Online Game Addiction
CYBERPSYCHOLOGY & BEHAVIOR Volume 9, Number 6, 2006 © Mary Ann Liebert, Inc.
Rapid Communication Motivations for Play in Online Games
NICK YEE
ABSTRACT An empirical model of player motivations in online games provides the foundation to understand and assess how players differ from one another and how motivations of play relate to age, gender, usage patterns, and in-game behaviors. In the current study, a factor analytic approach was used to create an empirical model of player motivations. The analysis revealed 10 motivation subcomponents that grouped into three overarching components (achievement, social, and immersion). Relationships between motivations and demographic variables (age, gender, and usage patterns) are also presented. INTRODUCTION of the player demographic are motivated differently, and whether certain motivations are more highly correlated with usage patterns or other in-game behaviors. Such a model has value for both researchers and game designers. For researchers, findings may clarify whether certain kinds of players are more susceptible to problematic usage, for example. For game developers, findings may clarify how certain game mechanics may attract or alienate certain kinds of players. While Bartle’s Player Types3 is a well-known player taxonomy of Multi-User Dungeon (MUD) users, the underlying assumptions of the model have never been empirically tested. For example, Bartle assumed that preference for one type of play (e.g., achievement) suppressed other types of play (e.g., socializing or exploring). Also, it has never been empirically shown that the four player types are indeed independent types. In other words, several of the types may correlate to a high degree. In essence, it would be hard to use Bartle’s model on a practical basis unless it was validated with and grounded in empirical data. In the following work, I describe a factor analytic approach to creating an empirically grounded player motivation model.
E
VERY DAY,
Rapid Communication Motivations for Play in Online Games
NICK YEE
ABSTRACT An empirical model of player motivations in online games provides the foundation to understand and assess how players differ from one another and how motivations of play relate to age, gender, usage patterns, and in-game behaviors. In the current study, a factor analytic approach was used to create an empirical model of player motivations. The analysis revealed 10 motivation subcomponents that grouped into three overarching components (achievement, social, and immersion). Relationships between motivations and demographic variables (age, gender, and usage patterns) are also presented. INTRODUCTION of the player demographic are motivated differently, and whether certain motivations are more highly correlated with usage patterns or other in-game behaviors. Such a model has value for both researchers and game designers. For researchers, findings may clarify whether certain kinds of players are more susceptible to problematic usage, for example. For game developers, findings may clarify how certain game mechanics may attract or alienate certain kinds of players. While Bartle’s Player Types3 is a well-known player taxonomy of Multi-User Dungeon (MUD) users, the underlying assumptions of the model have never been empirically tested. For example, Bartle assumed that preference for one type of play (e.g., achievement) suppressed other types of play (e.g., socializing or exploring). Also, it has never been empirically shown that the four player types are indeed independent types. In other words, several of the types may correlate to a high degree. In essence, it would be hard to use Bartle’s model on a practical basis unless it was validated with and grounded in empirical data. In the following work, I describe a factor analytic approach to creating an empirically grounded player motivation model.
E
VERY DAY,
References: 1. Woodcock, B.S. (2005). MMOG Chart. Available at: www.mmogchart.com/. Accessed September 13, 2006. 2. Yee, N. The demographics, motivations, and derived experiences of users of massively-multiuser online graphical environments. Presence: Teleoperators and Virtual Environments 15:309–329. 3. Bartle, R. (1996). Hearts, clubs, diamonds, spades: players who suit MUDs. Journal of Virtual Environments. Available at: www.brandeis.edu/pubs/jove/ HTML/v1/bartle.html. Accessed September 13, 2006. MOTIVATIONS FOR PLAY IN ONLINE GAMES 4. Young, K. (1998). Internet addiction: the emergence of a new clinical disorder. CyberPsychology & Behavior 1:237–244. 5. Bean, A. (2006). The internet’s dangerous power. Washington Square News. Retrieved April 11th, 2006 6. Kershaw, S. (2005). Hooked on the web: Help is on the way. The New York Times. Retrieved December 1st, 2005 Address reprint requests to: Nick Yee Department of Communication Stanford University Stanford, CA 94305 E-mail: nyee@stanford.edu