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Record W2283893534 · doi:10.5539/ibr.v9n3p79

Factors that Affect Commercial Banks Customers Intention towards Electronic Payment Services in Jordan

2016· article· en· W2283893534 on OpenAlexvenueno aff
Malek AL-Majali, Amin Ayed Bashabsheh

Bibliographic record

VenueInternational Business Research · 2016
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsnot available
Fundersnot available
KeywordsPaymentReliability (semiconductor)BusinessAffect (linguistics)SimplicityVariablesElectronic bankingTest (biology)MarketingPsychologyComputer scienceStatisticsThe InternetFinanceMathematics

Abstract

fetched live from OpenAlex

This study aims to identify the factors influencing commercial banks customers intention toward electronic payment services in Jordan (AL-Karak) province. To achieve the aim of the study, a questionnaire has been developed to explore the effect of the independent variables (relative advantages, simplicity, security, consciousness and self efficacy) on the dependent variable (banks customers intention toward E-payment services). Six hundred questioners had distributed and 543 were returned to be valid for the final analysis with response rate of 90.5%. SPSS v 18 was used to test the reliability and composite reliability, normal distribution and correlation between the study variables. Also, Amos v 8 software has been used to examine the study hypotheses. Results of this study indicates an acceptance to four hypotheses related to influence of security, self efficacy, consciousness and simplicity continually. We reject one hypotheses related to relative advantages on banks customers toward E-payment services adoption. Finally, set of recommendations had been present throughout the study.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.216
GPT teacher head0.467
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2016
Admission routes1
Has abstractyes

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