The rapid advancement of artificial intelligence (AI)-driven personalization in e-commerce has introduced algorithm-based dark patterns that subtly manipulate consumer behavior and potentially distort decision-making processes. This study aims to analyze the effect of personalization-based dark patterns on consumer decision distortion by integrating perspectives from behavioral economics and business ethics. A quantitative approach was employed using a scenario-based survey involving 212 active e-commerce users. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine both direct and indirect relationships among variables. The results indicate that personalization-based dark patterns have a positive and significant effect on consumer decision distortion. Additionally, cognitive biases, particularly loss aversion and anchoring effect, significantly mediate this relationship.
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The Effect of Personalization Algorithm-Based Dark Patterns on Consumer Decision Distortion: Integration of Business Ethics and Behavioral Economics Perspectives on E-Commerce Platforms
Algorithm-based dark patterns significantly contribute to distorted consumer decisions through both direct influence and cognitive mechanisms, confirming that digital consumers are not fully rational, as their decisions are shaped by psychological biases exploited through system design.