Actuarial Risk Management Practices and Firm Performance: The Mediating Role of E-Service Innovation
Bibliographic record
Abstract
Research on actuarial risk management practices (ARMP) and insurance firm performance has revealed inconsistent results. Therefore, a mediating factor such as innovation is needed to bridge between them. Studies exploring the relationship between ARMP and innovation have been largely qualitative. This study offered a quantitative model focusing on the mediating role of e-service innovation between ARMP and firm performance. The hypothesized relationships were tested using a structural equation model (SEM), with a sample from 98 Indonesian insurance companies and WarpPLS 7.0 as the analytical tool. The results indicated that ARMP significantly influenced e-service innovation but was insignificant for firm performance. Furthermore, the findings highlighted the significant role of e-service innovation in insurance firm performance, which implied that e-service innovation acts as a mediator in the relationship between ARMP and firm performance. The practical application of the research findings makes them directly relevant and beneficial to the insurance industry, especially in Indonesia.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".