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Record W2949318672 · doi:10.5430/ijfr.v10n5p167

The Mediating Effect of Attitude on Customers’ Behavioural Intention to Participate in Islamic Banking: Empirical Evidence

2019· article· en· W2949318672 on OpenAlexvenueno aff
Ibrahim Abiodun Oladapo, Normah Omar, Ruhaini Muda, Abdulazeez Adewuyi Abdurraheem

Bibliographic record

VenueInternational Journal of Financial Research · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTheory of reasoned actionIslamic bankingOriginalityPsychologyIslamRelevance (law)Value (mathematics)Structural equation modelingNorm (philosophy)Positive attitudeAction (physics)Positive relationshipSocial psychologyQuestionnaireMarketingBusinessPolitical scienceSociology

Abstract

fetched live from OpenAlex

Purpose - This paper examines the mediating effect of positive attitude (ATT) and subjective norm (SJN) on customers’ behavioral intention to participate in Islamic banking in Nigeria using the Theory of Reasoned Action (TRA) as a basis.Design/methodology/approach - Data were collected using a self-administered questionnaire with 274 samples. This study used the convenience sampling technique.Findings – The authors found a positive and significant relationship between the factors in the model. The findings highlight the relevance of attitude in the structural model. The findings further show that attitude has a positive effect in mediating the relationship between awareness, knowledge, religion and the behavioural intention of customers.Originality/value - This paper highlights the need for policymakers, regulators and Islamic banking operators to create proper awareness through effective communication with customers on the benefits of Islamic banking and the provision of a platform for knowledge enhancement to promote a positive attitude among the different segments of society and which in turn can influence their intention to participate in Islamic 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)0.005
metaresearch head score (Gemma)0.005
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 score0.126
Threshold uncertainty score0.586

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.118
GPT teacher head0.416
Teacher spread0.298 · 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

Citations19
Published2019
Admission routes1
Has abstractyes

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