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This research provides a detailed analysis of publications related to the Chat GPT topic using data from the Scopus database. The main purpose of this research is to find research gaps within ChatGPT research, especially in business. This research content is divided into four key areas: publication and citation structure, past research topics, author co-citation analysis, and keyword co-occurrence analysis. The research finds that Chat GPT is actively explored in medicine, social science, computer science, and engineering. Most past research is not directly related to business, but it can support business continuity. The research notes a balanced contribution from authors and identifies "Artificial Intelligence" as a leading keyword. Countries like the United States, China, and India are major contributors. Using Latent Dirichlet Allocation (LDA), the study categorizes previous work into five areas: remote learning, AI-based customer support, Learning Management Systems, academic research roles, and AI data handling. The co-citation analysis shows that authors, specifically Wu J. and Radford A., have significantly impacted the Chat GPT publication. For keyword co-occurrence, "Artificial Intelligence" and "ChatGPT" are keywords that have a strong link. The keywords cluster are divided into technology (red) and human-centric (green) topics.
Kurnianingrum et al. (Fri,) studied this question.