Adoption of ESG Practices by SMEs: Evidence from Canada
Bibliographic record
Abstract
Although small and medium-sized enterprises (SMEs) constitute more than 90 percent of businesses, employ around 70 percent of the global workforce, and create more than 50 percent of the global economic value, prior research pays disproportionate attention to large companies’ corporate responsibility (CR). In this study, we focused on SME adoption of environmental, social, and governance (ESG) practices as constituents of CR. By considering the complex and dynamic interdependencies among multiple stakeholders at multiple levels, we took a holistic and inductive approach to understand the interconnectedness between SMEs’ ESG consciousness and adoption. By conducting 32 semi-structured in-depth interviews with SME managers and the representatives of two stakeholders with critical roles in SMEs’ ESG adoption as finance and ESG-related support service providers, we developed a grounded theoretical model that explains the relationship between SMEs’ ESG consciousness and adoption. Our analysis reveals that depending upon their idiosyncratic circumstances, SMEs are conjointly under opposing enabling and constraining forces that have heterogeneous effects on their ESG consciousness and adoption. While their financial and human capital limitations, when coupled with competition, forces them to prioritize immediate survival and growth and constrain their ESG adoption, firm-specific incentives for positive impact and penalties for negative impact enable their ESG adoption. By accentuating the tension felt by SMEs between the choices to fit current versus future contexts, we contribute to the resource dependence and contingency theories in the SME CR field.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".