Mass Collaboration Project Recommendation Within Open-Innovation Design Networks

Abstract

Mass collaboration within the design engineering process supports the inclusion of unique perspectives when working on complex problems. Increasing the number of individuals providing input and support into these perplexing challenges can increase innovation, decrease product development times, and provide solutions that truly encompass the needs of the market. One of the greatest challenges within mass collaboration engineering projects is the organization of individuals within these large design efforts. Understanding which projects would most effectively benefit from additional designers or contributors is paramount to supporting mass collaboration design networks. Within such networks, there exists a large number of contributors as well as a large pool of potential projects. Matching individuals with the projects that they can provide the greatest benefit to or building a team of individuals for newly developed projects requires the consideration of previous performance and an understanding of individual competencies and design abilities. This work presents a framework which recommends individual project placement based on individual abilities and the project requirements. With this work, a pool of individuals and potential projects are simulated, and the application of a hybrid recommender system is explored. To complement the simulation, an additional case study with empirical data is performed to study the potential applicability of the proposed framework. Overall, it was found that recommended team compositions greatly outperform the baseline team development, most notably as greater consideration is placed on collaborative recommendations.