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Record W3111081329 · doi:10.5267/j.ac.2020.12.004

The role of budget participation in improving managerial performance

2020· article· en· W3111081329 on OpenAlexvenueno aff
Andi Mattulada Amir, Ridwan Ridwan, Muhammad Din, Nina Yusnita Yamin, Femilia Zahra, Muh. Fiqram Firman

Bibliographic record

VenueAccounting · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior and Marketing Influence
Canadian institutionsnot available
Fundersnot available
KeywordsModerationStructural equation modelingLocal governmentGovernment (linguistics)BusinessWork (physics)Intervening variableUnit (ring theory)Variable (mathematics)Government budgetAccountingEconomicsPublic administrationPolitical sciencePsychologyPublic financeSocial psychologyEngineeringMacroeconomicsMathematicsSociologyStatistics

Abstract

fetched live from OpenAlex

This study aims to analyze the effect of budget participation on the performance of the government apparatus of Palu City through budgetary slack with Culture kaili “Nosarara Nosabatutu” as moderating. This research was conducted in the regional apparatus organization of the Palu City Government in 41 regional work unit. Data were analyzed using Structural Equation Modeling with WARP PLS 7.0. The results show that budget participation has a positive effect on the performance of government officials, budget participation has a negative effect on budgetary slack and negative budgetary slack on the performance of the government apparatus. The results of this study also indicate that budgetary slack was a partial mediator between the effect of budget participation on the performance of local government officials. The Kaili culture variable “Nosarara Nosabatutu” cannot be proven as a moderator.

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)0.003
metaresearch head score (Gemma)0.009
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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.010
GPT teacher head0.221
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 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

Citations27
Published2020
Admission routes1
Has abstractyes

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