Marketing Islamic Financial Services: A Review, Critique, and Agenda for Future Research
Bibliographic record
Abstract
Islamic finance has experienced rapid growth globally, surpassing the USD 2 trillion mark in 2017. As a result, the literature related to Islamic finance and banking is rather rich. Despite the richness of the literature, our knowledge of the marketing issues related to Islamic finance is modest and somewhat ambiguous. Therefore, we review several decades of research about the Islamic finance in various parts of the world. We identify and discuss three main research themes that draw on different conceptualization and theoretical lenses. After synthesizing their respective findings, we propose several avenues for future research that integrate these three research themes with the goal of developing a more nuanced understanding of Islamic finance and its marketing. While we believe that our review will mainly serve as a crucial reinvigoration and launch point for future research on Islamic finance marketing, it is also of great practical benefit for policymakers of various countries and especially managers of financial service firms interested in marketing Islamic banking and financial services to their 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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".