The Effect of Video Loading Symbol on Waiting Time Perception

  • Woojoo Kim
  • Shuping XiongEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10290)


This study aimed to investigate the effect of different loading symbols and durations on waiting time perception of online video viewers. 60 young adults participated in this study and gave subjective ratings on waiting time perception through a 7-point Likert scale for 48 loading symbols (3 durations × 4 progress functions × 2 shapes × 2 embellishments). Results showed that duration and the progress function significantly influence the viewers’ waiting time perception, while shape and embellishment do not. Loading symbols with the repetitive and linear progress functions are perceived longer than those of the power and inverse power progress functions. To indicate loading progress and to use manipulated progress functions are recommended, and design factors such as shape and embellishment are considered to be less effective. The findings of this study may serve as a useful input for loading symbol designers in creating better loading symbols.


Video loading Time perception Symbol design Progress indicators Human-computer interface 


  1. 1.
    Angrilli, A., Cherubini, P., Pavese, A., Manfredini, S.: The influence of affective factors on time perception. Percept. Psychophys. 59(6), 972–982 (1997)CrossRefGoogle Scholar
  2. 2.
    Bleustein, C., Rothschild, D.B., Valen, A., Valatis, E., Schweitzer, L., Jones, R.: Wait times, patient satisfaction scores, and the perception of care. Am. J. Manag. Care 20(5), 393–400 (2014)Google Scholar
  3. 3.
    Branaghan, R.J., Sanchez, C.A.: Feedback preferences and impressions of waiting. Hum. Factors: J. Hum. Factors Ergon. Soc. 51(4), 528–538 (2009)CrossRefGoogle Scholar
  4. 4.
    Brandt, M.: 1 in 4 U.S. internet users watches online videos daily. Statista, 28 August 2014.
  5. 5.
    Ceaparu, I., Lazar, J., Bessiere, K., Robinson, J., Shneiderman, B.: Determining causes and severity of end-user frustration. Int. J. Hum.-Comput. Interact. 17(3), 333–356 (2004)CrossRefGoogle Scholar
  6. 6.
    Dabrowski, J., Munson, E.V.: 40 years of searching for the best computer system response time. Interact. Comput. 23(5), 555–564 (2011)CrossRefGoogle Scholar
  7. 7.
    De Pessemier, T., De Moor, K., Joseph, W., De Marez, L., Martens, L.: Quantifying subjective quality evaluations for mobile video watching in a semi-living lab context. IEEE Trans. Broadcast. 58(4), 580–589 (2012)CrossRefGoogle Scholar
  8. 8.
    eMarketer. US adults spend 5.5 hours with video content each day, 16 April 2015.
  9. 9.
    Fredrickson, B.L., Kahneman, D.: Duration neglect in retrospective evaluations of affective episodes. J. Pers. Soc. Psychol. 65, 45–55 (1993)CrossRefGoogle Scholar
  10. 10.
    Harrison, C., Amento, B., Kuznetsov, S., Bell, R.: Rethinking the progress bar. In: Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology, pp. 115–118. ACM (2007)Google Scholar
  11. 11.
    Kishore, A.: Why quality of experience is the most critical metric for internet video profitability. Guardian (2013)Google Scholar
  12. 12.
    Krishnan, S.S., Sitaraman, R.K.: Video stream quality impacts viewer behavior: inferring causality using quasi-experimental designs. IEEE/ACM Trans. Netw. 21(6), 2001–2014 (2013)CrossRefGoogle Scholar
  13. 13.
    Lin, Y.C., Yeh, C.H., Wei, C.C.: How will the use of graphics affect visual aesthetics? A user-centered approach for web page design. Int. J. Hum. Comput. Stud. 71(3), 217–227 (2013)CrossRefGoogle Scholar
  14. 14.
    Myers, B.A.: The importance of percent-done progress indicators for computer-human interfaces. In: Proceedings of the 1985 SIGCHI Conference on Human Factors in Computing Systems, San Francisco, California, CHI 1985, pp. 11–17. ACM Press, New York (1985)Google Scholar
  15. 15.
    Nah, F.F.H.: A study on tolerable waiting time: how long are web users willing to wait? Behav. Inf. Technol. 23(3), 153–163 (2004)CrossRefGoogle Scholar
  16. 16.
    Parush, A., Shwarts, Y., Shtub, A., Chandra, M.J.: The impact of visual layout factors on performance in Web pages: a cross-language study. Hum. Factors: J. Hum. Factors Ergon. Soc. 47(1), 141–157 (2005)CrossRefGoogle Scholar
  17. 17.
    Pruyn, A., Smidts, A.: Effects of waiting on the satisfaction with the service: beyond objective time measures. Int. J. Res. Mark. 15(4), 321–334 (1998)CrossRefGoogle Scholar
  18. 18.
    Szameitat, A.J., Rummel, J., Szameitat, D.P., Sterr, A.: Behavioral and emotional consequences of brief delays in human–computer interaction. Int. J. Hum. Comput. Stud. 67(7), 561–570 (2009)CrossRefGoogle Scholar
  19. 19.
    Thomas, E.A., Weaver, W.B.: Cognitive processing and time perception. Percept. Psychophys. 17(4), 363–367 (1975)CrossRefGoogle Scholar
  20. 20.
    Thomaschke, R., Haering, C.: Predictivity of system delays shortens human response time. Int. J. Hum. Comput. Stud. 72(3), 358–365 (2014)CrossRefGoogle Scholar
  21. 21.
    Thompson, D.A., Yarnold, P.R., Williams, D.R., Adams, S.L.: Effects of actual waiting time, perceived waiting time, information delivery, and expressive quality on patient satisfaction in the emergency department. Ann. Emerg. Med. 28(6), 657–665 (1996)CrossRefGoogle Scholar
  22. 22.
    Thum, M., Boucsein, W., Kuhmann, W., Ray, W.J.: Standardized task strain and system response times in human-computer interaction. Ergonomics 38(7), 1342–1351 (1995)CrossRefGoogle Scholar
  23. 23.
    Tognazzini, B.: Principles, techniques, and ethics of stage magic and their application to human interface design. In: Proceedings of the INTERACT 1993 and CHI 1993 Conference on Human Factors in Computing Systems, pp. 355–362. ACM (1993)Google Scholar
  24. 24.
    Treisman, M.: Temporal discrimination and the indifference interval: implications for a model of the” internal clock”. Psychol. Monogr.: Gen. Appl. 77(13), 1 (1963)CrossRefGoogle Scholar
  25. 25.
    Wobbrock, J.O., Findlater, L., Gergle, D., Higgins, J.J.: The aligned rank transform for nonparametric factorial analyses using only anova procedures. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 143–146. ACM (2011)Google Scholar
  26. 26.
    Zakay, D.: Attention allocation policy influences prospective timing. Psychon. Bull. Rev. 5(1), 114–118 (1998)CrossRefGoogle Scholar
  27. 27.
    Zakay, D., Tsal, Y.: Awareness of attention allocation and time estimation accuracy. Bull. Psychon. Soc. 27(3), 209–210 (1989)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Human Factors and Ergonomics Laboratory, Department of Industrial and Systems EngineeringKorea Advanced Institute of Science and Technology (KAIST)DaejeonSouth Korea

Personalised recommendations