The DOI matches its Crossref record; 125 of the 125 references it cites were found in a registry or an index.
Machine learning and AI in marketing–Connecting computing power to human insights
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Ma and Sun (2020) argue for machine learning methods for marketing research”
From A strategic framework for artificial intelligence in marketing (2021) · cites this paper as Ma and Sun (2020)
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
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LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
XGBoost: A Scalable Tree Boosting System
Chen, T., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939785 · Cited by 54,569
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565
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Research works printed but not linked: 6
- Lipton, Z.C., (2016), “The mythos of model interpretability,” arXiv preprint arXiv:1606.03490 · arXiv
- Malik, N., P.V. Singh, D.K. Lee, and K. Srinivasan, (2019), “A Dynamic Analysis of Beauty Premium,” working paper, SSRN: https://ssrn.com/abstract=3208162
- Mitchell, T., (1997), “Machine learning,” McGraw Hill, p. 2, ISBN 978-0071154673.
- Sutton, R.S. and A.G. Barto, (2018), “Reinforcement learning: An introduction,” MIT press.
- Watkins, 1992, Q-learning, Machine Learning, 8, 279
- Zhang, M. and L. Luo, (2019), “Can User-Posted Photos Serve as a Leading Indicator of Restaurant Survival? Evidence from Yelp,” working paper, SSRN: https://ssrn.com/abstract=3108288
Cited by
Sorted by citations
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., et al. (2023) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2023.102642 · Cited by 4,584
A strategic framework for artificial intelligence in marketing
Huang, M.-H., & Rust, R. T. (2021) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-020-00749-9 · Cited by 2,059
“Balducci and Marinova (2018) show a data-driven approach to marketing management and research and Ma and Sun (2020) argue for machine learning methods for marketing research, while Lehmann (2020) proposes a method to blend theory and data in the evolving world of marketing research.”
Peres, R., Schreier, M., Schweidel, D., & Sorescu, A. (2023) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2023.03.001 · Cited by 535
Leveraging Artificial Intelligence in Marketing for Social Good—An Ethical Perspective
Hermann, E. (2021) · Journal of Business Ethics · doi:10.1007/s10551-021-04843-y · Cited by 504
ChatGPT and consumers: Benefits, Pitfalls and Future Research Agenda
Paul, J., Ueno, A., & Dennis, C. (2023) · International Journal of Consumer Studies · doi:10.1111/ijcs.12928 · Cited by 345
Metadata
- Authors
- Ma, L., & Sun, B.
- Affiliations
- University of Maryland, College Park (US)
- Cheung Kong Graduate School of Business (CN)
- Corresponding author
- Ma, Liye
- Venue
- International Journal of Research in Marketing
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 37
- Issue
- 3
- Pages
- 481-504
- ISSN
- 0167-8116 (ISSN-L)
- OpenAlex
- W3081095512
- Cited by
- 602
- References
- 124
- Citations by year
- 2026: 83 · 2025: 111 · 2024: 156 · 2023: 108 · 2022: 99 · 2021: 39 · 2020: 6
- FWCI
- 24.77
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Research in Marketing (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
- Keywords
- machine learning in marketing, AI agents, marketing research methodology, model interpretability, unstructured data, large-scale data, tracking data, network data, decision support, causal inference, prescriptive analytics, marketing theory, research agenda
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.
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