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
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Blei, D. M. (2012) · Communications of the ACM · doi:10.1145/2133806.2133826 · Cited by 5,516
Artificial intelligence in service
Huang, M.-H., & Rust, R. T. (2018) · Journal of Service Research · doi:10.1177/1094670517752459 · Cited by 3,978
Haenlein, M., & Kaplan, A. (2019) · California Management Review · doi:10.1177/0008125619864925 · Cited by 2,655
Big data consumer analytics and the transformation of marketing
Erevelles, S., Fukawa, N., & Swayne, L. (2016) · Journal of Business Research · doi:10.1016/j.jbusres.2015.07.001 · Cited by 1,377
Luo, X., Tong, S., Fang, Z., & Qu, Z. (2019) · Marketing Science · doi:10.1287/mksc.2019.1192 · Cited by 1,262 · cited 2×
“, 2019); investigations of the impact of AI chatbot disclosures on customer purchases (Luo et al., 2019); considerations of effects on workforces (Davenport & Kirby, 2015) and redefinitions of AI-enabled workplaces (Chui et al.”
BackgroundUnderstanding the Role of Artificial Intelligence in Personalized Engagement Marketing
Kumar, V., Rajan, B., Venkatesan, R., & Lecinski, J. (2019) · California Management Review · doi:10.1177/0008125619859317 · Cited by 797 · cited 3×
“Technological advancements frequently lead to structural shifts in business paradigms, as is true of AI in marketing (Kumar et al., 2019).”
BackgroundPlatforms in the peer-to-peer sharing economy
Wirtz, J., So, K. K. F., Mody, M. A., Liu, S. Q., & Chun, H. H. (2019) · Journal of Service Management · doi:10.1108/josm-11-2018-0369 · Cited by 414
Self-citationCustomer participation in services: domain, scope, and boundaries
Dong, B., & Sivakumar, K. (2017) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-017-0524-y · Cited by 265
Martínez-López, F. J., & Casillas, J. (2013) · Industrial Marketing Management · doi:10.1016/j.indmarman.2013.03.001 · Cited by 198
Salminen, J., Yoganathan, V., Corporan, J., Jansen, B. J., & Jung, S.-G. (2019) · Journal of Business Research · doi:10.1016/j.jbusres.2019.04.018 · Cited by 99
Self-citation
Cited by
How to conduct a bibliometric analysis: An overview and guidelines
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2021.04.070 · Cited by 14,329
Mariani, M. M., Perez‐Vega, R., & Wirtz, J. (2022) · Psychology & Marketing · doi:10.1002/mar.21619 · Cited by 631
Self-citationAI-powered marketing: What, where, and how?
Kumar, V., Ashraf, A. R., & Nadeem, W. (2024) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2024.102783 · Cited by 509
Basu, R., Aktar, M. N., & Kumar, S. (2025) · Journal of Marketing Analytics · doi:10.1057/s41270-024-00355-6 · Cited by 35
A theoretical framework to guide AI ethical decision making
Ferrell, O. C., Harrison, D. E., Ferrell, L. K., Ajjan, H., & Hochstein, B. W. (2024) · AMS Review · doi:10.1007/s13162-024-00275-9 · Cited by 24
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)
- OpenAlex
- W3096690806
- Cited by
- 555
- References
- 10
- FWCI
- 18.66
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda. https://app.cochat.ai/papers/10.1016/j.jbusres.2020.10.044
- The paper
- Mustak, M., Salminen, J., Plé, L., & Wirtz, J. (2021). Artificial intelligence in marketing: Topic modeling, scientometric analysis, and research agenda. Journal of Business Research. https://doi.org/10.1016/j.jbusres.2020.10.044
Data sources
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.
