When customers are willing to disclose information in the insurance industry: A multi-group analysis comparing ten countries
Abstract
Purpose: The purpose of this paper is to show antecedents of customers’ information disclosure in the insurance industry and demonstrate central levers that foster customers’ information disclosure to companies in the insurance sector. Design/methodology/approach: A conceptual model is presented, which is empirically tested with 3,494 insurance customers from ten counties with structural equation modelling and multi-group analysis. Findings: Customer value in the insurance industry consists of three factors (customer value provided by the company, the agent, and the product) and affects information disclosure directly and indirectly (via satisfaction and trust). Research limitations/implications: Antecedents of customers’ information disclosure in the insurance industry were identified. Moreover, the authors show that, in line with resource exchange theory, customers are willing to disclose personal and behavioral data to an insurance company in exchange for lower premiums or additional services. Practical implications: Customers expect benefits in exchange for their personal data. In combination with new technologies (e.g. smartphones or wearables), companies can offer tailored products to their customers and can create a win -win situation for customers as well as insurance companies. Originality/value: The paper identifies the antecedents of customers’ information disclosure in the insurance industry with a conceptual model. This model is tested in ten countries and offers insights in established (e.g. USA) as well as emergent markets (e.g. Brazil).
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Value with respect to insurance services comprises firms, staff or agent and the insurance policy.
“Steiner and Maas (2018) described that value with respect to insurance services comprises of firms, staff or agent and the insurance policy.”
From Health insurance policy renewal: an exploration of reputation, performance, and affect to understand customer inertia (2022) · cites this paper as Steiner and Maas (2018)
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- Authors
- Steiner, P. H., & Maas, P.
- Affiliations
- University of St.Gallen (CH)
- Venue
- International Journal of Bank Marketing
- Publisher
- Emerald
- Published
- 2018-05-24
- Volume
- 36
- Issue
- 6
- Pages
- 1015-1033
- ISSN
- 0265-2323 (ISSN-L)
- OpenAlex
- W2803277171
- Cited by
- 24
- References
- 89
- Citations by year
- 2025: 3 · 2024: 4 · 2023: 4 · 2022: 6 · 2021: 4 · 2020: 2 · 2019: 1
- FWCI
- 3.98
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- International Journal of Bank Marketing (publishedVersion)
- Alexandria (UniSG) (University of St.Gallen) (submittedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Customer Service Quality and Loyalty
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Keywords
- customer information disclosure, insurance customers, customer value, customer satisfaction, consumer trust, resource exchange theory, personal data, behavioral data, insurance agents, insurance products, premium discounts, wearable devices, cross-national comparison
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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