Gender role orientation and gaming behavior revisited: examining mediated and moderated effects

ABSTRACT

The article presents results of a study that examined the influence of gender role orientations on gaming motivations, game genre preferences, and different play styles. Applying social role theory, it was hypothesized that femininity and masculinity influence gender-typed motivations (social interaction, competition, and challenge) and preferences (role-playing and action games) as well as gender-typed behavior (cooperative play and competitive play). After collecting empirical data through an online survey, hypotheses were tested by structural equation modeling. Moreover, moderating effects of sociodemographic characteristics (biological gender, age, and educational level) were examined. Findings provide evidence for the existence of gender-typed motives of play, genre preferences, and – mediated by motivations and preferences – gaming behavior. Group analyses support a biosocial model of gender-typed gaming behavior because gender-typing of motives, preferences, and play styles varies in strength and direction by biological gender, age, and educational level.

KEYWORDS:

• Gender roles
• digital games
• cooperative
• competitive
• social role theory

Disclosure statement

No potential conflict of interest was reported by the author.

Notes on contributor

Claudia Wilhelm, PhD, is a senior researcher at the Department of Media and Communication, University of Erfurt, Germany. Her research interests include media choice, gender differences in digital gaming, social aspects of digital gaming, and adolescent media use. [email: [email protected]].

Notes

1 Criteria such as construct reliability (CR) and average variance extracted (AVE) are more appropriate indicators of convergent validity, although Cronbach’s α is more commonly used (Hair et al., 2014). Hence, reliability of factors was tested by calculating Cronbach’s α as this criterion is used by other studies using measures such as BSRI (e.g., Engelberg & Melzer, 2015; Poels et al., 2012). Table A1 also provides CR and AVE values of all latent constructs. Hair et al. (2014, p. 619) state that a CR between .60 and .70 and an AVE of .50 are acceptable. However, these criteria are not commonly reported in communication research.

2 As bootstrap procedure cannot be applied using the FIML technique in Amos, missing values were replaced by using the EM (expectation maximization) method in SPSS. There were no significant differences in model estimates and model fit.