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Record W3171159963 · doi:10.5267/j.ac.2021.4.021

The association between cost-standard setting and work performance: The role of information asymmetry and goal complexity

2021· article· en· W3171159963 on OpenAlexvenueno aff
Saad Hussein

Bibliographic record

VenueAccounting · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
FundersTikrit University
KeywordsSpurious relationshipPath analysis (statistics)Work (physics)Rule of thumbVariable (mathematics)Association (psychology)VariablesComputer sciencePsychologyEconometricsPath (computing)Information asymmetryStatisticsMathematicsEconomicsEngineeringMicroeconomics

Abstract

fetched live from OpenAlex

This paper is an empirical testing of the association between Cost Standard-Setting (PSS) on Work Performance (WP), mediated With Information Asymmetry (IA), and Goal Complexity (GC). It is a rule of thumb that PSS setting can lower the level of IA and GC between the managers and the employees and leads to better WP. The present work uses a path model to measure the direct, indirect, and spurious effect between the dependent and independent variables of this study. Data were collected from ten corporate firms in Iraq via a pre-designed questionnaire survey, the questionnaire forms were distributed randomly to the firm’s top management personnel, departmental managers, engineers, accountants, and administrators who are involved in PSS. Around 350 forms were distributed for data collection, however, only 198 forms were considered for this analysis, the rest of the forms were discarded due to incompleteness or missing values. The findings of the study showed a significant direct effect of PSS on WP. Likewise, there was clear evidence of an indirect effect via the mediating variables (IA and GC). The influence of IA and GC confirms the strong association between the independent variable (PSS) on the dependent variable (WP).

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 score0.212
Threshold uncertainty score0.775

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.007
GPT teacher head0.205
Teacher spread0.198 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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