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Substantive use of artificial intelligence: The role of individual differences

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  • Klesel, Michael
  • Messer, Uwe

Abstract

Artificial intelligence (AI) is becoming increasingly powerful, enabling users to perform tasks more efficiently and effectively. However, not all users are equally able to take advantage of its capabilities. We draw on previous literature that has introduced the concept of "substantive use" - the reflective consideration of how to use a system's features - to better understand individual differences in the context of AI. We contribute to the current literature in three ways: First, we summarize the literature on technology use and describe its relevance for AI-related research. Second, we review the literature and show that IS has already begun to investigate individual differences to understand the use of AI systems. Third, we propose a theoretical model t hat accounts for the direct and configurational effects of individual differences on substantive use behavior.

Suggested Citation

  • Klesel, Michael & Messer, Uwe, 2024. "Substantive use of artificial intelligence: The role of individual differences," Working Paper Series 32, Frankfurt University of Applied Sciences, Faculty of Business and Law.
    • Handle: RePEc:zbw:fhfwps:306858
    DOI: 10.48718/8d9d-b049
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    1. Jussupow, Ekaterina & Spohrer, Kai & Heinzl, Armin & Gawlitza, Joshua, 2021. "Augmenting Medical Diagnosis Decisions? An Investigation into Physicians’ Decision-Making Process with Artificial Intelligence," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 137446, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    2. Pascal Hamm & Michael Klesel & Patricia Coberger & H. Felix Wittmann, 2023. "Explanation matters: An experimental study on explainable AI," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-21, December.
    3. Andrew Burton-Jones & Detmar W. Straub, 2006. "Reconceptualizing System Usage: An Approach and Empirical Test," Information Systems Research, INFORMS, vol. 17(3), pages 228-246, September.
    4. Ekaterina Jussupow & Kai Spohrer & Armin Heinzl & Joshua Gawlitza, 2021. "Augmenting Medical Diagnosis Decisions? An Investigation into Physicians’ Decision-Making Process with Artificial Intelligence," Information Systems Research, INFORMS, vol. 32(3), pages 713-735, September.
    5. Omar A. El Sawy & Arvind Malhotra & YoungKi Park & Paul A. Pavlou, 2010. "Research Commentary ---Seeking the Configurations of Digital Ecodynamics: It Takes Three to Tango," Information Systems Research, INFORMS, vol. 21(4), pages 835-848, December.
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