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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

<p>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.</p>

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.216
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 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

Citations6
Published2016
Admission routes1
Has abstractyes

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