Leveraging good university governance to enhance HEI's performance through the lens of ethical work climate
Bibliographic record
Abstract
This study examines the role of management control systems (MCS) in enhancing the performance of Higher Education Institutions (HEIs) in Indonesia, focusing on the interaction between enabling and coercive control systems within the framework of ethical work climate (EWC) and good university governance (GUG). The research highlights the importance of creating a positive ethical work environment to improve the effectiveness of MCS and governance practices. A survey was conducted with lecturers and administrative staff from private universities across Indonesia, with data analyzed using Structural Equation Modelling (SEM) to test the relationships between EWC, MCS, GUG, and HEI performance. The findings reveal that both Enabling and Coercive Control Systems positively influence HEI performance and contribute to the improvement of GUG. Additionally, a positive EWC strengthens the effectiveness of both control systems, fostering trust, transparency, and employee engagement. The study provides theoretical insights into how MCS and ethical climates shape governance and performance in higher education, with practical implications for HEIs administrators to optimize MCS, balance control systems, and cultivate an ethical work environment to enhance institutional success. Future research could further explore the impact of leadership styles and external factors on the effectiveness of these systems in different higher education contexts.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".