A solution looking for problems? A systematic literature review of the rationalizing influence of artificial intelligence on decision-making in innovation management

Maria Cristina Pietronudo, Gregoire Croidieu (gregcroidieu@yahoo.com) and Francesco Schiavone
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Maria Cristina Pietronudo: PARTHENOPE - Università degli Studi di Napoli “Parthenope” = University of Naples
Gregoire Croidieu: EM - EMLyon Business School
Francesco Schiavone: PARTHENOPE - Università degli Studi di Napoli “Parthenope” = University of Naples, PSB - Paris School of Business - HESAM - HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université

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Abstract: Given innovation's chaotic nature, organizations struggle to make decisions when managing innovation. Both academics and practitioners hope artificial intelligence can solve this problem and provide a solution to support and rationalize innovation processes. The literature on this topic, however, is fragmented. The goal of this paper is to systematically review the literature to guide future research. We build on the garbage can model, as our findings reveal that the rationalizing influences of AI on innovation management as a decision-making process is varied. Our results reveal four main influences that pave the way for future research: AI augmenting rationality, AI augmenting creativity, AI renewing the organizing of innovation, and AI triggering new challenges. Taken together, these findings suggest AI is not a tool that uniformly optimizes innovation management and decision-making but rather, is best understood as a multifaceted solution, with intended and unintended rationalizing influences, in search of problems to solve.

Keywords: Artificial intelligence; Decision making; Innovation; Innovation management; Garbage can model (search for similar items in EconPapers)
Date: 2022-09-01
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Citations: View citations in EconPapers (5)

Published in Technological Forecasting and Social Change, 2022, 182, 19 p. ⟨10.1016/j.techfore.2022.121828⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04325569

DOI: 10.1016/j.techfore.2022.121828

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