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Assessing User Competence: Conceptualization and Measurement

Author

Listed:
  • Barbara L. Marcolin

    (Faculty of Management, The University of Calgary, 2500 University Drive NW, Calgary, Alberta, Canada T2N 1N4)

  • Deborah R. Compeau

    (Faculty of Management, The University of Calgary, 2500 University Drive NW, Calgary, Alberta, Canada T2N 1N4)

  • Malcolm C. Munro

    (Faculty of Management, The University of Calgary, 2500 University Drive NW, Calgary, Alberta, Canada T2N 1N4)

  • Sid L. Huff

    (Faculty of Commerce and Administration, Victoria University of Wellington, Wellington, New Zealand)

Abstract

Organizations today face great pressure to maximize the bene its from their investments in information technology (IT). They are challenged not just to use IT, but to use it as effectively as possible. Understanding how to assess the competence of users is critical in maximizing the effectiveness of IT use. Yet the user competence construct is largely absent from prominent technology acceptance and it models, poorly conceptualized, and inconsistently measured. We begin by presenting a conceptual model of the assessment of user competence to organize and clarify the diverse literature regarding what user competence means and the problems of assessment. As an illustrative study, we then report the findings from an experiment involving 66 participants. The experiment was conducted to compare empirically two methods (paper and pencil tests versus self-report questionnaire), across two different types of software, or domains of knowledge (word processing versus spreadsheet packages), and two different conceptualizations of competence (software knowledge versus self-efficacy). The analysis shows statistical significance in all three main effects. How user competence is measured, what is measured, what measurement context is employed:all influence the measurement outcome. Furthermore, significant interaction effects indicate that different combinations of measurement methods, conceptualization, and knowledge domains produce different results. The concept of frame of reference, and its anchoring effect on subjects' responses, explains a number of these findings. The study demonstrates the need for clarity in both defining what type of competence is being assessed and in drawing conclusions regarding competence, based upon the types of measures used. Since the results suggest that definition and measurement of the user competence construct can change the ability score being captured, the existing information system (IS) models of usage must contain the concept of an ability rating. We conclude by discussing how user competence can be incorporated into the Task-Technology Fit model, as well as additional theoretical and practical implications of our research.

Suggested Citation

  • Barbara L. Marcolin & Deborah R. Compeau & Malcolm C. Munro & Sid L. Huff, 2000. "Assessing User Competence: Conceptualization and Measurement," Information Systems Research, INFORMS, vol. 11(1), pages 37-60, March.
    • Handle: RePEc:inm:orisre:v:11:y:2000:i:1:p:37-60
    DOI: 10.1287/isre.11.1.37.11782
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    References listed on IDEAS

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    Cited by:

    1. Andrew Burton-Jones & Camille Grange, 2013. "From Use to Effective Use: A Representation Theory Perspective," Information Systems Research, INFORMS, vol. 24(3), pages 632-658, September.
    2. Radhika Santhanam & De Liu & Wei-Cheng Milton Shen, 2016. "Research Note—Gamification of Technology-Mediated Training: Not All Competitions Are the Same," Information Systems Research, INFORMS, vol. 27(2), pages 453-465, June.
    3. Andrew Burton-Jones & Olga Volkoff, 2017. "How Can We Develop Contextualized Theories of Effective Use? A Demonstration in the Context of Community-Care Electronic Health Records," Information Systems Research, INFORMS, vol. 28(3), pages 468-489, September.
    4. Henri Barki & Ryad Titah & Céline Boffo, 2007. "Information System Use--Related Activity: An Expanded Behavioral Conceptualization of Individual-Level Information System Use," Information Systems Research, INFORMS, vol. 18(2), pages 173-192, June.
    5. Zabih-Allah Torabi & Mehdi Pourtaheri & Colin Michael Hall & Ayyoob Sharifi & Fazlollah Javidi, 2023. "Smart Tourism Technologies, Revisit Intention, and Word-of-Mouth in Emerging and Smart Rural Destinations," Sustainability, MDPI, vol. 15(14), pages 1-21, July.
    6. Weiling Ke & Lele Kang & Chuan-Hoo Tan & Chih-Hung Peng, 2021. "User Competence with Enterprise Systems: The Effects of Work Environment Factors," Information Systems Research, INFORMS, vol. 32(3), pages 860-875, September.
    7. Michael A. Erskine & Dawn G. Gregg & Jahangir Karimi & Judy E. Scott, 2019. "Individual Decision-Performance Using Spatial Decision Support Systems: A Geospatial Reasoning Ability and Perceived Task-Technology Fit Perspective," Information Systems Frontiers, Springer, vol. 21(6), pages 1369-1384, December.
    8. J. J. Po-An Hsieh & Arun Rai & Mark Keil, 2011. "Addressing Digital Inequality for the Socioeconomically Disadvantaged Through Government Initiatives: Forms of Capital That Affect ICT Utilization," Information Systems Research, INFORMS, vol. 22(2), pages 233-253, June.
    9. Crittenden, Victoria L. & Crittenden, William F. & Ajjan, Haya, 2019. "Empowering women micro-entrepreneurs in emerging economies: The role of information communications technology," Journal of Business Research, Elsevier, vol. 98(C), pages 191-203.
    10. Christoph Weinert & Christian Maier & Sven Laumer & Tim Weitzel, 2020. "Technostress mitigation: an experimental study of social support during a computer freeze," Journal of Business Economics, Springer, vol. 90(8), pages 1199-1249, September.
    11. Robert E. Crossler & France Bélanger, 2019. "Why Would I Use Location-Protective Settings on My Smartphone? Motivating Protective Behaviors and the Existence of the Privacy Knowledge–Belief Gap," Information Systems Research, INFORMS, vol. 30(3), pages 995-1006, September.
    12. Mun Y. Yi & Fred D. Davis, 2003. "Developing and Validating an Observational Learning Model of Computer Software Training and Skill Acquisition," Information Systems Research, INFORMS, vol. 14(2), pages 146-169, June.
    13. 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.
    14. Yoon, Jeewhan & Vonortas, Nicholas S. & Han, SungWon, 2020. "Do-It-Yourself laboratories and attitude toward use: The effects of self-efficacy and the perception of security and privacy," Technological Forecasting and Social Change, Elsevier, vol. 159(C).

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