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Analysis Of Collaborative Design Networks: A Case Study Of OpenIDEO

M. Fuge, Kevin Tee, Alice M. Agogino, Nathan Maton
Published 2014 · Engineering

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This paper presents a large-scale empirical study of OpenIDEO, an online collaborative design community. Using network analysis techniques, we describe the properties of this collaborative design network and discuss how it differs from common models of network formation seen in other social or technological networks. One major finding is that in OpenIDEO’s social network the highly connected members talk more to less connected members than each other—a behavior not commonly found in other social and collaborative networks. We discuss how some of the interventions and incentives inherent in OpenIDEO’s platform might cause this unique structure, and what advantages and disadvantages this structure has for coordinating distributed design teams. Specifically, its core-periphery structure is robust to network changes, but is at risk of decreasing design exploration ability if the core becomes too heavily clustered or loses efficiency. We discuss possible interventions that can prevent this outcome: encouraging core members to collaborate with periphery nodes, and increasing the diversity of the user population. 1 The Rise of Distributed Design Communities To solve increasingly complex design problems, companies are beginning to look outside of their existing talent pool to absorb and build off of ideas from distributed individuals or groups. This practice is called different names by different groups, including Open Innovation, Crowdsourcing, and Crowd Design, among others. It is practiced by a range of organizations, from large global corporations (e.g., P&G’s Connect+Develop program1) all the way down to small decentralized groups of individuals (the Open Source Software movement). Internet technologies enable regular people to cooperatively design better products, permitting a powerful ∗Address all correspondence to this author mark.fuge@berkeley.edu – Dept. Mechanical Engineering †kevintee@berkeley.edu – Dept. Computer Science ‡agogino@berkeley.edu – Dept. Mechanical Engineering 1http://www.pgconnectdevelop.com/ new kind of product development process. To increase the effectiveness of these distributed teams, it would be helpful to understand how they act differently than traditional groups, and how existing design and management practices need to be adapted to this new setting. This paper contributes to that understanding through the use of network analysis techniques. By comparing a real-world design network with prior models of collaborative networks, this paper presents two main contributions: (1) An empirical network analysis of OpenIDEO, an online design innovation network, which can act as a test bed for models of design networks. (2) A summary of key differences between observed behavior and existing network models, with discussion on the implications for directing design practice. Specifically, we explore the role of community structure in OpenIDEO, explaining how some of its common network properties predispose OpenIDEO to certain advantages and disadvantages when facilitating idea generation and collaboration. We find that OpenIDEO’s social interactions center around a core of users who communicate more frequently with members on the periphery than among themselves (an uncommon disassortative core-periphery social structure). This structure is more robust to network changes than standard social networks—a good thing for open innovation platforms in which participation is voluntary. However, the central core structure also represents a risk to potential idea generation effectiveness: high clustering within the core could precipitate design fixation on a small number of concepts as a result of complex contagion (repeated exposure to the same stimuli from multiple people) [1]. We discuss several possible interventions that can prevent this effect, such as promoting collaboration between core and periphery members and increasing diversity of participants. This paper provides a brief introduction of current network models and reviews previous studies of similar networks (e.g., Open Source Software networks, Co-Authorship networks, etc.). It then describes the OpenIDEO dataset and the network qualities we studied, and presents our empirical
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