An exploration of why gaming communities may make younger and non-normative gamers vulnerable to Internet Gaming Disorder

Abstract

Existing literature has primarily evaluated how exposure to video game content impacts social and behavioral functioning. However, the parameters of video game engagement have expanded with multiplayer online gaming, live streaming, and community discussion on social media platforms. This study sought to examine how participation in online gaming communities is associated with problematic gaming behaviors and symptoms of Internet gaming disorder (IGD). A sample of 1176 gamers completed an online survey investigating game habits, community involvement, and gaming preferences as measured by both the Game Community of Inquiry Scale (GCoIS) and the Internet Gaming Disorder Scale-Short form (IGDS9-SF). Results from a multiple hierarchical regression indicated several predictors of problematic gaming, including both community attractiveness and community receptiveness. Moreover, data showed that younger gamers and non-normative gamers were more vulnerable to problematic gaming and more severe symptoms of IGD.

Keywords

Game community of inquiry Internet gaming disorder online gaming problematic gaming

Introduction

Video game play is a global practice that will involve >3 billion active gamers in 2027 (Clement, 2023). Titles such as Fortnite and League of Legends engage broad communities of gamers from around the world and across the lifespan (Zhang and Kaufman, 2016). Such popularity has raised concerns about mental health, addiction, and social isolation (André et al., 2020). For instance, studies have associated video game use with isolation, poor social skills, and aggression (e.g. Anderson et al., 2013; Gentile et al., 2017). The impact of gaming on social and behavioral functioning has become so widespread that problematic gaming was recognized as a clinical disorder by the American Psychiatric Association (2018)—under the name of Internet gaming disorder (IGD)—and by the World Health Organization (2018)—under the name of gaming disorder (GD).

The literature has primarily emphasized how time spent playing video games and video game content impacts behavior and social functioning (e.g. Anderson et al., 2010; Coyne et al., 2020) for clinical and treatment-related reasons. However, gaming as digital entertainment is becoming an online experience that includes multiple practices and community instances. Rather than playing solo or just with family and friends, new generations prefer multiplayer titles and spend time watching others playing and discussing their gaming passion and experiences on social media (e.g. Discord, Reddit, Twitch.tv; Gandolfi et al., 2021; Taylor, 2018). Consequently, research has shifted to investigate the role of game communities in problematic gaming and social endeavors (Cote, 2017; Tang et al., 2019). While some studies have highlighted how these environments can host meaningful practices that may prevent game addiction (e.g. Gandolfi, 2022; Gandolfi et al., 2021; Jung, 2020a, 2020b), others point to how these settings promote and normalize disruptive attitudes associated with problematic gaming (e.g. Bell, 2021; Shen et al., 2016, 2020). Given the mixed findings, more research is needed to understand the association between game community involvement and IGD.

This article addresses the possible impact of participation in gaming groups on IGD by exploring community engagement and behavior. A sample of 1176 gamers completed an online survey that investigated game habits, community involvement, and gaming preferences as measured by both the Game Community of Inquiry Scale (GCoIS; Gandolfi et al., 2021) and the Internet Gaming Disorder Scale-Short form (IGDS9-SF; Pontes and Griffiths, 2015). A multiple regression analysis was completed with IGDS9-SF as the dependent variable. Results pointed at community dimensions that impact problematic gaming along with other predictors (e.g. ethnicity, time spent playing, typology of game). The article’s first section provides an overview of IGD and the potential role of game communities; the second describes the methodology and instruments used. The third addresses the results, and the fourth is for discussion and conclusions with implications and suggestions for future research.

Literature review

IGD and digital gaming

IGD has been described colloquially as game addiction, pathological gaming, and/or problematic gaming. Symptoms include preoccupation with gaming and the inability to manage time spent gaming, both which impact social, occupational, and other areas of functioning (American Psychiatric Association (APA), 2018; Anderson et al., 2013; Gentile et al., 2017). IGD has been associated with a significant impairment or distress and the presence of at least 5 out of 9 criteria, including:

preoccupation with Internet games, withdrawal symptoms, need to spend increasing amounts of time engaged in Internet games, unsuccessful attempts to control gaming, loss of interest in previous hobbies and entertainment, continued excessive use of Internet games despite problems, deception of others regarding the amount of online gaming, use of Internet games to escape or relieve a negative mood and jeopardizing or losing a significant relationship, job or educational or career opportunity. (Starcevic et al., 2020: 31)

As such, IGD tends to describe problematic gaming as a whole; other constructs such as GD (World Health Organization, 2018) tend to point at a more severe pathological pattern of gaming with possible associations with attention deficit/hyperactivity disorder (ADHD) and coping mechanism (Starcevic et al., 2020).

In the context of this study and its related research design, IGD has been preferred due to its broader scope. According to the APA (2018), IGD entails preoccupation with gaming, withdrawal symptoms when gaming is not possible (e.g. sadness, anxiety, irritability), and tolerance (i.e. the need to spend more time gaming to satisfy the urge to engage in video game play). In the literature, IGD has also been positively associated with experience with and hours spent gaming (Gentile et al., 2017), typology of gaming (e.g. competitive games with Multiplayer Online Battle Arenas like League of Legends; Bonnaire and Baptista, 2019), and money spent in video games (Na et al., 2017). The normative sample or majority of video game players considered are White, male, and heterosexual (Paaßen et al., 2017; Paul, 2018). However, preliminary evidence suggests that non-normative gamers have significantly higher levels of problematic gaming than their normative counterparts (Fox and Tang, 2017; Paul, 2018; for similar findings related to gambling, see Caler et al., 2017). This association can be related to the fact that non-White individuals are more exposed to mental health issues due to the inequality they face (from access to healthcare to stress and mental illness stigmas related to race and ethnicity; for example see Baima and Sude, 2020; Cockerham, 2013; Rose and Kalathil, 2019; Williams et al., 1997). As such, playing video games may work as a coping mechanism toward mental health struggles. There is indeed evidence that social media use and exposure to interactive technologies may have this function among at-risk populations, which rely on these practices to deal with stress, discrimination, and so on (e.g. Cho et al., 2021; Nagata et al., 2022; To et al., 2020; Vogel et al., 2021). Some other identified risk factors for IGD include being a young male (Gentile et al., 2017) and not being involved in a romantic relationship (Traş, 2019).

A component that may be related to the development of IGD and has not been properly addressed by current literature is community behavior on game-related platforms like Reddit, Steam, and Twitch.tv (Gandolfi and Ferdig, 2021; Kowert, 2020). Indeed, there is an increasing normalization of problematic gaming across gaming (e.g. Xbox Live), streaming (e.g. Twitch.tv), and social media platforms (e.g. Reddit; Beres et al., 2021; Ghosh, 2021; Türkay et al., 2020). IGD criteria have been found to be related to a heterogeneous group of players who show a variety of problematic behaviors, from disruptive interactions online to lack of control of gaming behaviors (Starcevic et al., 2020: 36), which may be informed by these communities (e.g. Canossa et al., 2021; Kowert, 2020; Tang et al., 2019).

These results suggest that game communities and relationships with other players may be promising foci to understand problematic gaming and its processes (Consalvo, 2012; Gandolfi et al., 2021; Taylor, 2018). Following this premise, Spada and Caselli (2017) found that players’ metacognition toward gaming (i.e. attitudes about online gaming and interaction with other gamers) is positively associated with IGD.

Positive and negative outcomes within game communities

It is important to recognize that the relationship between problematic gaming (broadly defined) and game communities is very complex and dynamic. It evolves and may appear differently depending on the culture of the community, which can be influenced by gaming content or context. Moreover, moderation strategies may influence the emergence of problematic gaming. For example, Bell (2021) discovered that online moderation practices were effective in minimizing harassment and supporting a positive online atmosphere in Telltale Games’ The Walking Dead forums. Moreover, this community’s members made more comments about game dilemmas, thus following the main TWD’s themes rather than on characters’ appearance. This type of discourse would likely differ from other types of gaming communities where there may be different opportunities for gamers to stigmatize characters’ identities with potential hegemonic implications (Bell, 2021). Similarly, studies of gaming communities via social media such as FIFA and Bloodstained revealed higher rates of racist interactions compared with gaming communities such as the SIMS that were associated with sexist interactions (Ghosh, 2021). These types of interactions seem to be enforced and promoted by game communities (Shen et al., 2020), which would imply a reinforcing mechanism (Fox et al., 2018; Gandolfi et al., 2021) of disruptive and problematic behaviors (Buchanan et al., 2018; Fox and Tang, 2017).

Problematic gaming is perhaps the most glaring in team-based games and eSports communities due to their competitive focus (Bonnaire and Baptista, 2019; Paul, 2018). After investigating players involved in League of Legends, Kou (2020) argued that problematic gaming is an emergent and functional process. Team-based and eSports incorporate competition as a large aspect of gaming, which is often stress-inducing and leads to reactive behaviors (Türkay et al., 2020). A commonly observed and normalized problematic behavior is veteran players’ responses toward novice players. For example, Shen et al. (2020) found World of Tanks veterans recognize the norms of the game and community (e.g. exposure to problematic behaviors and beliefs), and, therefore, establish a negative attitude toward novice players. Such a process can perpetuate these relationships over time (Beres et al., 2021; Shen et al., 2020). In addition, studies have identified players more inclined to social dominance as individuals who would be more likely to engage in cyber-aggression (Hilvert-Bruce and Neill, 2020; Jagayat and Choma, 2021; Tang et al., 2019) in game communities, which would be characterized by power structures and inequalities toward non-normative gamers (Bowman et al., 2013; Chess and Shaw, 2015; Paaßen et al., 2017; Paul, 2018).

Conversely, there is also evidence that online game communities can act as positive and inclusive spaces for individuals to freely express themselves and develop healthy attitudes toward gaming (Jung, 2020a; Squire, 2010). This can create an environment with measurable benefits for emotional and mental health (Gandhi et al., 2021) that can support minority players and at-risk populations (To et al., 2020; Vogel et al., 2021). Indeed, social learning in these communities may provide cognitive stimuli and social support that may go against the status quo; this would be facilitated by alternative and informal communities, which are spreading in online environments (Ashuri et al., 2018). Recently, research has also provided evidence that technology is playing an increasingly important role in affecting addictive behaviors as a factor of risk, but also as a possible solution (Guo et al., 2023; Sun and Zhang, 2021). For instance, active player engagement in communicative and interactive gaming communities creates a source of social bonding and mutual understanding (Gandolfi et al., 2021; Halverson, 2012; Sirola et al., 2019) as well as an increased perception of safety and learning (Jung, 2020a, 2020b). Game communities that work as an ideological safe space where inclusion, mutual acceptance, shared values/beliefs, and community appeal are important factors for attracting new members and establishing positive and healthy gaming communities (Gong et al., 2019; Hayday et al., 2021). For example, Adinolf and Turkay (2018) found patterns of inclusion, community support, and shared values/beliefs in eSports and other competitive environments, showing that players can develop strategies for countering negative and disruptive behaviors. Twitch.tv streamers can also play a role in a positive Internet environment as they help other players develop skills and knowledge through creational efforts; they also foster a community that is friendly and healthy through conversations on chat (Faas et al., 2018).

Game communities of inquiry

Differences exist between gaming communities, even those with commonalities in game preferences and content. This points to the importance of the community itself. A central and established framework for better understanding how the community component interacts with IGD is Game Community of Inquiry (GCoI; Soyturk et al., 2020). The GCoI was inspired by the Community of Inquiry framework (Garrison et al., 2001) and it was developed to better understand how game communities learn and behave together online (Gandolfi et al., 2021). Using the Game Community of Inquiry Scale (GCoIS) that is based on the GCoI, Soyturk and colleagues (2020) surveyed 1000 participants about their community experience. The authors identified three main components that characterize game communities and their behaviors: community attractiveness (the appeal of a community), community receptiveness (the openness of a community), and community cognition (cognitive challenges within a community).

Preliminary studies using the GCoIS found that young male gamers tend to be more involved and supported by game communities (Gandolfi et al., 2021). In addition, players who are more active within their respective gaming community (e.g. those who more often post, comment, and share content) rate a higher degree of community attractiveness, community receptiveness, and community cognition compared with those who engage less often. The literature has further suggested that negative attitudes toward the dangers of online gaming are related to higher scores of community attractiveness and lower scores of community receptiveness (Gandolfi et al., 2021). In other words, gaming communities may be considered appealing and safe for problematic gamers. However, these players would not be able to identify and engage with members of those communities due to their lack of social online skills. Finally, community cognition has been positively associated with hours spent with other players online and positive attitudes toward online gaming (Gandolfi, 2022; see also Adinolf and Turkay, 2018; Hayday et al., 2021).

Summary of the literature review

Online gaming is an important media practice for millions of individuals. There is preliminary evidence that game communities may influence both positive community support and problematic gaming. While some studies have highlighted how IGD and problematic behaviors can be fostered in these outlets, other research has pointed at the benefits of online gaming in terms of inclusion, empathy, and cognitive involvements. Despite these early and important investigations, there are not enough analytical efforts aimed at understanding how the community (from participation to perception) interacts with IGD. This study aimed to address this gap by deploying the GCoIS framework and investigating its association with IGD scores along with other possible predictors. Three research questions led to the following analyses:

RQ1: Are gender, education, ethnicity, and relationship status predictors of IGD?

RQ2: Are time spent playing, preferred game type, game experience, and money spent on digital entertainment predictors of IGD?

RQ3: Are the three GCoIS subfactors predictors of IGD?

RQ1 and RQ2 served as baseline questions according to the previous literature on the topic and provided control variables. RQ3 was used to address the potential role of gaming communities in informing IGD and relevant behaviors.

Method

The present study relied on cross-sectional analyses involving 1176 online gamers. The research design was based on an online questionnaire developed and disseminated with the survey platform Qualtrics via Reddit and Steam in Fall 2019 and Winter 2020. Thirty-six game-related channels (e.g. r/truegaming, r/rust, DOTA2 Steam portal) were used to post the survey after moderator approval. The study itself was approved and monitored by the authors’ University Institutional Review Board.

Procedure and sample

Participants were given a consent form prior to data collection. After agreeing, they completed an online survey structured in two main parts. First, participants provided demographic information (e.g. gender, age, race) and answered game-related questions (e.g. preferred game genre of their reference game community, time spent in playing video games, and how long they had been playing video games). Demographic and game-related information of the participants is reported in Table 1. The next section describes the measures for the second part.

Table 1.

Descriptive statistics for independent variables used in the study.

Variables	M (SD) or N (%)	Coding
Gender
Male	1037 (89.4)	Male = 1
Female	123 (10.6)	Female = 0
Race
Caucasian	924 (79.7)	Caucasian = 1
Other	236 (20.3)	All Others = 0
Education level
Less than college	705 (60.8)	Bachelor’s degree and higher = 1
College and higher	455 (39.2)	Less than Bachelor’s degree = 0
Relationship status
Single	725 (62.5)	Single = 1
Others (married, divorced, separated)	435 (37.5)	Others = 0
Age	26.66 (7.62)
How long have you played video games?	19.31 (7.7)
Hours spending time playing video games (weekly)	21.66 (13.2)
Money spent on expenses related to gaming	624.69 (983.951)
Game Genre of their game community
Battle Royale (e.g. Fortnite)	95 (8.2)
Multiplayer Online Battle Arena (e.g. DOTA2)	73 (6.3)
Action (e.g. For Honor)	61 (5.3)
Team-based action (e.g. Overwatch)	469 (39.9)
Massively multiplayer online (e.g. EVE online)	161 (13.8)
Strategy (e.g. Starcraft 2)	59 (5.1)
Sport (e.g. FIFA)	118 (10.2)
Sand box (e.g. Minecraft)	105 (9.1)

Measures

IGDS9-SF

The short form of IGD Scale (IGDS9-SF) used in this study included nine items that were adapted based on the definition of the IGD in the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (APA, 2018; Pontes and Griffiths, 2015). The scale aims to examine the severity of IGD on both online (e.g. playing with others with a positive attitude) and offline (e.g. playing too frequently) gaming activities during the past 12-month period. Participants were asked to answer each question in the scale using a 5-point Likert-type Scale from Never (1) to Very Often (5). The scoring range goes from 9 to 45; higher scores indicate a greater number of symptoms related to IGD. This unidimensional scale has demonstrated high internal consistency (α = .87) (Pontes and Griffiths, 2015) and in the current sample (α = .82). An overall score of 32 has been indicated as the optimal cutoff point to evaluate if IGD is present or not (Qin et al., 2020). According to this parameter, in the context of this study, n = 243 participants (20.6%) were found to fall under this category.

GCoIS

The GCoIS was developed to measure the educational components of game-related platforms or communities in terms of community inquiry aspects (Soyturk et al., 2020). The scale uses a 5-point Likert-type scale ranging from Strongly Disagree (1) to Strongly Agree (5). The results obtained from a cross-validation study using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) revealed that the GCoIS consisted of a final of 14 items in three subscales: community attractiveness (CA), community receptiveness (CR), and community cognition (CC; Soyturk et al., 2020). The CA subscale is the appeal of a given game community (p. 6), the CR subscale measures the ability of a community to deal with its own members and their requests (p. 7), and the CC aims to examine how a community stimulates its followers from a cognitive perspective (p. 8). The results from the factorial validity process indicated that the GCoIS has good psychometric properties (Soyturk et al., 2020). In the context of this study, the result of the internal consistency, measured using coefficient alpha (α), was also satisfactory for all subscales (CA, α = .838; CR, α = .879, CC, α = .754).

Data analyses

The initial sample of participants who completed the survey was n = 1322. After removing invalid responses (e.g. redundant answers) (Curran, 2016), a final sample of n = 1176 video game players was considered. This study investigated if the GCoIS subscales (CA, CR, CC), demographic variables (gender, age, race, educational level, relationship status), and game-related habits (time spent for playing video games, duration of gaming involvement, video game genre of the favorite game community) accounted for a significant amount of variance in the participant’s rated IGDS9-SF severity level. Categorical independent variables (gender, race, income, education level) were dummy coded prior to the analysis. Race was split in White and non-White to explore differences between normative and non-normative gamers (e.g. Chess and Shaw, 2015; Cote, 2017; Paul, 2018), which may point at relevant differences (see Jagayat and Choma, 2021). A three-block hierarchical multiple regression analysis was used to test the model, which included the changes in the variance of the dependent variable (i.e. IGD as measured by IGDS9-SF), while adding the set of predictors described above (demographics for the first block, game habits for the second block, GCoIS subfactors for the third bloc). All the analyses in this study were performed using IBM SPSS Version 21.

Results

Prior to the data analysis process, assumptions for the hierarchical multiple regression were examined. The linearity, normality, homoscedasticity, independence of errors, and multicollinearity were checked, and all these assumptions held. After addressing the assumptions, a hierarchical multiple linear regression analysis was conducted to investigate the three research questions. Table 2 shows the model summary results for each block and corresponding regression coefficients within blocks. The results suggested that each block significantly added to the prediction of the IGDS9-SF level of gamers. Together, the final model explained approximately 11% of the variance in gamers’ IGDS9-SF level, which is acceptable in social research (e.g. Li, 2018), with a medium effect size (effect size = 0.26; Cohen, 1988).

Table 2.

Summary of multiple regression analysis for variables predicting IGD.

Variable	Block 1			Block 2			Block 3
	B (SE)	β	t	B (SE)	β	t	B (SE)	β	t
Gender	–.075 (.066)	–.035	–1.121	–.051 (.066)	–.024	–.773	.008 (.067)	.004	.112
Age	–.009 (.003)	–.100	–2.738**	.003 (.007)	.032	.433	.003 (.007)	.038	.512
Race	–.162 (.053)	–.096	–3.073**	–.141 (.051)	–.084	–2.742**	–.133 (.051)	–.079	–2.616**
Educational level	–.017 (.046)	–.013	–.375	.031 (.045)	.023	.679	.042 (.045)	.031	.935
Relationship status	–.027 (.048)	–.020	–.572	–.081 (.047)	–.059	–1.738	–.065 (.046)	–.048	–1.407
Games
Battle Royale				–.109 (.078)	–.044	–1.398	–.086 (.077)	–.035	–1.114
Multiple Player Online Battle				–.042 (.087)	–.015	–.488	–.017 (.086)	–.006	–.202
Action Role-Playing				.078 (.098)	.025	.801	.071 (.097)	.023	.732
Massively Multiple Online				–.063 (.064)	–.033	–.977	–.043 (.064)	–.023	–.670
Strategy				–.023 (.092)	–.008	–.244	.008 (.092)	.003	.092
Sport				–.209 (.070)	–.096	–3.008**	–.178 (.070)	–.082	–2.556***
Sandbox				–.140 (.072)	–.062	–1.953	–.114 (.071)	–.051	–1.603
Hours spent on video gaming				.013 (.002)	.250	7.880*	.013 (.002)	.250	7.935*
How long playing video gaming				–.013 (.006)	–.143	–2.015***	–.013 (.006)	–.144	–2.038**
Money spent on video gaming				–.001 (.000)	–.024	–.771	–.000 (.000)	–.034	–1.074
Game Community of Inquiry
Attractiveness							.026 (.007)	.126	3.443*
Receptiveness							–.024 (.006)	–.131	–3.737*
Cognition							.007 (.008)	.032	.890

SE: standard error; IGD: Internet gaming disorder.

p < .001, ^**p < .01, ^***p < .05.

In the first block, race (i.e. White vs non-White) was a significant predictor of gamers’ IGDS9-SF level. White gamers had around .162 points lower IGDS9-SF scores than other gamers (β = −.096). Age was also a significant predictor of the IGDS9-SF in this sample. Specifically, one unit increase in age resulted in .009 points decrease on gamers’ IGDS9-SF level (β = .100) after controlling other predictors in the model.

The analysis was repeated for Block 2 with predictor variables from Block 1 and new predictors (i.e. game genres, time spent in playing video games, how much time spent annually for gaming, and duration of game play involvement). The model in Block 2 was statistically significant. The results suggested that those who play sports games (e.g. FIFA, Madden) had approximately .209 points lower IGD than those playing team-action games (e.g. Overwatch, Valorant). In addition, one unit increase in time spent on playing games resulted in .013 points increase on gamers’ IGDS9-SF level (β = .250) after controlling other predictors in the model. Conversely, years of playing videogames was a negative predictor of IGDS9-SF. In other words, one unit increase in the years of playing videogames resulted in .013 points decrease on gamers’ IGDS9-SF score (β = −.143) after holding other predictors in the model constant.

In the last block, Block 3, community dimensions of the GCoIS were added to the model. Attractiveness (CA) was a positive predictor of gamers’ IGDS9-SF (B = .026), whereas receptiveness (CR) was found to be a negative one (B = −.24). In more detail, one unit increase in CA scores resulted in .026 points increase on gamers’ IGDS9-SF score (β = .126) after controlling other predictors in the model. Conversely, one unit increase in CR scores resulted in .024 points decrease on gamers’ IGDS9-SF level (β = −.131) after holding other predictors in the model constant. Community cognition (CC) was not found to be a significant predictor of IGDS9-SF.

Discussion

This study explored potential predictors of IGD (as measured by IGDS9-SF) by looking at both known and novel factors. The first research question was created to explore if gender, education, ethnicity, and relationship status were predictors of IGD. White gamers showed lower IGDS9-SF scores than other players. This finding suggests that problematic gaming may represent a further burden for non-normative gaming populations that do not match the hegemonic profile of the White male gamer—something that was suggested by Broman and Hakansson (2018). This is also aligned with the extensive literature on addiction and underserved populations (e.g. Cockerham, 2013). In other words, playing video games may work as a coping mechanism as other technological practices such as social media use for at-risk populations to manage stressful situations (e.g. Cho et al., 2021; Nagata et al., 2022). Non-normative gamers would play video games to deal with stress, anxiety, and stigmas related to their situation; this behavior would follow well-known patterns about substance abuse and addiction (from smoking to alcoholism) as a way to react to (and indirectly reinforce) systemic inequalities (Rainey et al., 2018; Saloner and Cook, 2013; Verkuyten, 2008). However, while it is possible that non-normative gamers may be inordinately impacted by IGD, further investigation is needed to determine other possible mediating variables.

It is important to note that several studies have addressed how online gaming contributes to hostility against non-normative individuals (Bowman et al., 2013; Cote, 2017; Gray, 2017; Hilvert-Bruce and Neill, 2020). With previous research indicating that community engagement predicts IGD and problematic gaming (Fox and Tang, 2017; Paaßen et al., 2017; Tang et al., 2019), it is possible that negativity works as a reinforcing mechanism for problematic gaming in non-normative gamers. In other words, this may work as a sort of internalized oppression (e.g. see Banks and Stephens, 2018), where victims tend to mimic and reiterate their oppressors’ behaviors. Therefore, future research should address both how these inequalities operate and possible interventions that may mitigate this process. This is important because non-normative gamers, who often are minorities, are at higher risk of problematic gaming in comparison with normative ones (Blake and Sauermilch, 2021). At the same time, this category of players can particularly benefit from the social and community interactions related to online gaming (Blake and Sauermilch, 2021).

The second research question sought to address whether time spent playing games, preferred game type, length of gaming experience, and money spent on digital entertainment act as predictors of IGD. Data analyses showed that time spent playing was a positive predictor of IGD, a finding that is aligned with previous evidence (e.g. Gentile et al., 2017). However, the years of experience playing videogames was a negative predictor of IGD. This may be related to the normalization of social gaming and the social impact on younger players. More specifically, younger players are more at risk for IGD and, therefore, tend to show disruptive behaviors (Gandolfi et al., 2021). Across gaming preferences, those who preferred sports games had lower IGD than those who engaged in team-based action games. This result echoes previous evidence about how video game violence itself would not be related to game addiction (Przybylski and Weinstein, 2019). Moreover, these findings may relate to the relationships with other players, as high levels of competition dependent on team engagement in team-based action games is often associated with hostile and disruptive behavior (Bonnaire and Baptista, 2019; Paul, 2018).

The final research question examined whether the three GCoIS subfactors (attractiveness, receptiveness, and cognition) were predictors of IGD. Results suggested that the community played an important role in informing IGD. More specifically, attractiveness was found to be a positive predictor of the sample’s IGD level. This may be related to the fascination that young adults have with online gaming environments (Gandolfi et al., 2021), which may attract problematic gamers who would tend to learn and reinforce addictive behaviors and attitudes by looking at other players (see Buchanan et al., 2018; Fox and Tang, 2017). An ongoing review of attractiveness is important because players who use gaming as a distraction and show problematic gaming behaviors may neglect other social relationships and activities of daily living (Gentile et al., 2017; Sirola et al., 2019). This result is also aligned with the previous literature on the role of metacognition toward gaming and its influence on gaming addiction (Spada and Caselli, 2017). Finally, it may imply that online gaming environments work as a negative distraction for problematic gamers, who would tend to learn and reinforce addictive behaviors and attitudes by looking at other players (for similar reflections, see Buchanan et al., 2018; Fox and Tang, 2017).

Results from the third research question also showed that receptiveness was found to be a negative predictor of IGD. This can be associated with how a supportive community can mitigate gaming addiction and its impact on social skills through social learning (Bandura, 1977; Gandolfi, 2022). In other words, developing communities (as developers, publishers, community managers, or moderators) that promote and support their own members with an open and inclusive approach may mitigate the risk of problematic gaming. As such, it can be argued that more attention should be given to the climate of the gaming community (perhaps even labeled Internet gaming climate) in case it needs to be enriched with a better interplay between old and new members (Gandolfi et al., 2021) or additional supervision (e.g. netiquette, moderators, publishers’ efforts and communication; for similar suggestions, see Parmet, 2021). Looking at related results from the first research question, this is particularly important for developing inclusive and accessible gaming environments (for similar conclusions regarding the potential of media outlets in this regard, see To et al., 2020; Vogel et al., 2021).

Finally, the cognitive dimension of GCoIS did not seem to play a significant role in influencing IGD. This result suggests additional inquiries targeting if and how cognitive involvement (e.g. planned interventions) with and among peers may make a difference in addressing problematic gaming. Indeed, there is preliminary evidence that cognitive processes may occur in game communities with important learning and emotional applications (Gandolfi, 2022; Jung, 2020a). However, these examples tend to be related to informal learning; more efforts are required to explore how to design and deliver cognitive challenges and tasks that can benefit players and their wellbeing.

These findings support recommendations for the ongoing monitoring and research of IGD for at least two reasons. First, the recent changes in the video game industry and culture may have a stronger influence on younger video game players (i.e. older players tend to have less IGD). The fact that video games have become more social in nature may have a stronger influence on problematic gaming and disruptive behaviors. Second, eSports are growing in popularity around the world (Reitman et al., 2020). With that growth comes an increase in the competitive side of online gaming; this may foster IGD instances among players. As such, it is important to reflect on how a positive and healthy idea of competition may be communicated to gaming audiences.

Limitations

The current study has five main limitations. First, it focuses on a broad notion of online gamers. Such a lens is supported by the literature on online gaming (e.g. Cote, 2017; Massanari, 2017; Paul, 2018), but it may oversimplify gamers’ profiles and groupings. As such, future research should examine more specific gaming audiences (e.g. built around preferred game titles) or game practices (e.g. speedrunning, which is the idea that players attempt to complete the game as fast as possible). Second, this study does not explore how different platforms (e.g. Reddit vs Twitch.tv) and gamer profiles (e.g. competitive vs collaborative players) may impact IGD. Therefore, additional investigations could focus on well-defined game communities and related behaviors for gathering more pragmatic insights.

Third, the research design depends on self-reported data, which may have been biased. Therefore, there is a need for analyses deploying different methodologies, including social media and gaming activity data (e.g. game log/metrics, social posts). Fourth, participants were recruited from gaming communities on Steam and Reddit. As such, they were active gamers and this can limit the generalizations of this study’s results (Lehdonvirta et al., 2021). Fifth, IGD and online gaming are complex and multifaceted phenomena that require interdisciplinary efforts to be properly framed. As such, additional approaches and research perspectives should be considered to expand the scope of this article. Flipping the coin, IGD can become a way to better understand game communities and their endeavors, from group dynamics to leading identities and sense of belonging. This will require future research that cuts across disciplines.

Conclusion

This study has shed light on how multiple factors may impact problematic gaming with an emphasis on game communities, whose importance is dramatically increasing in digital entertainment (Gandolfi et al., 2021; Jung, 2020a). The goal of this study was to explore the possible impact video game communities have on problematic gaming (i.e. IGD) across non-normative and normative gamers. Investigating video game communities is an important area of research as it is often overlooked when attempting to understand problematic gaming or when developing proper countermeasures to negative gaming outcomes. Results from this study indicated that younger gamers and non-normative gamers were more vulnerable to problematic gaming and more severe symptoms of IGD. This suggests the need for ongoing research on how gamers interact and behave online in order to identify protective variables for these communities and to inform possible interventions as needed. In addition, future research should shed light on how game communities may become bearers of inclusion, support, and cognitive stimuli.

Footnotes

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Enrico Gandolfi

Richard E Ferdig

Karlie Krause

Grace Morris

Author biographies

Enrico Gandolfi is an Assistant Professor and the Program Coordinator of the Educational Technology program at Kent State University. His research interests include games and simulations for learning, augmented, and virtual reality in education, and online communities of inquiry. He is the author of several journal articles (e.g. for Computers & Education, New Media and Society, Technology in Society, British Journal of Educational Technology) and book chapters (for publishers such as Routledge, MIT Press, ETC Press) about these topics. He has been CO-PI and senior personnel for grants funded by the National Science Foundation and the National Endowment for Humanities.

Richard E Ferdig is the Summit Professor of Learning Technologies and Professor of Educational Technology at Kent State University. His research, teaching, and service focus on combining cutting-edge technologies with current pedagogic theory to create innovative learning environments. His research interests include online education, educational games and simulations, the role of faith in technology, and what he labels a deeper psychology of technology. Rick was the founding Editor-in-Chief of the International Journal of Gaming and Computer Mediated Simulations, is the current Editor-in-Chief of the Journal of Technology and Teacher Education.

Karlie Krause completed her doctoral training at Midwestern University, Glendale before moving to Houston, Texas for a predoctoral internship at the University of Texas Health Sciences Center. She then went on to complete her postdoctoral fellowship at Akron Children’s Hospital in Akron, Ohio. She has now returned home to work as a pediatric neuropsychologist at Phoenix Children’s. Although she specializes in working with children and adolescents with complex medical and neurologic histories, her research has focused on the impact of video games on cognitive and psychological functioning.

Ilker Soyturk is an education expert who is currently working for the Ministry of National Education in the General Directorate of Measurement, Evaluation and Examination Services. He has published several peer-reviewed articles on gamers’ behaviors on top-ranked journals such as New Media and Society and Journal of Affective Disorders.

Grace Morris is a PhD student and Research Assistant in the program of Educational Technology at Kent State University.

Sarah Delahanty currently serves as the Director of Research and Education and is tasked with developing and managing research program for five pediatric medical subspecialties (neurology, neurosurgery, rehabilitation medicine, developmental and behavioral pediatrics, and neurobehavioral health). Using organizational science principles, she provides mentorship to MDs, PhDs, fellows, medical students, undergraduate and graduate students, and staff. In addition, she sustains her own funded research program of research focusing on traumatic injury.

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