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Record W4301187872 · doi:10.21554/hrr.092210

EDUCATIONAL DETERMINANTS OF ONLINE BANKING ADOPTION IN BOSNIA AND HERZEGOVINA

2022· article· en· W4301187872 on OpenAlexaff
Nesiba Smajic, Irfan Djedović, Ensar Mekić

Bibliographic record

VenueJournal Human Research in Rehabilitation · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsTD Bank Group
Fundersnot available
KeywordsUsabilityStructural equation modelingTechnology acceptance modelPath analysis (statistics)PsychologyTest (biology)MarketingComputer-assisted web interviewingBanking industrySurvey data collectionBusinessSocial psychologyComputer scienceStatisticsAccountingMathematicsHuman–computer interaction

Abstract

fetched live from OpenAlex

This study aims to empirically investigate the determinants of online banking adoption in Bosnia and Herzegovina. The data presented in this paper was collected via a survey from 321 randomly selected banking users in Bosnia and Herzegovina. The survey was developed based on the Technology Acceptance Model (TAM) framework to test the constructs and their ability to predict consumers’ behavioral intentions. All gathered data was processed and analyzed using the AMOS software version 24. We performed the Structural Equation Modeling (SEM) for path analysis to estimate the strength of the hypothesized relationships among the determinants. The findings indicate a significant impact of Attitude, Perceived Usefulness, and Perceived Ease of Use on consumers’ Intention to Use online banking services. Further, the research has proven that Perceived Usefulness and Perceived Ease of Use impact consumers’ Attitudes toward online banking adoption. The path analysis result suggests that Perceived Usefulness strongly mediates the relationship between Perceived Ease of Use and Attitude.

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)0.015
metaresearch head score (Gemma)0.003
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 score0.154
Threshold uncertainty score0.757

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.254
GPT teacher head0.535
Teacher spread0.281 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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