The Role of Trust and Data Sharing Willingness in Users’ Acceptance of Insurance Telematics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Understanding customers’ attitudes toward insurance telematics can significantly affect knowledge management practices within the insurance industry. This study explores the factors affecting users’ acceptance of insurance telematics. A theoretical adoption model was proposed by extending the technology acceptance model and theory of planned behavior with a priorly proved construct trust and a new construct data sharing willingness (DSW). Trust is built upon perceived usefulness, ease of use, and DSW in this research. The findings display that trust is crucial in increasing a positive feeling toward insurance telematics, which affects users’ acceptance of insurance telematics, along with subjective norms. DSW was found to impact users’ level of trust significantly. Theoretically, these findings imply that trust offers a significant passage for factors influencing consumers’ adoption of technology. Practically, the findings shed light on assisting the auto insurance industry in its digital transformation and designing interventions to improve consumers’ adoption of insurance telematics. The authors also suggest regulators take actions to oversee the technology to ensure customer privacy protection and fair market competition.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it