Privacy concerns and willingness to disclose personal data in exchange for personalization: A study of telematics car insurance through the lens of protection motivation theory

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Volume Title

School of Business | Master's thesis

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Date

2025-05-21

Department

Major/Subject

Mcode

Degree programme

Master's Programme in Marketing

Language

en

Pages

81

Series

Abstract

Personalization plays a pivotal role in modern digital businesses by tailoring experiences to individual users based on their data, preferences, and behaviors. This approach can, for example, enhance user satisfaction, improve decision-making, and foster stronger customer relationships. However, it also raises significant privacy concerns due to the extensive collection and processing of personal information it entails. This thesis studies how individuals navigate the trade-off between the benefits of personalization and the risks of personal data disclosure, using telematics-based insurance as the empirical context – where personalized insurance offerings are made possible through the continuous collection and analysis of personal and driving behavioral data. Grounded in Protection Motivation Theory (PMT), the study conceptualizes privacy concern as a form of protection motivation triggered by privacy threats. The model incorporates PMT’s core constructs – threat appraisal (perceived severity and vulnerability), coping appraisal (self-efficacy), and evaluations of privacy risks and rewards – to explain consumers’ privacy concerns and their willingness to disclose personal information. A quantitative research approach was employed, using survey data gathered from users and potential users of telematics-based insurance. Structural Equation Modelling (SEM) was applied to examine the hypothesized relationships. Given the study’s focus on prediction and explained variance, SEM was chosen for its strength in handling models with multiple interdependent relationships and observed indicators. The measurement model was evaluated to establish the reliability and validity of the constructs, followed by an assessment of the structural model to examine the hypothesized relationships among the latent variables. The measurement model demonstrated good reliability and validity across all constructs, confirming the robustness of the instruments and supporting the model’s adequacy for structural analysis. The findings of structural model revealed that perceived severity and vulnerability of privacy threats increase privacy concerns, while self-efficacy reduces them, highlighting the role of customers’ confidence in mitigating the risks. Privacy concerns negatively impact the willingness to disclose personal information, emphasizing privacy concerns as a key barrier to data sharing. Moreover, perceived costs of protecting privacy intensify concerns, whereas perceived rewards from personalization do not statistically significantly reduce them. The model demonstrated strong predictive ability, offering valuable insights into the psychological drivers of privacy decisions that resonate most strongly with consumers. The study contributed to the understanding of the privacy trade-off in personalization and offered practical implications for insurance companies and policymakers in addressing privacy concerns and mitigating risks of potential discrimination. Additionally, it contributed to the limited body of research on privacy concerns within the insurance industry context.

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Supervisor

Vassinen, Antti Abel

Keywords

protection motivation theory, personalization, privacy concerns, willingness to disclose personal data, telematics car insurance, structural equation modeling (SEM)

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https://urn.fi/URN:NBN:fi:aalto-202506124606

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[gradu] Kauppakorkeakoulu / BIZ