Islamic Banking-A Cross Cultural Patronage Study among the Students in Chennai
Bibliographic record
Abstract
Islamic Banking is any banking activity performed on the basis of Islamic laws of jurisprudence. The two fundamental sources of Islamic law are the Quran, (which is the Holy Book of Muslims) and the Sunnah, (which are the Traditions of the final Prophet Muhammad (Peace Be upon Him)). Islamic methods of finance revolve around a few basic concepts, the most important of which is the prohibition of ‘Riba’ or interest. Although this concept is relatively new in the Indian context, the Islamic financial world is now a global force to reckon with and India might miss out if the opportunity is not capitalized. It is worth mentioning that the total value of Islamic business stands at $6.7 trillion and is growing at a rate of 16-20% a year. Also, the fact that many non-Muslim countries like UK have opened full-fledged Islamic banks point to the fact that Islamic finance or Islamic banking is not limited to Muslims alone. Therefore, India’s ambitions of becoming an Asian financial hub cannot be met without capitalizing into the pool of Islamic Finance. The purpose of this paper is to study the level of awareness of students of private universities in Chennai and to study the cross cultural patronage factors towards Islamic Banking. The findings reveal that the respondents have a positive and favorable patronage towards Islamic Banking due to their perception about its success in other parts of the world.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".