Digital drivers of digital transformation in 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
This study aimed to investigate digital drivers of digital transformation success in public sector organizations. Based on prior related studies, three digital drivers were selected as key drivers, which are digital government, digital leadership, and digital HRM. Gathering data by online questionnaires from public sector employees, the study based on SmartPLS 3.0 statistics found significant and positive impacts of these three drivers on digital transformation success. Interestingly, the results refer to the success of digital transformation is greatly subject to digital HRM and possibly this effect is due to the fact that the basic aim of digital government and digital leadership is to enhance the operations of the digitization process through adopting digital-oriented public administration mentality, creating public value, setting shared digital vision and strategy, communicating digital change goals, initiating digital organizational culture, which is basically guided and can be attained through efficient and effective digital HRM practices. Hence, the study contributes to the literature through underlying three digital drivers of digital transformation success. It calls scholars for considering these drivers when examining success factors of digital transformation and practitioners when redesigning organizations to adapt digital change.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.025 |
| 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