Chiara Giachino, Martin Cepel, Elisa Truant, Augusto Bargoni
{"title":"Artificial intelligence-driven decision making and firm performance: a quantitative approach","authors":"Chiara Giachino, Martin Cepel, Elisa Truant, Augusto Bargoni","doi":"10.1108/md-10-2023-1966","DOIUrl":null,"url":null,"abstract":"PurposeThe 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/approachWe surveyed 346 managers in the United States using established scales from the literature and leveraged p modelling to analyse the data.FindingsResults 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/valueThese 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.","PeriodicalId":4,"journal":{"name":"ACS Applied Energy Materials","volume":"117 36","pages":""},"PeriodicalIF":5.4000,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Energy Materials","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1108/md-10-2023-1966","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
引用次数: 0
Abstract
PurposeThe 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/approachWe surveyed 346 managers in the United States using established scales from the literature and leveraged p modelling to analyse the data.FindingsResults 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/valueThese 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.
期刊介绍:
ACS Applied Energy Materials is an interdisciplinary journal publishing original research covering all aspects of materials, engineering, chemistry, physics and biology relevant to energy conversion and storage. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important energy applications.