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Record W4389000852 · doi:10.33094/ijaefa.v17i2.1254

Determinants of internet banking usage in emerging markets: Evidence from Egypt

2023· article· en· W4389000852 on OpenAlexaboutno aff
Shereen Aly Hussien Aly Abdou

Bibliographic record

VenueInternational Journal of Applied Economics Finance and Accounting · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
FundersHelwan University
KeywordsThe InternetBusinessQuarter (Canadian coin)MarketingFinanceGeography

Abstract

fetched live from OpenAlex

This study aims to investigate the determinants of internet banking (IB) usage in emerging markets under five determinants that include demographic characteristics of bank clients, perceived risk, financial awareness, the bank's technology infrastructure, and perceived relative advantages of internet banking in Egypt. In accordance with a qualitative approach; a questionnaire was designed including five distinct variables of internet banking usage. The questionnaire was administered to the clients of Egyptian banks within the first quarter of 2023, with a total response of 384 participants. The study found that the perceived risk, financial awareness, the bank's technology infrastructure, and perceived relative advantages of internet banking have a significant effect on the attitudes of clients' usage of internet banking (IB) in Egypt. In addition to age, educational qualification, and occupation. Financial awareness and the demographic characteristics of age have a significant effect on the attitudes of clients' usage of internet banking in Egypt, but monthly income and gender have no significance in Egypt. Internet banking presents an opportunity for bank units to achieve entrepreneurial endeavours by providing benefits to their clients and to the overall economy.

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)0.000
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 score0.217
Threshold uncertainty score0.614

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.015
GPT teacher head0.239
Teacher spread0.224 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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