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Record W4281702441 · doi:10.18280/ijsdp.170321

The Influences of Attitude, Religiosity, and Subjective Norm on Muslim’s Donation Intention During COVID-19 Lockdown in Malaysia

2022· article· en· W4281702441 on OpenAlexvenueno aff
Mohamad Syahmi Mat Daud, Hairunnizam Wahid, Mohd Ali Mohd Noor

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
FundersUniversiti Kebangsaan Malaysia
KeywordsReligiosityDonationPsychologySocial psychologyCoronavirus disease 2019 (COVID-19)Norm (philosophy)Structural equation modelingTheory of planned behaviorAffect (linguistics)Infectious disease (medical specialty)DiseasePolitical scienceMedicineLawEconomicsStatisticsMathematics

Abstract

fetched live from OpenAlex

Charitable giving appears to be one of the most critical approaches to mitigating the impact of the global crisis such as coronavirus disease (COVID-19) on the poor and vulnerable people in Malaysia. Therefore, this study investigates the influences of religiosity, subjective norms, and attitude on donation intention among Malaysian Muslims during the coronavirus disease (COVID-19) lockdown in Malaysia. This study obtained a primary dataset consisting of 328 responses among Muslims throughout 14 states and the Federal Territories of Malaysia. Partial least square-structural equation modeling (PLS-SEM) were employed to analyze the primary data. Consequently, the results have found that religiosity and attitude are significant factors that directly predict monetary donation intention. Furthermore, attitude acted as a mediator in the relationship between religiosity and subjective norms on Malaysian Muslims’ donation intention. Ultimately, this study proposed relevant policies to identify specific factors that affect the donation intention as a practical response for vulnerable groups impacted by COVID-19 in Malaysia.

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 score0.027
Threshold uncertainty score0.363

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 communication0.0000.000
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 head0.012
GPT teacher head0.246
Teacher spread0.234 · 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

Citations15
Published2022
Admission routes1
Has abstractyes

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