Investigating entrepreneurial resilience toward sustainable competitive advantage: Does local culture matter?
Bibliographic record
Abstract
Literature indicates it is crucial to understand how SMEs could adapt to changes in their external environment to develop a competitive advantage. However, there is limited discussion on how entrepreneurs’ resilience functions as one of the dynamic capabilities required to maintain their company’s competitive advantage in the long-term. This study aimed to investigate how entrepreneurial resilience affects SMEs ability to achieve sustainable competitive advantage. It also analyzed the moderating role of local cultural values in reinforcing resilience and sustainable competitive advantage. The population consisted of 400 handicraft SME who were thought of as Bali’s sustaining tourism sector. The results demonstrated that financial capital, human capital, and social capital were factors that shaped entrepreneurial resilience, where the ownership of capital and access to capital helped entrepreneurs when encountering changes in the dynamic and challenging industrial environment, where the role of financial capital was one of the sources of resilience that had the most significant influence. Furthermore, entrepreneurial resilience also contributed significantly to the achievement of sustainable competitive advantage. Moreover, the results also indicate that local cultural values strengthen entrepreneurial resilience because entrepreneurs who adjusted to local cultural norms developed in the surrounding community tended to respond positively to their behavior. Positive environmental acceptance could reinforce entrepreneurial resilience, given the psychological support of their behavior.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".