Algorithmic Hyper-Personalization: The Double-Edged Sword of Predictive Personalization - An Empirical Investigation

Abstract

Algorithmic hyper-personalization has emerged as a dominant strategy in digital marketing, enabling brands to deliver unprecedented levels of relevance and convenience. However, the same technologies that create value for consumers also risk triggering perceptions of surveillance, invasion of privacy, and psychological discomfort often described as "creepiness." This study investigates the paradoxical nature of hyper-personalization, examining the tipping point where perceived benefits are outweighed by psychological costs. Drawing on privacy calculus theory, reactance theory, and the persuasion knowledge model, we develop and test a theoretical model that balances personalization benefits against privacy concerns. Using survey data from 487 consumers in Pakistan, we find that hyper-personalization follows an inverted U-shaped relationship with customer loyalty: moderate levels of personalization maximize loyalty, while excessive personalization diminishes it through increased perceptions of creepiness. Perceived creepiness fully mediates the relationship between hyper-personalization and loyalty, with privacy concerns and perceived surveillance serving as key mechanisms. Consumer characteristics including privacy literacy, trust propensity, and prior brand relationship moderate these effects. The findings contribute to personalization literature by identifying the curvilinear nature of personalization effects and establishing creepiness as a critical mediating mechanism. For practitioners, we provide actionable guidelines for calibrating personalization intensity, implementing transparency measures, and designing consent mechanisms that preserve consumer trust.

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