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Traditional, virtual, and digital intermediaries in university-industry collaboration: exploring institutional logics and bounded rationality

Lookup NU author(s): Professor James Cunningham



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


University-industry collaboration now extends beyond traditional intermediary structures and logics, enabled by more contemporary virtual networks and digital formats. This however poses new strategic and operational challenges for effective and responsive knowledge transfer. The purpose of this paper is to compare traditional models of knowledge transfer intermediaries in university-industry collaboration with emerging, virtual (network-based) and digital intermediaries by exploring their structures (thus institutional logics) and their services (their agency). We synthesise literature to form a comprehensive analytical framework to assess the structure and agency of twenty international knowledge transfer intermediaries from around the world. Further running a cluster analysis using multiple correspondence analysis method and following its results we propose a unique combination of institutional logic and bounded rationality lenses, which allowed us to identify four types of knowledge transfer intermediaries: rigid, rigid-unbounded, agent-bounded and agile. Our unique framework contributes to existing knowledge focused on traditional forms of knowledge transfer intermediaries, by identifying and positing institutional logics for emerging contemporary virtual and digital intermediaries in university-industry collaboration.

Publication metadata

Author(s): Albats E, Alexander AT, Cunnningham JA

Publication type: Article

Publication status: Published

Journal: Technological Forecasting and Social Change

Year: 2022

Volume: 177

Print publication date: 01/04/2022

Online publication date: 21/01/2022

Acceptance date: 01/01/2022

Date deposited: 17/02/2022

ISSN (print): 0040-1625

ISSN (electronic): 1873-5509

Publisher: Elsevier Inc.


DOI: 10.1016/j.techfore.2022.121470


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