Strategic Intelligence of Small and Medium Enterprises Embedded in Global Supply Chains: A Framework for Resilience in the Face of Systemic Risks
Bibliographic record
Abstract
This study highlights the challenges and resilience of SMEs embedded in global supply chains that are vulnerable to systemic risks. SMEs, constituting a significant portion of the global economy, have been largely overlooked in supply chain resilience literature. Faced with crises like the COVID-19 pandemic or geopolitical tensions, SMEs often adopt a wait-and-see approach, seeking to reduce uncertainty before making tangible commitments. Our proposed conceptual framework highlights strategic intelligence as a key dynamic capability to diminish uncertainty, reduce the waiting time for SMEs, and prompt them to commit tangible resources to restore balance in a new context. Three sub-capabilities of strategic intelligence are identified: supply network visibility, environmental sensing, and timely responsiveness. External moderating determinants, such as external social capital and government support, can also help overcome the limitations of SMEs' internal resources. This study calls for future empirical research to explore these relationships and address current gaps in the understanding of SMEs' supply chain resilience. It particularly encourages testing this model using the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach. By focusing on strategic intelligence, inter-organizational resource sharing, and government support, it provides practical insights for managers and policymakers, emphasizing the importance of enhancing SME resilience in the face of systemic disruptions. This, in turn, contributes to the resilience of our economies in an increasingly complex and uncertain world.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".