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The study emphasizes the importance of impactful journals and the need for interdisciplinary research, particularly for addressing AI’s ethical and regulatory challenges. It demonstrates that machine learning can identify research topics, while ChatGPT supports topic labelling, content generation, and more efficient academic writing. By combining methods, the research uncovers thematic structures, reveals gaps, highlights emerging areas, and proposes a methodology for selecting influential papers to bridge computational and traditional reviews.","A literature review of artificial intelligence research in business and management using machine learning and ChatGPT  \nAuthor:  \nGuler, N ; Kirshner, SN; Vidgen , R  \nPublication details:  \nData and Information Management v. 8  \nChapter No. 3 2543-9251 (ISSN)  \nPublication Date:  \n2024-09-01  \nPublisher DOI:  \n[https://doi.org/10.1016/j.dim.2024.100076](https://doi.org/10.1016/j.dim.2024.100076)  \nLicense:  \n[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nLink to license to see what you are allowed to do with this resource.  \nDownloaded from [http://hdl.handle. net/1959.4/103460](http://hdl.handle. net/1959.4/103460) in [https://](https://)[ ](https://)[unsworks. unsw.edu.au](unsworks. unsw.edu.au) on 2025-04-18  \n8 (2024) 100076  \nContents lists available at ScienceDirect  \nData and Information Management  \njournal [homepage: www.journals.elsevier.com/data-and-information-management](homepage: www.journals.elsevier.com/data-and-information-management)  \n| A literature review of artificial intelligence research in business and management using machine learning and ChatGPT\u003Cbr>*\u003Cbr>Nazmiye Guler, Samuel N. Kirshner\u003Cbr>University of New South Wales Business School, Sydney, 2052, Australia |  |  |  |\n| --- | --- | --- | --- |\n| Richard Vidgen\u003Cbr>University of New South Wales Business School, Sydney, 2052, Australia University of Hull, School of Management, Hull, HU6 7RX, UK |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Artificial intelligence\u003Cbr>Machine learning\u003Cbr>Topic modelling Computational literature reviews ChatGPT |  | This paper investigates applying AI models and topic modelling techniques to enhance computational literature reviews in business, management, and information systems. The study highlights the significance of impactful journals and emphasises the need for interdisciplinary and transdisciplinary research, especially in addressing AI’s ethical and regulatory challenges. We demonstrate the effectiveness of combining machine learning and ChatGPT in the literature review process. Machine learning is used to identify research topics, and ChatGPT assists researchers in labelling the topics, generating content, and improving the efficiency of academic writing. By leveraging topic modelling techniques and ChatGPT, we uncover and label topics within the literature, shedding light on the thematic structure and content of the research field, allowing researchers to uncover meaningful insights, identify research gaps, and highlight rapidly expanding research areas. Additionally, we contribute to the literature review process by introducing a methodology that identifies impactful papers, helping to bridge the gap between computational literature reviews and traditional literature reviews. |  |\n\n1. Introduction  \nArtificial Intelligence (AI) has emerged as a disruptive force, transforming industries, reshaping our lives and work, and significantly impacting businesses, institutions, and societies. Driven by advancements in machine learning and deep learning, as well as the exponential growth of available data, AI technologies have enabled machines to handle complex tasks such as natural language processing, image recognition, and decision-making (Brynjolfsson & Mcafee, 2014; Jordan & Mitchell, 2015; Shollo et al., 2022).  \nAI’s rapid evolution has led to its widespread adoption, with applications ranging from automating mundane tasks to enhancing productivity and enabling new forms of human-computer collaboration across various sectors, including healthcare, finance, manufacturing, and education (Agrawal et al., 2019; Autor, 2015, pp. 237–260; Chui et al., 2018; Dwivedi et al., 2021). The technology offers significant potential for businesses and society by improving efficiency, reducing costs, and  \nproviding better decision-making capabilities (Dwivedi et al., 2023; Ransbotham et al., 2017). Combined with big data, AI has the potential to surpas","cbCaisP7bhd0T7sb","https://ap.wps.com/l/cbCaisP7bhd0T7sb","pdf",49945384,1,26,"English","en",105,"# Introduction\n## Background on AI in business and management\n## Capabilities of machine learning and large language models\n## Opportunities and risks for organizations and society","[{\"question\":\"How does the paper use machine learning in the literature review process?\",\"answer\":\"Machine learning is used to identify and extract research topics from the literature. 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