Strategic Planning in the Malaysian Insurance Industry
Bibliographic record
Abstract
This article examines the extent of planning and the key essential characteristics of planning in the Malaysian insurance industry. A survey was conducted with the support of the Malaysian Insurance Institute. A total of 59 insurance companies involved in life, general and composite businesses were given the questionnaire. A total of 36 companies responded the questionnaire. The findings showed that all the insurance companies con-ducted formal planning. However, only one quarter have a planning unit/section. The extent of planning conducted varies according to the type of insurance business and existence of planning unit/section. The Chief Executive Officers are highly involved in the planning activities. Planning activities are focused at the functional level, short term, and the medium term. There are more firms conducting an internal analysis than an external analysis. As for corporate objectives, underwriting profit is rated as most important and the sum insured as least important. It was also found that nearly 89 % of the firm pursed a combination strategy of growth and stability. Overall, nearly 75 % considered the effectiveness of the planning system in achieving corporate goals, and about 37 % considered it as most important towards better performance. Implications of the findings are also discussed. 1.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".