How internal factors determine digital transformation: The moderating role of leader's project management competence
Bibliographic record
Abstract
Digital transformation refers to technological application with a comprehensive shift in corporate governance's mindset, structure, and strategy. In particular, digital transformation project management is key in ensuring that digital transformation initiatives are implemented on schedule and achieve the set goals. This study investigates the importance of a leader's project management competence and other internal factors in successfully implementing digital transformation projects. Through Partial Least Squares Structural Equation Modeling (PLS-SEM), data collected from questionnaires administered to 436 small and medium-sized enterprises (SMEs) in Thanh Hoa, Vietnam shows that all four internal factors included in the model directly affect the transform digital ability and indirectly affects the level of digital transformation of SMEs, in which the most decisive influence comes from digital transformation strategy, followed by the influence of corporate culture, technology platform and finally workforce competence. More specifically, this study has demonstrated that an enterprise's digital transformation can be considered a project, and the leader's project management competence determines the project's success. The project management capacity of the enterprise leaders not only directly affects the digital transformation results but also plays a positive moderator in the association between digital transformation capability and the digital transformation level of SMEs. The results suggest several recommendations for leaders of SMEs in Thanh Hoa Province to improve project management capacity, thereby promoting the digital transformation process.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".