Determinants of low adoption of Islamic banking in Pakistan
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the reasons behind low penetration of Islamic banking in Pakistan. Specifically, the study investigates the differentiation of Islamic banks (IBs) from conventional banks, the role of religion in choosing Islamic banking and the perception of IBs amongst the consumers. Design/methodology/approach The study uses a mixed-method approach, qualitative research along with a survey of users of conventional and Islamic banking. Factor analysis identified underlying dimensions and cluster analysis ascertained the differences between users and non-users of Islamic banking. Inferential statistics were used to test purported hypotheses. Findings The study finds that the users and non-users both perceive that Islamic banking is not completely interest-free. Furthermore, consumers presume that IBs are more of eyewash and are not truly practicing Islamic banking. Moreover, religion is not a major factor that attracts new users but there are also other important factors in marketing Islamic banking, such as service quality, convenience, branch network, etc. Originality/value This is one of the sparse studies in the field of Islamic banking consumer behaviour, which uses focus groups of users and non-users, and in-depth interviews of experts, to identify the issues and factors considered relevant and important by the users rather than relying only on literature review. Furthermore, it also provides a profile of users versus non-users of Islamic banking which is very useful for segmentation and targeting of customers.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".