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

Abhinna Baxi Bhatnagar et al.

International Journal of Technology Intelligence and Planning2024https://doi.org/10.1504/ijtip.2024.140634review
AJG 1
Weight
0.38

What the paper says

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.

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https://doi.org/https://doi.org/10.1504/ijtip.2024.140634

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@article{abhinna2024,
  title        = {{A bibliometric review of the generative artificial intelligence research landscape in marketing}},
  author       = {Abhinna Baxi Bhatnagar et al.},
  journal      = {International Journal of Technology Intelligence and Planning},
  year         = {2024},
  doi          = {https://doi.org/https://doi.org/10.1504/ijtip.2024.140634},
}

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

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Evidence weight

0.38

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.18 × 0.4 = 0.07
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.