AI Literacy for the top management: An upper echelons perspective on corporate AI orientation and implementation ability

Marc Pinski (), Thomas Hofmann () and Alexander Benlian ()
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Marc Pinski: Technical University of Darmstadt
Thomas Hofmann: Technical University of Darmstadt
Alexander Benlian: Technical University of Darmstadt

Electronic Markets, 2024, vol. 34, issue 1, No 24, 23 pages

Abstract: Abstract We draw on upper echelons theory to examine whether the AI literacy of a firm’s top management team (i.e., TMT AI literacy) has an effect on two firm characteristics paramount for value generation with AI—a firm’s AI orientation, enabling it to identify AI value potentials, and a firm’s AI implementation ability, empowering it to realize these value potentials. Building on the notion that TMT effects are contingent upon firm contexts, we consider the moderating influence of a firm’s type (i.e., startups vs. incumbents). To investigate these relationships, we leverage observational literacy data of 6986 executives from a professional social network (LinkedIn.com) and firm data from 10-K statements. Our findings indicate that TMT AI literacy positively affects AI orientation as well as AI implementation ability and that AI orientation mediates the effect of TMT AI literacy on AI implementation ability. Further, we show that the effect of TMT AI literacy on AI implementation ability is stronger in startups than in incumbent firms. We contribute to upper echelons literature by introducing AI literacy as a skill-oriented perspective on TMTs, which complements prior role-oriented TMT research, and by detailing AI literacy’s role for the upper echelons-based mechanism that explains value generation with AI.

Keywords: AI orientation; AI implementation; AI literacy; Attention-based view; Upper echelons theory (search for similar items in EconPapers)
JEL-codes: L22 M15 O30 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s12525-024-00707-1

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