The impact of creativity and digital leadership on decision-making quality: Implications for public service performance
Bibliographic record
Abstract
This study investigates the relationships between creativity, digital leadership, decision-making quality, and public service performance in Sidoarjo Regency. The primary objective is to examine how creativity and digital leadership influence decision-making quality and, subsequently, public service performance. A quantitative approach utilizing a cross-sectional study design was employed. Data were collected from 200 employees of public service institutions in Sidoarjo Regency using Google Forms and direct interviews. The main variables were assessed using Likert scales, measuring creativity, digital leadership, decision-making quality, and public service performance. The analysis involved descriptive and inferential statistics, including regression analysis and mediation analysis. The findings reveal significant positive relationships between creativity, digital leadership, decision-making quality, and public service performance. Creativity and digital leadership were found to positively impact decision-making quality, which in turn influenced public service performance. The implications suggest that fostering a culture of creativity and digital leadership is crucial for enhancing decision-making quality and, consequently, improving public service performance. Public service managers should invest in initiatives to develop creativity and digital leadership skills among employees and prioritize transparent decision-making processes. Furthermore, the study highlights the need for continuous monitoring and evaluation to ensure sustained improvements in public service delivery. The novelty lies in examining the interplay between creativity, digital leadership, decision-making quality, and public service performance within the context of Sidoarjo Regency, providing valuable insights for public service management in the region.
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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 | 0.006 | 0.015 |
| 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 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".