The interplay between social responsibility and institutional investment in achieving sustainable business outcomes
Bibliographic record
Abstract
This study examines how institutional investment and CSR interact to promote sustainable business practices in Canada, emphasizing ESG reporting and ethical accountability as key drivers. The study employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze survey data from 385 Canadian senior managers and sustainability officers. A Likert-scale questionnaire assessed ESG policy integration, portfolio engagement, ESG reporting, and CSR’s moderating role. The study found ESG reporting and strong CSR commitment significantly drive sustainable practices in Canadian firms, while ESG policy integration and portfolio engagement showed limited direct impact. Social responsibility’s moderating effect on institutional investment dimensions was weak, underscoring the need for transparency, ethical accountability, and deep strategic integration of ESG and CSR beyond compliance for long-term sustainability. The study concludes that ESG reporting and CSR commitment drive sustainability, while ESG policy integration and engagement have limited impact. Firms must deeply embed ESG/CSR beyond compliance, prioritizing transparency and ethical accountability for long-term sustainable outcomes. Institutional investors should assess ESG implementation depth, not just policies. Corporations must embed CSR/ESG into core strategies, prioritize transparent reporting, and avoid greenwashing through actionable practices to drive long-term sustainability beyond superficial compliance.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".