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Record W2800962226 · doi:10.5539/ibr.v11n5p42

An Exploratory Study on Customers’ Selection in Choosing Islamic Banking

2018· article· en· W2800962226 on OpenAlexvenueno aff
Mahiswaran Selvanathan, Dineswary Nadarajan, Amelia Farzana Mohd Zamri, Subaashnii Suppramaniam, Ahmad Muzammir Muhammad

Bibliographic record

VenueInternational Business Research · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIslamReputationIslamic bankingBusinessMarketingQuality (philosophy)Simple random sampleSelection (genetic algorithm)Retail bankingBanking industryExploratory researchAccountingComputer science

Abstract

fetched live from OpenAlex

Islamic banking industry is growing very rapidly by offering high quality schemes where free interest and better customer services are provided. Strong Islamic banking movements is forcing the industry to come up with new strategy to compete the market. The purpose of this study is to determine and identify the factors that influence the consumers to choose Islamic bank products or services. Data is collected using non probability simple random sampling around Selangor area. The analysis shows that bank reputation, religious and cost benefit factors are significant which influence customers’ selection on Islamic banking. Bank reputation and cost benefit has positive relationship on choosing Islamic banking. Convenient is not significant factor of influence customers’ selection on choosing Islamic banking. As a conclusion, the study prove that religion shows negative relationship on choosing Islamic banking. This shows that religion is not the main factor but the risk of selecting a bank is important.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.128
Threshold uncertainty score0.894

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.061
GPT teacher head0.359
Teacher spread0.298 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations30
Published2018
Admission routes1
Has abstractyes

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