Background: Over the past 10 years, gamer profiles have been developed to understand the reason underlying players’ intrinsic motivation. While the research undertaken has led to the creation of distinct models (e.g., BrainHex and Hexad typologies), there is a lack of studies on the prevalence of these profiles among a specific population and the association between the target population’s profiles and their personality traits, gaming behavior, and sociodemographic characteristics.
Methods: Thus, the present study aimed to (a) establish the gamer profiles of French undergraduate law students, (b) examine the relationships between the participants’ profiles and their personality traits, gaming behavior, and sociodemographic characteristics, with a view to the development of serious games specifically intended for this population. In total, 753 French undergraduate students participated in the study, completing an online questionnaire. Data were analyzed using Latent Profile Analysis (LPA), chi-square, and ANOVA.
Results: The main findings show that among participants, the two most prevalent gamers’ profiles are Mastermind (45%) and Seeker (22.7%); followed by three less represented archetypes: Conqueror (12.9%), Daredevil (9.7%), and Achiever (9.7%). These archetypes are associated with the participants’ Big Five personality traits. Specifically, Daredevils, Masterminds, and Seekers have high mean scores on Extraversion ( p < .001); Achievers and Seekers have high mean scores on Agreeableness ( p < .001); Seekers and Achievers have high mean scores on Neuroticism ( p < .001); and Seekers, Masterminds, and Achievers have high mean scores on Openness ( p < .001). The unveiled profiles are also significantly associated with the participants’ gaming behavior including their playing frequency (p < .001), game types ( p = .031), and sociodemographic characteristics ( p < .001). For example, Masterminds are more likely to be female than the other four profiles ( p < .001), while Conquerors and Daredevils are more likely to have a low socio-economic status compared to those with intermediate and high socio-economic status ( p = .49).
Conclusion: These findings can be used to design serious/educational games tailored to the studied population.