Player Personas and Game Choice

Vahlo, Jukka; Koponen, Aki

doi:10.1007/978-3-319-08234-9_149-1

Cited by 6 publications

(8 citation statements)

References 19 publications

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“…In the study by Vahlo and Koponen (2018), being motivated to play and appreciating in-game challenges predicted weakly game choice (i.e., a habit of playing a variety of digital game genres). In comparison, the effect of gameplay activity preference on game choice was moderate.…”

Section: Research Questionsmentioning

confidence: 95%

“…58-59;Vahlo 2017). Vahlo and Koponen (2018). The observed variables were constructed by calculating mean sums for each scale.…”

Section: Game Choicementioning

confidence: 99%

“…To further investigate whether GAIN can be regarded as contingent with the CGD model, a cluster analysis should be conducted. Based on the results presented by Vahlo and Koponen (2018), identified player types can then be utilized in predicting players' game choices. The second and the third research questions of this study are:…”

Section: Research Questionsmentioning

confidence: 99%

“…Motivations to play and other psychographic segmentations (see Hartmann and Klimmt 2006;Hamari and Tuunanen 2014, p. 34), as well as player behavioral approaches, have been utilized in constructing player typologies and player personas, that is, representative players (Drachen et al 2009;Vahlo and Koponen 2018). Player typologies based on behavior data (e.g., psychometric measurements, game logs, and telemetrics) aim to scrutinize patterns in dynamic gameplay interaction.…”

Section: Introductionmentioning

confidence: 99%

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Validating gameplay activity inventory (GAIN) for modeling player profiles

Vahlo

Smed

Koponen

2018

User Model User-Adap Inter

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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.

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Section: Research Questionsmentioning

confidence: 95%

“…58-59;Vahlo 2017). Vahlo and Koponen (2018). The observed variables were constructed by calculating mean sums for each scale.…”

Section: Game Choicementioning

confidence: 99%

Section: Research Questionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Validating gameplay activity inventory (GAIN) for modeling player profiles

Vahlo

Smed

Koponen

2018

User Model User-Adap Inter

Self Cite

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“…Out of the overall body of player research, the player preference literature can be further divided into four main categories: motivation to play, player behavior, gaming intensity, and gameplay type preferences ([46] [48] [52]) asks why we play games. Motivations to play studies can be divided into general models and contextual models.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Five-Factor Inventory of Intrinsic Motivations to Gameplay (IMG)

Vahlo¹,

Hamari²

2019

Proceedings of the Annual Hawaii International Conference on System Sciences

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In this study, we develop and validate Intrinsic Motivations to Gameplay (IMG) inventory. In Study 1, psychometric properties of a preliminary 10-item version of IMG were investigated by employing an online survey data collected among Finnish and Danish population (N = 2,205). In Study 2, a 23-item version of IMG was developed based on further interview data and survey data collected among Canadian population (N = 1,322). The 23-item version of IMG revealed five factors of intrinsic motivations for gameplay: Relatedness, Autonomy, Competence, Immersion, and Fun. In Study 3, a third survey was conducted among Finnish and Japanese participants (N = 2,057) to design a Self-Determination theory (SDT) informed confirmatory factor analysis (CFA). The CFA validated a 15-item version of IMG inventory, which can be utilized widely in studies on digital gaming and gamification to better understand player preferences.

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Driven, Imaginative, and Casual Game Experiences

Vahlo

Koponen

2020

Game User Experience and Player-Centered Design

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Player Personas and Game Choice

Cited by 6 publications

References 19 publications

Validating gameplay activity inventory (GAIN) for modeling player profiles

Validating gameplay activity inventory (GAIN) for modeling player profiles

Five-Factor Inventory of Intrinsic Motivations to Gameplay (IMG)

Driven, Imaginative, and Casual Game Experiences

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