Digital drivers of digital transformation in public sector organizations
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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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".