From Observation to Co-Creation: A Netnographic Review of AI-Driven Consumer Behavior
DOI:
https://doi.org/10.47709/brilliance.v5i1.5885Keywords:
AI-netnography, consumer behavior, co-creation, human–AI interaction, qualitative marketing researchAbstract
As artificial intelligence (AI) continues to transform the landscape of digital consumer experiences, traditional netnographic methods face significant disruption—both in terms of methodological execution and theoretical framing. This systematic review explores how the growing presence of AI, functioning not only as a technological tool but also as an agentic participant, reshapes the way netnography is practiced in marketing and consumer behavior research. Recognizing a widening gap in the literature, the study aims to synthesize current conceptual and methodological advancements at the intersection of ethnographic inquiry and intelligent systems. Employing the PRISMA 2020 protocol for systematic reviews, 43 peer-reviewed journal articles published between 2013 and 2024 were retrieved from Scopus and Web of Science databases. A thematic synthesis of these articles reveals five dominant themes: the methodological evolution of netnography within online consumer communities; the role of AI in predictive personalization; emerging patterns of consumer–AI value co-creation; new relational models of human–AI interaction; and ethical and epistemological challenges posed by AI-augmented ethnography. These themes collectively inform the development of a novel conceptual framework, AI-Netnography which positions both human and algorithmic agents as co-constructors of meaning, identity, and experience. By reimagining netnographic inquiry for AI-mediated environments, this review not only advances the field of qualitative marketing research but also proposes new pathways for ethically responsible and epistemologically inclusive digital consumer studies.
References
AbouElgheit, E. (2024). The Role of Generative AI in Shaping Millennials and Gen Z’s Orientation Toward Luxury Products. Journal of Marketing Development and Competitiveness, 18(2). https://doi.org/10.33423/jmdc.v18i2.7012
Alhitmi, H. K., Mardiah, A., Al-Sulaiti, K. I., & Abbas, J. (2024). Data security and privacy concerns of AI-driven marketing in the context of economics and business field: an exploration into possible solutions. Cogent Business & Management, 11(1). https://doi.org/10.1080/23311975.2024.2393743
Bartl, M., Kannan, V. K., & Stockinger, H. (2016). A review and analysis of literature on netnography research. International Journal of Technology Marketing, 11(2), 165. https://doi.org/10.1504/IJTMKT.2016.075687
Brüns, J. D., & Meißner, M. (2024). Do you create your content yourself? Using generative artificial intelligence for social media content creation diminishes perceived brand authenticity. Journal of Retailing and Consumer Services, 79, 103790. https://doi.org/10.1016/j.jretconser.2024.103790
Chandrakumar, H. (2024). The Use of AI-Driven Personalization for Enhancing the Customer Experience for Gen-Z. Open Journal of Business and Management, 12(06), 4472–4481. https://doi.org/10.4236/ojbm.2024.126225
Chintalapati, S., & Pandey, S. K. (2022). Artificial intelligence in marketing: A systematic literature review. International Journal of Market Research, 64(1), 38–68. https://doi.org/10.1177/14707853211018428
Dessart, L., Veloutsou, C., & Morgan-Thomas, A. (2015). Consumer engagement in online brand communities: a social media perspective. Journal of Product & Brand Management, 24(1), 28–42. https://doi.org/10.1108/JPBM-06-2014-0635
Giuggioli, G., & Pellegrini, M. M. (2023). Artificial intelligence as an enabler for entrepreneurs: a systematic literature review and an agenda for future research. International Journal of Entrepreneurial Behavior & Research, 29(4), 816–837. https://doi.org/10.1108/IJEBR-05-2021-0426
Guerra-Tamez, C. R., Kraul Flores, K., Serna-Mendiburu, G. M., Chavelas Robles, D., & Ibarra Cortés, J. (2024). Decoding Gen Z: AI’s influence on brand trust and purchasing behavior. Frontiers in Artificial Intelligence, 7. https://doi.org/10.3389/frai.2024.1323512
Hermann, E. (2022). Leveraging Artificial Intelligence in Marketing for Social Good—An Ethical Perspective. Journal of Business Ethics, 179(1), 43–61. https://doi.org/10.1007/s10551-021-04843-y
Hermann, E., & Puntoni, S. (2024). Artificial intelligence and consumer behavior: From predictive to generative AI. Journal of Business Research, 180, 114720. https://doi.org/10.1016/j.jbusres.2024.114720
Hsieh, P.-L., & Wei, S.-L. (2017). Relationship formation within online brand communities: Bridging the virtual and the real. Asia Pacific Management Review, 22(1), 2–9. https://doi.org/10.1016/j.apmrv.2016.10.008
Kozinets, R. V., & Gambetti, R. (2021). Netnography Unlimited: Understanding Technoculture Using Qualitative Social Media Research. In Netnography Unlimited. Routledge. https://doi.org/10.4324/9781003001430
Kumar, A., Bapat, G., Kumar, A., Hota, S. L., Abishek, G. D., & Vaz, S. (2024). Unlocking Brand Excellence: Harnessing AI Tools for Enhanced Customer Engagement and Innovation. RAiSE-2023, 204. https://doi.org/10.3390/engproc2023059204
Lagadec, D. Le, Jackson, D., & Cleary, M. (2024). Artificial intelligence in nursing education: Prospects and pitfalls. Journal of Advanced Nursing, 80(10), 3883–3885. https://doi.org/10.1111/jan.16276
Raji, M. A., Olodo, H. B., Oke, T. T., Addy, W. A., Ofodile, O. C., & Oyewole, A. T. (2024). E-commerce and consumer behavior: A review of AI-powered personalization and market trends. GSC Advanced Research and Reviews, 18(3), 066–077. https://doi.org/10.30574/gscarr.2024.18.3.0090
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ, n71. https://doi.org/10.1136/bmj.n71
Pongsakornrungsilp, S., Bradshaw, A., & Schroeder, J. E. (2008). Brand Community as Co-Creation Value in the Service-Dominant Logic of Marketing. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1103970
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
Tang, R., De Donato, L., Bes?inovi?, N., Flammini, F., Goverde, R. M. P., Lin, Z., Liu, R., Tang, T., Vittorini, V., & Wang, Z. (2022). A literature review of Artificial Intelligence applications in railway systems. Transportation Research Part C: Emerging Technologies, 140, 103679. https://doi.org/10.1016/j.trc.2022.103679
Teepapal, T. (2025). AI-driven personalization: Unraveling consumer perceptions in social media engagement. Computers in Human Behavior, 165, 108549. https://doi.org/10.1016/j.chb.2024.108549
Vla?i?, B., Corbo, L., Costa e Silva, S., & Dabi?, M. (2021). The evolving role of artificial intelligence in marketing: A review and research agenda. Journal of Business Research, 128, 187–203. https://doi.org/10.1016/j.jbusres.2021.01.055
Zaglia, M. E. (2013). Brand communities embedded in social networks. Journal of Business Research, 66(2), 216–223. https://doi.org/10.1016/j.jbusres.2012.07.015
Zulaikha, S., Mohamed, H., Kurniawati, M., Rusgianto, S., & Rusmita, S. A. (2020). Customer Predictive Analytics Using Artificial Intelligence. The Singapore Economic Review, 1–12. https://doi.org/10.1142/S0217590820480021
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Ahmad Mundzir, Kuniarti Pratiwi

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.














