Too Sensitive, Too Much? Triggers and Consequences of Perceived Surveillance in Digital Data-Driven Advertising

Digital data-driven advertising requires the collection, processing, and storage of personal data, which raises questions on its impact on individuals’ perceptions of dataveillance. Yet it remains unclear which specific characteristics of the data drive such perceptions. This study examines how the sensitivity and amount of data used in digital data-driven advertisements influence perceived surveillance and its subsequent impacts on advertising effectiveness and privacy resignation. Through a 2 (data sensitivity: low vs. high) × 2 (data amount: single- vs. multiple-type) online experiment (N = 1,256), we find that ads using high-sensitivity data or multiple data types evoke stronger perceived surveillance than those using low-sensitivity data or a single data type, although increasing the amount of data when sensitivity is high does not further increase perceived surveillance. Consequently, perceived surveillance negatively impacts attitudes toward the ad, brand, and platform, but does not significantly influence privacy resignation. These findings extend our understanding about triggers of perceived surveillance by examining two key features in data-driven advertising: data sensitivity and data amount. They also illustrate the unintended consequences of data use on not only the ad and the brand, but also the platform, highlighting the important role of perceived surveillance in shaping consumer responses.