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Record W2945649464 · doi:10.5430/ijfr.v10n3p181

The Effect of Transparency, Competency and Religiosity on Public Officers’ Ethical Behaviour

2019· article· en· W2945649464 on OpenAlexvenueno aff
Mazurina Mohd Ali, Nurul Izzah Mohamed Azam, Erlane K Ghani

Bibliographic record

VenueInternational Journal of Financial Research · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
FundersUniversiti Teknologi MARA
KeywordsReligiosityTransparency (behavior)PsychologyPublic relationsSocial psychologyInterpersonal communicationEnforcementEthical valuesLaw enforcementBusinessPolitical scienceSociologyLawSocial science

Abstract

fetched live from OpenAlex

This study examines the relationship between transparency, competency and religiosity with the ethical behaviour of the public officers in Malaysia. This study utilises the questionnaire survey as the research instrument distributed to the public officers from eleven enforcement agencies in Malaysia. Using multiple regression analysis on 71 respondents, this study shows that both transparency and competency have significant relationship with the ethical behaviour of the public officers in the enforcement agencies, and the relationship is significant. Personal actions and interpersonal relationships, two-way communication, reinforcement, and decision-making are among the factors contributing to the significant relationship. However, religiosity does not have a significant relationship on the public sector officers’ ethical behaviour. The findings from this study provide some implications towards the enforcement agencies in improving their ethical behaviour in performing their tasks. This is because transparency and competency play an important role in improving ethical behaviour in the work place.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.329
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 source (direct Gemma or distilled Codex), 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

Citations1
Published2019
Admission routes1
Has abstractyes

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