Motivations of Play in MMORPGs

Every day, millions of people interact with each other in online environments known as Massively-Multiplayer Online Role-Playing Games (MMORPGs). MMORPG players, who on average are 26 years old, typically spend 22 hours a week in these environments. Articulating motivational differences among different users is the precursor to understanding the emergence of more complex behaviors and interactions in these environments, as well as providing a framework to differentiate one user from another. Such a framework provides the foundation to explore whether different sections of the demographic are motivated differently, and whether certain motivations are more highly correlated with usage patterns or in-game preferences or behaviors. The following paper describes a study that used online survey data to create an empirical model of player motivations in MMORPGs and how those resulting motivations correlate with demographic variables and usage patterns. Bartle’s (1996) Player Types is a well-known taxonomy of Multi-User Domain (MUD) users derived from his experiences in creating and managing MUDs. These 4 Types - Achievers, Socializers, Explorers, and Killers - each have different in-game preferences and motivations for using the MUD environment. For example, Explorers are users who are interested in understanding the mechanics and rules of the system as well as mapping out the world, while Socializers are users who enjoy chatting, interacting and role-playing with other users. Bartle’s model provides an important foundation in understanding the motivations of different players, however, it suffers from three significant weaknesses. First, the proposed components of each Type may not be highly correlated. Second, the proposed Types might be overlapping and not truly distinct Types. And finally, the purely theoretical model provides no way to assess users as to what Type they might be. To resolve these weaknesses and build a more solid foundation for understanding player motivations, an empirical analysis was performed. A list of 40 questions that related to player motivations was generated based on Bartle’s Types and anecdotal information from earlier surveys of MMORPG players. Examples of these statements are: How important is it to you to level up as quickly as possible? How much do you enjoy helping other players? How often do you make up stories and histories for your characters? The response option for every question was a 5-point uni-polar construct-specific scale. For example, - Not Important At All - Slightly Important - Moderately Important - Very Important - Tremendously Important Data was then collected from 3000 MMORPG players through online surveys publicized at online portals that catered to MMORPG players from several popular MMORPGs - EverQuest, Dark Age of Camelot, Ultima Online, and Star Wars Galaxies. A factor analysis was then performed on this data to separate the statements into clusters where items within each cluster were as highly correlated as possible while clusters themselves were as uncorrelated as possible. The methodology achieved three goals that overcame the inherent weaknesses of Bartle’s model. First, it ensured that the components of each motivation were indeed correlated. Second, it ensured that different motivations were indeed different. And finally, it would provide a way to assess player motivations. In a sense, this methodology was testing Bartle’s Types for validity and correcting for inherent problems with a purely theoretical model. One important theoretical distinction between Bartle’s types and the factors resulting this study is that Bartle argued that every player was predominantly motivated by one of the four types, whereas the factor model assumes that factors are uncorrelated and therefore it is possible for a player to score high on several factors. This is analogous to contemporary personality assessment tools (such as the Big-5). Just because someone scores high on Extraversion doesn’t mean they can’t also score high on Neuroticism. In other words, in the factor model, scoring high in one factor doesn’t exclude a player from any other factor, whereas Bartle’s model implicitly does so. Bartle’s model tries to categorize players into boxes. The factor model scores players on every factor. A factor analysis using principal components extraction produced 7 factors. All resulting factors had a Cronbach’s alpha of over .70. These factors were: 1) Achievement - The desire to advance the character as quickly as possible, as well as accumulate rare equipment and items, in order to become powerful within the context of the game. 2) Casual Social Interaction (Chat) - An interest in chatting and gossiping with other players. 3) Immersion - The desire to be immersed in a fantasy world and try out new roles and personalities with different characters. 4) Serious Social Interaction (Relationship) - An interest in forming strong, supportive relationships where personal issues can be shared. 5) Competition - The desire to challenge and compete with other players. In more extreme cases, the desire to annoy, manipulate or dominate other players. 6) Escapism - The interest in the virtual world derives from wanting to escape from the real world. 7) Explorer - The desire to explore the game’s mechanics and geography. By calculating the factor scores for the 3000 respondents, the data also allowed an understanding of how these motivations mapped onto gender and age differences as well as hours of play per week. T-tests on the motivation scores grouped by gender showed that male players were significantly more motivated by Competition and Achievement (p’s < .001, r’s = .26 & .19) while female players were significantly more motivated by Casual and Serious Social Interaction (p’s < .001, r’s = .10 & .26). Age was negatively correlated with Achievement (r = -.30), Casual Social Interaction (r = -.10) and Competition (r = -.34). And finally, the number of hours played per week was positively correlated with Achievement (r = .16), Serious Social Interaction (r = .12) and Escapism (r = .12). The empirical model developed in this study provides a solid foundation for future research in MMORPGs by providing a model to understand player motivations, a tool to assess those motivations, and thus also a means to understand usage patterns, in-game behaviors and demographic variables in relation to player motivations.