Abstract In this study, we develop and validate Motives of Autonomous Players (MAP) inventory. Several models on videogame motives have been published recently, but typically these models focus either on specific videogame types, on individual games, or on a particular theory on human motivation. The MAP model takes an integrative approach that considers why people play games in general. This is done by adopting an inductive bottom-up research attitude and by focusing on motives that can be argued to be broadly applicable for all kinds of videogames, ranging from casual mobile games to massively multiplayer online role-playing games. Since the MAP model is based on extensive player data that represent a great variety of player motives, the results are widely applicable in player modeling and in understanding player–game interaction at large. The initial MAP model was developed by analyzing open-ended gaming motive descriptions (N = 1,648) by a content analysis procedure. A preliminary 101-item MAP inventory was included in a UK-based survey (N = 600). A nine-factor model was identified and further validated as a 34-item version by making a confirmatory factor analysis with a USA-based survey data (N = 600). Additional analyses on construct validity were performed for investigating how motives to play videogames predict players’ game enjoyment factors that were kept analytically distinct from general motivational factors to play videogames.

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.

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.