The impact of creativity and digital leadership on decision-making quality: Implications for public service performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it