A Comparative Analysis of Customers’ Satisfaction for Conventional and Islamic Insurance Companies in Pakistan
Bibliographic record
Abstract
Islamic finance and insurance are penetrating in international markets especially after world economic crisis since 2008. This research is an attempt to analyze customers’ satisfaction for the services of conventional and Islamic insurance companies in Pakistan. A modified SERVQUAL model is used to measure the service quality in the constructs of reliability, responsiveness, empathy, convenience and Shariah compliance. For this purpose primary data of 400 customers, 173 from conventional and 227 from Islamic insurance companies, is estimated through propensity score matching as well as linear, non-linear and non-parametric classification techniques. The results on service quality indicate significant gap between expectation and perception of overall insurance industry. No significant difference of service quality is found between conventional and Islamic insurance companies in the constructs of reliability, responsiveness, convenience and empathy. The findings suggest a significant improvement in the service quality of conventional and Islamic insurance industry. Particularly, the conventional insurance companies need to focus on young people, private employees and lower income groups, whereas the Islamic insurance companies have to put more efforts to improve Shariah compliance and to attract self-employed and higher income groups.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".