Digital Transformation of Small and Medium Enterprises: Aspects of Public Support
Bibliographic record
Abstract
The purpose of this study is to identify the necessary public support measures for small and medium-sized enterprises (SMEs) and provide policy makers with guidance on how to facilitate a successful digital transformation. The study is based on a representative survey of 425 Latvian SMEs carried out in spring 2021. We combine three analyses: a survey among SMEs, qualitative comparative analysis and regression analysis. The results of this study show that a significant number of SMEs are convinced that they will not be able to cope with digital transformation without various kinds of assistance, with direct financial support from the state or EU funds and tax incentives playing a major role. The range of public support required is rather wide, from staff training, mentoring and increasing the potential workforce to tax relief and direct financial support. We found statistically significant differences in public support needed depending on the size of SMEs and their ability to independently manage digital transformation. These findings could be useful for policymakers, managers and practitioners to identify various forms of public support that can maximize the impact of digital transformation not only on business, but also on society as a whole.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".