Perceived inclusion of Islamic finance: The effects of attitudes, experience, literacy, religiosity, and social influences
Bibliographic record
Abstract
The existing literature lacks demand-side perception studies on Islamic finance and its determinants, especially in developing markets. This study examines the influences of attitudes towards Islamic finance (ATTIF), financial experience (FE), Islamic financial literacy (IFL), religiosity (RL), and social influences (SI) on the perceived inclusiveness of Islamic finance (PIIF). This study used purposive sampling to obtain 400 questionnaire respondents from Zanzibar, Tanzania. The Smart-PLS (4) was used for analysing the data. We discovered mixed perceptions among the respondents regarding the inclusiveness of Islamic finance. Furthermore, the findings revealed a positive and significant influence of ATTIF, FE, SI, and RL on PIIF. ATTIF mediates the effects of RL and SI. The effect of ATTIF on PIIF is mitigated by IFL. The two most significant factors that determine PIIF are SI and ATTIF. RL was high among respondents but less important in determining PIIF. Islamic financial institutions should design products that fit society’s needs by considering socio-cultural and economic dynamics. These findings have important policy implications for improving the inclusiveness of Islamic financial markets. This study provides new insight into the inclusion of Islamic finance and its determinants.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".