Bibliographic record
Abstract
This paper attempts to find out the factors important in developing a suitable marketing strategy for insurance companies in Saudi Arabia. It investigates the reasons for buying insurance by the current users of insurance, reasons for not buying insurance by non-users of insurance and the issues and problems faced by Saudi Insurance industry.The study is based primarily on primary data collected randomly from 500 users of insurance, from 400 non-users of insurance and 80 insurance executives through structured questionnaire in Jeddah city of Saudi Arabia. The three questionnaires were developed in English and translated into Arabic for effective response due to Saudi Culture, and language. The response from the three groups of respondents were analyzed using simple statistical techniques such as percentages, mean, chi-square tests, factor analysis, and ANOVA analysis with the help of Statistical Package for the Social Sciences (SPSS).The results of the study show that the social and regulatory factors played crucial role in the consumer’s decision in purchasing insurance. However it was also found that the public at large is unaware about the benefits of insurance, and various types of insurance products. The insurance companies shall focus of promotional marketing strategies. The marketer’s primary focus should be on promotional activities.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".