Exploring the role of digital leadership and digital transformation on the performance of the public sector organizations
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
Previous studies rarely discuss how digital leadership influences the performance of public organizations. So far, those who have conducted research only discuss the performance of public organizations. The purpose of this research is to analyze the relationship between digital leadership and organizational performance, digital leadership and digital transformation and the relationship between digital transformation and organizational performance in public government organizations. The research method is a quantitative survey, research data obtained by distributing online questionnaires to 765 employees of public organizations. Data analysis used a structural equation model (SEM) with SmartPLS 3.0 software. The stages of data analysis are validity, reliability and significance tests. The sampling technique used is non-probability sampling. The questionnaire used in this study uses a Google form distributed to respondents. The questionnaire measurement method uses a Likert scale of 5. The independent variables used in this study are digital leadership and digital transformation. The dependent variable used in this study is the performance of the public organizations. The results of this study indicate that digital leadership had a positive and significant effect on organizational performance, digital leadership had a positive and significant effect on digital transformation and digital transformation had a positive and significant effect on organizational performance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| 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