VOSS: Teams Emerging from the Crowd: Virtual Team Structure as a Predictor of Idea Goodness in Online Innovation Communities

创新是美国经济的核心驱动力。组织不断寻求以更少的资源更具创新性。先进的信息和通信技术使组织能够从用户社区的各种建议中提取产品、服务和政策的良好想法，这是一种在不增加资源支出的情况下增加创新的方法。但是，将想法产生外包给非专家会增加产生的想法的质量和适用性的差异，并相应地限制组织平等关注所有建议的能力。这项研究的目的是探索组织如何更有效地利用“群体的力量”来在一片贡献的海洋中发现和识别最有希望的想法。最近，对团队设计的大量研究表明，团队的结构组成可以解释团队产生好想法的能力的变化。将一群面向大众的选民概念化为“新兴虚拟团队”，使我们能够探索一些假设，即何时被具有特定结构的新兴虚拟团队投票选为“好的”的想法实际上将成为一个组织应该花费其有限的注意力资源的想法。在这个为期两年的项目中，我们收集和分析了用户在三个社区创新论坛上提交和投票的想法，开发了一种高效有效地从坏想法中筛选好想法的算法，并在其他社区创新论坛上测试了该算法的预测能力。

Innovation is a core driver of the U.S. economy. Organizations are constantly seeking to be more innovative with fewer resources. Advanced information and communication technologies that allow organizations to extract good ideas for products, services, and policies from the diverse suggestions of user communities represent one way to increase innovation without a concomitant increase in resource expenditures. But the outsourcing of idea generation to non-specialists increases the variance in the quality and applicability of the ideas that are generated and, concordantly, strains an organization's ability to give equal attention to all suggestions. The goal of this study is to explore how organizations might more effectively harness "the power of the crowd" to find and recognize the most promising ideas within a sea of contributions.Recently, a good deal of research into team design has shown that the structural composition of a team can explain variations in the team's ability to generate good ideas. Conceptualizing a collective of crowd-soucing voters as an "emergent virtual team" allows us to explore a number of hypotheses about when the ideas that are voted to be "good" by emergent virtual teams with particular structures will, in fact, be the ideas upon which an organization should spend its limited resources of attention. In this two-year project, we collect and analyze ideas submitted and voted on by users in three community-based innovation forums, develop an algorithm for efficiently and effectively filtering good ideas from bad, and test the predictive power of the algorithm on other community-based innovation forums.