Audience Interpretation of AI-Driven Product Visualization and Virtual Influencers in Culinary Marketing: S–O–R and Parasocial Approaches
DOI:
https://doi.org/10.59261/bustechno.v7i4.777Keywords:
AI-Driven Product Visualization, Culinary Digital Marketing, Parasocial Interaction, Stimulus–Organism–Response, Virtual InfluencersAbstract
Background: Artificial intelligence has transformed how culinary products are visualized and communicated in digital marketing.
Objective: This study develops a model of audience interpretation of AI-driven product visualization and virtual influencers in culinary digital marketing by integrating the Stimulus–Organism–Response (S–O–R) framework and Parasocial Interaction Theory.
Methods: An interpretive qualitative approach with a phenomenological design was applied. Data were obtained from 12 informants: 10 audience members aged 18–35 who had encountered AI-driven culinary content on Instagram or TikTok, one digital marketing practitioner, and one AI expert. In-depth interviews, digital observations, and documentation were analyzed using NVivo 15 through open, axial, and selective coding, thematic categorization, and triangulation.
Results: The findings show that AI-driven product visualization and virtual influencers function as stimuli that attract initial attention through aesthetic, modern, and realistic visuals. Audiences process these stimuli through cognitive, affective, and conative evaluations, including assessments of visual realism, message credibility, AI transparency, emotional appeal, uncertainty, and content authenticity. Audience responses appear in the form of engagement, information seeking, intention to try, and purchase intention, but depend on the consistency between promotional visuals and the actual products.
Conclusion: This study recommends a human–AI hybrid strategy combining AI-generated visual appeal, transparency, authentic evidence, and human involvement.
References
Alejandro, A., & Zhao, L. (2024). Multi-method qualitative text and discourse analysis: A methodological framework. Qualitative Inquiry, 30(6), 461–473.
Ampornklinkaew, C. (2025). The role of social media influencers in influencing consumers’ imitation intentions. Digital Business, 5(2), 100143. https://doi.org/10.1016/j.digbus.2025.100143
Beuving, J., & Vries, G. (2025). Doing qualitative research: The craft of naturalistic inquiry. Routledge.
Califano, G., & Spence, C. (2024). Assessing the visual appeal of real/AI-generated food images. Food Quality and Preference, 116, 105149. https://doi.org/10.1016/j.foodqual.2024.105149
Cao, N., Isa, N. M., Perumal, S., & Chen, C. (2025). Perceived Value, Consumer Engagement, and Purchase Intention in Virtual Influencer Marketing: The Role of Source Credibility and Generational Cohort. Journal of Theoretical and Applied Electronic Commerce Research, 20(2), 150. https://doi.org/10.3390/jtaer20020150
DataReportal. (2026). Digital 2026: Indonesia. DataReportal. https://datareportal.com/reports/digital-2026-indonesia
DataReportal, & Social, W. A. (2025). Digital 2025: Indonesia. https://scholar.google.com/scholar?q=%22Digital+2025%3A+Indonesia%22
Djafarova, E., & Davies, J. (2025). Exploring the impact of virtual vs human influencers on purchase intentions in fashion/beauty industry. Journal of Digital Economy. https://doi.org/10.1016/j.jdec.2025.11.001
Elliott, R. A., Pfaff, K., & Cruz, E. (2025). Interpretive Description for the Novice Researcher: Reflections and Recommendations From a Doctoral Research Journey. International Journal of Qualitative Methods, 24, 16094069251410040.
Godulla, A. (2026). Para-Social Interaction Theory: Mass Communication and Para-Social Interaction: Observations On Intimacy at a Distance–by Donald Horton and R. Richard Wohl (1956). In Key works: Theories in communication studies (pp. 13–25). Springer.
Gu, C., Jia, S., Lai, J., Chen, R., & Chang, X. (2024). Exploring Consumer Acceptance of AI-Generated Advertisements: From the Perspectives of Perceived Eeriness and Perceived Intelligence. Journal of Theoretical and Applied Electronic Commerce Research, 19(3), 2218–2238. https://doi.org/10.3390/jtaer19030108
Hamrang, T. M., Payandeh, R., & Jafari Haftkhani, N. (2026). Consumer Acceptance of AI-Generated Advertising in Digital Business: Roles of Perceived Intelligence, Eeriness, and Holistic Thinking. Eeriness, and Holistic Thinking.
Horton, D., & Wohl, R. R. (1956). Mass communication and para-social interaction: Observations on intimacy at a distance. Psychiatry, 19(3), 215–229. https://doi.org/10.1080/00332747.1956.11023049
Hub, I. M. (2026). What is Influencer Marketing? The Ultimate Strategy Guide for 2026. Influencer Marketing Hub. https://influencermarketinghub.com/influencer-marketing/
Kechri, K., Kleisiari, C., Kyrgiakos, L. S., Vasileiou, M., Tosiliani, D. D., Angelopoulos, V., Kleftodimos, G., & Vlontzos, G. (2025). Emerging technologies for investigating food consumer behavior: A systematic review. Comprehensive Reviews in Food Science and Food Safety, 24(6), e70340.
Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. SAGE Publications. https://scholar.google.com/scholar?q=Naturalistic+inquiry
Misra, A., Dinh, D. T., & Ewe, S. Y. (2024). The more followers the better? The impact of food influencers on consumer behaviour in the social media context. British Food Journal, 126(12), 4018–4035. https://doi.org/10.1108/BFJ-01-2024-0096
NVivo. (2025). NVivo (Version 15). Lumivero. https://lumivero.com/products/nvivo/
Rini, L., Schouteten, J. J., Faber, I., Frøst, M. B., Perez-Cueto, F. J. A., & De Steur, H. (2024). Social media and food consumer behavior: A systematic review. Trends in Food Science & Technology, 143, 104290. https://doi.org/10.1016/j.tifs.2023.104290
Vivek, R., Nanthagopan, Y., & Piriyatharshan, S. (2023). Beyond Methods: Theoretical Underpinnings of Triangulation in Qualitative and Multi-Method Studies. SEEU Review, 18(2), 105–122. https://doi.org/10.2478/seeur-2023-0088
Wiesner, C. (2022). Doing qualitative and interpretative research: reflecting principles and principled challenges. Political Research Exchange, 4(1), 2127372. https://doi.org/10.1080/2474736X.2022.2127372
Yu, J., Dickinger, A., So, K. K. F., & Egger, R. (2024). Artificial intelligence-generated virtual influencer: Examining the effects of emotional display on user engagement. Journal of Retailing and Consumer Services, 76, 103560. https://doi.org/10.1016/j.jretconser.2023.103560
Ziakis, C., & Vlachopoulou, M. (2023). Artificial Intelligence in Digital Marketing: Insights from a Comprehensive Review. Information, 14(12), 664. https://doi.org/10.3390/info14120664
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Fitri Firdausi Nuzula
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlike 4.0 International (CC-BY-SA). that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
