Artificial intelligence and machine learning in corporate governance: A bibliometric analysis

Article type: Research Article

Authors: Samara, Husni Hasan^{a; *} | Qudah, Hanan Ahmad^{b; 1} | Mohsin, Hayder Jerri^{c; 2} | Abualhijad, Seba^{d; 3} | Bani Hani, Laith Yousef^{e; 4} | Al Rahamneh, Samer^f | AlQudah, Mohammad Zakaria^{g; 5}

Affiliations: [a] Department of Accounting, Faculty of Economics, University of Valencia, Valencia, Spain | [b] Department of Financial and Banking Sciences, Amman University College, Al-Balqa Applied University, Amman, Jordan | [c] College of Administrative Technology, Southern Technical University, Basra, Iraq | [d] Department of Business Information Technology, Faculty of Business Administration, Princess Sumaya University for Technology, Amman, Jordan | [e] Department of Commerce and Management Studies, Andhra University, India | [f] Department of Finance and Accounting, University of Extremadura, Badajoz, Spain | [g] Department of Accounting and Finance, University of Valencia, Valencia, Spain

Correspondence: [*] Corresponding author: Husni Hasan Samara, Department of Accounting, Faculty of Economics, University of Valencia, Valencia, Spain. E-mail: [email protected]. ORCID: 0009-0002-8807-8349.

Note: [1] ORCID: 0000-0002-4611-4244.

Note: [2] ORCID: 0009-0007-6743-9826.

Note: [3] ORCID: 0000-0004-6652-1041.

Note: [4] ORCID: 0000-0002-4233-1516.

Note: [5] ORCID: 0000-0002-2781-1028.

Abstract: BACKGROUND:The study deeply explores the thriving domains of artificial intelligence (AI) and machine learning (ML) in corporate governance. OBJECTIVE:The study aims to thoroughly examine the rapidly developing fields of artificial intelligence (AI) and machine learning (ML) in corporate governance. METHODS:After completing an in-depth analysis of 229 research studies published between 2008 and 2023 (using software tools such as RStudio, VOSviewer, and Excel),), the study reveals a notable increase in publications since 2022. Corporate social responsibility (CSR), environmental, social, and governance (ESG) issues, executive remuneration, and sustainability are all considered as important key focal areas of focus. Scholars in this field are notably at the forefront from Taiwan, the United States, and China. IMPLICATIONS:However, the study stress the necessity for further researches to estimate the efficacy of different AI and ML methodologies. This may guide evidence-based governance practices various industries and geographical areas.

Keywords: AI, ML Corporate Governance, bibliometric analysis, trends, research gaps

DOI: 10.3233/HSM-240114

Journal: Human Systems Management, vol. Pre-press, no. Pre-press, pp. 1-27, 2024