The Strategic Engagement of Organizations in Sustainability Partnerships
Bibliographic record
Abstract
Cross-sector partnerships are created to address sustainability issues that organizations are uncapable to solve alone. Research has mainly focused on small partnerships, providing limited understanding on what drives organizations to partner, how they are structured for partnering, and what they achieve from partnering. This research focuses on four large sustainability partnerships each with a minimum of approximately 100 organizations from across sectors. 224 organizations were surveyed (response rate = 26%, valid, unbiased, reliability > 70%) using a resource- based view and community capitals approach to assess organizational drivers to join a sustainability partnership and outcomes they gain from partnering, and a contingency view for structural features to address sustainability. Results show that society-oriented resources such as contributing to community sustainability are the most valuable drivers for organizations to join partnerships, and the most valuable outcomes gained from partnering, contrasting with the literature which focuses mainly on business-oriented capitals. Findings also show that informal structural features (plans, policies, partnering) are the most common for addressing sustainability, in alignment with contingency theory. Furthermore, organizations achieve the outcomes that drive them to partner, confirming the value of large partnerships. No evidence was found to support relationships between drivers and structures, nor between structures and outcomes.
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".