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Biases in Retrospective Self-Reports of Time Use: An Empirical Study of Computer Users

Author

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  • Fred Collopy

    (The Weatherhead School of Management, Case Western Reserve University, Cleveland, Ohio 44106-7235)

Abstract

Research on information systems (IS) use has often relied upon retrospective self-reports. One example is when the amount of time spent using a system is reported, often as an indication of user acceptance. In this study, self-assessments of computer usage are compared with computer-monitored interactive use and connect time for 401 managers and professionals. When self-assessed use was compared with logged interactive use, there was a 32% difference in the average amount of use (3.9 vs. 2.7 hours/day). When the self-assessment was compared with total connect time, the averages were similar to each other (3.9 vs. 4.0 hours). In both comparisons, though, there were considerable differences between individual self-assessments and logged time (with a median absolute percentage difference of 47% when compared with logged connect time). Individual estimates of use tended to regress toward the population's mean use. Those whose use of the system was relatively light overestimated their use, while heavy users underestimated theirs. When a test of the relationship between user satisfaction and amount of use was conducted using self-assessments, the relationship was not statistically significant. When the same test was conducted using logged connect time, the relationship was significant. These results suggest that care should be exercised in operationalizing the user acceptance construct through self-reports of time use.

Suggested Citation

  • Fred Collopy, 1996. "Biases in Retrospective Self-Reports of Time Use: An Empirical Study of Computer Users," Management Science, INFORMS, vol. 42(5), pages 758-767, May.
    • Handle: RePEc:inm:ormnsc:v:42:y:1996:i:5:p:758-767
    DOI: 10.1287/mnsc.42.5.758
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    Cited by:

    1. Hunter, Gary K., 2019. "On conceptualizing, measuring, and managing augmented technology use in business-to-business sales contexts," Journal of Business Research, Elsevier, vol. 105(C), pages 201-213.
    2. Morteza Ghobakhloo & Sai Hong Tang, 2015. "Information system success among manufacturing SMEs: case of developing countries," Information Technology for Development, Taylor & Francis Journals, vol. 21(4), pages 573-600, October.
    3. Lee, Yong-Ki & Lee, Choong-Ki & Lee, Seung-Kon & Babin, Barry J., 2008. "Festivalscapes and patrons' emotions, satisfaction, and loyalty," Journal of Business Research, Elsevier, vol. 61(1), pages 56-64, January.
    4. Fabian von Schèele & Darek M. Haftor, 2014. "Cognitive Time Distortion on the Performance of Economic Organizations," Systems Research and Behavioral Science, Wiley Blackwell, vol. 31(1), pages 77-93, January.
    5. Amalia R. Miller & Kamalini Ramdas & Alp Sungu, 2023. "Browsers Don’t Lie? Gender Differences in the Effects of the Indian COVID-19 Lockdown on Digital Activity and Time Use," NBER Working Papers 31919, National Bureau of Economic Research, Inc.
    6. Grappi, Silvia & Montanari, Fabrizio, 2011. "The role of social identification and hedonism in affecting tourist re-patronizing behaviours: The case of an Italian festival," Tourism Management, Elsevier, vol. 32(5), pages 1128-1140.
    7. von Schéele, Fabian & Haftor, Darek M., 2018. "Temporal workload in economic organizations: A hidden non-linear condition of economic efficiency," Journal of Business Research, Elsevier, vol. 88(C), pages 415-420.
    8. Michael Ahearne & Eli Jones & Adam Rapp & John Mathieu, 2008. "High Touch Through High Tech: The Impact of Salesperson Technology Usage on Sales Performance via Mediating Mechanisms," Management Science, INFORMS, vol. 54(4), pages 671-685, April.

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