Title: Text-based insights on generative AI applications in economics and business

Authors: Morteza Alaeddini; Alireza Asgari; Shahab Ahmadi

Addresses: ICN Business School, Université de Lorraine, CEREFIGE, F-54000 Nancy, France ' Clermont School of Business, 63000 Clermont-Ferrand, France; Université Grenoble Alpes, Grenoble-INP, CERAG, 38400 Saint-Martin-d'Hères, France ' Université Grenoble Alpes, Grenoble-INP, CERAG, 38400 Saint-Martin-d'Hères, France

Abstract: This study provides a thorough bibliometric text-based analysis of generative artificial intelligence (GenAI) research in economics and business. Based on an analysis of 1,613 peer-reviewed articles from Scopus and Web of Science published between 2021 and 2025, the study uses co-occurrence networks, topic modelling and burst analysis to map the intellectual structure of GenAI literature. The key findings reveal GenAI to be a rapidly evolving and increasingly interdisciplinary field, with research hotspots in AI ethics, sentiment analysis, digital transformation, and higher education. Emerging trends include AI-assisted writing, consumer behaviour and strategic management applications. The study highlights the growing integration of GenAI in business functions and educational contexts, while also identifying ethical and methodological challenges. This work offers valuable insights for scholars, educators and practitioners seeking to understand the dynamic landscape and future directions of GenAI in business and economics.

Keywords: generative artificial intelligence; GenAI; large language model; LLM; bibliometric analysis; thematic analysis; network analysis; research trends; interdisciplinary; management; economics; finance.

DOI: 10.1504/IJGAIB.2026.151805

International Journal of Generative Artificial Intelligence in Business, 2026 Vol.1 No.1/2, pp.175 - 198

Received: 23 Jul 2025
Accepted: 03 Aug 2025
Published online: 20 Feb 2026 *