This paper investigates the influence of recommendation badges in consumer decision-making. Online recommendation badge is a type of digital nudge that affects customer’s behavior to a desired direction while preserving all the available options and maintaining the same economic incentives. With the advances of Artificial Intelligence/Machine Learning (AI/ML), algorithmically driven nudges have been widely employed to influence individuals and collective behaviors that include undesired consequences for both end-users and firms. Drawing on nudge literature and cognitive theory, this study focuses on two types of recommendation badges in the world’s largest e-commerce platform and proposed the concept of ambiguous badge (e.g., Amazon’s Choice) and specific badge (e.g., Best Seller) based on the accountability of the recommendation generation and badge placement. More specifically, we hypothesize that: (1) consumers’ preference will be modified by the badge when the recommendation doesn’t not match their preference, (2) ambiguous badge will modify consumer’s preference more than specific badge, (3) recommendation badge from a large and well-known platform is more likely to affect user decision and modify consumer’s preference than from a small and unfamous platform, (4) consumers have higher choice confidence with specific badge than that with ambiguous badge. This study will contribute to the literature on nudge, biased recommendation agent/systems research, and the dark side of IS by unveiling the impact of accountability on user preference manipulation in online recommendation.

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