Industry exposure to artificial intelligence, board network heterogeneity, and firm idiosyncratic risk

Hudson, Kerry and Morgan, Robert E ORCID: https://orcid.org/0000-0001-8981-3144 2024. Industry exposure to artificial intelligence, board network heterogeneity, and firm idiosyncratic risk. Journal of Management Studies 10.1111/joms.13127

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Abstract

Despite the growing impact of artificial intelligence (AI) in business, there is little research examining its effects on firm idiosyncratic risk (IR). This is an important issue for boards: as key conduits of firm–environment information flows via board interlock networks, traditional risk oversight functions are being increasingly augmented with strategic decision-making and communications. Accordingly, we explore how AI and board interlocks independently and interactively affect IR, focusing on the heterogeneity of the board's network ties. We hypothesize these effects within signalling theory, positing that a firm's AI exposure and board network will differentially affect market perceptions of risk contingent on their perceived cost and relative signal strength under different environmental conditions. We find that while AI and board network heterogeneity both favourably affect risk, operating in a high-AI industry while occupying a network position that spans industry boundaries mitigates these effects, leading to an increase in IR for firms in the most technologically advanced industries. Additional analyses of diversification corroborate these theoretical mechanisms: as a costly signal of competence across multiple domains, diversification enables firms to simultaneously engage with AI and diverse knowledge networks without market penalties. Our findings offer practical insights for directors and avenues for theoretical development.

Item Type:	Article
Date Type:	Published Online
Status:	In Press
Schools:	Business (Including Economics)
Publisher:	Wiley
ISSN:	0022-2380
Date of First Compliant Deposit:	19 July 2024
Date of Acceptance:	26 June 2024
Last Modified:	25 Jul 2024 13:15
URI:	https://orca.cardiff.ac.uk/id/eprint/170683

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