Factors that influence cooperation in networks for innovation and learning

Rory Sie*, Marlies Bitter-Rijpkema, Slavi Stoyanov, Peter Sloep

*Corresponding author for this work

    Research output: Contribution to journalArticleAcademicpeer-review

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    Networked cooperation fails if the available partnerships remain opaque. A literature review and Delphi study uncovered the elements of a fruitful partnership. They relate to personality, diversity, cooperation, and management. Innovation networks and learning networks share the same cooperative intention, but they too often fail as members of the network do not know which partnerships are valuable. If one plans to build a support service that provides insight into the value of future cooperation, one first needs to know what contributes to effective and efficient cooperation. In addition to carrying out a literature review, we invoked the eDelphi method to answer this question. eDelphi is a method to solicit knowledge from experts anonymously and without geographical constraints. Observations from two eDelphi rounds are reported in this article. The first round focused on factor generation and determined which factors influence cooperation networks; it was conducted with two groups of six representative experts. Experts list open communication, a positive attitude, trust, keeping appointments, and personality as influential factors for cooperation networks. A team of four moderators categorised the factors in a second round, resulting in four core clusters: personal characteristics, diversity, effective cooperation, and managerial aspects. Interestingly the experts failed to list some factors that are mentioned in the literature. This finding is discussed.
    Original languageEnglish
    Pages (from-to)377-384
    Number of pages8
    JournalComputers in Human Behavior
    Early online date27 May 2014
    Publication statusPublished - Aug 2014


    • Cooperation
    • Networked innovation
    • Recommender systems
    • Coalition formation
    • Delphi method
    • Hierarchical cluster analysis


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