Factors Influencing the Intentions of Non-Muslims in India to Accept Islamic Finance as an Alternative Financial System
Bibliographic record
Abstract
This study aims to investigate the impact of attitude & behaviour, subjective norms and religiosity on intention to accept Islamic finance as an alternative financial system in India. An adopted questionnaire with 20 items was used to gather data from 932 respondents. The sample was taken from north India and three religions: Hindu, Christianity and Sikhism acted as strata for this study. The data was tested for association between three variables on intention to accept Islamic finance. Statistical tools like reliability analysis, independent variable correlation, sample adequacy and regression analysis were used. The findings of this study suggest that subjective norms and religiosity is the influencer in accepting Islamic finance as an alternative. Attitude & behaviour has no relevance in developing intentions about accepting Islamic Finance. The research is original and its implications will be helpful for targeting non-Muslim customers with customized products and create awareness among people that Islamic finance is not only for Muslims.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".