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Record W2363289403

Factors Affecting the Adoption of Internet Banking AmongstIIUMâ students: A Structural Equation Modeling Approach (SEM)

2012· article· en· W2363289403 on OpenAlexvenueno aff
Nabil Hussein Al-Fahim

Bibliographic record

VenueThe Journal of Internet Banking and Commerce · 2012
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsnot available
Fundersnot available
KeywordsStructural equation modelingConfirmatory factor analysisConstruct (python library)The InternetGoodness of fitReliability (semiconductor)Construct validityTechnology acceptance modelLatent variableComputer sciencePsychologyConvergent validityPsychometricsUsabilityArtificial intelligenceWorld Wide WebMachine learningClinical psychology
DOInot available

Abstract

fetched live from OpenAlex

This article examines the factors that determine the internet banking adoption amongst International Islamic University Malaysia (IIUM) and its causal effects using a theoretical model based on the Technology Acceptance Model (TAM). The research model consists of four exogenous latent constructs, namely, awareness, perceived usefulness, trust and perceived risk and endogenous latent construct namely Internet banking adoption. Data relating to constructs were collected from 200 university’s students in (IIUM) and subjected to structural Equation Modeling (SEM) analysis. Confirmatory Factor Analysis (CFA) was performed to examine the reliability, construct validity, convergent validity and goodness of fit of structural models and measurement models. The hypothesized structural model fits the data well. The results show that the significant factor that leads to the adoption of internet banking is perceived usefulness but awareness, trust and risk have negative significant towards the use of internet banking.

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)0.006
metaresearch head score (Gemma)0.001
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 score0.183
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.164
GPT teacher head0.375
Teacher spread0.211 · 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

Citations13
Published2012
Admission routes1
Has abstractyes

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