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

传统的团队是由两个或两个以上的人组成的，为实现共同目标而共同努力的组织。 然而，由于互联网的出现，个人现在能够以越来越大的灵活性和流动性组成团队。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。这项研究将使用一个非常大的样本，中国在线游戏团队，试图解决尚未回答的问题，人们如何去选择队友时，从事这种类型的短暂的团队合作，以及是否有些方法的队友选择比其他更好的表现。这项研究将在中国上海复旦大学的徐云杰博士的指导下进行。徐博士是定量方法学方面的顶尖专家，对中国最大和最受欢迎的在线游戏社区有着前所未有的了解。该项目将利用一个主要由数字跟踪数据组成的数据集，这是一种基于虚拟系统环境中用户活动的自动记录信息的大数据。使用数字跟踪数据的一个主要好处是其获取的不引人注目的性质;当前数据都是在虚拟在线游戏的背景下收集的，允许不显眼地跟踪团队模式。此外，将使用称为指数随机图建模（ERGM）的特定网络分析方法来测试所提出的假设。在该方法中，观察到的关系网络（即，团队组合关系）通过基于个体差异、观察到的网络中存在的关系配置、甚至其他关系自变量来估计参数来建模。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.