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<.001; **p<.01,
shown in Figure 1). The model was modified by excluding item Skill 3 and Time 1 because
both had the squared multiple correlation (variance) of less than .30, or a factor loading of
less than .50 (Holmes-Smith, Coote, and Cunningham, 2004). Based on the examinations of
the modification indices, Challenge 1 and Challenge 3 were allowed to correlate with each
other. The Modification Index measures how much chi-square is expected to decrease if this
parameter is set free and the model is re-estimated. The largest modification index shows the
parameter that improves the fit most when set free. The revised model has greatly improved
its fit indices (CMIN/df=1.03; Bollen-Stine p= .98; AGFI=.94; TLI=1.00; CFI=1.00;
RMSEA=.01; CAIC=230.13).
The convergent validity of the revised model is moderately achieved (rs .54-.80; p<.001) with
the threshold value of .50 used in this (Holmes-Smith, Coote, and Cunningham, 2004). The
method of evaluating pattern and structure coefficients (Thompson, 1997) were used in
determining whether the Skill, Challenge, Involvement and Time constructs are empirically
distinguishable, while they are usually viewed as theoretically interrelated. Large correlations
between latent constructs (>.90) indicate a lack of discriminant validity (Holmes-Smith,
Coote, and Cunningham, 2004). The revised model showed good discriminate validity (all
p<.001) between: Skill and Challenge (r=.55), Skill and Involvement (r=.63), Skill and Time
(r=.65) Challenge and Time (r=.74) and Challenge and Involvement (r=.90). However, the
discrimination between the constructs of Time and Involvement was low (r=.95). Collapsing
the model to a three factor model was not a significant improvement so the four factor flow
model is a good fit to the data.
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Legend:
e15
.32
.52
.56***
Skill
.69***
.48
e2
Skill 2
.27**
Skill 3
.08
e3
Challenge 1
e4
.65***
.69
.71*** Challenge 2
Challenge
.61***
.83***
.50
e5
.37
e6
Challenge 3
.66***
Challenge 4
FLOW
.96***
Involvement 1
.63***
.60***
Involvement
e7
e8
.36
e9
Involvement 2
.67***
.72***
.92***
.43
(Never experienced - Always experienced):
Challenge 1: I feel challenged
Challenge 2: I feel passionate about winning the game
Challenge 3: I enjoy winning each level of the game
Challenge 4: I feel daring
.39
e17
.92
Skill 1: Compared to other players I know, I consider my
skills for playing games are in: Low level – Top
level
Skill 2: How good is your playing skill? Bad – Good
Skill 3: The games I play are mostly: Difficult – Easy
.42
e16
.72***
e1
Skill 1
.45
Involvement 3
e10
(Never experienced - Always experienced):
Involvement 1: I feel involved with the game
Involvement 2: I get immersed by the game
Involvement 3: I feel "carried away" by the game
Involvement 4: I feel as if I were part of the game
.52
e11
Involvement 4
e18
.85
Time
.47***
.61***
Time 1
.22
e12
.37
Time 2
.77***
Time 3
e13
.59
(Never experienced - Always experienced):
Time 1: I forget time when I play
Time 2: When I play good games, I forget about time
Time 3: When I play good games, I lose track of time
e14
***p<.001; **p<.01
CMIN/df=1.88; Bollen-Stine p= .11; AGFI=.88; TLI=.91; CFI=.92; RMSEA=.06; CAIC=336.68
Figure 1: A Higher Order Analysis of Flow in Online Games Playing with Standardised
Estimate
In order to measure the predictive validity of the revised model, this study investigated the
impact of Flow upon Replay intention. The scale of Replay intention was represented by
three statements about the gamers’ intention to play the games again (i.e. “How likely are you
to play games again, if the opportunity arose?”, “After I play game, I will play it again with
my friends”, and “How likely are you to play games over the next week?”). Using composite
factors for the constructs of Skill, Challenge, Involvement and Time, first-order factors loaded
onto Flow before affecting Replay intention (illustrated in Figure 2). The analysis revealed
that the factors of Skill, Challenge, Involvement and Time were significant (p<.001), and the
model had good fit indices. These findings support the higher-order model for the data in this
study.
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e1
.32
e2
.49
.24
e3
e4
.78
.58
Skill
.57***
.70***
Challenge
.61***
Involvement
Flow
.60
.77***
.76***
Replay
intention
e5
Time
CMIN/df=2.31; Bollen-Stine p=.40; AGFI=.94; TLI=.97; CFI=.99; RMSEA=.08; CAIC=79.47
***p<.001
Figure 2: Test of Predictive Validity
Discussion
Given the relative novelty of quantitative research on the experience of Flow, we sought to
develop a robust and practical flow measurement for online games playing. Overall, the Flow
construct and measurements proposed and tested were found to have reasonable convergent
and discriminant validities. Additional analysis revealed that Skill, Challenge, Involvement
and Time in the higher-order model of flow were a good fit to the data and explained the
replay intention. The model in Figure 2 was tested for the causal link of Flow to replay
intention, and shows the relative influences of the four factors in the decision-making process.
Gamers’ perceptions of a game’s Challenge and Time while playing were the strongest link to
intend to replay, while Skill and Involvement were somewhat weaker. The link of Flow to the
gamers’ intentions to replay was a significant.
There are many limitations in this study, and caution must be used in interpreting these
findings. Participants were asked to complete self-report scales that described their past game
playing experiences. These responses may be biased because the post-experience self-report
survey method, or may be insensitive to detecting the experiences of game playing. Future
study should consider alternative data collection methods (e.g. real time collection) and other
possible factors such as the influence of habitual play (Said, Mizerski and Lam, 2003). In
addition, the test of predictive validity in the current study did not use the real behavioural
outcomes as it only measured the self-reported intention that is presumed to capture factors
that influence future behaviour (Ajzen 1988). Anywhere possible, the longitudinal studies
that conduct a test of predictive validity using real data of playing are highly recommended.
Researchers did not try to map the antecedents and consequences of flow. Rather, researchers
only seek to capture the factors that indicate flow, i.e. Skill, Challenge, Involvement and Time
distortion. Thus, future studies should conduct model comparisons in order to fully
understand the state of flow in online games playing. Finally, multiplayer online games, like
the focus of this study, may vary in the experiences induced, and be very different from
simple web games. By identifying different flow experiences of different game genres,
studies should contribute more to the game designs that may facilitate these flow perceptions.
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References
Ajzen, I., 1988. Attitudes, Personality, and Behavior. Chicago, IL: The Dorsey Press
Chen, H., Wigan, R. T., and Nilan, M. S., 1999. Optimal Experience of Web Activities.
Computers in Human Behavior. 15(5), 585-608.
Csikszentmihalyi, M., 1975. Play and Intrinsic Rewards. Journal of Humanistic Psychology.
15, 41-63.
Csikszentmihalyi, M., 1977. Beyond Boredom and Anxiety. San Francisco, CA: Jossey-Bass.
Csikszentmihalyi, M., 1993. The Evolving Self: A Psychology for the Third Millennium.
New York: Harper-Collins.
Csikszentmihalyi, M., 1997. Finding Flow: The Psychology of Engagement with Everyday
Life. New York: Basic Books.
Csikszentmihalyi, M., and Csikszentmihalyi, I. S., 1988. Optimal Experience: Psychological
Studies of Flow in Consciousness. Cambridge: Cambridge University Press.
Csikszentmihalyi, M., and LeFevre, J., 1989. Optimal Experience in Work and Leisure.
Journal of Personality and Social Psychology. 56(5), 815-822.
Fuller, M. and Jenkins, H., 1995. Nintendo® and New World Travel Writing: A Dialogue. In
Cybersociety: Computer-Mediated Communication and Community, Jones, S. G.
(Ed.), Thousand Oaks: Sage Publications, 57-72.
Ellis, G. D., Voekl, J. E., and Morris, C., 1994. Measurement and Analysis Issues with
Explanation of Variance in Daily Experience Using the Flow Model. Journal of
Leisure Research. 26(4), 337-356.
Ghani, J. A., and Deshpande, S. P., 1994. Task Characteristics and the Experience of Optimal
Flow in Human-Computer Interaction. Journal of Psychology. 128(4), 381-391.
Hoffman, D. L., and Novak, T. P., 1996. Marketing in Hypermedia Computer-Mediated
Environments: Conceptual Foundations. Journal of Marketing. 60(July), 50-68.
Holmes-Smith, P., Coote, L. and Cunningham, E., 2004. Structural Equation Modeling: From
the Fundamentals to Advanced Topics. ACSPRI-Summer Training Program,
Canberra, Australia.
Koufaris, M., Kambil, A. & LaBarbera, P. A., 2002. Consumer Behaviour in Web-based
Commerce: An Empirical Study. International Journal of Electronic Commerce. 6(2),
115-138.
Neuman, P., 1998. Are Computers Addictive? Communication of the ACM. 40(3), 128.
Pine, J. I. B, Peppers, D. & Rogers, M., 1995 Do You Want to Keep Your Customers
Forever? Harvard Business Review. 73(2), 692-703.
ANZMAC 2005 Conference: Marketing Research and Research Methodologies (quantitative)
113
Rice, M., 1997. What Makes Users Revisit a Web Site? Marketing News. 31(6), 12.
Sadowski Jr., W., and Stanney, K., 2002. Measuring and Managing Presence in Virtual
Environments, in Stanney, K. (Ed.), Handbook of Virtual Environments Technology.
Mahwah, NJ: Lawrence Erlbaum Associates, Inc., p. 791-806.
Said, L. R., and Mizerski, D., 2003. Applying the NBD to Predict the Online Game Play of
Youth and Young Adults. Poster presented in the Marketing and Public Policy
Conference, AMA, Burton, S., Andrews, J.C., and Mazis, M. B. (Eds.). Washington
DC.
Said, L. R., Mizerski, D., and Lam, D., 2003. Comparing the Effect of Habit in the Online
Game Play of Australian and Indonesian Gamers. Proceedings of the ANZMAC,
Geurson, G., Kennedy, R., and Tolo, M. (Eds.), University of South Australia,
Adelaide.
Novak, T. P., Hoffman, D. L., and Duhachek, A., 2003. The Influence of Goal-Directed and
Experiential Activities on Online Flow Experiences. Journal of Consumer Psychology.
13(1&2), 3-16.
Novak, T. P., Hoffman, D., and Yung, Y., 2000. Measuring the Customer Experience in
Online Environments: A Structural Modeling Approach. Marketing Science. 19(1),
22-42.
Thompson, B., 1997. The Importance of Structure Coefficients in Structural Equation
Modeling Confirmatory Factor Analysis. Educational and Psychological
Measurement. 57(1), 5-19.
Webster, J., Trevino, L. K., and Ryan, L., 1993. The Dimensionality and Correlates of Flow
in Human Computer Interactions. Computers in Human Behavior. 9(4), 411-426.
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