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实现更大的创新。先进的信息和通信技术使各组织能够从用户群体的各种建议中提炼出关于产品、服务和政策的好主意，这是在不增加资源支出的情况下增加创新的一种方法。但是，将创意外包给非专业人士，增加了创意质量和适用性的差异，同时也限制了组织对所有建议给予同等重视的能力。本研究的目的是探索组织如何更有效地利用“群众的力量”，在大量的贡献中发现和识别最有前途的想法。最近，大量关于团队设计的研究表明，团队的结构组成可以解释团队产生好想法的能力的变化。 概念化集体的人群作为一个“新兴的虚拟团队”的投票者，使我们能够探索一些假设时，被投票为“好”的新兴的虚拟团队具有特定的结构，事实上，是一个组织应该花费其有限的资源的注意力的想法。在这个为期两年的项目中，我们收集和分析了用户在三个基于社区的创新论坛中提交和投票的想法，开发了一种算法，可以有效地过滤好的想法，并在其他基于社区的创新论坛上测试该算法的预测能力。

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.