Impact of AI Recommendations’ transparency in E-Commerce: Understanding the impact of covert and overt personalization

Abstract

With the increasing usage of Artificial Intelligence in marketing and e-commerce, techniques such as product recommendations have been enhanced through usage of consumers’ behavior and preferences to optimize their efficiency. While this has many benefits, it also raises concerns over data privacy and usage. This study addresses a gap in the current literature by examining how the transparency of AI recommendations - defined as overt and covert - affects perceived benefits and privacy concerns, and how these effects may vary depending on the customer journey stage that the recommendation is made. The research was based on a 2x2 between-subjects experimental design with transparency (overt vs covert) and customer journey stage (pre-purchase vs post-purchase) as the two manipulated factors. The results indicate that transparency alone does not have a significant impact on perceived benefits or privacy concerns, refuting some assumptions in previous research. However, higher perceived benefits were associated with lower privacy concerns. Additionally, the impact of transparency on perceived benefits was significant in the pre-purchase phase, but not in the post-purchase phase. These findings offer a new look that connects transparency and timing in the context of recommendations in e-commerce, while expanding the existing literature to the realm of AI-generated recommendations.