Title: A bibliometric review of the generative artificial intelligence research landscape in marketing

Authors: Abhinna Baxi Bhatnagar; Akash Kumar Srivastava; Priyanka Kataria; Susheel Shukla; Jaspal Singh

Addresses: IIMT College of Management, IIMT Group of Colleges, Greater Noida, Uttar Pradesh 201310, India ' IIMT College of Management, IIMT Group of Colleges, Greater Noida, Uttar Pradesh 201310, India ' IIMT College of Management, IIMT Group of Colleges, Greater Noida, Uttar Pradesh 201310, India ' IIMT College of Management, IIMT Group of Colleges, Greater Noida, Uttar Pradesh 201310, India ' IIMT College of Management, IIMT Group of Colleges, Greater Noida, Uttar Pradesh 201310, India

Abstract: This investigation aims to conduct a bibliometric examination to synthesise the publication trends of generative artificial intelligence (GenAI) in marketing. Consequently, we add to the literature by scrutinising the historical, current, and prospective research on GenAI in marketing. The data for this study is derived from the Scopus database, and 257 documents were selected for bibliometric analysis after implementing inclusion and exclusion criteria. Citations analysis is employed to identify the most influential publications and contributors. Productivity analysis is utilised to ascertain the most prolific authors and sources. Bibliometric analysis unveils the four thematic clusters of research on GenAI are marketing orientation, service innovation, customer relationship management, and generative language models. Co-citation analysis is conducted to discern citation patterns and identify highly referenced documents. Additionally, the thematic trends of GenAI in marketing are conversational AI, and AI-human collaboration, large language models, pre-trained language models, generative adversarial networks.

Keywords: generative artificial intelligence; GenAI; generative language models; bibliometric analysis; bibliographic coupling.

DOI: 10.1504/IJTIP.2024.140634

International Journal of Technology Intelligence and Planning, 2024 Vol.13 No.3, pp.287 - 307

Received: 03 Nov 2023
Accepted: 15 Jul 2024
Published online: 28 Aug 2024 *

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