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Record W2001094742 · doi:10.5539/ibr.v5n12p83

Traditional Banks Conversion Motivation into Islamic Banks: Evidence from the Middle East

2012· article· en· W2001094742 on OpenAlexvenueno aff
Farooq Alani, Hisham Yaacob

Bibliographic record

VenueInternational Business Research · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIslamIslamic bankingPhenomenonProfit (economics)BusinessIslamic financeShariaAccountingCommerceMarketingEconomics

Abstract

fetched live from OpenAlex

The increasing awareness on Islamic banking and finance has created a huge demand for shari’ah based or shari’ah compliant products. Banks, especially are trying to capture this huge market by either converting themselves into a full fledge Islamic banks or opening a window for the Islamic based transactions. This study highlights the reasons why traditional banks turned towards Islamic model. The phenomenon of traditional banks turning into Islamic form was reinforced by the success of these banks averting the recent world economic crises. This study examined this phenomenon through four axes, first is the law, second is the risk and profit rates, third is about the customer needs for Islamic products, final one the lessons of successful conversions the region. This study concludes that there is a statistical significance between the trend towards the switching to Islamic banks and the low risk nature with high levels of profits that characterized Islamic banks. Also, religious (Islam) has influenced the conversion towards Islamic model. The study put forward several recommendations. Perhaps the most important call is to unite, integrate and increase interdependence between the Islamic banks. It is also important for the Islamic banks to innovate and create new products for the benefit of the consumers at large.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.266
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.003
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.178
GPT teacher head0.312
Teacher spread0.134 · 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; both teacher heads agree on what is shown here.

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

Citations17
Published2012
Admission routes1
Has abstractyes

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