2020
Vahlo, Jukka; Karhulahti, Veli-Matti
Challenge Types in Gaming Validation of Video Game Challenge Inventory (CHA) Journal Article
In: International Journal of Human-Computer Studies, vol. 143, no. 102473, pp. 1-13, 2020, ISSN: 1071-5819.
Abstract | Links | Tags: Challenge, Confirmatory factor analysis, Exploratory factor analysis, Player preferences, Scale validation, Survey
@article{Vahlo2020,
title = {Challenge Types in Gaming Validation of Video Game Challenge Inventory (CHA)},
author = {Jukka Vahlo and Veli-Matti Karhulahti},
url = {https://www.sciencedirect.com/science/article/pii/S1071581920300756},
doi = {10.1016/j.ijhcs.2020.102473},
issn = {1071-5819},
year = {2020},
date = {2020-11-01},
journal = {International Journal of Human-Computer Studies},
volume = {143},
number = {102473},
pages = {1-13},
abstract = {Challenge is a key motivation for videogame play. But what kind of challenge types videogames include, and which of them players prefer? This article helps to answer the above questions by developing and validating Videogame Challenge Inventory (CHA), a psychometrically sound measurement for investigating players’ challenge preferences in videogames. Based on a review of literature, we developed a 38-item version of CHA that was included in a social media user survey (N = 813). An exploratory factor analysis (EFA) revealed a latent structure of five challenge types: Physical, Analytical, Socioemotional, Insight, and Foresight. CHA was amended in another EFA with USA-based survey data (N = 536). The second EFA suggested a four-factor structure similar to the first EFA. A confirmatory factor analysis was executed after an item screening process with a 12-item version of CHA via UK-based survey data (N = 1,463). The 12-CHA had an acceptable fit to the data, and the model passed construct, convergent, and discriminant validity tests. The usefulness of the validated 12-CHA is shown by connecting the discovered challenges and their preferences to known videogame play motivations and to habits of playing specific videogame genres.},
keywords = {Challenge, Confirmatory factor analysis, Exploratory factor analysis, Player preferences, Scale validation, Survey},
pubstate = {published},
tppubtype = {article}
}
Challenge is a key motivation for videogame play. But what kind of challenge types videogames include, and which of them players prefer? This article helps to answer the above questions by developing and validating Videogame Challenge Inventory (CHA), a psychometrically sound measurement for investigating players’ challenge preferences in videogames. Based on a review of literature, we developed a 38-item version of CHA that was included in a social media user survey (N = 813). An exploratory factor analysis (EFA) revealed a latent structure of five challenge types: Physical, Analytical, Socioemotional, Insight, and Foresight. CHA was amended in another EFA with USA-based survey data (N = 536). The second EFA suggested a four-factor structure similar to the first EFA. A confirmatory factor analysis was executed after an item screening process with a 12-item version of CHA via UK-based survey data (N = 1,463). The 12-CHA had an acceptable fit to the data, and the model passed construct, convergent, and discriminant validity tests. The usefulness of the validated 12-CHA is shown by connecting the discovered challenges and their preferences to known videogame play motivations and to habits of playing specific videogame genres.
2019
Li, Xiaozhou; Lu, Chien; Peltonen, Jaakko; Zhang, Zheying
A Statistical Analysis of Steam User Profiles Towards Personalized Gamification Proceedings Article
In: Koivisto, Jonna; Hamari, Juho (Ed.): CEUR Workshop Proceedings, pp. 217-228, CEUR-WS, 2019, ISSN: 1613-0073.
Abstract | Links | Tags: Exploratory factor analysis, Gamification, Personalized gamification, Preference, Steam, User profile
@inproceedings{Li2019,
title = {A Statistical Analysis of Steam User Profiles Towards Personalized Gamification},
author = {Xiaozhou Li and Chien Lu and Jaakko Peltonen and Zheying Zhang},
editor = {Jonna Koivisto and Juho Hamari},
url = {https://urn.fi/URN:NBN:fi:tuni-201910244073},
issn = {1613-0073},
year = {2019},
date = {2019-04-08},
booktitle = {CEUR Workshop Proceedings},
volume = {2359},
pages = {217-228},
publisher = {CEUR-WS},
abstract = {Gamification is widely used as motivational design towards enhancing the engagement and performance of its users. Many commonly adopted game design elements have been verified to be effective in various domains. However, the designs of such elements in the majority of the target systems are similar. Due to inevitable differences between users, gamification systems can perform more effectively when users are provided with differently and personally designed features according to their preferences. Many studies have suggested such requirements towards personalizing gamified systems based on the users’ preferences, with categorizing gamification users and identifying their preferences as the initial step. This study proposes a preliminary analysis of the factors that categorize user preference in a game community, based on the user profiles data of the Steam platform. It shall not only facilitate understanding of players’ preferences in a game community but also lay the groundwork for the potential personalized gamification design.},
keywords = {Exploratory factor analysis, Gamification, Personalized gamification, Preference, Steam, User profile},
pubstate = {published},
tppubtype = {inproceedings}
}
Gamification is widely used as motivational design towards enhancing the engagement and performance of its users. Many commonly adopted game design elements have been verified to be effective in various domains. However, the designs of such elements in the majority of the target systems are similar. Due to inevitable differences between users, gamification systems can perform more effectively when users are provided with differently and personally designed features according to their preferences. Many studies have suggested such requirements towards personalizing gamified systems based on the users’ preferences, with categorizing gamification users and identifying their preferences as the initial step. This study proposes a preliminary analysis of the factors that categorize user preference in a game community, based on the user profiles data of the Steam platform. It shall not only facilitate understanding of players’ preferences in a game community but also lay the groundwork for the potential personalized gamification design.