Advertisement

Motivation and Emotion

, Volume 35, Issue 4, pp 393–402 | Cite as

Flow experience in physical activity: Examination of the internal structure of flow from a process-related perspective

  • Masato Kawabata
  • Clifford J. Mallett
Original Paper

Abstract

Considering the phenomenology of flow experience reflects attentional processes, Nakamura and Csikszentmihalyi (Handbook of positive psychology, Oxford University Press, New York, 2002) classified the components of flow experience into proximal conditions and the characteristics of a subjective state while being in flow. The present study was conducted to clarify the concept of flow through examination of the interrelationships among the components from a process-related perspective. A total of 1,048 participants completed the Japanese versions of the Flow State Scale-2 (Kawabata et al. in Psychol Sport Exerc 9:465–485, 2008), and based on their scores, 591 respondents were considered to be in a flow state during their physical activity. A proposed higher-order confirmatory factor model and a full structural equation model were tested for the flow respondents. The results of the higher-order model indicated that the 9 flow factors were empirically classified into the flow state and its proximal condition. Furthermore, the outcomes of the full structural model preliminarily supported the hypothesized sequential relationships among flow factors.

Keywords

Flow experience Internal structure Attentional process Structural equation modeling Physical activity 

Notes

Acknowledgements

This project was partially supported by the University of Queensland Graduate School Research Travel Grant. We are grateful to Mihaly Csikszentmihalyi and Jeanne Nakamura of Claremont Graduate University for sharing their insights into the flow concept. We also thank Robert Eklund of Florida State University and the anonymous reviewers for constructive comments on earlier drafts of this article.

References

  1. Bagozzi, R. P., & Kimmel, S. K. (1995). A comparison of leading theories for the prediction of goal-directed behaviours. British Journal of Social Psychology, 34, 437–461.CrossRefGoogle Scholar
  2. Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238–246.PubMedCrossRefGoogle Scholar
  3. Bentler, P. M. (2006). EQS 6 structural equations program manual. Encino, CA: Multivariate Software.Google Scholar
  4. Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.Google Scholar
  5. Byrne, B. M. (2006). Structural equation modeling with EQS: Basic concepts, applications, and programming (2nd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  6. Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. New York: Harper & Row.Google Scholar
  7. Csikszentmihalyi, M. (1997). Finding flow: The psychology of engagement with everyday life. New York: Harper Collins.Google Scholar
  8. Csikszentmihalyi, M. (2000). Beyond boredom and anxiety (25th anniversary ed.). San Francisco: Jossey-Bass (original work published in 1975).Google Scholar
  9. Csikszentmihalyi, M., & Csikszentmihalyi, I. (Eds.). (1988). Optimal experience: Psychological studies of flow in consciousness. New York: Cambridge University Press.Google Scholar
  10. Csikszentmihalyi, M., Rathunde, K., & Whalen, S. (1993). Talented teenagers: The roots of success and failure. Cambridge, UK: Cambridge University Press.Google Scholar
  11. Davidson, R. J. (1994). Asymmetric brain function, affective style, and psychopathology: The role of early experience and plasticity. Development and Psychopathology, 6, 741–758.CrossRefGoogle Scholar
  12. Dietrich, A. (2004). Neurocognitive mechanisms underlying the experience of flow. Consciousness and Cognition, 13, 746–761.PubMedCrossRefGoogle Scholar
  13. Fan, X., & Sivo, S. A. (2005). Sensitivity of fit indexes to misspecified structural or measurement model components: Rationale of two-index strategy revised. Structural Equation Modeling, 12, 343–367.CrossRefGoogle Scholar
  14. Hu, L., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to under parameterized model misspecification. Psychological Methods, 3, 424–453.CrossRefGoogle Scholar
  15. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.CrossRefGoogle Scholar
  16. Jackson, S. A., & Eklund, R. C. (2002). Assessing flow in physical activity: The Flow State Scale-2 and Dispositional Flow Scale-2. Journal of Sport & Exercise Psychology, 24, 133–150.Google Scholar
  17. Jackson, S. A., & Eklund, R. C. (2004). The flow scales manual. Morgantown, WV: Fitness Information Technology.Google Scholar
  18. Jackson, S. A., Kimiecik, J. C., Ford, S., & Marsh, H. W. (1998). Psychological correlates of flow in sport. Journal of Sport & Exercise Psychology, 20, 358–378.Google Scholar
  19. Jackson, S. A., & Marsh, H. W. (1996). Development and validation of a scale to measure optimal experience: The Flow State Scale. Journal of Sport & Exercise Psychology, 18, 17–35.Google Scholar
  20. Jackson, S. A., Thomas, P. R., Marsh, H. W., & Smethurst, C. J. (2001). Relationships between flow, self-concept, psychological skills, and performance. Journal of Applied Sport Psychology, 13, 129–153.CrossRefGoogle Scholar
  21. Kawabata, M., Mallett, C. J., & Jackson, S. A. (2008). The Flow State Scale-2 and Dispositional Flow Scale-2: Examination of their factorial validity and reliability for Japanese adults. Psychology of Sport and Exercise, 9, 465–485.CrossRefGoogle Scholar
  22. MacCallum, R., Roznowski, M., & Necowitz, L. B. (1992). Model modifications in covariance structure analysis: The problem of capitalization on chance. Psychological Bulletin, 111, 490–504.PubMedCrossRefGoogle Scholar
  23. Marsh, H. W., Dowson, M., Pietsch, J., & Walker, R. (2004a). Why multicollinearity matters: a reexamination of relations between self-efficacy, self-concept, and achievement. Journal of Educational Psychology, 96, 518–522.CrossRefGoogle Scholar
  24. Marsh, H. W., Hau, K.-T., & Wen, Z. (2004b). In search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler’s (1999) findings. Structural Equation Modeling, 11, 320–341.CrossRefGoogle Scholar
  25. Marsh, H. W., & Jackson, S. A. (1999). Flow experiences in sport: Construct validation of multidimensional, hierarchical state and trait responses. Structural Equation Modeling, 6, 343–371.CrossRefGoogle Scholar
  26. Massimini, F., & Carli, M. (1988). The systematic assessment of flow in daily experience. In M. Csikszentmihalyi & I. Csikszentmihalyi (Eds.), Optimal experience: Psychological studies of flow in consciousness (pp. 266–287). New York: Cambridge University Press.Google Scholar
  27. Moneta, G. B. (2004). The flow experience across cultures. Journal of Happiness Studies, 5, 115–121.CrossRefGoogle Scholar
  28. Nakamura, J., & Csikszentmihalyi, M. (2002). The concept of flow. In C. R. Snyder & S. J. Lopez (Eds.), Handbook of positive psychology (pp. 89–105). New York: Oxford University Press.Google Scholar
  29. Pearce, J. M., Ainley, M., & Howard, S. (2005). The ebb and flow of online learning. Computers in Human Behavior, 21, 745–771.Google Scholar
  30. Rheinberg, F. (2008). Intrinsic motivation and flow. In J. Heckhausen & H. Heckhausen (Eds.), Motivation and action (pp. 323–348). New York: Cambridge University Press.CrossRefGoogle Scholar
  31. Steiger, J. H. (1990). Structural model evaluation and modification: An interval estimation approach. Multivariate Behavioral Research, 25, 173–180.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.School of Human Movement StudiesThe University of QueenslandBrisbaneAustralia

Personalised recommendations