop these notions through an effective communication framework so that they can also be implemented for the improvement of virtual teams. It could be the case that other factors in MOBA games diminish the effect on social presence, or that the predetermined factors do in fact have no relationship with social presence. What could influence the relationship is the way that players interact with either the game environment or playable characters. As collaboration in a temporary team is always required in this type of game, players may pre-assume that there are always other players in their game, so the

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perception of social presence is actually non-existent or not relevant. The second explanation is linked to playable characters in the game. As people play together, they might identify what is happening on screen more with the playable characters than with the players playing them.

For example, someone might blame a bad play on “Yasuo” (a playable character in League of Legends) instead of blaming the bad play on “Player X”, which again could diminish the perception of others in the virtual environment. The literature review by Mora-Cantallops &

Sicilia (2018) on MOBA games show that player experiences and unpleasant (toxic) behavior in-game are topics that had a great research interest. Because toxic behavior plays a role in MOBA games, social presence might be affected by this as well. Further research on this topic could provide businesses and institutions with valuable insights on remote working and learning, as group conflicts and unpleasant behavior can be better explored through this proxy of MOBA games. There is a lack of research to compare the results of the relationship between the amount of online gaming and social presence to. It would therefore be interesting to see further research explore social outcomes in these games on a deeper level and confirm the (lack of a) relationship between online gaming and social presence.

Methodological limitations on the results of this study could also include the lack of equal distribution between the amount of time played the different types of online games. It was already mentioned that further research should collect an objective sample with a more equal distribution of gender as well. Also, collecting data on the amount of time played online games in hours instead of categories may provide more accurate analysis results in a similar study, although this type of data was originally chosen to improve reliability in given answers on the amount of online gaming. New studies could also opt for collecting data on a single type of game, instead of the 7 different variables created in this study. The last methodological limitation is related to the recorded social presence scores in students. It might be the case that these students were negatively biased in their opinion on online education, as the COVID-19

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pandemic has forced the online learning environment to become the only option for the students in this sample. The COVID-19 pandemic has not been something people would generally see as something positive, and the measures taken to fight the virus (like forcing online education) would normally not be implemented on such a scale. One might argue that there are thus negative associations with online education in the current time, which influences the responses on the items to measure social presence. Nevertheless, while this study is limited in generalizability due to its sampling method, the information gathered can still be useful for the future. The relationship that has been researched also needs to be further empirically explored and confirmed.

The potential practical implications that have been given are linked to the way online gaming as a leisure activity can be used to influence certain outcomes that are relevant for learning or working in a virtual environment. Student gamers that follow online education should opt for playing Battle Royale games because of its positive relationship with social presence, and thus with various learning outcomes like perceived learning and satisfaction. The implications also suggest that gender may have a bigger effect on outcomes in online learning that were previously established in related literature (Johnson, 2011). Certainly in certain sub- groups of people, like gamers and students, the results and their implications may differ. This study suggests that male student gamers have been better prepared for the forced shift towards online education and its effects on social presence. Deeper empirical studies could determine what the most important effects of online gaming and gender are on important learning outcomes to determine how to improve online education in the future. Institutions and businesses alike can learn from the effects of online gaming as its activities and influences keep evolving in this growing digital world.

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Conclusion

In short, this study aimed to explore the relationship between online gaming and social presence, an influential outcome in the online learning environment. Since the COVID-19 pandemic influenced this environment so drastically and made remote ways to communicate and interact with others, like online gaming, so popular, the motivation for this paper was found to help understand the effects of the given relationship and its implications during this unusual situation. Data on students studying from home during the COVID-19 pandemic was collected through a cross-sectional survey, so this study could explore the research goal using statistics.

The hypotheses were tested using linear regression and the PROCESS macro by Hayes (2017) for moderation and mediation analysis. Results indicated that both the main hypothesis, as well as the moderation hypothesis were not supported. This study will now answer the research question: “To what extent is the amount of online gaming related to social presence in students studying from home during the COVID-19 pandemic and how is this relation moderated by the type of online game played?”. The amount of online gaming in general is not related to social presence in students studying from home during the COVID-19 pandemic in this study. The type of online game played did not moderate the relationship, but was shown to have a different impact on social presence, as the amount of time playing the Battle Royale type of game was shown to be positively related to social presence in these students. Gender mediates the relationship between the amount of online gaming and social presence in such a way that more online gaming indirectly increased social presence scores through the higher probability of being a male. Institutions should be aware of these relationships as online education evolves during, and after, the COVID-19 pandemic and #PlayApartTogether becomes #PlayTogether again.

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Appendix

Item-Total Statistics

Scale Mean if Item Deleted

Scale Variance if Item Deleted

Corrected Item- Total Correlation

Cronbach's Alpha if Item

Deleted 1 Likert scale - The instructor facilitated

discussions in the online course.

31,16 33,781 ,240 ,743

2 Likert scale - I feel comfortable interacting with other participants in the online course.

31,22 29,853 ,549 ,705

3 Likert scale - I feel comfortable participating in the online course discussions.

31,40 28,663 ,661 ,690

4 Likert scale - I feel comfortable conversing through the online communication medium.

31,00 31,129 ,444 ,720

5 Likert scale - Online education is an excellent medium for social interaction.

32,67 28,935 ,616 ,695

6 Likert scale - The instructor created a feeling of an online community.

32,13 31,629 ,434 ,721

7 Likert scale - I am able to form distinct individual impressions of some course participants even though we communicated only via an online education medium.

31,67 33,355 ,221 ,748

8 Likert scale - The introductions enabled me to form a sense of online community.

32,29 32,562 ,325 ,734

9* NEW Likert scale - Discussions using the medium of online education tend to be more impersonal than face–to–face discussions.

32,65 32,715 ,248 ,746

10 Likert scale - I feel my point of view is acknowledged by other participants in the online course.

31,27 34,910 ,148 ,752

11 Likert scale - I feel comfortable introducing myself in the online course.

30,94 32,383 ,330 ,734

12* NEW Likert scale - Messages in the online course were impersonal.

32,08 33,365 ,328 ,734

Reliability Statistics Cronbach's

Alpha N of Items

,745 12

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Descriptive Statistics

N Minimum Maximum Mean Std. Deviation Variance

MEAN.SP 63 1,83 4,08 2,8823 ,50792 ,258

Valid N (listwise) 63

GEN.2

Frequency Percent Valid Percent

Cumulative Percent

Valid Less than 1 hour a week 4 6,3 6,3 6,3

Between 1-2 hours a week

10 15,9 15,9 22,2

Between 2-4 hours a week

8 12,7 12,7 34,9

Between 4-7 hours a week

14 22,2 22,2 57,1

Between 7-12 hours a week

5 7,9 7,9 65,1

Between 12-20 hours a week

9 14,3 14,3 79,4

More than 20 hours a week

13 20,6 20,6 100,0

Total 63 100,0 100,0

Hours - MOBA

Frequency Percent Valid Percent

Cumulative Percent

Valid Less than 1 hour a week 40 63,5 63,5 63,5

Between 1-2 hours a week

6 9,5 9,5 73,0

Between 2-4 hours a week

3 4,8 4,8 77,8

Between 4-7 hours a week

6 9,5 9,5 87,3

Between 7-12 hours a week

5 7,9 7,9 95,2

Between 12-20 hours a week

2 3,2 3,2 98,4

More than 20 hours a week

1 1,6 1,6 100,0

Total 63 100,0 100,0

(38)

Hours - FPS

Frequency Percent Valid Percent

Cumulative Percent

Valid Less than 1 hour a week 36 57,1 57,1 57,1

Between 1-2 hours a week

12 19,0 19,0 76,2

Between 2-4 hours a week

4 6,3 6,3 82,5

Between 4-7 hours a week

4 6,3 6,3 88,9

Between 7-12 hours a week

3 4,8 4,8 93,7

More than 20 hours a week

4 6,3 6,3 100,0

Total 63 100,0 100,0

Hours - RTS

Frequency Percent Valid Percent

Cumulative Percent

Valid Less than 1 hour a week 51 81,0 81,0 81,0

Between 1-2 hours a week

6 9,5 9,5 90,5

Between 2-4 hours a week

1 1,6 1,6 92,1

Between 4-7 hours a week

3 4,8 4,8 96,8

Between 7-12 hours a week

1 1,6 1,6 98,4

Between 12-20 hours a week

1 1,6 1,6 100,0

Total 63 100,0 100,0

(39)

Hours - MMO

Frequency Percent Valid Percent

Cumulative Percent

Valid Less than 1 hour a week 52 82,5 82,5 82,5

Between 1-2 hours a week

5 7,9 7,9 90,5

Between 2-4 hours a week

2 3,2 3,2 93,7

Between 12-20 hours a week

2 3,2 3,2 96,8

More than 20 hours a week

2 3,2 3,2 100,0

Total 63 100,0 100,0

Hours - BR

Frequency Percent Valid Percent

Cumulative Percent

Valid Less than 1 hour a week 44 69,8 69,8 69,8

Between 1-2 hours a week

5 7,9 7,9 77,8

Between 2-4 hours a week

6 9,5 9,5 87,3

Between 4-7 hours a week

1 1,6 1,6 88,9

Between 7-12 hours a week

4 6,3 6,3 95,2

More than 20 hours a week

3 4,8 4,8 100,0

Total 63 100,0 100,0

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