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Record W3111344294 · doi:10.5267/j.ac.2020.11.018

Past performance and earning management: The moderating effect of employee relative earning

2020· article· en· W3111344294 on OpenAlexvenueno aff
Ronny Kountur, Bramantyo Djohanputro, Martdian Ratna Sari

Bibliographic record

VenueAccounting · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsModerationNull hypothesisVariablesVariable (mathematics)BusinessStock (firearms)Stratified samplingEconometricsStatisticsEconomicsMathematicsEngineering

Abstract

fetched live from OpenAlex

Investors are facing doubt on the quality of earning reported. They require some indicators to detect the quality of earning reported. The use of past performance as indicators of current and future earning management is challengeable since there are contradicting results in the sign of the relationship between past performance and earning management. Another variable may moderate their relationship. Therefore, it is the purpose of this study to know if employee’s relative earning is the moderating variable in the relationship between past performance and earning management. One hundred thirty-five companies listed in Indonesia Stock Exchange were selected with the use of stratified random sampling. The data is analyzed using sub-group analysis followed by the Chow F test and the linear regression analysis. Earning management is the dependent variable, whereas past performance is the independent variable, and employee relative earning is the moderating variable. The null hypotheses were rejected. A significant negative association exists between past performance and earning management, while the employee relative earning was found to be the moderating variable. The effect of past performance to earning management increases as employee relative earning getting lower.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.243
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.001
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 head0.007
GPT teacher head0.194
Teacher spread0.187 · 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.

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
Published2020
Admission routes1
Has abstractyes

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