COVID-19 adversities: setting an agenda for research on SME resilience
Bibliographic record
Abstract
Purpose This study presents a systematic literature review (SLR) of research on small to medium enterprises (SME) resilience during the COVID-19 pandemic, synthesizing entrepreneurial responses through the lens of ambidexterity, crisis adaptation and relational support. Design/methodology/approach Unlike narrative literature reviews, which are considered less comprehensive, an SLR was deemed appropriate for this study. Its methodological rigour enabled a systematic search of several bibliographic databases, resulting in an initial sample of 2,616. Rigorous and structured qualification criteria were applied to ensure that suitable articles were selected for analysis, resulting in 175 articles. Findings This study revealed that, due to the pandemic's significant impact on small businesses, their owners had to be ambidextrous in pivoting between exploration and exploitation. This included leveraging their capabilities while adventurously applying technology and innovation, and being flexible, agile and able to tolerate ambiguity. This involved entrepreneurially identifying previously unexploited opportunities, viewing “dire circumstances” as opportunities and continuing business in the face of mounting COVID-19 adversities. Research limitations/implications This study has both academic and practical implications, as well as social and policy implications. Its perspectives encourage additional research and policy initiatives to mitigate the impacts of a crisis on SMEs. SME owners acquire knowledge in dealing with adversities and learn how to promote a resilient workforce during a pandemic. Originality/value This paper is unique in that it integrates exploration, bricolage and ambidexterity within the context of SME resilience, developing a model of SME resilience that incorporates entrepreneurial adaptability and relational networks.
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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 | 0.094 | 0.157 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".