Towards Understanding Collaboration Within Circular Business Models

  • Phil BrownEmail author
  • Nancy Bocken
  • Ruud Balkenende
Part of the CSR, Sustainability, Ethics & Governance book series (CSEG)


This chapter presents emerging work on mapping collaborative activities related to Circular Business Model (CBM) implementation. Findings pertain to the importance of collaboration presenting specific types demonstrated while pursuing CBMs. Future research areas are highlighted to explore potential impacts of collaboration upon CBMs.

Collaboration is essential to simultaneously ensuring economic, environmental and social performance throughout a product’s life cycle(s). This chapter addresses the following question: What types of collaboration are presented by companies pursuing Circular Business Models?

Companies developing CBMs, a subcategory of sustainable business models, explore life cycle perspectives through aspiring to slow and close resource loops. The required system change is beyond individual companies and requires transition towards inter-organisational collaborative networks. Collaboration, although vital, is also described as “opaque”, an “amorphous meta-concept”, and a “black box”. Collaboration is therefore a highly diverse and expansive concept, explored here within the specific focus to its implications and interactions upon CBMs.

A literature study combining research fields of CBMs and collaboration within sustainable supply chain management was conducted. Specific types of collaboration crucial to CBMs are presented. These are applied through a proposed framework to describe eight Dutch companies pursuing CBMs. Their collaborative processes are analysed through pattern matching and cross-case analysis. Based on this, initial characteristics of the collaborative activities that are linked to CBMs are proposed.


Circular Business Models (CBMs) Companies Pursuing Fairphone Circular Play visionVision 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Authors and Affiliations

  1. 1.Delft University of TechnologyDelftNetherlands
  2. 2.University of CambridgeCambridgeUK
  3. 3.Lund UniversityLundSweden

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