Centre for Applied Research on Social Sciences and Law

Unpacking value creation and value capture in collaborative networks for sustainability

Paper

Our study elucidates collaborative value creation and private value capture in collaborative networks in a context of sustainability. Collaborative networks that focus on innovative solutions for grand societal challenges are characterized by a multiplicity and diversity of actors that increase the complexity and coordination costs of collective action.

These types of inter-organizational arrangements have underlying tensions as partners cooperate to create collaborative value and compete to capture or appropriate value on a private or organizational level, resulting in potential and actual value flows that are highly diffuse and uncertain among actors. We also observe that network participants capture value differentially, often citing the pro-social (e.g. community, belonging, importance) and extrinsic benefits of learning and reputation as valuable, but found it difficult to appropriate economic or social benefits from that value. Differential and asymmetric value appropriation among participants threatens continued network engagement and the potential collective value creation of collaborative networks.

Our data indicates that networked value creation and capture requires maintaining resource complementarity and interdependency among network participants as the network evolves.

We develop a framework to assess the relational value of collaborative networks and contribute to literature by unpacking the complexities of networked value creation and private value capture in collaborative networks for sustainability.

Reference Divito, L., & Good, J. (2021). Unpacking value creation and value capture in collaborative networks for sustainability. Paper presented at ISIRC 2021, 13th International Social Innovation Research Conference
“Enabling the change! Social innovation and enterprises for a better future”, Milan, Italy.

Publication date

Sep 2021

Author(s)

Jason Good

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Research database