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Record W4402842373 · doi:10.5267/j.dsl.2024.8.003

Examining the adoption decision of Islamic electronic banks in Jordan

2024· article· en· W4402842373 on OpenAlexvenueno aff
Mefleh Faisal Mefleh Al-Jarrah, Abdalla Mohammad Al Badarin, Shadi Khalifeh Alahmad, Kholood Ahmed Tanash

Bibliographic record

VenueDecision Science Letters · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIslamBusinessIslamic bankingOperations managementEngineeringGeographyArchaeology

Abstract

fetched live from OpenAlex

This study aims to examine the factors affecting Jordanian Islamic banks’ customers’ adoption of electronic banks. The study relied on quantitative methods, where the study designed a questionnaire based on the Technology Acceptance Model (TAM) which derived from the Theory of Reasoned Action (TRA). The study sample consisted of 470 respondents. The study applied the Partial Least Squares Structural Equation Modeling (PLS-SEM), where the results of the Chi-square test and the standardized root mean square residual (SRMR) test showed the validity of the model for analysis. The results showed that usefulness, privacy, and awareness affect the adoption of e-banking, and that ease to use and electronic skills affect the adoption of e-banking through usefulness. The results also show that Infrastructure does not affect the adoption of e-banking, and Convenience does not affect the adoption of e-banking through Intention to use e-banking. The study advises Islamic banks to spread awareness about the importance of electronic banking and design easy-to-use electronic services that enjoy privacy and security.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.876
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.017
GPT teacher head0.254
Teacher spread0.237 · 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 designOther design
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

Citations2
Published2024
Admission routes1
Has abstractyes

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