Channels matter: Multimodal connectedness, types of co-players and social capital for Multiplayer Online Battle Arena gamers

Meng, Jingbo; Williams, Dmitri; Shen, Cuihua

doi:10.1016/j.chb.2015.06.007

Cited by 40 publications

(23 citation statements)

References 52 publications

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“…This may be a result of the fact that these environments are very fast-moving, and there is, thus, less opportunity to engage in explicit communication and incorporate everyone's views in making game time decisions. Our findings echo a growing recognition of more automated, tacit coordination in gameplay [50], while complementing previous research that emphasizes the role of communication in enhancing players' social relationships such as trust and interaction [37,54].…”

Section: Discussionsupporting

confidence: 86%

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What Makes a Strong Team?

Kim

Engel

Woolley

et al. 2017

Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing

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Recent research has demonstrated that (a) groups can be characterized by a collective intelligence (CI) factor that measures their ability to perform together on a wide range of different tasks, and (b) this factor can predict groups' performance on other tasks in the future. The current study examines whether these results translate into the world of teams in competitive online video games where selforganized, time-pressured, and intense collaboration occurs purely online. In this study of teams playing the online game League of Legends, we find that CI does, indeed, predict the competitive performance of teams controlling for the amount of time played as a team. We also find that CI is positively correlated with the presence of a female team member and with the team members' average social perceptiveness. Finally, unlike in prior studies, tacit coordination in this setting plays a larger role than verbal communication.

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Section: Discussionsupporting

confidence: 86%

“…The final sample was 97% male, and the average age was 22 years old, which is close to the demographic makeup found in other research on League players [29,37]. All-male teams comprised about 85% of the sample.…”

Section: Samplesupporting

confidence: 78%

What Makes a Strong Team?

Kim

Engel

Woolley

et al. 2017

Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing

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“…Further, it has been proposed that MPOGs include multiple different subgenres of games such as simulation and real-time strategy games, first-person shooting games, RPGs, or fighting and action games. [2526] All these game genres have been reported to be associated with an increased risk of IGD in literature. This has also been seen in the present study, where the effect of genre of games was not significant when MPOG factor was considered in the multivariate regression model, suggesting that MPOGs subsumed the effect of these individual genre of games.…”

Section: Discussionmentioning

confidence: 99%

Gaming disorder among medical college students from India: Exploring the pattern and correlates

et al. 2019

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Background:In the extant literature, apart from few published case reports describing patients with severe form of gaming disorder (GD), there is a lack of studies describing the pattern and correlates of GD existing in the Indian settings. Thus, the present study aimed to explore the extent and pattern of gaming behavior in a sample of medical college students from India and explore its association with the sociodemographic, psychological (depressive symptoms), and Internet gaming characteristics.Materials and Methods:This Internet-based cross-sectional study was conducted as an online survey among 306 medical students by the Behavioral Addictions Clinic at a tertiary care teaching college in India. The severity of problematic gaming behavior and depressive symptoms was assessed using the Internet GD Scale-Short Form (IGDS9-SF) and Patient Health Questionnaire-9 (PHQ-9). A semi-structured questionnaire was used to collect information regarding sociodemographic and Internet gaming characteristics of the participants. Statistical analysis was done using SPSS software version 21.0, with two-tailed P < 0.05 taken as significant and P < 0.01 as highly significant results.Results:We identified 173 (55.6%) current gamers, with 11 (3.6%) Internet GD gamers based on the Diagnostic and Statistical Manual of Mental Disorders -5 criteria in the current study sample. A preference for multiplayer online gaming pattern (β =0.17, P = 0.005), spending greater amount of time in playing digital games (β = 0.53, P < 0.01), and higher PHQ-9 scores (β =0.25, P < 0.01**) representing greater depressive symptom severity were associated with statistically significantly greater scores on the IGDS9-SF, indicative of a higher risk for having GD.Conclusions:GD is a cause of concern among medical students in India. There is an urgent need to create awareness about it among students and concerned authorities. Further, there is a need to develop effective screening and treatment strategies suited for our population. The risk factors identified in the current study can be utilized to screen those at high risk of developing the same.

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“…It "can be defined as the various modalities through which people maintain their connections with each other in everyday life" [5] (p. 79). The concept focuses on a headcount of the number of communication channels used for social interaction among people [17,18]. The slightly broader concept of "media manifold" [19] (p. 220) goes beyond the focus of social channels and also considers broadcasting media and other apps.…”

Section: Theoretical Framework: Polymodal Mediamentioning

confidence: 99%

What Characterizes the Polymodal Media of the Mobile Phone? The Multiple Media within the World’s Most Popular Medium

Hilbert

Aravindakshan

2018

MTI

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While the mobile phone is the world's most popular media device, it is actually not one single medium, but is effectively used as a different medium by different user groups. The article characterizes polymodal differences in mobile apps usage among different user groups, including gender, education, occupation, screen size, and price. We monitored the complete app usage of 10,725 smartphone users for one month each (56 million sessions, recording almost 1 million hours). Our key contribution consists in developing and analyzing a theoretical framework to classify the over 16,000 apps used into five categories. Exploring nine research questions we provide a broad characterization by asking who, with which characteristics, uses which kinds of apps in what extensity and intensity? For example, it is not the young and high occupational grades that use the mobile phone as a human-to-machine computer (including gaming and artificial intelligence tools). Large screen size is related to extensive long sessions, while a small screen size is related to intensive frequent usage. The results go beyond providing ample empirical evidence for the inherently polymodal nature of the mobile phone, but also proposes a framework on how to deal with it analytically.

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Channels matter: Multimodal connectedness, types of co-players and social capital for Multiplayer Online Battle Arena gamers

Cited by 40 publications

References 52 publications

What Makes a Strong Team?

What Makes a Strong Team?

Gaming disorder among medical college students from India: Exploring the pattern and correlates

What Characterizes the Polymodal Media of the Mobile Phone? The Multiple Media within the World’s Most Popular Medium

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