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Cited by 555

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda

Mustak, Mekhail, Salminen, Joni, Plé, Loïc, Wirtz, Jochen·2021·Journal of Business Research
DOI 10.1016/j.jbusres.2020.10.044
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Abstract

Abstract The rapid advancement of artificial intelligence (AI) offers exciting opportunities for marketing practice and academic research. In this study, through the application of natural language processing, machine learning, and statistical algorithms, we examine extant literature in terms of its dominant topics, diversity, evolution over time, and dynamics to map the existing knowledge base. Ten salient research themes emerge: (1) understanding consumer sentiments, (2) industrial opportunities of AI, (3) analyzing customer satisfaction, (4) electronic word-of-mouth–based insights, (5) improving market performance, (6) using AI for brand management, (7) measuring and enhancing customer loyalty and trust, (8) AI and novel services, (9) using AI to improve customer relationships, and (10) AI and strategic marketing. The scientometric analyses reveal key concepts, keyword co-occurrences, authorship networks, top research themes, landmark publications, and the evolution of the research field over time. With the insights as a foundation, this article closes with a proposed agenda for further research.

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Metadata

Authors
Mustak, M., Salminen, J., Plé, L., & Wirtz, J.
Venue
Journal of Business Research
Publisher
Elsevier BV
Published
2021-01
Volume
124
Pages
389-404
ISSN
0148-2963 (ISSN-L)
DOI
10.1016/j.jbusres.2020.10.044
OpenAlex
W3096690806
Cited by
555
References
10
FWCI
18.66
Type
journal article
Language
en
Open access
closed

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.