Collaborative Sustainable Business Models: Understanding Organizations Partnering for Community Sustainability
Bibliographic record
Abstract
Cross-sector social partnerships (CSSPs) are relevant units of analysis for understanding sustainable business models (SBMs). This research examines how organizations value their motivations to participate in large sustainability-focused partnerships, how they perceive the value captured, and their structures implemented to address sustainability partnerships. Two hundred and twenty-four organizations partnering within four large sustainability CSSPs were surveyed using an augmented resource-based view (RBV) theoretical framework. Results show that partners were motivated by and captured value related to sustainability-, organizational-, and human-oriented resources, and that organizations prefer more informal than formal structural elements to implement their partnerships’ sustainability strategies. Contributions to SBM and CSSP fields are revealed. SBM thinking is a provocation toward seeking integrated sustainable value creation, helping show the value of large CSSPs. Conversely, by conceiving of large, pluralistic CSSPs as “collaborative SBMs,” we extend the idea of the “business model” to the societal level, exploring how value is captured in partnership.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".