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Record W2164917790 · doi:10.5539/ass.v11n4p310

Islamic Banking-A Cross Cultural Patronage Study among the Students in Chennai

2015· article· en· W2164917790 on OpenAlexvenueno aff
Yaaseen Masvood, Y. Lokeswara Choudary

Bibliographic record

VenueAsian Social Science · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIslamIslamic bankingBusinessGeographyArchaeology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.303
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2015
Admission routes1
Has abstractyes

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