Generative Artificial Intelligence in Consumer Co-Creation: A Marketing Framework of Perceived Control, Psychological Ownership, and Purchase Intentions

Authors

  • Amirhossein Nafei Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei, Taiwan

https://doi.org/10.22105/masi.v3i2.107

Abstract

The rapid diffusion of generative artificial intelligence (GenAI) into consumer-facing design platforms is changing how firms involve customers in new product development (NPD). Yet the marketing consequences of this change cannot be inferred from creativity gains alone. A system may improve the objective quality of an idea while simultaneously weakening the consumer’s sense of control, authorship, authenticity, and ownership over the resulting product. This conceptual article develops a dual-pathway framework explaining when GenAI-enabled co-creation strengthens or undermines consumer responses. The enabling pathway proposes that interfaces preserving directive control and meaningful consumer contribution increase perceived control and creative self-efficacy, which reinforce perceived authorship and psychological ownership. The displacement pathway proposes that excessive system decision authority dilutes perceived authorship and may reduce product authenticity, particularly for identity-relevant products. Psychological ownership and authenticity subsequently shape purchase intention, willingness to pay, advocacy, and continued participation. The framework predicts a non-linear relationship between GenAI decision authority and psychological ownership: assistance may add too little value, collaboration can preserve agency while expanding capability, and autonomous generation can displace the consumer from the creative act. Process transparency, consumer artificial intelligence literacy, customization depth, and product identity relevance are identified as boundary conditions. The article contributes to consumer co-creation, artificial intelligence in marketing, psychological ownership, and innovation research by shifting attention from whether GenAI improves output creativity to how the allocation of creative agency shapes market outcomes. A three-study validation program and actionable design principles are proposed for future empirical research.

Keywords:

generative artificial intelligence; , consumer co-creation; , new product development;, perceived control; , perceived authorship; , psychological ownership;, purchase intention

References

  1. [1] Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48, 24–42. https://doi.org/10.1007/s11747-019-00696-0
  2. [2] Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49, 30–50. https://doi.org/10.1007/s11747-020-00749-9
  3. [3] Puntoni, S., Reczek, R. W., Giesler, M., & Botti, S. (2021). Consumers and artificial intelligence: An experiential perspective. Journal of Marketing, 85(1), 131–151. https://doi.org/10.1177/0022242920953847
  4. [4] De Bruyn, A., Viswanathan, V., Beh, Y. S., Brock, J. K.-U., & von Wangenheim, F. (2020). Artificial intelligence and marketing: Pitfalls and opportunities. Journal of Interactive Marketing, 51, 91–105. https://doi.org/10.1016/j.intmar.2020.04.007
  5. [5] Haefner, N., Wincent, J., Parida, V., & Gassmann, O. (2021). Artificial intelligence and innovation management: A review, framework, and research agenda. Technological Forecasting and Social Change, 162, 120392. https://doi.org/10.1016/j.techfore.2020.120392
  6. [6] Truong, Y., & Papagiannidis, S. (2022). Artificial intelligence as an enabler for innovation: A review and future research agenda. Technological Forecasting and Social Change, 183, 121852. https://doi.org/10.1016/j.techfore.2022.121852
  7. [7] Bouschery, S. G., Blazevic, V., & Piller, F. T. (2023). Augmenting human innovation teams with artificial intelligence: Exploring transformer-based language models. Journal of Product Innovation Management, 40(2), 139–153. https://doi.org/10.1111/jpim.12656
  8. [8] Hoyer, W. D., Chandy, R., Dorotic, M., Krafft, M., & Singh, S. S. (2010). Consumer cocreation in new product development. Journal of Service Research, 13(3), 283–296. https://doi.org/10.1177/1094670510375604
  9. [9] Bendapudi, N., & Leone, R. P. (2003). Psychological implications of customer participation in co-production. Journal of Marketing, 67(1), 14–28. https://doi.org/10.1509/jmkg.67.1.14.18592
  10. [10] Fuchs, C., Prandelli, E., & Schreier, M. (2010). The psychological effects of empowerment strategies on consumers’ product demand. Journal of Marketing, 74(1), 65–79. https://doi.org/10.1509/jmkg.74.1.65
  11. [11] Franke, N., Schreier, M., & Kaiser, U. (2010). The “I designed it myself” effect in mass customization. Management Science, 56(1), 125–140. https://doi.org/10.1287/mnsc.1090.1077
  12. [12] Doshi, A. R., & Hauser, O. P. (2024). Generative AI enhances individual creativity but reduces the collective diversity of novel content. Science Advances, 10(28), eadn5290. https://doi.org/10.1126/sciadv.adn5290
  13. [13] Lee, B. C., & Chung, J. (2024). An empirical investigation of the impact of ChatGPT on creativity. Nature Human Behaviour, 8, 1906–1914. https://doi.org/10.1038/s41562-024-01953-1
  14. [14] McGuire, J., De Cremer, D., & Van de Cruys, T. (2024). Establishing the importance of co-creation and self-efficacy in creative collaboration with artificial intelligence. Scientific Reports, 14, 18525. https://doi.org/10.1038/s41598-024-69423-2
  15. [15] Meincke, L., Nave, G., & Terwiesch, C. (2025). ChatGPT decreases idea diversity in brainstorming. Nature Human Behaviour, 9, 1107–1109. https://doi.org/10.1038/s41562-025-02173-x
  16. [16] Jaakkola, E. (2020). Designing conceptual articles: Four approaches. AMS Review, 10, 18–26. https://doi.org/10.1007/s13162-020-00161-0
  17. [17] MacInnis, D. J. (2011). A framework for conceptual contributions in marketing. Journal of Marketing, 75(4), 136–154. https://doi.org/10.1509/jmkg.75.4.136
  18. [18] Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039
  19. [19] Griffin, A., & Hauser, J. R. (1993). The voice of the customer. Marketing Science, 12(1), 1–27. https://doi.org/10.1287/mksc.12.1.1
  20. [20] Cooper, R. G. (1990). Stage-gate systems: A new tool for managing new products. Business Horizons, 33(3), 44–54. https://doi.org/10.1016/0007-6813(90)90040-I
  21. [21] Nambisan, S., & Baron, R. A. (2009). Virtual customer environments: Testing a model of voluntary participation in value co-creation activities. Journal of Product Innovation Management, 26(4), 388–406. https://doi.org/10.1111/j.1540-5885.2009.00667.x
  22. [22] Füller, J. (2010). Refining virtual co-creation from a consumer perspective. California Management Review, 52(2), 98–122. https://doi.org/10.1525/cmr.2010.52.2.98
  23. [23] Franke, N., & Piller, F. T. (2004). Value creation by toolkits for user innovation and design: The case of the watch market. Journal of Product Innovation Management, 21(6), 401–415. https://doi.org/10.1111/j.0737-6782.2004.00094.x
  24. [24] von Hippel, E. (2005). Democratizing innovation. MIT Press.
  25. [25] Norton, M. I., Mochon, D., & Ariely, D. (2012). The IKEA effect: When labor leads to love. Journal of Consumer Psychology, 22(3), 453–460. https://doi.org/10.1016/j.jcps.2011.08.002
  26. [26] Moreau, C. P., Bonney, L., & Herd, K. B. (2011). It’s the thought (and the effort) that counts: How customizing for others differs from customizing for oneself. Journal of Marketing, 75(5), 120–133. https://doi.org/10.1509/jmkg.75.5.120
  27. [27] Troye, S. V., & Supphellen, M. (2012). Consumer participation in coproduction: “I made it myself” effects on consumers’ sensory perceptions and evaluations of outcome and input product. Journal of Marketing, 76(2), 33–46. https://doi.org/10.1509/jm.10.0205
  28. [28] Schreier, M., Fuchs, C., & Dahl, D. W. (2012). The innovation effect of user design: Exploring consumers’ innovation perceptions of firms selling products designed by users. Journal of Marketing, 76(5), 18–32. https://doi.org/10.1509/jm.10.0462
  29. [29] Dahl, D. W., Fuchs, C., & Schreier, M. (2015). Why and when consumers prefer products of user-driven firms: A social identification account. Management Science, 61(8), 1978–1988. https://doi.org/10.1287/mnsc.2014.1999
  30. [30] Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072
  31. [31] Dellermann, D., Ebel, P., Söllner, M., & Leimeister, J. M. (2019). Hybrid intelligence. Business & Information Systems Engineering, 61(5), 637–643. https://doi.org/10.1007/s12599-019-00595-2
  32. [32] Hui, M. K., & Bateson, J. E. G. (1991). Perceived control and the effects of crowding and consumer choice on the service experience. Journal of Consumer Research, 18(2), 174–184. https://doi.org/10.1086/209250
  33. [33] Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68–78. https://doi.org/10.1037/0003-066X.55.1.68
  34. [34] André, Q., Carmon, Z., Wertenbroch, K., Crum, A., Frank, D., Goldstein, W., Huber, J., van Boven, L., Weber, B., & Yang, H. (2018). Consumer choice and autonomy in the age of artificial intelligence and big data. Customer Needs and Solutions, 5, 28–37. https://doi.org/10.1007/s40547-017-0085-8
  35. [35] Tierney, P., & Farmer, S. M. (2002). Creative self-efficacy: Its potential antecedents and relationship to creative performance. Academy of Management Journal, 45(6), 1137–1148. https://doi.org/10.2307/3069429
  36. [36] Pierce, J. L., Kostova, T., & Dirks, K. T. (2001). Toward a theory of psychological ownership in organizations. Academy of Management Review, 26(2), 298–310. https://doi.org/10.5465/amr.2001.4378028
  37. [37] Pierce, J. L., Kostova, T., & Dirks, K. T. (2003). The state of psychological ownership: Integrating and extending a century of research. Review of General Psychology, 7(1), 84–107. https://doi.org/10.1037/1089-2680.7.1.84
  38. [38] Peck, J., & Shu, S. B. (2009). The effect of mere touch on perceived ownership. Journal of Consumer Research, 36(3), 434–447. https://doi.org/10.1086/598614
  39. [39] Belk, R. W. (1988). Possessions and the extended self. Journal of Consumer Research, 15(2), 139–168. https://doi.org/10.1086/209154
  40. [40] Morhart, F., Malär, L., Guèvremont, A., Girardin, F., & Grohmann, B. (2015). Brand authenticity: An integrative framework and measurement scale. Journal of Consumer Psychology, 25(2), 200–218. https://doi.org/10.1016/j.jcps.2014.11.006
  41. [41] Napoli, J., Dickinson, S. J., Beverland, M. B., & Farrelly, F. (2014). Measuring consumer-based brand authenticity. Journal of Business Research, 67(6), 1090–1098. https://doi.org/10.1016/j.jbusres.2013.06.001
  42. [42] Logg, J. M., Minson, J. A., & Moore, D. A. (2019). Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes, 151, 90–103. https://doi.org/10.1016/j.obhdp.2018.12.005
  43. [43] Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015). Algorithm aversion: People erroneously avoid algorithms after seeing them err. Journal of Experimental Psychology: General, 144(1), 114–126. https://doi.org/10.1037/xge0000033
  44. [44] Castelo, N., Bos, M. W., & Lehmann, D. R. (2019). Task-dependent algorithm aversion. Journal of Marketing Research, 56(5), 809–825. https://doi.org/10.1177/0022243719851788
  45. [45] Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019). Resistance to medical artificial intelligence. Journal of Consumer Research, 46(4), 629–650. https://doi.org/10.1093/jcr/ucz013
  46. [46] Berger, J., & Heath, C. (2007). Where consumers diverge from others: Identity signaling and product domains. Journal of Consumer Research,

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Published

2026-06-10

Issue

Vol. 3 No. 2 (2026): Management Analytics and Social Insights (MASI)

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How to Cite

Nafei, A. (2026). Generative Artificial Intelligence in Consumer Co-Creation: A Marketing Framework of Perceived Control, Psychological Ownership, and Purchase Intentions. Management Analytics and Social Insights, 3(2), 94-100. https://doi.org/10.22105/masi.v3i2.107

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