Big Data in Insurance: Understanding Customer Resistance and Ethical Boundaries

Big data is revolutionizing the insurance industry by enabling personalized risk assessments and pricing, yet these practices also raise ethical concerns. This study fills a gap in the literature by integrating the theories of contextual integrity and protected values to understand customer responses to the expanding use of big data applications in car insurance. These frameworks conceptualize ethical boundaries as limits to acceptable information flows and to trade-offs when data practices conflict with contextual norms or non-negotiable values. Using cross-national survey data from Switzerland (N = 1,080) and the United States (N = 1,084), we investigate customer objections to insurers collecting data on driving behavior, social media activity, online shopping, or creditworthiness. Our findings show that objections vary significantly by context, with collecting driving style data perceived as more acceptable than using credit information. While perceived benefits and trust in institutions lower objections, endorsement of protected values intensifies them. Customers holding strong protected values also exhibit absolute trade-off resistance, even when offered monetary incentives. A cluster analysis reveals four distinct customer segments, each demonstrating unique attitudes and resistance levels. Overall, the findings underscore the importance of acknowledging ethical boundaries and tailoring data strategies to heterogeneous customer segments.

The online version contains supplementary material available at https://doi.org/10.1007/s44206-026-00252-8.