The impact of workforce practices on firms’ sustainability performance: An empirical study of Canadian listed firms
Bibliographic record
Abstract
This study examines the impact of workforce practices on firms’ environmental and social performance. The mediating impact of firms’ financial performance and the moderating impact of firm age on workforce practices and environmental/social performance are also investigated. Data were collected through the Refinitiv database from a sample of 224 large, actively traded Canadian firms listed on the Toronto Stock Exchange (TSX). A linear regression model was used to test the effect of various workforce practices on firms’ environmental and social performance. The findings have important implications for the direct and indirect impacts of workforce practices on firms’ environmental and social performance. While the direct impact was found to be significant, firms’ financial performance was found to fully mediate the workforce-environment/social performance relationship. The findings also demonstrated that the impact of firm age on workforce practices and environmental/social performance via financial performance was significant. The study draws on the signaling theory to empirically investigate the contextual aspects that affect the association between various workforce practices and firms’ sustainability performance. The findings can be utilized by firms to select the right mix of practices to tailor workforce management and achieve better sustainability performance in their environmental and social initiatives.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".