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User Response Data: The Potential for Errors and Biases

Author

Listed:
  • Ellen M. Hufnagel

    (Department of Information Systems and Decision Sciences, The University of South Florida, 4202 E. Fowler Avenue—CIS 1040, Tampa, Florida 33620-7800)

  • Christopher Conca

    (Department of Information Systems and Decision Sciences, The University of South Florida, 4202 E. Fowler Avenue—CIS 1040, Tampa, Florida 33620-7800)

Abstract

Surveys that require users to evaluate or make judgments about information systems and their effect on specific work activities can produce misleading results if respondents do not interpret or answer questions in the ways intended by the researcher. This paper provides a framework for understanding both the cognitive activities and the errors and biases in judgment that can result when users are asked to categorize a system, explain its effects, or predict their own future actions and preferences with respect to use of a system. Specific suggestions are offered for wording survey questions and response categories so as to elicit more precise and reliable responses. In addition, possible sources of systematic bias are discussed, using examples drawn from published IS research. Recommendations are made for further research aimed at better understanding how and to what extent judgment biases could affect the results of IS surveys.

Suggested Citation

  • Ellen M. Hufnagel & Christopher Conca, 1994. "User Response Data: The Potential for Errors and Biases," Information Systems Research, INFORMS, vol. 5(1), pages 48-73, March.
    • Handle: RePEc:inm:orisre:v:5:y:1994:i:1:p:48-73
    DOI: 10.1287/isre.5.1.48
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    Citations

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    Cited by:

    1. Lei Zhu & Izak Benbasat & Zhenhui Jiang, 2010. "Let's Shop Online Together: An Empirical Investigation of Collaborative Online Shopping Support," Information Systems Research, INFORMS, vol. 21(4), pages 872-891, December.
    2. Naresh K. Malhotra & Sung S. Kim & Ashutosh Patil, 2006. "Common Method Variance in IS Research: A Comparison of Alternative Approaches and a Reanalysis of Past Research," Management Science, INFORMS, vol. 52(12), pages 1865-1883, December.
    3. Liao, Kun & Deng, Xiaodong & Marsillac, Erika, 2013. "Factors that influence Chinese automotive suppliers’ mass customization capabilities," International Journal of Production Economics, Elsevier, vol. 146(1), pages 25-36.
    4. Pluyter, J.R., 2012. "Designing immersive surgical training against information technology-related overload in the operating room," Other publications TiSEM d48c5727-92fd-41b1-be5d-5, Tilburg University, School of Economics and Management.
    5. Shiyong Wu & Wenxin Chen & Wei Chen & Wen Zheng, 2022. "Effects of Cultural Intelligence and Imposter Syndrome on School Belonging through Academic Resilience among University Students with Vocational Backgrounds," IJERPH, MDPI, vol. 19(13), pages 1-16, June.
    6. Zied Mani & Inès Chouk, 2019. "Impact of privacy concerns on resistance to smart services: does the ‘Big Brother effect’ matter?," Post-Print hal-03217977, HAL.
    7. J. Vaníček, 2004. "Data gathering for science and research," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 50(1), pages 29-34.
    8. Teo, T. S. H. & King, W. R., 1997. "An assessment of perceptual differences between informants in information systems research," Omega, Elsevier, vol. 25(5), pages 557-566, October.
    9. Jahyun Goo, 2010. "Structure of service level agreements (SLA) in IT outsourcing: The construct and its measurement," Information Systems Frontiers, Springer, vol. 12(2), pages 185-205, April.
    10. France Belanger & Rosann Webb Collins & Paul H. Cheney, 2001. "Technology Requirements and Work Group Communication for Telecommuters," Information Systems Research, INFORMS, vol. 12(2), pages 155-176, June.

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