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Record W2936058016 · doi:10.5430/ijfr.v10n2p75

The Relationship Between Knowledge, Trust, Intention to Pay Zakah, and Zakah-Paying Behavior

2019· article· en· W2936058016 on OpenAlexvenueno aff
S. Martono, Ahmad Nurkhin, Fatimah Luthfiyah, Fachrurrozie Fachrurrozie, Ahmad Rofiq, Sumiadji Sumiadji

Bibliographic record

VenueInternational Journal of Financial Research · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
FundersUniversitas Negeri Semarang
KeywordsChristian ministryBusinessQuality (philosophy)Service (business)Path analysis (statistics)Data collectionService qualityPopulationMarketingComputer scienceMedicinePolitical scienceStatistics

Abstract

fetched live from OpenAlex

The purpose of this study is to investigate the effect of knowledge and trust on intention to pay zakah. This study also tests the the effect of knowledge, trust, and intention to pay zakah on zakah-paying behavior. The population of the research comprises the employees of the Ministry of Religion, specifically in the Semarang municipal region. The method of data collection used is a questionnaire which has been developed from those used by previous researchers. Path analysis was used to analyze the data by using warpPls 6.0. The results show that knowledge and trust have a postive and significant effect on the employees’ intention to pay zakah and their zakah-paying behavior. Intention to pay zakah has no impact of on zakah-paying behavior. Knowledge has a high positive effect on intention to pay zakah and zakah-paying behavior. This research suggests that zakah organizations should increase their trust by improving their performance and service quality. They should organize education and dissemination activities to improve zakah payers’ knowledge.

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)0.002
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 score0.214
Threshold uncertainty score0.379

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.082
GPT teacher head0.383
Teacher spread0.302 · 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

Citations28
Published2019
Admission routes1
Has abstractyes

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