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Incentives, Framing, and Reliance on Algorithmic Advice: An Experimental Study

Ben Greiner¹; Philipp Grünwald²; THOMAS LINDNER³; Georg Lintner²; Martin Wiernsperger⁴

¹ Institute for Markets and Strategy, Wirtschaftsuniversität Wien, 1020 Vienna, Austria; and School of Economics, University of New South Wales, Sydney, New South Wales 2052, Australia · ² Wirtschaftsuniversität Wien, 1020 Vienna, Austria · ³ Institute for International Business, Wirtschaftsuniversität Wien, 1020 Vienna, Austria; and Institute for Management and Marketing, University of Innsbruck, 6020 Innsbruck, Austria; and Department of International Economics, Government and Business, Copenhagen Business School, 2000 Frederiksberg, D · ⁴ Samuel Curtis Johnson Graduate School of Management, Cornell University, Ithaca, New York 14853;

Managerial decision makers are increasingly supported by advanced data analytics and other artificial intelligence (AI)-based technologies, but they are often found to be hesitant to follow the algorithmic advice. We examine how compensation contract design and framing of an AI algorithm influence decision makers’ reliance on algorithmic advice and performance in a price estimation task. Based on a large sample of almost 1,500 participants, we find that compared with a fixed compensation, both compensation contracts based on individual performance and tournament contracts lead to an increase in effort duration and to more reliance on algorithmic advice. We further find that using an AI algorithm that is framed as also incorporating human expertise has positive effects on advice utilization, especially for decision makers with fixed pay contracts. By showing how widely used control practices, such as incentives and task framing, influence the interaction of human decision makers with AI algorithms, our findings have direct implications for managerial practice. This paper was accepted by David Simchi-Levi, Special Issue on the Human-Algorithm Connection. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.02777 .

DOI

10.1287/mnsc.2022.02777

Volume

72 (1)

Pages

302-322

Language

en

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