Journal of Intelligent Marketing Management

Journal of Intelligent Marketing Management

When the Market Buys from Itself: The Rise of Recursive Economies in Intelligent Ecosystems

Editor-in-Chief Lecture

Authors
1 Ph.D. in Business Administration, University of Tehran, Tehran, Iran; Department of Management, Nabi Akram Institute of Higher Education, Tabriz, Iran
2 Ph.D. in Business Administration, University of Tehran, Tehran, Iran; Researcher, Amin Intelligent Management Research Group, Tehran, Iran
3 Department of Business Administration, Abrar Institute of Higher Education, Tehran, Iran; Researcher, Amin Intelligent Management Research Group, Tehran, Iran
Abstract
In the classical marketing tradition, the market has been a discovery mechanism for preferences: demand is the input and the firm's response is the output. This editorial argues that in contemporary intelligent ecosystems this direction has been reversed, giving rise to what we term the recursive economy: a condition in which demand signals, transacting actors, the product-validation process, and even the training data of next-generation models are all generated by the very intelligent systems that were supposed to discover demand. Adopting an interpretive–analytical method with abductive logic, the study draws on an interpretive systematic review of 21 works, 20 of which were published from 2025 onwards. Findings are organized into four levels of recursion: signal recursion (fabricated reviews and social proof), actor recursion (virtual influencers and simulated respondents), transaction recursion (agentic commerce in which the buyer is itself a model), and learning recursion (next-generation models trained on the market's own outputs). We argue that the central challenge is no longer information asymmetry in its classical sense but self-referential information, and that foundational marketing constructs such as electronic word of mouth, brand equity and loyalty now face a construct-validity crisis because their measurement channels have become endogenous. A three-criterion framework is proposed for separating legitimate intelligent marketing from recursive fabrication, and six research questions are put forward as an agenda for forthcoming issues.
Keywords

Allouah, A., Besbes, O., Figueroa, J. D., Kanoria, Y., & Kumar, A. (2026). What is your AI agent buying? Evaluation, biases, model dependence, & emerging implications of agentic e-commerce. In Proceedings of the ACM Web Conference 2026 (pp. 8697–8700). ACM. https://doi.org/10.1145/3774904.3792943
Badade, A. B., Dhanaraj, R. K., & Selvarajan, S. (2026). Hybrid model for astroturfing detection using content analysis in the political domain on social media. Social Network Analysis and Mining, 16, 54. https://doi.org/10.1007/s13278-025-01557-1
Cheng, L.-K. (2026). Virtual influencer increases unethical consumer behavior due to increased moral disengagement. International Journal of Human–Computer Interaction, 42(3), 1549–1561. https://doi.org/10.1080/10447318.2025.2524501
Coeckelbergh, M. (2025). LLMs, truth, and democracy: An overview of risks. Science and Engineering Ethics, 31(1), 4. https://doi.org/10.1007/s11948-025-00529-0
Davlembayeva, D., Chari, S., & Papagiannidis, S. (2025). Virtual influencers in consumer behaviour: A social influence theory perspective. British Journal of Management, 36(1), 202–222. https://doi.org/10.1111/1467-8551.12839
Furner, C. P., & Zinko, R. (2026). When trust breaks: The impact of AI-generated reviews on consumer trust and purchase intention in entrepreneurial marketing. Journal of Enterprising Culture, 34(1), 109–114. https://doi.org/10.1142/S0218495826500032
Gonzalez, G. R., Habel, J., & Hunter, G. K. (2026). AI agents, agentic AI, and the future of sales. Journal of Business Research, 202, 115799. https://doi.org/10.1016/j.jbusres.2025.115799
Karimova, G. Z., & Rasool, S. (2026). AI-facilitated astroturfing in medical tourism: Evidence from YouTube comment sections. Journal of Global Marketing, 1–18. https://doi.org/10.1080/08911762.2026.2717514
Lim, W. M., Agarwal, R., Mishra, A., & Mehrotra, A. (2025). The rise of fake reviews: Toward a marketing-oriented framework for understanding fake reviews. Australasian Marketing Journal, 33, 178–198. https://doi.org/10.1177/14413582241283505
Limilia, P. (2025). How people perceived and engage with political astroturfing accounts. Communication Research and Practice, 11(4), 512–529. https://doi.org/10.1080/22041451.2025.2531637
Muzumdar, P., Cheemalapati, S., RamiReddy, S. R., Singh, K., Kurian, G., & Muley, A. (2025). The dead internet theory: A survey on artificial interactions and the future of social media. Asian Journal of Research in Computer Science, 18(1), 67–73. https://doi.org/10.9734/ajrcos/2025/v18i1549
Ranjan, H., & Dwivedi, S. (2025). Detecting LLM-generated fake reviews: A leakage-free multi-model framework. International Journal of Engineering and Information Management, 1(4), 59–80. https://doi.org/10.52756/ijeim.2025.v01.i04.003
Rao, V. S., Kumar, A., Lakkaraju, H., & Shah, N. B. (2025). Detecting LLM-generated peer reviews. PLOS ONE, 20(9), e0331871. https://doi.org/10.1371/journal.pone.0331871
Ren, G., Wang, H., & Yang, Y. (2025). Cross-domain fake review detection based on deep learning multi-level generic features extraction fusion. Informatica, 49(18). https://doi.org/10.31449/inf.v49i18.7071
Shabankareh, M., Kalantari Daronkola, H., Sarhadi, A., & Thaichon, P. (2025). The influence of artificial intelligence quality on business-to-business relationships on e-commerce platforms. International Journal of Advertising, 44, 1483–1522. https://doi.org/10.1080/02650487.2025.2557053
Shumailov, I., Shumaylov, Z., Zhao, Y., Papernot, N., Anderson, R., & Gal, Y. (2024). AI models collapse when trained on recursively generated data. Nature, 631(8022), 755–759. https://doi.org/10.1038/s41586-024-07566-y
Shumailov, I., Shumaylov, Z., Zhao, Y., Papernot, N., Anderson, R., & Gal, Y. (2025). Author correction: AI models collapse when trained on recursively generated data. Nature, 640(8058), E6. https://doi.org/10.1038/s41586-025-08905-3
Saura García, C. (2025). Synthetification of public opinion: Impacts on deliberative democracies. Ethics and Information Technology, 27, 54. https://doi.org/10.1007/s10676-025-09870-1
van Berlo, Z. M. C., & Breves, P. L. (2025). Disclosing the virtual nature of virtual influencers: The effect of disclosure prominence and the role of product digitality. Computers in Human Behavior Reports, 19, 100742. https://doi.org/10.1016/j.chbr.2025.100742
Wang, J., Gong, M., & Li, Y. (2025). Can AI take our surveys for us? Evaluating LLMs as stand-ins for human respondents. In Proceedings of the 2025 16th International Conference on E-business, Management and Economics (pp. 266–273). ACM. https://doi.org/10.1145/3760557.3760597
Zeng, H.-K., & Lin, C. A. (2025). Consumer evaluation of virtual vs. human influencers via source credibility, perceived social similarity, and consumption motivation. Journal of Theoretical and Applied Electronic Commerce Research, 20(3), 168. https://doi.org/10.3390/jtaer20030168