AI-Driven Personalization and Consumer Decision-Making: How Perceived Value and Cognitive Bias Shape Consumer Responses

Objective: This study aims to analyze the influence of AI-driven personalization on Consumer Decision-Making through the mediating role of Perceived Value and to test the moderating role of Cognitive Bias in the relationships between AI-driven personalization and perceived value, as well as between Perceived Value and Consumer Decision-Making. Research Design & Methods: This study used a quantitative approach by surveying consumers who have experience interacting with AI-based digital platforms. The study population included all consumers who had experience using AI-based digital platforms in the past six months. The sample size was 240 respondents. Data were collected through a structured questionnaire and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Findings: The results of the study indicate that AI-Driven Personalization does not directly influence Consumer Decision-Making. However, AI-Driven Personalization has been shown to have a positive effect on Perceived Value, and Perceived Value has a positive effect on Consumer Decision-Making. In addition, Perceived Value has been shown to significantly mediate the relationship between AI-Driven Personalization and Consumer Decision-Making. The results of the moderation test indicate that Cognitive Bias does not moderate the relationship between AI-Driven Personalization and Perceived Value, but strengthens the relationship between Perceived Value and Consumer Decision-Making. Contribution: This research provides a theoretical contribution by expanding the application of SOR Theory in the context of AI-based marketing by proving that the influence of AI-based personalization on consumer decisions occurs through the mechanism of forming perceived value. This research also enriches Behavioral Decision Theory by showing that psychological factors in the form of Cognitive Bias play a role in strengthening the transformation of value evaluations into actual consumer decisions. Novelty: The novelty of this research lies in the integration of AI-Driven Personalization, Perceived Value, and Cognitive Bias in a single conceptual model that combines the perspectives of SOR Theory and Behavioral Decision Theory. This research shows that the influence of AI-Driven Personalization on Consumer Decision-Making does not occur directly, but rather through Perceived Value as a mediator, while Cognitive Bias acts as a moderator that strengthens the relationship between Perceived Value and Consumer Decision-Making.