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Artificial intelligence-driven decision making and firm performance: a quantitative approach

Chiara Giachino, Martin Cepel, Elisa Truant, Augusto Bargoni

Management Decision

ISSN: 0025-1747

Article publication date: 10 June 2024

1242

Abstract

Purpose

The purpose of this study is to investigate the relationship between artificial intelligence (AI) and decision making in the development of AI-related capabilities. We investigate if and how AI-driven decision making has an impact on firm performance. We also investigate the role played by environmental dynamism in the development of AI capabilities and AI-driven decision making.

Design/methodology/approach

We surveyed 346 managers in the United States using established scales from the literature and leveraged p modelling to analyse the data.

Findings

Results indicate that AI-driven decision making is positively related to firm performance and that big data-powered AI positively influences AI-driven decision making. Moreover, there is a positive relationship between big data-powered AI and the development of AI capability within a firm. It is also found that the control variables of firm size and age do not significantly affect firm performance. Finally, environmental dynamism does not have a positive and significant moderating effect on the path connecting big data-powered AI and AI-driven decision making, while it exerts a positive moderating effect on the development of AI capability to strengthen AI-driven decision making.

Originality/value

These findings extend the resource-based view by highlighting the capabilities developed within the firm to manage big data-powered AI. This research also provides theoretically grounded guidance to managers wanting to align their AI-driven decision making with superior firm performance.

Keywords

Citation

Giachino, C., Cepel, M., Truant, E. and Bargoni, A. (2024), "Artificial intelligence-driven decision making and firm performance: a quantitative approach", Management Decision, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/MD-10-2023-1966

Publisher

:

Emerald Publishing Limited

Copyright © 2024, Emerald Publishing Limited

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