Peter, R, Roshith, V, Lawrence, S, Mona, A.E, Narayanan, K and Fathima, Y (2025) 'Gen AI – Gen Z: understanding Gen Z’s emotional responses and brand experiences with Gen AI-driven, hyper-personalized advertising.' Frontiers in Communication, 10. e155455.
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Abstract
Introduction: Gen Z, a tech-savvy consumer group, has highly evolved in its approach to new-age advertising. The rise of Generative Artificial Intelligence (Gen AI) has revolutionized advertising by enabling hyper-personalized content, making it essential to understand its influence on Generation Z (Gen Z) population. This study explores the responses of Gen Z participants in India to Generative Artificial Intelligence based, hyper-personalized advertisements, with a specific focus on emotional responses and brand interactions which are significant predictors of advertisement success. Methods: Using qualitative research methods, semi-structured interviews were conducted with 40 Gen Z participants. Thematic analysis of the data was performed to understand the major themes pertaining to emotional responses and brand interactions to this form of Gen AI-driven advertising. Results: Two major themes and five sub-themes were revealed through thematic analysis. The first theme, diverse emotional responses, encompassed two sub-themes, curiosity and interest as well as fear and suspicion. The second major theme, enhanced brand experience, encompassed three sub-themes of advanced targeted marketing; initial attraction and brand engagement; and brand connection and loyalty, as perceived by the participants. Discussion: Findings imply that brands can harness Gen AI-driven, hyper-personalized advertisements to evoke meaningful emotions, enhancing consumer loyalty and building stronger, more personal connections with their audience.
Item Type: | Article |
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Keywords: | Gen AI advertisements, hyper-personalization, emotional responses, brand interaction, brand engagement, brand connection, brand loyalty |
Subjects: | B Philosophy. Psychology. Religion > BF Psychology |
Divisions: | School of Sciences |
Date Deposited: | 04 Sep 2025 16:49 |
Last Modified: | 04 Sep 2025 16:49 |
ISSN: | 2297-900X |
URN: | https://researchspace.bathspa.ac.uk/id/eprint/17249 |
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