Partner Strategic Capabilities for Capturing Value from Sustainability-Focused Multi-Stakeholder Partnerships
Bibliographic record
Abstract
As social and ecological problems escalate, the role of collective capacity and knowledge is becoming more critical in reaching solutions. This capacity and knowledge are dispersed among diverse stakeholder organizations. Thus, organizations in the private, public and civil society sectors are experiencing pressure to address these complex challenges through collaborative action in the form of multi-stakeholder partnerships. One major challenge to securing and maintaining partner engagement in these voluntary collaborative initiatives is defining the value proposition for prospective and existing partner organizations. Understanding the relationship between different forms of partner involvement and the subsequent resources that partners stand to gain is necessary to articulate the value proposition of the partnership to partners. This study conducts a survey of partner organizations from 15 different sustainability-focused multi-stakeholder partnerships in Canada. We compare three partner strategies for implementation and value capture and discover that each strategy is associated with different partner-level resource outcomes. Our findings indicate that product stewardship strategies are associated with financial and organizational capital, marketing and promotion with human capital, and internal implementation structures with shared capital. This study has implications for multi-stakeholder partnership researchers and practitioners because it suggests the possibility that certain partner-level outcomes could rely on the partner, as well as partnership implementation strategies.
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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 | 0.013 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".