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The Value of Privacy - Empirical Research, Using Drivers as an Example

Author

Listed:
  • Adam Sliwinski
  • Lukasz Kurylowicz

Abstract

Purpose: The paper assesses expectations regarding the amount of discount in insurance premium, which could compensate insured for loss of privacy should it transpire that one’s driving style is to be monitored. Design/Methodology/Approach: The analysis is carried out using data collected through a survey conducted on a sample of clients of insurance companies. As part of the analysis, Pearson's Chi-Square Test is used. It is based on comparing empirical values with expected values, where expected values are treated as variables that would occur if there were no relationship between them. Findings: Analysis showed that 75% of the respondents who would allow the possibility of concluding a UBI type contract at all would expect a discount of up to 30% of their current premium. While a maximum of 15% discount relates to as many as 37,45% of the respondents. The factors like gender, age, education, and place of residence of the respondents influence the level of expectations. Practical Implications: The paper conducts an empirical study on the impact of the loss of privacy on the discount in insurance premium thus the topic might be of interest to insurance companies and their clients making the final insurance purchase decision. Originality/Value: Is the first study when UBI expectations are explained in terms of Poland one of the biggest insurance market in CEE region. The findings might have a practical and science value. From practical point of view, it gives the insurance professionals the knowledge of the expected level of possible decrease in premium that might result in intensification of UBI. From scientific point of view the study provides useful information for further research especially in terms of factors that determine the level of expectations.

Suggested Citation

  • Adam Sliwinski & Lukasz Kurylowicz, 2021. "The Value of Privacy - Empirical Research, Using Drivers as an Example," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 1), pages 936-953.
  • Handle: RePEc:ers:journl:v:xxiv:y:2021:i:special1:p:936-953
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    References listed on IDEAS

    as
    1. Aidan Hollis & Jason Strauss, "undated". "Privacy, Driving Data and Automobile Insurance: An Economic Analysis," Working Papers 2008-13, Department of Economics, University of Calgary, revised 14 Feb 2008.
    2. Sebastian Derikx & Mark de Reuver & Maarten Kroesen, 2016. "Can privacy concerns for insurance of connected cars be compensated?," Electronic Markets, Springer;IIM University of St. Gallen, vol. 26(1), pages 73-81, February.
    3. Lilia Filipova, 2007. "Monitoring and Privacy in Automobile Insurance Markets with Moral Hazard," Discussion Paper Series 293, Universitaet Augsburg, Institute for Economics.
    4. Lilia Filipova, 2007. "Monitoring and Privacy in Automobile Insurance Markets with Moral Hazard," Working Papers 026, Bavarian Graduate Program in Economics (BGPE).
    5. Tamara Dinev & Paul Hart, 2006. "An Extended Privacy Calculus Model for E-Commerce Transactions," Information Systems Research, INFORMS, vol. 17(1), pages 61-80, March.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Privacy; insurance telematics; client acceptance; usage-based insurance (UBI).;
    All these keywords.

    JEL classification:

    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design

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