Factors affecting support services in small and medium enterprises: Evidence from Vietnam small and medium information technology enterprises
Bibliographic record
Abstract
To meet the needs of innovation, to improve the competitiveness of information technology based on the professional abilities and resources, internal business is not enough, but there is a need to seek external support through business support services such as market search support, trade promotion, legal advice, human resource training, support supply and technology transfer support. There are several services to support businesses and to increase efficiency when they are developed in both quantity and scale of service provision and quality, as well as service structure. So how do we analyze the factors that affect information technology support services in Vietnam? Answering this question helps to suggest solutions and policies to promote the development of the business support service sector and contributes to the rapid development of information technology enterprises in Vietnam. This paper uses information from a survey data of 315 Vietnamese information technology (IT) of small and medium sized enterprises and 460 support service providers in 2018. Data are processed through STATA software version 14.0, and SPSS 20.0 software. The results indicate the Vietnamese business units need to improve to develop the information technology service market, significantly.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".