Applying Flow Theory to Predict User-Perceived Performance of Tablets

  • James ScovellEmail author
  • Rina Doherty
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9748)


A users’ perception of interactive device performance is influenced by their feeling of being in control and that there is a sense of constant progress. A system will be able to keep users in the flow by meeting expectations and keeping up with their inputs and commands. The concept of flow has been discussed since the 1960’s and has been used in the context of computing devices; however, the ability to operationally define and quantitatively measure this construct is limited. This paper describes a study that tested a new framework for measuring flow as it relates to User-Perceived Performance (UPP) of tablets.


User-perceived performance Severity-Duration Mean Opinion Score (MOS) User experience (UX) Flow Tablet Computer performance 


  1. 1.
    Csikszentmihalyi, M.: The flow experience and its significance for human psychology. In: Csikszentmihalyi, M., Csikszentmihalyi, I.S. (eds.) Optimal Experience: Psychological Studies of Slow in Consciousness, pp. 15–35. Cambridge University Press, Cambridge (1988)CrossRefGoogle Scholar
  2. 2.
    Shneiderman, B.: Designing the User Interface: Strategies for Effective Human-Computer Interaction, 1st edn. Addison-Wesley, Reading (1987)Google Scholar
  3. 3.
    Dabrowski, J., Munson, E.: 40 Years of searching for the best computer system response time. Interact. with Comput. 23, 555–564 (2011). Elsevier B.VCrossRefGoogle Scholar
  4. 4.
    Seow, S.C.: Designing and Engineering Time: The Psychology of Time Perception in Software. Addison-Wesley, Amsterdam (2008)Google Scholar
  5. 5.
    Zakay, D., Hornik, J.: How much time did you wait in line? A time perception perspective. In: Time and Consumer Behaviour (1991)Google Scholar
  6. 6.
    Angrilu, A., Cherubini, P., Pavase, A., Manfredini, S.: The influence of affective factors on time perception. Percept. Pyschophys. 59, 972–982 (1997)CrossRefGoogle Scholar
  7. 7.
    Goldstein, B.E.: Sensation and Perception, 5th edn. University of Pittsburg, Pittsburg (1999)Google Scholar
  8. 8.
    Jota, R., Ng, A., Dietz, P., Wigdor, D.: How fast is fast enough? A study of the effects of latency in direct touch pointing tasks. In: Proceedings of S1GCHI Conference on Human Factors in Computing Systems (CHI 2013), pp. 2291–2300. ACM, New York, NY, USA (2013)Google Scholar
  9. 9.
    Doherty, R., Sorenson, P.: Keeping users in the flow: mapping system responsiveness with user experience. Procedia Manuf. 3, 4384–4391 (2015)CrossRefGoogle Scholar
  10. 10.
    Mangan, T.: White paper perceived performance. Tuning a system for what really matters. TMurgent Technologies (2003).
  11. 11.
    Miller, R.B.: Response time in man-computer conversational transactions. In: International Business Machines (IBM) Corporation, Fall Joint Computer Conference, Poughkeepsie, New York (1968)Google Scholar
  12. 12.
    Card, S.K., Moran, T.P., Newell, A.: The Psychology of Human-Computer Interaction. Lawrence Erlbaum Associates, Hillsdale (1983)Google Scholar
  13. 13.
    Anderson, G., Doherty, R., Baugh, E.: Diminishing returns? Revisiting perception of computing performance. In: Proceedings of CHI, pp. 2703–2706 (2011)Google Scholar
  14. 14.
    Verheij, I.: White paper quantifying and rating perceived performance of a virtual desktop system application (2011).
  15. 15.
  16. 16.
    Tolia, N., Andersen, D.G., Satyanarayanan, M.: Quantifying interactive user experience on thin clients. In: Proceedings of the IEEE. Carnegie Mellon University (2006)Google Scholar
  17. 17.
    Norman, D.: The Design of Everyday Things. Basic Books, New York (2002). ISBN 978-0-465-06710-7Google Scholar
  18. 18.
    ITU-R.: Methodology for the subjective assessment of the quality of television pictures. Recommendation BT.500-13, Geneva (2012)Google Scholar
  19. 19.
    Mittal, V., Ross Jr, W.T., Baldasare, P.M.: The asymmetric impact of negative and positive attribute-level performance on overall satisfaction and repurchase intentions. J. Mark. 62, 33–47 (1998)CrossRefGoogle Scholar
  20. 20.
    Oliva, T.A., Oliver, R.L., Bearden, W.O.: The relationship among consumer satisfaction, involvement, and product performance. Behav. Sci. 40(April), 104–132 (1995)CrossRefGoogle Scholar
  21. 21.
    Oliver, R.L.: Cognitive, affective, and attribute bases of the satisfaction response. J. Consum. Res. 20(December), 418–430 (1993)CrossRefGoogle Scholar
  22. 22.
    Peeters, G., Czapinski, J.: Positive-negative asymmetry in evaluations: the distinction between affective and informational negativity effect. Eur. Rev. Soc. Psychol. 1, 33–60 (1990)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Intel Corporation, Platform Evaluation and Competitive AssessmentHillsboroUSA

Personalised recommendations