An instrument to build a gamer clustering framework according to gaming preferences and habits. (September 2016)

• Record Type: Journal Article
• Authors: Manero, Borja; Torrente, Javier; Freire, Manuel; Fernández-Manjón, Baltasar
• Is Part Of: Computers in human behavior. Volume 62(2016)
• Page Start: 353
• Page End: 363
• Publication Date: 2016-09
• Subjects: Educational games -- Classification of gamers -- Gaming preferences and habits -- Instruments for serious games -- Applied games; Interactive computer systems -- Periodicals; Man-machine systems -- Periodicals; 004.019
• DOI: 10.1016/j.chb.2016.03.085
• Languages: English
• ISSNs: 0747-5632

Abstract

Abstract: In the era of digital gaming, there is a pressing need to better understand how people's gaming preferences and habits affect behavior and can inform educational game design. However, instruments available for such endeavor are rather informal and limited, lack proper evaluation, and often yield results that are hard to interpret. In this paper we present the design and preliminary validation (involving N = 754 Spanish secondary school students) of a simple instrument that, based on a 10-item Game Preferences Questionnaire (GPQ), classifies participants into four 'clusters' or types of gamers, allowing for easy interpretation of the results. These clusters are: (1) Full gamers, covering individuals that play all kinds of games with a high frequency; (2) Hardcore gamers, playing mostly first-person shooters and sport games; (3) Casual gamers, playing moderately musical, social and thinking games; and (4) Non-gamers, who do not usually play games of any kind. The instrument may have uses in psychology and behavioral sciences, as there is evidence suggesting that attitudes towards gaming affects personal attitudes and behavior. Besides, we propose applying the instrument to help designers of educational games to get better tailored their games to their target audiences. Graphical abstract: Highlights: Developing an instrument to quickly classify gamers according to their preferences and habits. Data from 754 students confirmed the instrument validation. We found 4 different gamers profiles: Hardcore, Casual, Well-rounded gamers and No gamers. Results are in accordance with the reviewed literature in the field. Gaming preferences and habits could influence educational games effectiveness.