EAPSI: Team Assembly and Performance in a Large Sample of Chinese Online Gamers

传统团队是由两个或更多人共同努力实现共同目标的成熟的、清晰划分的团队。然而，由于互联网的出现，个人现在能够更灵活、更灵活地组建团队。例如，来自不同背景的地理上分散的个人可以暂时联合起来完成一个独特的项目，然后解散。这项研究将使用非常大的中国网络游戏团队样本，试图解决人们在参与这种短暂的团队合作时如何选择队友，以及某些队友选择方法是否会比其他方法产生更好的表现这一尚未回答的问题。这项研究将在上海复旦大学中国博士的指导下进行。徐博士是量化方法论方面的顶尖专家，他对中国一些最大、最受欢迎的网络游戏社区拥有前所未有的访问权限。该项目将利用一个主要由数字跟踪数据组成的数据集，这是一种大数据类型，源于基于虚拟系统环境中的用户活动自动记录信息。使用数字跟踪数据的一个主要好处是它的获取不引人注目；当前的数据都是在虚拟在线游戏的环境中收集的，允许对团队模式进行不明显的跟踪。此外，建议的假设将使用一种特定的网络分析方法进行测试，该方法称为指数随机图建模(ERGM)。在该方法中，通过基于个体差异、存在于观察网络中的关系配置、甚至其他关系独立变量来估计参数，来对观察到的关系网络(即，团队集合关系)进行建模。该NSF EAPSI奖是与中国科技部合作资助的。

Traditional teams are well-established, clearly delineated groups of two or more people working together to achieve a common goal. However, due to the advent of the Internet, individuals are now able to assemble into teams with increasing flexibility and fluidity. For instance, geographically dispersed individuals from a diverse array of backgrounds can temporarily join forces to complete a unique project, and then subsequently disband. This study will use a very large sample of Chinese online gaming teams in attempts to resolve the yet unanswered questions of how people go about choosing teammates when engaging in this type of ephemeral teamwork and whether some methods of teammate selection result in better performance than others. This research will be conducted at Fudan University in Shanghai, China under the guidance of Dr. Yunjie Xu. Dr. Xu is a foremost expert in quantitative methodology, and has unprecedented access to some of China's largest and most popular online gaming communities.This project will utilize a dataset that is largely comprised of digital trace data, which is a type of big data that stems from the automatic recording of information based on user activity within the context of a virtual system. One primary benefit of using digital trace data is the unobtrusive nature of its acquisition; the current data were all collected within the context of a virtual online game, allowing for inconspicuous tracking of teaming patterns. Furthermore, the proposed hypotheses will be tested using a specific network analytic method known as exponential random graph modeling (ERGM). In this method, an observed network of relationships (i.e., team assembly ties) is modeled by estimating parameters based on individual differences, relational configurations present in the observed network, and even other relational independent variables. This NSF EAPSI award is funded in collaboration with the Chinese Ministry of Science and Technology.