The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Artificial intelligence (AI) in strategic marketing decision-making: a research agenda
Abstract
Purpose: The purpose of this paper is to review literature about the applications of artificial intelligence (AI) in strategic situations and identify the research that is needed in the area of applying AI to strategic marketing decisions. Design/methodology/approach: The approach was to carry out a literature review and to consult with marketing experts who were invited to contribute to the paper. Findings: There is little research into applying AI to strategic marketing decision-making. This research is needed, as the frontier of AI application to decision-making is moving in many management areas from operational to strategic. Given the competitive nature of such decisions and the insights from applying AI to defence and similar areas, it is time to focus on applying AI to strategic marketing decisions. Research limitations/implications: The application of AI to strategic marketing decision-making is known to be taking place, but as it is commercially sensitive, data is not available to the authors. Practical implications: There are strong implications for all businesses, particularly large businesses in competitive industries, where failure to deploy AI in the face of competition from firms, who have deployed AI to improve their decision-making could be dangerous. Social implications: The public sector is a very important marketing decision maker. Although in most cases it does not operate competitively, it must make decisions about making different services available to different citizens and identify the risks of not providing services to certain citizens; so, this paper is relevant to the public sector. Originality/value: To the best of the authors’ knowledge, this is one of the first papers to probe deployment of AI in strategic marketing decision-making.
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Citations
Cites
Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making
Jarrahi, M. H. (2018) · Business Horizons · doi:10.1016/j.bushor.2018.03.007 · Cited by 2,772
Organizational Decision-Making Structures in the Age of Artificial Intelligence
Shrestha, Y. R., Ben-Menahem, S. M., & von Krogh, G. (2019) · California Management Review · doi:10.1177/0008125619862257 · Cited by 1,108
Business models and business model innovation: Between wicked and paradigmatic problems
Foss, N. J., & Saebi, T. (2018) · Long Range Planning · doi:10.1016/j.lrp.2017.07.006 · Cited by 696
Brock, J. K.-U., & von Wangenheim, F. (2019) · California Management Review · doi:10.1177/1536504219865226 · Cited by 622
Cognitive and Motivational Biases in Decision and Risk Analysis
Montibeller, G., & von Winterfeldt, D. (2015) · Risk Analysis · doi:10.1111/risa.12360 · Cited by 578
Personality Traits of Entrepreneurs: A Review of Recent Literature
Kerr, S. P., Kerr, W. R., & Xu, T. (2018) · Foundations and Trends® in Entrepreneurship · doi:10.1561/0300000080 · Cited by 548
Emerging Technology and Business Model Innovation: The Case of Artificial Intelligence
Lee, J., Suh, T., Roy, D., & Baucus, M. (2019) · Journal of Open Innovation: Technology, Market, and Complexity · doi:10.3390/joitmc5030044 · Cited by 441
Behavioural biases in investment decision making – a systematic literature review
Kumar, S., & Goyal, N. (2015) · Qualitative Research in Financial Markets · doi:10.1108/qrfm-07-2014-0022 · Cited by 331
Big data, big decisions: The impact of big data on board level decision-making
Merendino, A., Dibb, S., Meadows, M., Quinn, L., Wilson, D., Simkin, L., & Canhoto, A. (2018) · Journal of Business Research · doi:10.1016/j.jbusres.2018.08.029 · Cited by 239
The Influence of Context on the Strategic Decision‐Making Process: A Review of the Literature
Shepherd, N. G., & Rudd, J. M. (2014) · International Journal of Management Reviews · doi:10.1111/ijmr.12023 · Cited by 234
Cited by
Marketing with ChatGPT: Navigating the Ethical Terrain of GPT-Based Chatbot Technology
Rivas, P., & Zhao, L. (2023) · AI · doi:10.3390/ai4020019 · Cited by 191
Exploring the Applications of Artificial Intelligence in Marketing: A Topic Modelling Analysis
NOVA Information Management School (NOVA IMS), L., Rita, P., Omran, W., University of Minho, F. O. E. A. M., Ramos, R. F., Instituto Politécnico de Coimbra, E., Costa, T., & NOVA Information Management School (NOVA IMS), L. (2025) · Tourism & Management Studies · doi:10.18089/tms.20250103 · Cited by 7
Sudirman, S., Ahmad, K., & Hood, Z. (2026) · International Journal of Public Administration · doi:10.1080/01900692.2025.2539345
Metadata
- Authors
- Stone, M., Aravopoulou, E., Ekinci, Y., Evans, G., Hobbs, M., Labib, A., Laughlin, P., Machtynger, J., & Machtynger, L.
- Venue
- The Bottom Line
- Publisher
- Emerald
- Published
- 2020-04-13
- Volume
- 33
- Issue
- 2
- Pages
- 183-200
- ISSN
- 0888-045X (ISSN-L)
- OpenAlex
- W3018262553
- Cited by
- 363
- References
- 30
- FWCI
- 9.94
- Type
- journal article
- Language
- en
- Open access
- closed
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- Stone, M., Aravopoulou, E., Ekinci, Y., Evans, G., Hobbs, M., Labib, A., Laughlin, P., Machtynger, J., & Machtynger, L. (2020). Artificial intelligence (AI) in strategic marketing decision-making: a research agenda. The Bottom Line. https://doi.org/10.1108/bl-03-2020-0022
Data sources
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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.