Recovery Speed of Micro, Small, and Medium Enterprises (MSMEs) Following the COVID-19 Pandemic: The Influence of Entrepreneurial Capacity and Characteristics
Bibliographic record
Abstract
This study examines the effect of entrepreneurial capacity, both directly and indirectly, through entrepreneurial characteristics on the performance recovery speed of Micro, Small, and Medium Enterprises (MSME) due to the COVID-19 pandemic.The entrepreneurial capacity consists of financial access and market access.Meanwhile, entrepreneurial characteristics include the need for achievement, risk-taking propensity, and internal locus of control.The data were obtained through a field survey of MSME entrepreneurs engaged in the food and beverage sector in three cities including; Semarang, Surakarta, and Salatiga, Indonesia.The total sample was 397 respondents and used SEM-PLS analysis to test the hypothesis.This study shows that financial access, the need for achievement, and the internal locus of control positively affect MSMEs' performance recovery speed after the COVID-19 pandemic.Furthermore, the mediating effect testing demonstrates that the need for achievement and internal locus of control mediate the effect of financial access on the performance recovery speed of MSMEs.Therefore, stakeholders interested in developing the MSMEs are suggested to intensify their efforts to increase their ability to access finance and strengthen the entrepreneurial characteristics among the MSME entrepreneurs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".