2025
Vahlo, Jukka; Tuuri, Kai
Eight types of video game experience Journal Article
In: Entertainment Computing, vol. 52, 2025, ISSN: 1875-9521.
Abstract | Links | Tags: Game experience types, Game preferences, Latent class analysis, Survey
@article{Vahlo2025b,
title = {Eight types of video game experience},
author = {Jukka Vahlo and Kai Tuuri },
url = {https://doi.org/10.1016/j.entcom.2024.100882
https://www.sciencedirect.com/science/article/pii/S1875952124002507
},
doi = {10.1016/j.entcom.2024.100882},
issn = {1875-9521},
year = {2025},
date = {2025-01-31},
urldate = {2025-01-31},
journal = {Entertainment Computing},
volume = {52},
abstract = {The study of game experience is a well-established area within game research, supported by numerous models. These models, while valuable, often focus on analyzing game experiences within specific contexts rather than facilitating comparative analyses. Addressing this research gap, our study empirically identifies prevalent game experience types that are common across various games. By analyzing 5,372 game experience descriptions provided by 1,193 survey respondents, this research employs a survey design inspired by the flow of qualitative interviews, facilitating a comprehensive understanding of the diverse factors shaping these experiences. Through latent class analysis, we delineate eight distinct game experience types: Compelling Challenge, Immersive Exploring, Creative Caring, Energetic Rushing, Competitive Shooting, Cheerful Bouncing, Strategic Management, and Daily Dwelling. Each type is analyzed in terms of both the variables from the latent class analysis and additional survey variables, enhancing our understanding of their unique and comparative characteristics. This approach sheds light on the multifaceted nature of game experiences and broadens our insights into player engagement across different game genres, offering practical implications for game design, marketing, and future research.},
keywords = {Game experience types, Game preferences, Latent class analysis, Survey},
pubstate = {published},
tppubtype = {article}
}
The study of game experience is a well-established area within game research, supported by numerous models. These models, while valuable, often focus on analyzing game experiences within specific contexts rather than facilitating comparative analyses. Addressing this research gap, our study empirically identifies prevalent game experience types that are common across various games. By analyzing 5,372 game experience descriptions provided by 1,193 survey respondents, this research employs a survey design inspired by the flow of qualitative interviews, facilitating a comprehensive understanding of the diverse factors shaping these experiences. Through latent class analysis, we delineate eight distinct game experience types: Compelling Challenge, Immersive Exploring, Creative Caring, Energetic Rushing, Competitive Shooting, Cheerful Bouncing, Strategic Management, and Daily Dwelling. Each type is analyzed in terms of both the variables from the latent class analysis and additional survey variables, enhancing our understanding of their unique and comparative characteristics. This approach sheds light on the multifaceted nature of game experiences and broadens our insights into player engagement across different game genres, offering practical implications for game design, marketing, and future research.
2024
Macey, Joseph; Palomäki, Jussi; Castrén, Sari
Using latent class analysis to identify Finnish gambler types and potential risk Journal Article
In: International Gambling Studies, vol. 25, iss. 1, pp. 22-45, 2024, ISSN: 1445-9795.
Abstract | Links | Tags: Gambling, Latent class analysis
@article{nokey,
title = {Using latent class analysis to identify Finnish gambler types and potential risk},
author = {Joseph Macey and Jussi Palomäki and Sari Castrén},
url = {https://doi.org/10.1080/14459795.2024.2401521},
doi = {10.1080/14459795.2024.2401521},
issn = {1445-9795},
year = {2024},
date = {2024-09-28},
journal = {International Gambling Studies},
volume = {25},
issue = {1},
pages = {22-45},
abstract = {The trend of increasing liberalization in gambling markets has been matched by a need for both effective approaches to promote responsible gambling practices and for improved prevention strategies. Given that the majority of players do not experience problematic gambling, it is in the public interest that knowledge is generated which helps identify activities or clusters of activities which are associated with at-risk behaviors. This study uses a representative sample of the Finnish population aged 15–74, to identify distinct types of gamblers based on their behavioral patterns and predictors of class membership via Latent Class Analysis. Cross-sectional random sample data were collected in 2019 (n = 3148). In addition to confirming existing knowledge for gamblers characterized by high engagement and high risk, it offered insights into three further classes: the largest (ME-HR, 45%), was characterized by moderate engagement, but participated in activities associated with higher levels of risk. Additionally, low-risk classes were differentiated by both gambling preferences and demographic characteristics. Given that the largest class was associated with significant potential for the development of problematic behaviors, this work makes several recommendations for preventative actions, including targeted awareness campaigns and psychoeducation addressing erroneous beliefs about gambling.},
keywords = {Gambling, Latent class analysis},
pubstate = {published},
tppubtype = {article}
}
The trend of increasing liberalization in gambling markets has been matched by a need for both effective approaches to promote responsible gambling practices and for improved prevention strategies. Given that the majority of players do not experience problematic gambling, it is in the public interest that knowledge is generated which helps identify activities or clusters of activities which are associated with at-risk behaviors. This study uses a representative sample of the Finnish population aged 15–74, to identify distinct types of gamblers based on their behavioral patterns and predictors of class membership via Latent Class Analysis. Cross-sectional random sample data were collected in 2019 (n = 3148). In addition to confirming existing knowledge for gamblers characterized by high engagement and high risk, it offered insights into three further classes: the largest (ME-HR, 45%), was characterized by moderate engagement, but participated in activities associated with higher levels of risk. Additionally, low-risk classes were differentiated by both gambling preferences and demographic characteristics. Given that the largest class was associated with significant potential for the development of problematic behaviors, this work makes several recommendations for preventative actions, including targeted awareness campaigns and psychoeducation addressing erroneous beliefs about gambling.