Business resilience in the Sustainable Development Goals (SDGs) era: A conceptual review
Bibliographic record
Abstract
Amidst the global COVID-19 pandemic, the term resilience has gained significant momentum in global news and management studies. Although scholars from different domains have investigated resilience, there is a need to provide clarity on its definitions and assessment (Anderson, 2015). This paper provides a conceptual review on resilience and explores business resilience as a framework to guide sustainability strategy by mitigating social and environmental risks. The study contributes to the literature on resilience and tabulates the key definitions of business resilience covered in a sample of 80 peer-reviewed articles and books (Hillmann & Guenther, 2021; McKnight & Linnenluecke, 2017). We challenge the existing literature on adaptive capacity models that are short in anticipating unprecedented operational disruptions. To build business resilience we argue for the adoption of the Sustainable Development Goals (SDGs). Given their strategic outlook until 2030, the SDGs offer a framework for corporate sustainability that helps decision-makers within organizations identify social and environmental risks and establish business strategies that build resilience and meet the expectations of a firm’s diverse stakeholders
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".