"Managerial overreliance on AI-augmented decision-making processes: How the use of AI-based advisory systems shapes choice behavior in R&D investment decisions"
by Keding, Christoph; Meissner, Philip (2021)
AI-augmented decision-making processes promise to transform strategic decisions around innovation management. However, despite a growing body of research on algorithmic management, very little is known about the behavioral effects of the AI-augmented decision-making process. This article utilizes a psychological perspective to research the interaction of artificial intelligence and human judgment, suggesting that AI-based advice affects human decision-making behavior and skews perceptions of decision outcomes. We present a vignette-based decision experiment involving 150 senior executives to examine the perception of AI-augmented decision-making at the individual level. In contrast to earlier research on algorithm aversion, we find that employing AI-based advisory systems positively affects choice behavior and amplifies decision quality perception. We further show how this overreliance on an AI-augmented decision-making process can be explained through both a higher degree of trust in the advisor and the attribution of a more structured process. This paper contributes to the emerging discussion as to the role of AI in management and the novel phenomenon of algorithm appreciation by investigating the interplay of human and artificial intelligence in strategic decision-making to show that AI-based advice is perceived as more trustworthy than human advice in an R&D investment context.
KeywordsAi-Augmented Decision-Making, Algorithmic Decision-Making, Strategy, Algorithm Appreciation, Overreliance
ThemesAI and Management
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