Pengaruh Generative AI Marketing terhadap Purchase Intention melalui Customer Trust dan Perceived Personalization pada Pengguna E-Commerce - Destiyana Yuwanti; Imam Yuwono; Raka Saputra (Lembaga Teewan Journal Solutions, 2026)
The rapid advancement of Generative Artificial Intelligence(Generative AI) has transformed digital marketing by enabling businesses to deliver more adaptive, interactive, and personalized customer experiences. Despite its growing adoption in e-commerce, the underlying mechanisms through which Generative AI Marketing influences Purchase Intention remain insufficiently explored, particularly in the context of Indonesian e-commerce users. This study aims to examine the effect of Generative AI Marketing on Purchase Intention by investigating the mediating roles of Perceived Personalizationand Customer Trust. A quantitative explanatory research design was employed using a survey of 50 Indonesian e-commerceusers who had previously interacted with AI-powered marketing features. Respondents were selected through purposive sampling, and the collected data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS software. The findings reveal that Generative AI Marketing has a positive and significant effect on Perceived Personalization, Customer Trust, and Purchase Intention. Furthermore, Perceived Personalization significantly enhances both Customer Trust and Purchase Intention, while Customer Trust positively influences Purchase Intention. The mediation analysis further demonstrates that Perceived Personalizationand Customer Trust, both individually and sequentially, mediate the relationship between Generative AI Marketing and Purchase Intention. These findings suggest that the effectiveness of Generative AI in digital marketing extends beyond its capability to generate personalized recommendations; it also depends on its ability to foster consumers' perceptions of personalization and build trust, which ultimately encourage Purchase Intention. This study contributes to the literature by integrating the Stimulus–Organism–Response (S–O–R) Theory, Technology Acceptance Model (TAM), Relationship Marketing Theory, and Signaling Theory into a comprehensive framework for explaining consumer Purchase Intention in AI-driven e-commerce. The findings also provide practical insights for e-commerce businesses in designing AI-powered marketing strategies that emphasize personalization, transparency, and trust to strengthen customer engagement and purchasing behavior.