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The ongoing digitalization is accompanied by a plethora of new types of consumer data, enabling firms to deliver highly personalized services. Thus, data-based insurances (i.e. tariffs tailored to data collected about insurants’ behavior) gain increasing prevalence (Huang & Rust, 2013). However, these insurances remain challenging for firms, as they ground on consumers’ willingness revealing personal data, while consumers fear an intrusion of privacy (Mothersbaugh et al., 2012). Despite the growing need in understanding the key drivers of consumers’ adoption of these potentially intrusive tariffs (Ostrom et al., 2015), yet, extant literature focusses on data disclosure in e-commerce. However, prior research findings might not necessarily generalize to consumers’ value assessment and adoption of data-based insurances, as their revolutionary nature comes with a new variety of risks associated with data tracking. E.g., based on tracked health data via wearables (e.g. sleep rhythm), insurers may draw extensive conclusions about individuals’ lifestyles or health status.
Grounded on Privacy Calculus Theory (Culnan & Armstrong, 1999), two experimental studies show that type of compensation for disclosing data (non-financial [e.g. material gifts] vs. financial rewards [e.g. discounts]) interacts with the sensitivity of tracked data (low vs. high), thus altering cost-benefit assessments and subsequent behavior. Further, this interactional effect is contingent on the insurance context. Study 1 examines this interplay in a life insurance context, showing if financial rewards are provided, higher levels of data sensitivity increase purchase intentions of data-based insurances. Increased utility perceptions (due to an increased fit to consumers’ needs) are the underlying mechanism, mediating the positive effect of data sensitivity on purchase intention. Contrary, we find a negative effect of data sensitivity on purchase intention, if non-financial rewards are provided, driven by increased risk perceptions. Whereas study 1 focuses on a life insurance context, study 2 expands this perspective to car insurances. In contrast to health data, consumers can influence tracked driving behavior (e.g. braking behavior) more actively, hedonic motives are more salient, and the data give a less comprehensive picture of consumers’ lifestyles. Results show the mechanisms identified in study 1 are reversed in this new context. Study 2 demonstrates that higher levels of data sensitivity now increase purchase intentions, if non-financial rewards are provided. Enhanced utility and hedonic value perceptions are the underlying mechanisms. These benefits then outweigh risk perceptions and drive subsequent consumer response.
In sum, our findings provide service marketers with a nuanced understanding of key contributors to and inhibitors of consumer adoption of data-based insurances, as well as related underlying value assessments. This research contributes to the discussion about distinct rewards’ effectiveness for data disclosure (Hui et al., 2007) in a service context, and furthermore adds to the ongoing debates on boundaries of highly personalized, data-based services (MSI, 2016).