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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F04130081%3A_____%2F24%3AN0000021" target="_blank" >RIV/04130081:_____/24:N0000021 - isvavai.cz</a>

Result on the web

<a href="https://www.emerald.com/insight/content/doi/10.1108/md-10-2023-1966/full/html" target="_blank" >https://www.emerald.com/insight/content/doi/10.1108/md-10-2023-1966/full/html</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1108/MD-10-2023-1966" target="_blank" >10.1108/MD-10-2023-1966</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

50204 - Business and management

Project

—

Continuities

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

MANAGEMENT DECISION

ISSN

0025-1747

e-ISSN

1758-6070

Volume of the periodical

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Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

23

Pages from-to

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UT code for WoS article

001240267500001

EID of the result in the Scopus database

2-s2.0-85195277913