Citation

Gan, Chin Lay and Lee, Yi Yong and Liew, Tze Wei and Tan, Su Mae and Ahmad, Faizan and Prasetio, Adhi (2025) Cognitive and affective factors in AI virtual influencer marketing: A stimulus–organism–response and pleasure–arousal–dominance model approach. Digital Business, 5 (2). p. 100150. ISSN 2666-9544

Text Cognitive and affective factors in AI virtual influencer marketing_ A stimulus–organism–response and pleasure–arousal–dominance model approach.pdf - Published Version Restricted to Repository staff only Download (1MB)

Abstract

Despite the exponential rise of AI-powered virtual influencer (VI), the role of user-centric elements, particularly experiential content and emotional factors in shaping consumer decision-making remains underexplored. This study adopts the Stimulus–Organism–Response (S-O-R) framework and incorporates the Pleasure-ArousalDominance (PAD) model to conceptualize emotional responses in the VI context. The present research in tegrates affective (perceived enjoyment, hedonic motivation) and cognitive (satisfaction) emotional states to better understand consumer responses to VI. We surveyed 248 VI followers and employed structural equation modeling to test the conceptual framework. The findings reveal that: (1) Perceived interactivity significantly influences both affective (emotional states, perceived enjoyment, hedonic motivation) and cognitive states (satisfaction); (2) perceived risk and visual appeal significantly influence emotional states; (3) visual appeal significantly influences perceived enjoyment; and (4) hedonic motivation and satisfaction significantly influence willingness to buy. The findings provide practical guidance for designing emotionally engaging VI campaigns by leveraging key stimuli to influence consumer behavior.

Item Type:	Article
Uncontrolled Keywords:	Purchase intention
Subjects:	H Social Sciences > HF Commerce > HF5001-6182 Business > HF5410-5417.5 Marketing. Distribution of products
Divisions:	Faculty of Business (FOB) Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	30 Sep 2025 01:29
Last Modified:	04 Oct 2025 14:54
URII:	http://shdl.mmu.edu.my/id/eprint/14531

Downloads

Edit (login required)