less than â.50 (Holmes-Smith, Coote, and Cunningham, 2004). Based on the examinations of the modification indices, Challenge 1 and Challenge 3 were ...
Measuring The State Of Flow In Playing Online Games Laila Refiana, Dick Mizerski, Jamie Murphy, University of Western Australia Abstract The literature on gaming suggests that people may experience the state of flow when playing online games. The main objective of this study is to develop a measurement of a gamer’s reported state of Flow using data gathered from an online survey of 218 gamers. The final Flow construct had four factors that fit the data. The factors were “Skill”, “Challenge”, “Involvement” and “Time”. This study found that all factors were best represented as firstorder factors for identifying the flow construct. A test of the final model provides evidence of convergent, discriminant and predictive validities of the Flow construct. The implications of these findings are discussed.
Introduction People may find the excitement of playing online games because of the great graphic and sound. Gamers’ intention to play online games again in the future is a very important factor to the existence of online games since customer return is a main goal of all industries (Pine, Peppers & Rogers 1995; Rice 1997), including online games. Nonetheless, retaining gamers is a challenge to online games industry with its incredible number of game choices available compared to few years ago. If online gamers do not experience enjoyment in playing, they will unlikely return to the same game nor extend the subscription of the game (cf. Koufaris, Kambil & Labarbera 2002). This study seeks to examine the experiential or emotional factors in playing online games. In doing so, this study applies the theory of flow (Csikszentmihalyi, 1975) as a foundation for looking at online games playing experiences. The concept of flow refers to optimal and very pleasing activities experienced by individuals with whole involvement, concentration and a sense of time distortion (Chen, Wigan, and Nilan, 1999). The theory of flow has been proposed in an explanation for enjoying the experiences from using the Internet (Hoffman and Novak, 1996; Novak, Hoffman, and Duhachek, 2003). It has been argued that creating compelling online experiences, such as surfing the Internet or online shopping depends on facilitating a state of Flow (Hoffman, and Novak 1996; Novak, Hoffman, and Yung 2000). It is reported that self-consciousness vanishes, one’s sense of time is distorted and a favourable state of mind is fulfilled as a consequence of achieving Flow in web experiences (Novak, Hoffman, and Yung, 2000). Fuller and Jenkins (1995, p. 60) described the experience of playing games as, “Once immersed in playing … all that matters is staying alive long enough to move between levels.” During game playing, gamers are reported to be “mesmerised” (Webster, Trevino, and Ryan, 1993), and take no notice of thoughts and perceptions outside the game. The theory of flow (Csikszentmihalyi, 1975) considers the experiential behaviour (e.g., involvement in playing online games) as an important part of the consumption. Therefore, the application of Flow Theory should be able to be extended to the online game playing environment. A reliable and valid measurement of flow should be based on well established theory and practice. However, there are many and diverse flow constructs available in the literature (e.g. Chen, Wigan, and Nilan, 1999; Novak, Hoffman, and Yung, 2000). The existing
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measurements of Flow usually contain some aspect of enjoyment, and are situational-related. For example, Ghani and Deshpande (1994) examined Flow among computer users in the workplace, while Trevino and Webster (1992) explored the state of Flow during emailing. Given the tradition of reporting a situational-specific application of Flow, this research proposes and tests a model of Flow that adapts previous measures for measuring the online game play experience.
Previous Flow Constructs and Measures The measurements of Flow in the literature are usually survey-based data derived from using statements and various scales to rate the statements. There are many factors that are considered to influence flow. For example, Csikszentmihalyi (1990, 1993, and 1997) has discussed 8 flow factors. A study by Said et al. (2003) reported on a survey of 456 gamers and their open-ended responses about their reasons for playing online games. The findings showed the most frequent responses related to four factors titled Skill, Challenge, Involvement and Time. (e.g. “immersing”, “more challenging because I play against human players”, “reduce boredom”, “playing against others”, “enable to show high scores in the Internet”, “kill time”). Skill and Challenge In further developing a Flow measure for online games playing, some of the multipleindicators were adapted from prior studies that reported Flow experiences on the Web (Novak, Hoffman, and Duhachek, 2003; Novak et al., 2000). Ghani and Deshpande (1994) found perceptions of skill and challenge affect the occurrence of Flow among computer users in the workplace. Csikszentmihalyi (1997) introduced the “flow channel segmentation models” that defined flow in terms of the equivalence of skills and challenges. Novak et al. (2000) used items, “How would you rate your skill at using the Web, compared to other things you do on the computer?” and “How would you rate your skill at using the Web, compared to the sport or game that you are best at?” for Skill. “How much does the Web challenge you, compared to the sport or game you are best at?” and “How much does the Web challenge you, compared to other things you do on the computer?” was used for measuring Challenge. It has been argued that the antecedents of Flow include perceived matching game play skills with game challenges (cf., Csikszentmihalyi, 1977; Csikszentmihalyi and LeFevre, 1989; Ellis, Voekl, and Morris, 1994; Hoffman and Novak, 1996; Novak et al., 2000). In other words, the circumstances in which Flow occurred for online games are believed to be related to an interaction with the gamers’ perception of their playing skill and the challenges the online games present to them. Involvement Flow studies concerning human-computer interactions show that the enjoyment and degree of involvement during computer use can produce perceptions of positive emotions (e.g. Sandelands and Buckner, 1989; Starbuck and Webster, 1991; Webster and Martocchio, 1992). Involvement in online games playing is thought to occur when gamers focus their energy and attention on a set of game stimuli presented in the virtual world (cf. Sadowski & Stanney, 2002). Novak et al. (2003) collected involvement responses aroused by the Web experience such as, “I was completely absorbed by the site”, “I was very involved in my searching” and “I often feel totally immersed when browsing”. This study adapts these statements as a basis for survey items measuring involvement in Flow.
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Time Flow has been associated with a distortion in the perception of time (Csikszentmihalyi, 1977; Novak, Hoffman, and Duhachek, 2003).The game play experience can be described as a pleasurable and exciting activity (Neuman, 1998) that makes gamers unaware of the time passed while they are playing the game. One of the statement adapted was, “Time disappears when I visit this site” (Novak, Hoffman, and Duhachek, 2003). This and other tested statements are shown in the legend of Figure 1.
Methodology In an effort to collect data from online gamers, several methods were attempted. An online survey was promoted with incentives in the largest game’s chat rooms. Over 800 respondents ultimately completed the online survey. Two-hundred and eighteen (n=218) online gamers were found based on reporting they played online games in the last 7 days. This period was used based on previous research with online games (Said, Mizerski, and Lam, 2003) that showed the frequency of play fit an NBD pattern, and that heavy users (the most likely to experience flow) would play at least once a week. The flow measurement in this study had statements representing four constructs, Skill, Challenge, Involvement and Time. Each statement was rated with a five-point scale. Participants completed the 14 multiple indicators (that represented the four factors of Flow) with the scales shown in the Figure 1 legend.
Results All paths were positive and all 14 indicator variables were significant (***p
