Social Capital, Resource Acquisition, and Firm Performance: Evidence from Vietnam's Tourism Sector
Bibliographic record
Abstract
Social capital is the relationship network between firms and stakeholders for mutual benefit.Based on the social capital theory, the study is carried out to explain the formation of supportive resources and improve the firm performance in response to the crisis after the COVID-19 pandemic.This research explores the components of social capital (formal capital involves government officials, and informal social capital includes relationships with relatives, friends, association members, and business partners) that influence firm performance through the mediating role of resource acquisition (financial and customer resource acquisition) in tourism.The study used Partial Least Squares Structural Equation Modeling (PLS-SEM) with a sample size of 207 managers of tourism firms in the Ba Ria -Vung Tau province.The results show that social capital was significantly and positively related to firm performance.In addition, the study also explored the partial mediating roles of financial resource acquisition and customer resource acquisition between social capital and firm performance.The results have brought practical significance for managers to focus on building social capital to increase access to resources to deal with the crisis after the COVID-19 pandemic.Finally, some limitations and further research directions are proposed in this study.
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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 | 0.002 |
| 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) | 0.002 | 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".