Exploring the potential of SMEs to build individual, organizational, and community resilience through sustainability‐oriented business practices
Bibliographic record
Abstract
Abstract Small‐ and medium‐sized enterprises (SMEs) can have significant resources, capacities, and influence in their communities, suggesting they have the potential to be agents for transformative sustainability. However, SMEs will need to move beyond firm‐centered sustainable business practices towards strategic approaches that encompass and contribute to resilience‐building processes. Amid the unfolding COVID‐19 pandemic, we explored what types of sustainable business practices of SMEs can contribute to individual, organizational, and community resilience. We identified six clusters of practice that are important in this regard. The clusters are not solely technical or “environmental” but rather illustrative of deeper sustainable values shaped by organizational structure, culture, and behavior. This paper suggests that SMEs can pursue transformative approaches to sustainability that are more environmentally, socially, and economically sustainable and better able to withstand shocks like the COVID‐19 pandemic and can be significant contributors to community resilience. We conclude with a series of future research priorities critical to examine a largely unexplored nexus in the private sector, the linkages and dynamics between sustainability practice, resilience building, and broader community pathways.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".