Management summary: Research problem Ongoing digitalization results in both threats as opportunities for the insurance sector. Increased transparency stimulates switching behavior and shifts the insurance market to a more price based competition. Together with recent developments such as the ban on intermediary commissions and the separation of banking and insurance activities, the traditional business model is put under pressure. By fully reaping the benefits of mobile technologies, such as portability, social interactivity, context sensitivity, connectivity and individuality, a variety of opportunities for innovative insurance services arises. A more differentiated product portfolio can shift the price based competition to a more quality focus which enables insurers to operate in more niche markets focusing on higher margins. In the last few years, privacy concerns associated with the consumer use of mobile technologies, have been the subject of many research papers. A number of privacy studies empirically verified the negative effect of perceived privacy concerns on the intention of use online and mobile services. As the disclosure of personal information is often necessary in obtaining online and mobile services, privacy concerns could inhibit people’s intention to use them as well. This could have major implications for the adoption of mobile insurance since privacy concerns regarding the insurance industry are already relatively high in general. Therefore, it is essential, in the development of future mobile insurance services, to understand the role of associated privacy concerns. Accordingly, this study aims to increase understanding of mobile insurance related privacy concerns, its relation on consumer’s ‘likelihood of use’ and potential compensating factors as perceived usefulness and expected monetary benefits. Therefore, the objective of this research is to further develop understanding towards the mitigating effect of perceived usefulness and monetary rewards on privacy concerns regarding the likelihood of use for mobile insurance services. In line with this objective the following main research question is developed: RQ In what way can privacy concerns, affecting the likelihood of use mobile insurance services, be mitigated by expected monetary benefits and perceived usefulness? Domain on Mobile Insurance For a clear and consistent understanding of this research question the definition of mobile insurance for this study is defined as “insurance products and services based on context sensitive mobile technologies”. Hereby insurance products and services involve all direct customer focused activities of an insurer. Thus, both the insurance policy itself and supportive services are involved. Context sensitivity of mobile technologies involves the ability to both gather and respond to real or simulated data unique to current location, environment, and time. Mobile insurance covers a broad field of insurance services. In order to get a better understanding on the scope of mobile insurance a categorization is made. This categorization is based on an explorative scan to all worldwide mobile insurance services. These worldwide mobile insurance services are subsequently categorized on its consumer functionalities and validated with insurance industry and technology experts. The final categorization, with a brief elaboration per category is listed below: 1. Usage based insurance; With a usage-based insurance premium, consumers pay only premium for actual use of their insurance. 2. Behavioral rewarding; By rewarding customers for less risky behavior, the insurer is trying to reduce the risk of accidents. 3. Up-to-date insurance package; By using personal (context sensitive) information of consumers, relevant personalized insurance products could be provided. 4. Preventative information services; Consumer context information offers insurers the opportunity to provide consumers with relevant context related preventative information. 5. Accident detection & prevention; By detecting (potential) accidents as early as possible, damages could be prevented and minimized. 6. Mobile accessibility; Mobile technologies facilitate a communication channel for sales and services. 7. Personal dashboards; By measuring individual behavior, insight could be provided in risk profiles of consumers to increase risk awareness. 8. Additional informative services; Context sensitive information offers opportunities for several semi-insurance services. Theoretical background on the concept of privacy Within literature a variety of definitions and interpretations for privacy is present, however a unified account of privacy has yet to emerge. This study interprets the definition of privacy as a tradable interest; “an interest that individuals have in sustaining a ‘personal space’ free from interference by other people and organizations”. Subsequently, this definition is operationalized to facilitate the measurement of privacy. A commonly used (reverse) operationalization of privacy in literature is the measurement of privacy concerns. Therefore, privacy is measured in this study by privacy concerns. Due to its plurality and inconsistency, a unified account for privacy is still absent in literature. Some scholars used another approach and instead of searching for an inclusive definition of privacy, they developed a typology for privacy. Recent literature defined seven types of privacy of which three are relevant for the application of (current) mobile insurance: Privacy of location and space "The right to move about in public or semi-public space without being identified, tracked, or monitored." Privacy of behavior and action "The ability to behave in public, semi-public or one’s private space without having actions monitored or controlled by others." Privacy of data and image "Concerns about making sure that individuals’ data is not automatically available to other individuals and organizations and that people can exercise a substantial degree of control over that data and its use.” A majority of consumers considers the disclosure of personal information as essential in modern life. The disclosure of personal information is however contrary with the definition of privacy; sustaining a ‘personal space’. Consequently, numerous studies consistently concluded that people are very concerned about their online privacy. Aforementioned contradiction imply that individuals consider a utilitarian trade-off between perceived benefits and sacrifices of disclosing personal information. Hereby privacy concerns have to be considered as a sacrifice. Previous literature states that providers can mitigate the negative effect of privacy concerns on the ‘likelihood of use’ in two ways; (1) by offering privacy policies regarding the handling and use of personal information and (2) by offering benefits such as monetary rewards or convenience. These compensating are further operationalized as expected monetary benefits and perceived usefulness. No existence of a direct relation between the construct of privacy concerns, perceived usefulness and expected monetary benefits is found in literature. However, several IT adoption studies in literature suggest an indirect relation through the construct of likelihood of use. Hereby, the likelihood of use is positively affected by the perceived usefulness and expected monetary benefits and negatively affected by privacy concerns. These findings are combined in a conceptual model which is validated for the case of mobile insurance by the explorative assessment. Analysis and results In order to provide an answer on the main research question, two quantitative assessments are conducted. By means of a consumer survey and multiple regression, an explorative assessment is conducted to the relations between the constructs of likelihood of use, privacy concerns, perceived usefulness and expected monetary benefit. Hereby, the conceptual model is validated. By means of a conjoint survey, a more in-depth assessment to the buy-off value of privacy is conducted for all relevant types of privacy, for the case of Pay-As-You-Drive (PAYD) insurance. Explorative assessment The construct of perceived usefulness appears to be in general the strongest predictor for the likelihood of use mobile insurance. The relation between these two constructs is significant for all categories of mobile insurance. Mobile insurance services with a higher perceived usefulness are likely to raise more interest of consumers for future use. The relation between the construct of expected monetary benefits and the likelihood of use shows to be positive as well, however not significant for all categories of mobile insurance. Expected monetary benefits appear not to be a significant predictor for the use of mobile accessibility. Overall it can be concluded that mobile insurance services with a higher expected monetary benefit for the consumer are likely to raise more interest of consumers for future use. In contrast to previous constructs, the relationship between the construct of privacy concerns and the likelihood of use appears to be negative, however not significant for all categories of mobile insurance. Privacy concerns appear not to be a significant predictor for the use of Accident detection and prevention and Mobile accessibility. Overall it can be concluded that mobile insurance service with raised privacy concerns are likely to have a negative impact on the likelihood of use mobile insurance. Altogether, it can be concluded that the likelihood of use mobile insurance services is primarily driven by its perceived usefulness. Thereafter, consumers’ likelihood of use mobile insurance services is driven by raised expectations on accompanied monetary benefits and inhibited by increased privacy concerns. However, not for every category of mobile insurance the predictors have a significant relation with the likelihood of use, no significant contra relations are found. These findings seem to support the relations as found in literature. Conjoint assessment Although the explorative assessment shows us that monetary benefits are not the strongest predictor for consumers’ likelihood of use mobile insurance services, the conjoint assessment is used for a more in-depth analysis to the buy-off value of privacy. For this analysis, the buy-off value of privacy is determined for all individual relevant types of privacy for the case of pay-as-you-drive (PAYD) insurance. PAYD insurance is an automobile insurance whereby the premium is dependent on the actual car-use. Most common used indicators for car-use are mileages, and driving behavior. Respondents are willing to sell their privacy of location and space through continuously disclosing the GPS-location of their car for a financial compensation of 2,27 per month. Privacy of behavior and actions appears to have slightly higher buy-off value since respondents are willing to continuously provide insight in their car-acceleration, car-deceleration and steering behavior, for a financial compensation of 2,98 per month. Regarding the privacy of data and image two buy-off values are determined related to the internal and external (secondary) use of personal information. Hereby, secondary use is operationalized as the unauthorized use of personal information for personalized advertisement. Respondents are willing to sell their privacy of data image for third party advertisement for a financial compensation of 2,77 per month. In contrast to the external use of personal information, respondents are willing to pay a monthly contribution of 2,91 for internal (insurance related) personalized advertisement. However, these outcomes cannot blind be generalized to the entire population, it can be concluded that respondents derive more disutility from external use of privacy related information than internal use. Discussion and conclusion In conclusion, we can say that privacy concern are likely to rise with the use of mobile insurance services. However these concerns can be compensated by both perceived usefulness of the service and an expected monetary benefits. The compensation by the expectation for financial benefits appears to have a smaller effect than compensation by elevated perceptions on the usefulness of a mobile insurance service. However when the expectation on monetary benefits is amplified with a financial compensation, the buy-off values for different types of privacy appear to be rather small. Hereby, consumers perceive their privacy of behavior and action as more valuable than their privacy of location and space. Regarding privacy of data and image, the buy-off value seems to be dependent on the one who exploits their data; the data holder or an external party. While the use of consumers’ personal information for personalized advertisement by the data holder appears to be beneficial, personalized advertisement by third parties is perceived as adversely. This study is the first attempt in literature in which the buy-off value for different types of privacy is determined. As this study proves, is the buy-off value of privacy varying for different types of privacy, supporting its plurality. A plural approach on privacy could provide a more detailed method for future technology acceptance studies. Emerging trends, such as the ongoing digitalization, quantified-self, internet of things and big data require the disclosure of different sets of personal and contextual information. Consequently, different types of privacy may be involved affecting consumer adoption to another extent. Therefore, it is recommended to include a plural construct of privacy in future technology acceptance studies. Further research is recommended to evaluation the value of privacy for other mobile (insurance) services. A comparison between the values of privacy of these individual services may result in interesting insights for technology adoption and privacy literature. By proving the existence of multiple types of privacy dependent on the specific characteristics of concerned (mobile) services, this study validates the findings of Nikou (2012) that IT artifact should no longer be treated as ‘Black-Box’. Further, analysis methods such as factor analysis and structural equation modeling (SEM) have not been applied in the explorative survey. By applying SEM in further research on the explorative dataset to examine both the effect of individual constructs per categories of mobile insurance and a generic constructs on the likelihood of use, could result in interesting insights, in line with Nikou’s (2012) findings, that IT artifact should no longer be treated as ‘Black-Box’.