Leveraging resources and dynamic capabilities for organizational resilience amid COVID-19
Bibliographic record
Abstract
Purpose: The aim of this study was to explore the effectiveness of the resource-based view (RBV) and dynamic capabilities (DCs) to settle the problem of how and why a firm could achieve successful resilience under the context of the COVID-19. Design/methodology/approach: A survey was conducted among 596 Chinese firms, and a structural equation model was applied. Findings/results: The empirical results indicate that both valuable, rare, inimitable, and non-substitutable (VRIN) and non-VRIN resources can promote better organisational resilience (OR). Moreover, DCs could mediate the relationship between the RBV and OR. Specifically, DCs could fully mediate the connection between non-VRIN resources and OR, while they can only partially mediate the relationship between VRIN resources and OR. Practical implications: The results of this study provide recommendations for how to proceed in environments where significant crises and outbreaks may occur. These findings are useful for business decision-making and enabling companies to develop new business strategies. Originality/value: Previous studies have investigated the drivers of OR from the perception of business strategies and practices. This study is the first to empirically test DCs as intermediary variable from RBV to promote the resilience of enterprises in the context of COVID-19.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".