How Perceptions Variance Shapes Brand Culture: Deconstructing Consumer Cognition Through a Comparison of Generative AI and Human-generated Texts
DOI:
https://doi.org/10.54097/p7p3gb13Keywords:
Generative AI, human-generated text, brand culture, consumer perception, csultural consistencyAbstract
This study investigated how differences in consumer perceptions of content origin—specifically between Gen-AI and human creation—influence brand culture, with a focus on addressing the underexplored connection between content authorship and brand cultural formation. Drawing on the Elaboration Likelihood Model (ELM), brand signal theory, and cultural consistency theory, two empirical studies were carried out to unpack the cognitive and affective mechanisms underlying this relationship. Study 1 compared consumer perceptions across four dimensions—brand authenticity, credibility, electronic word-of-mouth (EWOM) intentions, and brand attitudes. Findings indicated no significant differences in these perceptions between the two AI tools. Study 2 further explored the impact of human-generated versus DeepSeek-generated texts on brand culture. Results revealed that human-authored narratives significantly outperformed AI-generated content in enhancing perceptions of authenticity, credibility, EWOM intentions, brand attitudes, and brand culture. Brand credibility is the only factor with a significant positive effect on brand culture. Additionally, text origin (human vs. AI) played a moderating role, with human authorship strengthening the resonance between perceptual dimensions and brand culture. The AI origin functions as a negative peripheral cue under the ELM framework triggered heuristic skepticism. The study offered practical insights for marketers on integrating Gen-AI into brand communication while preserving cultural coherence.
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[1] Awasthi, Yogesh, T. Garikayi, L. T. Fundisi, and B. Mukhalela: A Comparative Study: Evaluating ChatGPT and DeepSeek AI Tools in Practice, International Journal of Open Information Technologies, Vol.13(2025) No.5, p.67–70. https://injoit.org/index.php/j1/article/view/2091
[2] Koswara, A.: AI-Driven Content in Crafting Persuasive Marketing Messages: A Linguistic Analysis of ChatGPT vs DeepSeek, JELE: Journal of English Literature and Education, Vol. 1 (2025) No.1, p.15-24. https://darussalampalbar.com/index.php/jele/article/view/17
[3] Nguyen, N.M., G.H. Hoang, N.T.M. Vu, L.D. Bui, and A.S. Ta: How Differently Do Cognitive and Affective Country Image Affect Brand Loyalty, Journal of Asia Business Studies, Vol. 19 (2025) No.1, p.1-22. https://doi.org/10.1108/JABS-07-2023-0279.
[4] Liu-Thompkins, Y., L. Khoshghadam, A.A. Shoushtari, and S. Zal: What Drives Retailer Loyalty? A Meta-Analysis of the Role of Cognitive, Affective, and Social Factors Across Five Decades, Journal of Retailing, Vol. 98 (2022) No.1, p.92-110. https://doi.org/10.1016/j.jretai.2022.02.005.
[5] Hollebeek, L.D., and K. Macky: Digital Content Marketing’s Role in Fostering Consumer Engagement, Trust, and Value: Framework, Fundamental Propositions, and Implications, Journal of Interactive Marketing, Vol. 45 (2019), p.27-41. https://doi.org/10.1016/j.intmar.2018.07.003.
[6] Lukose, A., R.S. Cleetus, H. Divya, T.M. Saravanakumar, and J. Jose: Exploring the Intersection of Brands and Linguistics: A Comprehensive Bibliometric Study, International Review of Management and Marketing, Vol. 15 (2025) No.1, p.257-271. https://doi.org/10.32479/irmm.17538.
[7] Nyagadza, B., E.M. Kadembo, and A. Makasi: When Corporate Brands Tell Stories: A Signalling Theory Perspective, Cogent Psychology, Vol. 8 (2021) No.1, p.1897063. https://doi.org/10.1080/23311908.2021.1897063.
[8] Lütjens, H., M. Eisenbeiss, M. Fiedler, and T. Bijmolt: Determinants of Consumers’ Attitudes towards Digital Advertising: A Meta-Analytic Comparison across Time and Touchpoints, Journal of Business Research, Vol. 153 (2022), p.445-466. https://doi.org/10.1016/j.jbusres.2022.07.039.
[9] Obilo, O.O., E. Chefor, and A. Saleh: Revisiting the Consumer Brand Engagement Concept, Journal of Business Research, Vol. 126 (2021), p.634-643. https://doi.org/10.1016/j.jbusres.2019.12.023.
[10] Babu, M.S.H.: A Study on Consumers' Psychology on Marketing Tools, Philosophy and Progress, Vol. 55 (2016) No.1-2, p.125-164. https://doi.org/10.3329/pp.v55i1-2.26394
[11] Kootenaie, M.F., and S.M. Kootenaie: Investigating the Relationship between Brand and Consumer Behavior, Journal of Social, Management and Tourism Letters, Vol. 2021 (2021), p.1-10. https://www.htpub.org/article/Journal-Of-Social%2C-Management-And-Tourism-Letter/vol/2021/articleid/995
[12] Borghi, M., and M.M. Mariani: The Role of Emotions in the Consumer Meaning-Making of Interactions with Social Robots, Technological Forecasting and Social Change, Vol. 182 (2022), p.121844. https://doi.org/10.1016/j.techfore.2022.121844.
[13] Wieczerzycki, M.: Consumer Response to the Use of AI in Value Co-Creation in Online Communities—The Case of Nexus Mods, Journal of Consumer Behaviour, (2025) Advance Online Publication. https://doi.org/10.1002/cb.2509.
[14] Petty, R.E., and J.T. Cacioppo: The Elaboration Likelihood Model of Persuasion (Springer, Netherlands 1986).
[15] Mudambi, S.M., and D. Schuff: What Makes a Helpful Online Review? A Study of Customer Reviews on Amazon.com, MIS Quarterly, Vol. 34 (2010) No.1, p.185-200. https://doi.org/10.2307/20721420
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