Organizational Commitments in Financial Service Audit With Antecedents of Organizational Justice and Job Satisfaction
Bibliographic record
Abstract
Public trust in a profession is determined by the reliability, accuracy, timeliness, and quality of services or services that can be provided by the auditor profession. Although the auditor's work and performance procedures have tended to be supervised and determined strictly and formally by external institutions, studies have shown that the context of the internal environment has an effect on improving auditor performance. This study seeks to analyze the effect of organizational justice and job satisfaction in the auditor's environment on organizational commitment. It focuses more on behavioral accounting, specifically relating to the auditor's work environment, by taking the object of the treatment's influence in the organization's internal participation and involvement of auditors that is reflected by organizational commitment. By using the Structural Equation Model (SEM), the findings show that procedural justice and interactional justice are empirically proven to influence organizational commitment. On the other hand, distributive justice has no effect on organizational commitment, and job satisfaction has also been proven empirically to have no effect on organizational commitment. These results provide input for public accounting firms so as not to overlook the fairness factor in providing rewards to auditors. In the case of fairness the awarding of rewards / awards to the auditor is not only limited to the amount of reward, but also the process for determining the amount of the reward.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".