2022
Vahlo, Jukka; Karhulahti, Veli-Matti
Two Overwatch Player Profiles Book Section
In: Ruotsalainen, Maria; Törhönen, Maria; Karhulahti, Veli-Matti (Ed.): Modes of Esports Engagement in Overwatch, pp. 11-30, Palgrave Macmillan, 2022, ISBN: 978-3-030-82766-3.
Abstract | Links | Tags: Electronic sports, Overwatch, Player profiles
@incollection{Vahlo2022,
title = {Two Overwatch Player Profiles},
author = {Jukka Vahlo and Veli-Matti Karhulahti},
editor = {Maria Ruotsalainen and Maria Törhönen and Veli-Matti Karhulahti},
url = {https://link.springer.com/book/10.1007/978-3-030-82767-0},
doi = {https://doi.org/10.1007/978-3-030-82767-0},
isbn = {978-3-030-82766-3},
year = {2022},
date = {2022-03-10},
booktitle = {Modes of Esports Engagement in Overwatch},
pages = {11-30},
publisher = {Palgrave Macmillan},
abstract = {What are Overwatch players like? The goal of this chapter is to map out surface characteristics of those who play Overwatch in order to better understand the game and its players’ roles in the larger context of esports play and players. To do this, we employ relatively large-scale esports survey data and carry out game-specific cluster analysis: the surveyed players are divided into groups, which are identified based on the players’ gaming habits. Ultimately, our study will identify six esports player clusters, two of which include distinct Overwatch players. By comparing these two Overwatch player clusters with each other as well as with other esports player clusters, this chapter sets hypotheses that help interpreting the rest of this book’s chapters and, we hope, also the accumulating and diversifying literature on esports players more widely.},
keywords = {Electronic sports, Overwatch, Player profiles},
pubstate = {published},
tppubtype = {incollection}
}
What are Overwatch players like? The goal of this chapter is to map out surface characteristics of those who play Overwatch in order to better understand the game and its players’ roles in the larger context of esports play and players. To do this, we employ relatively large-scale esports survey data and carry out game-specific cluster analysis: the surveyed players are divided into groups, which are identified based on the players’ gaming habits. Ultimately, our study will identify six esports player clusters, two of which include distinct Overwatch players. By comparing these two Overwatch player clusters with each other as well as with other esports player clusters, this chapter sets hypotheses that help interpreting the rest of this book’s chapters and, we hope, also the accumulating and diversifying literature on esports players more widely.
2018
Vahlo, Jukka; Smed, Jouni; Koponen, Aki
Validating gameplay activity inventory (GAIN) for modeling player profiles Journal Article
In: User Modeling and User-Adapted Interaction, vol. 28, no. 4–5, pp. 425–453, 2018, ISSN: 15731391.
Abstract | Links | Tags: Confirmatory factor analysis, Digital games, Media choice, Player profiles, Scale development, Scale validation
@article{Vahlo2018a,
title = {Validating gameplay activity inventory (GAIN) for modeling player profiles},
author = {Jukka Vahlo and Jouni Smed and Aki Koponen},
doi = {10.1007/s11257-018-9212-y},
issn = {15731391},
year = {2018},
date = {2018-11-13},
urldate = {2018-11-13},
journal = {User Modeling and User-Adapted Interaction},
volume = {28},
number = {4–5},
pages = {425–453},
publisher = {Springer Netherlands},
address = {Dordrecht},
abstract = {In the present study, we validated Gameplay Activity Inventory (GAIN), a short and psychometrically sound instrument for measuring players' gameplay preferences and modeling player profiles. In Study 1, participants in Finland (N= 879) responded to a 52-item version of GAIN. An exploratory factor analysis was used to identify five latent factors of gameplay activity appreciation: Aggression, Management, Exploration, Coordination, and Caretaking. In Study 2, respondents in Canada (N= 1322) and Japan (N= 1178) responded to GAIN, and the factor structure of a 15-item version was examined using a Confirmatory Factor Analysis. The results showed that the short version of GAIN has good construct validity, convergent validity, and discriminant validity in Japan and in Canada. We demonstrated the usefulness of GAIN by conducting a cluster analysis to identify player types that differ in both demographics and game choice. GAIN can be used in research as a tool for investigating player profiles. Game companies, publishers and analysts can utilize GAIN in player-centric game development and targeted marketing and in generating personalized game recommendations.},
keywords = {Confirmatory factor analysis, Digital games, Media choice, Player profiles, Scale development, Scale validation},
pubstate = {published},
tppubtype = {article}
}
In the present study, we validated Gameplay Activity Inventory (GAIN), a short and psychometrically sound instrument for measuring players' gameplay preferences and modeling player profiles. In Study 1, participants in Finland (N= 879) responded to a 52-item version of GAIN. An exploratory factor analysis was used to identify five latent factors of gameplay activity appreciation: Aggression, Management, Exploration, Coordination, and Caretaking. In Study 2, respondents in Canada (N= 1322) and Japan (N= 1178) responded to GAIN, and the factor structure of a 15-item version was examined using a Confirmatory Factor Analysis. The results showed that the short version of GAIN has good construct validity, convergent validity, and discriminant validity in Japan and in Canada. We demonstrated the usefulness of GAIN by conducting a cluster analysis to identify player types that differ in both demographics and game choice. GAIN can be used in research as a tool for investigating player profiles. Game companies, publishers and analysts can utilize GAIN in player-centric game development and targeted marketing and in generating personalized game recommendations.
Vahlo, Jukka; Koponen, Aki
Player Personas and Game Choice other
2018.
Abstract | Links | Tags: Gameplay preference categories, Player profiles, Player types, Player typologies
@other{Vahlo2018,
title = {Player Personas and Game Choice},
author = {Jukka Vahlo and Aki Koponen},
editor = {Newton Lee},
doi = {10.1007/978-3-319-08234-9_149-1},
isbn = {978-3-319-08234-9},
year = {2018},
date = {2018-01-16},
booktitle = {Encyclopedia of Computer Graphics and Games},
publisher = {Springer},
abstract = {Player personas are representations of player profile types, based on identified patterns in gameplay type preferences, motivations to play, or player behavior data. Player personas are constructed to support player-centric game design and targeted marketing.},
keywords = {Gameplay preference categories, Player profiles, Player types, Player typologies},
pubstate = {published},
tppubtype = {other}
}
Player personas are representations of player profile types, based on identified patterns in gameplay type preferences, motivations to play, or player behavior data. Player personas are constructed to support player-centric game design and targeted marketing.