Unpacking Inter-Organizational Collaboration Capabilities in the Circular Economy
Bibliographic record
Abstract
In Circular Economy (CE) research, while the importance of inter-organisational collaboration is recognised, there is a significant gap in understanding the specific challenges and capabilities needed for successful multi-actor collaboration. This study addresses this gap by exploring the impact of internal and external challenges on inter-organisational collaboration in complex CE projects and identifying key capabilities to overcome these barriers. This paper uses an inductive action research approach to examine a European project focused on developing circular solutions for commercial plastic waste. The research, conducted over three years and involving participatory observation and interviews, highlights critical collaborative barriers such as inadequate facilitation, communication gaps, and role confusion. It also identifies four essential collaboration capabilities: collaborative integration, adaptive resource management, empathic relationship-building, and inclusive mutual support, contributing to CE and inter-organisational relationships literature. These findings indicate that while crucial in complex CE projects, effective collaboration capabilities have broader relevance across various project types, challenging the notion of unique collaboration capabilities for CE. Instead we suggest the need for new CE collaboration practices supported by the sequential orchestration of key capabilities.
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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 | 0.021 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.001 | 0.018 |
| Research integrity | 0.002 | 0.002 |
| 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".