The role of quality assurance in improving the distribution of organizational performance
Bibliographic record
Abstract
The continuity of the organization was disrupted when the COVID-19 pandemic hit the world in early 2020, and many organizations were forced to adapt to emergencies. Programs that have been developed for the long term must be modified to suit the situation. This paper aims to evaluate the impact of the pandemic and analyze the ongoing impact of transformational leadership on the distribution of organizational performance mediated by organizational learning, total quality management and quality assurance, and altruism as moderating variables. The study was conducted by using Partial Least Square to analyze the behavior of the highest leadership of the Child Welfare Institution (CWI) of the Ministry of Social Affairs of the Republic of Indonesia, with a sample of 185 accredited institutions throughout Indonesia. The results of the study indicate that several factors affect the process of evaluating organizational performance. The LKSAs need to improve the quality of their organization's performance by following the requirements of the Ministry of Social Affairs consistently and continuously in implementing the fulfillment of the quality standards. The contribution of novelty in this study is that the total quality management variable is not able to improve organizational performance. The surprising finding is that the consistency of the distribution of total quality management implementation has no effect when the highest leadership is unable to carry out the sustainability of the standards that have been painstakingly prepared long before the pandemic occurred. However, the quality assurance can increase the distribution of organizational performance substantially.
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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 | 0.010 | 0.024 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".