Information Communication Technology-Enabled Platforms and P&C Insurance Consumption: Evidence from Emerging & Developing Economies
Bibliographic record
Abstract
Many business domains are benefitted with the fast diffusion of Information Communication Technology (ICT) -enabled platforms in society. But, ICT-enabled services have not received similar popularity across all domains despite rapid growth in ICT-based services. In the area of property and casualty insurance, the ICT adoption rate has been slow compared to other domains. The earlier literature in the domain attributed it to the product complexity. This paper argues that the impact of ICT-enabled platforms may have different outcomes on property and casualty insurance consumption due to risk aversion. In insurance literature, risk aversion has been proxied by many factors. As per the literature, secondary education has a positive impact on PCI (Property & Casualty Insurance) consumption while tertiary education has a negative impact on PCI consumption. In the context, this current study has empirically tested the impact of ICT-enabled platforms on PCI consumption in emerging markets and developing countries. The study found that secondary education has no significant impact on adoption of ICT platforms. However, tertiary education reduces the negative impact of ICT-enabled platform whereas Uncertainty Avoidance Index (UAI) increases the magnitude of negative impact of ICT adoption on PCI consumption.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".