A systematic literature review on collaboration in circular economy
Bibliographic record
Abstract
The potential of a circular economy (CE) to improve the economic and environmental performance of supply chains has captured the interest of academics and practitioners. Recent studies indicate that collaboration is one of the key pillars of the CE, enabling its benefits to spread along and beyond the supply chain. Most studies that focus on collaboration issues in the context of the CE are in the early stages of development, and in many, collaboration is not the focus of the study but only a side element to the discussion. This paper presents a systematic review of the literature on collaboration in the CE and considers the extent to which research studies provide insights on collaboration in the CE and its contribution to enhanced sustainability of supply chains. We conducted a systematic search of the business studies databases Scopus, Business Source Premier, and ABI/INFORM for academic articles published from 1998 to October 2019 which discuss or analyse collaboration in the context of CE practices: resource reduction, reuse, remanufacturing, recycling, and eco-design. To select articles, the following keywords were used: circular economy, industrial symbiosis, eco-design, remanufacture, collaboration, cooperation, coordination, and alliance. The search generated 89 articles. To perform content analysis, we considered the following dimensions of collaboration derived from the literature (Austin & Seitanidi, 2012; Bahinipati & Deshmukh, 2014; Ghisellini et al., 2016; Masi et al., 2018; Mason et al., 2007; Vereecke & Muylle, 2006): types of collaboration, the levels within the CE where collaboration is used, and effects of collaboration on the performance of a supply chain and its individual members. Studies on the boundaries, bridges, barriers and benefits of various types and levels of collaboration significantly contribute to existing knowledge on collaboration and the CE. While there is a large body of knowledge on collaboration and on the CE, few studies focused on both. In particular, we found a gap in the literature in terms of studies that explore collaboration on the macro level of the CE and between organizations at the same level in the supply chain, i.e. horizontal collaboration. Additionally, we found that despite the growing number of studies that examine the effects of collaboration in the CE on the economic and environmental performance of organizations and their supply chains, only in a few studies was relational and structural economic performance discussed in conjunction with social performance.
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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 | 0.022 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.036 | 0.031 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".