The Role of Trust and Data Sharing Willingness in Users’ Acceptance of Insurance Telematics
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".