Literature review in the generative AI era: How to make a compelling contribution

• Main Authors: PAN, Shan L., NISHANT, Rohit, TUUNANEN, Tuure, NAH, Fiona Fui-hoon
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Artificial Intelligence and Robotics; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/9525; https://ink.library.smu.edu.sg/context/sis_research/article/10525/viewcontent/1_s2.0_S0963868723000343_pv.pdf
• Institution: Singapore Management University

As we write this editorial for this special issue, we are amidst the significant technological changes that are continuing to shape society. Since the emergence of ChatGPT in November 2022, humanity has become aware of the potential of generative AI (i.e., AI that can generate content) and large language models (LLMs) (i.e., AI models trained on a massive corpus of unstructured data). There is growing debate and discussion about the promise and perils of generative AI for the future of work, and academia is not immune. Premier journals in the IS domain, such as Information Systems Research, have published editorials on what the emergence of generative AI means for IS research (see Susarla et al., 2023). Other journals have also published editorials on the role of generative AI – whether it is an assistant or a co-author/collaborator (e.g., Offiah and Khanna, 2023, Nah et al., 2023). These editorials have discussed various AI capabilities and limitations. However, they also assert that human researchers must fact-check the interpretation of the LLMs because they are prone to hallucinations and may be trained on irrelevant data, resulting in inaccurate inferences. In this editorial, we will explore what the emergence of generative AI and LLMs means for literature reviews, in general, and literature reviews in the IS domain, in particular.