Understanding why people play games and participate in different kinds of gameful and playful activities has been a major vein of research in the overlap of game studies, media psychology, computer science and information systems science. While many models have been developed for measuring motivations to play, only a few studies have focused on understanding why players choose a particular game instead of the other options, and what kind of tools could be developed for investigating and predicting players’ game choice.
In this 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 () 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 () and Japan () 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. As a result, we argued that GAIN can be used in research as a tool for investigating player profiles worldwide. The GAIN approach can inform player-centric game development by providing actionable data on what kinds of gameplay activities different player clusters prefer and what they dislike. The model can also be used in targeted marketing and in generating personalized game recommendations for target player clusters.
Since GAIN can be used as a method for analyzing player segments, the model can inform us how to make games more inclusive and attractive to new and versatile player-audiences. It is important to note that the model is not as general as motivations to play models: the factors in players’ gameplay appreciation will change as game development takes new directions – much in the same way as new game genres emerge in gaming communities and discourses.
Our work continues next with a study on players’ challenge type preferences. We aim to understand better how challenge type preferences and gameplay activity type preferences are related to each other, and what new we can learn about the gameplay experience by considering these two dimensions of gameplay together.
Want to read more? Go see: Vahlo, J., Smed, J. & Koponen, A. User Model User-Adap Inter (2018). https://doi.org/10.1007/s11257-018-9212-y