Abstract
Mass collaboration efforts can increase innovation and design possibilities. Incorporation of open innovation into the product development process allows for a vast array of unique perspectives and ideas. However, with the broad expansion of design possibilities, coordination of these development processes is paramount. To best make use of open innovation in product development, increased organizational efforts must be considered. The mass collaboration of individuals must account for individual intellectual abilities (competencies), working experience and even personality traits or idiosyncrasies. Approaches to this problem require the fusion of social network analysis with quantifiable design impacts. This work proposes a simulation framework that evaluates the design potential of a project team based on individual attributes and the team network structure. The overall contribution of this work comes from the exploration of team structure, focusing on network composition metrics such as centrality and network density, while attempting to understand the role of individual ability and positioning on the success of the design process. This work aims to garner a more thorough understanding of how the network structure of design teams correlates with their potential performance through a generalized simulation framework, applicable to future crowd and design initiatives.
Highlights
Mass collaboration efforts have been applied using a variety of techniques
Our work primarily focuses on user production while supplementing the organizational component of traditional production with social network analysis to allow for greater user production while minimizing expensive managerial overhead (Hamel 2011)
Once the interests have been individually assigned, we look for commonality of interests between members to determine whether a connection should be made between these two individuals based on a threshold of common interests
Summary
Crowdsourcing has been employed by numerous organizations as a method to garner ideas from the masses through the use of design challenges (Howe 2006). This method of mass collaboration generally places the organizational structure of the effort under the sponsoring company, only allowing for the crowd to generate potential solutions without complete engagement in the design process (Brabham 2008). From the network graph shown, it is observed that there were multiple members that had a degree of one or two, indicating that they were partially removed from the design effort This led to poor information sharing from these members, decreasing their design score. This network had a density of only 0.397, indicating that only approximately 40% of all possible connections were utilized
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