The role of social media marketing in attracting investment capital in industrial parks in the context of COVID-19
Bibliographic record
Abstract
With the complicated Covid-19 epidemic currently affecting many countries, there is an important position and role for social media marketing not only in attracting investment capital of countries but also in other fields, since it is a good way for people to connect and circulate work. This study aims to analyze the factors affecting the attraction of investment capital to Vietnam's industrial parks, focusing on considering social media marketing factors. The study's data comes from a survey of 256 enterprises operating in Vietnam (Including both active firms and enterprises in the group of potential investors with industrial parks). The data were analyzed using factor analysis and multivariate regression. The results of the study show that social media marketing had a positive effect on attracting investment capital into industrial parks of Vietnam (Standardized Coefficients = 0.329); besides, there are also positive effects of other factors such as human resources, industrial park infrastructure, local policies with varying degrees of influence. Based on those factors, the author offers recommendations regarding attracting investment capital to industrial parks actively.
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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 | 0.003 |
| 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 | 0.002 | 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".