From social good to operational good? How women influence sustainable operations
Bibliographic record
Abstract
Purpose Women can play an important part in integrating sustainability into operations and supply chain management. Yet, it is unclear how their broader representation across the organization influences operational practices and outcomes. Drawing on social role theory, we theorize and empirically examine how women impact sustainable operations in terms of resource usage, product responsibility, and emissions. We further theorize that firms with a greater proportion of women are more averse to unsustainable behavior and thus respond with greater urgency to improve sustainability performance when underperformance occurs as a mechanism. Design/methodology/approach Our empirical analysis uses a fixed effects panel estimation strategy on a sample of 17,532 observations from 2003 to 2023 across 3,867 firms using the Refinitiv V2 Environmental, Social and Governance database. Findings Having more women managers improves firm emissions performance. Women on the board have a broader impact, positively influencing resource usage, product responsibility, and emissions. In the post-hoc analysis, increasing representation of women at the manager and board levels was associated with year-to-year improvements in these same sustainable operations metrics. These improvements are most substantial when the firm underperformed industry peers in the prior year. Originality/value Our empirical results suggest that women can positively impact sustainable operations when they are in a decision-making position. Managerially, we advance a gender-based approach to governing sustainability issues in operations and supply chain management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".