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Record W2344940147 · doi:10.4172/1204-5357.1000123

Factors Influencing Intention to Adopt Internet Banking by Postgraduate Students of the University of Ibadan, Nigeria

2015· article· en· W2344940147 on OpenAlexvenueno aff
Funmilola Olubunmi Omotayo, Adebayo AK

Bibliographic record

VenueThe Journal of Internet Banking and Commerce · 2015
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsnot available
Fundersnot available
KeywordsThe InternetTheory of planned behaviorSoutheastern NigeriaControl (management)PsychologyUsabilityTechnology acceptance modelMarketingPositive attitudeQuestionnaireBusinessSociologySocial psychologyComputer scienceWorld Wide WebSocial scienceSocioeconomics

Abstract

fetched live from OpenAlex

This study examines the factors influencing intention to adopt internet banking by postgraduate students of the University of Ibadan Nigeria. The study adopted the Technology Acceptance Model and Theory of Planned Behaviour as the theoretical framework. The survey instrument employed involved design and administration of a total of 522 survey questionnaires within the University. The results of the study reveal no significant relationship between demographic characteristics of the students and intention to adopt internet banking, while the individual factors (attitude, trust, perceived usefulness, perceived ease of use and perceived behavioural control), and social factor (subjective norms) significantly influenced intention of the students to adoption internet banking. The study concludes that even though some students are yet to adopt the use of internet banking, their attitude towards online banking is favourable. It is therefore recommended that banks should intensify efforts to improve the security of online banking platform as well as continue to educate their customers on the perceived benefits of internet banking in addition to making the platform more user-friendly and easy to use.

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)0.000
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 score0.023
Threshold uncertainty score0.345

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.098
GPT teacher head0.337
Teacher spread0.238 · 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

Citations23
Published2015
Admission routes1
Has abstractyes

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