Factors influencing the Digital Transformation of Vietnam SMEs
Bibliographic record
Abstract
This study investigates the key factors influencing the Digital Transformation (DT) of small and medium-sized enterprises (SMEs) in Vietnam, a sector crucial for national economic development and competitiveness. Drawing from both organizational and technological perspectives, the research identifies drivers, challenges, and success factors related to the adoption of digital tools and processes. Using a quantitative methodology, data were collected via an online survey targeting 321 SMEs owners, managers, and administrators across multiple regions in Vietnam. The analysis, conducted through Structural Equation Modeling (SEM), reveals that leadership competencies, firm size, access to digital infrastructure, and external market pressures are significant determinants of DT success. Additionally, the study highlights the role of DT in enhancing business sustainability through improved operational efficiency, customer engagement, and market reach. These findings contribute to a deeper understanding of the DT journey within developing economies and offer actionable insights for policymakers and stakeholders aiming to accelerate digital adoption among SMEs in Vietnam.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".