Digital Game Dynamics Preferences and Player Types

Abstract

In this study, we examine digital game preferences by identifying game dynamics, i.e. player-game interaction modes, of 700 contemporary digital games, and players' (N = 1717) desire to play games with specific types of dynamics. Based on statistical analysis of the data, 5 game dynamics preference categories ("assault," "manage," "journey," "care," and "coordinate") In this study, we aim to provide new knowledge on how players' gaming preferences and different types of digital games can be analyzed within a single research framework based on activity theoretical considerations and game design concepts. While earlier research has argued challenge and competition as the principal reasons to play digital games (Sherry et al., 2006) and social interaction, immersion, and achievement as the key human motivations to play online games (Yee, 2006), player preferences in specific types of games are still largely unknown. The approach presented in this study should not be equated to motivations to play studies (e.g. Yee, 2006;Sherry et al., 2006) or to the studies of player behavior types (e.g., Bartle, 2003) but rather taken as complementary work aimed at identifying player-game interaction preference types. The knowledge of what keeps players involved with gameplay is paramount for developing player-centric games with better market fit (see Adams, 2014).We start by discussing how our approach relates with prior studies on player typologies. We then set the analytical framework for our investigation by defining the theoretical concepts relevant to our study, and finally present our research questions of identifying (1) the core game dynamics of contemporary digital games; (2) players' game dynamics preferences; and (3) player types based on game dynamics preferences. The results are followed by a discussion that will bring our approach into dialogue with related prior research. Player Typologies and Preferences for Playing Digital GamesPrior player categorizations have focused on either motivations to play or behavioral dimensions of players' play styles (Hamari & Tuunanen, 2014, p. 34). Categorizations of players by play styles (e.g., Bartle, 2003;Mulligan & Patrovsky, 2003;Tseng, 2010) and motivations to play (e.g., Bateman, Lowenhaupt & Nacke, 2011;Przybylski, Rigby & Ryan, 2010;Sherry et al., 2006;Yee, 2006) have mostly analyzed gaming habits or players' personality traits instead of examining playing as a form of computer-mediated designed interaction. These categorizations also do not typically include different types of digital games in the analysis. Behavioral observations of players' play styles are usually based on only one game, most typically an online game, or at least on a set game genre, whereas studies on motivations to play aim to explore the reasons why people play, for example, mobile games, online multiplayer games, or digital games in general.In contrast, the shortcoming of genre categories as well as other design-oriented classifications, such as design patterns approach (Björk & Holopainen, 20...

Validating gameplay activity inventory (GAIN) for modeling player profiles

“…Recently, a third approach has been proposed for profiling players which could be used for predicting game choice. Vahlo et al (2017) call this approach game preference research. Since the third approach aims to bring together characteristics of gameplay and players' sustained preferences for specific kinds of gameplay experiences, this approach can be situated in between player behavior and motivations to play models.…”

“…The purpose of this study is to develop and validate a psychometrically sound short instrument for measuring players' gameplay activity preferences in contemporary digital games, and to demonstrate the potential of the instrument in constructing player profiles and in predicting game choice. The introduced Gameplay Activity Inventory (GAIN) is based on a theoretical model and on the Core Game Dynamics (CGD) scale presented by Vahlo et al (2017). The original CGD scale is a 28-item Likert-7 measurement, which the authors (ibid.)…”

“…However, the fivefactor model of the CGD scale has not yet been validated. Furthermore, and as Vahlo et al (2017) note, more items should be developed and included in the inventory. The inventory should also be balanced and shortened in order to make it more applicable to surveys.…”

View, Play and Pay? – The Relationship between Consumption of Gaming Video Content and Video Game Playing and Buying

“…With the rapid development of video game culture player behaviour, and especially game-related consumer behaviour have become a popular topic of research over the past decade, examining elements such as player-types and behaviours [3,45], gaming preferences [15], and motivations to play [40,48,50].…”

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