The impact of internal forces on acceptance of takaful by insurance policy-holders in India
Bibliographic record
Abstract
Purpose Insurance is a modern risk-management tool. Although the idea is novel, its practice is not free of interest, uncertainty and elements of gambling. Takaful has been introduced as an alternative to modern insurance. India has an established insurance industry, and although the country has the second largest Muslim population in the world, takaful has not been introduced there. Moreover, no research has examined how internal forces affect policy-holders to buy new insurance products such as takaful in India. This study aims to examine whether internal factors influence individual insurance policy-holders to open up to takaful . As internal factors reflect the innovative nature of policy-holders, this paper seeks to determine whether there is significant difference in the innovative nature of two independent sample groups (e.g. between Muslims and non-Muslims) in participating in takaful . Design/methodology/approach New product adoption theory is used in developing the hypotheses and a questionnaire. Snowball sampling method is used in this survey, with a sample size of 909 respondents, including Muslim and non-Muslim policy-holders. The internal forces that encourage potential policy-holders to participate in takaful is the independent variable here, while the respondents’ actual willingness to participate in takaful is the dependent variable. Religion and level of education are used as control variables, and regression and T -tests are performed to analyze the data. Findings Results show that the internal factors have significant impact at 1 per cent on the acceptance of takaful by policy-holders. There is also a significant difference in the innovative nature between Muslims and non-Muslims. Mean values from the T -test show that Muslims are more innovative than non-Muslims in India, offering a good sign for India to start offering takaful , as Muslims could be the core customer base. Research limitations/implications This study focuses on internal factors influencing individual policy-holders’ willingness to participate in takaful . The findings can be the starting point for future research exploring the influence of external factors on such willingness to participate with potential benefits to local authorities, investors, insurance companies and the public in India. Originality/value This study provides crucial information about the demand side of takaful in India. The innovative nature of Indian policy-holders signals positive potential for operators to offer takaful in India and to concerned regulatory bodies to expedite its introduction to the market.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".