Social Networks, Transactive Memory and Team Performance: An Experimental Investigation

团队越来越多地被用于各种组织环境中，包括教育、医疗保健、政府和营利性公司。关于如何提高其有效性的信息有望改善美国组织的业绩和竞争力及其成员的福祉。我们提出的研究不仅对传统工作环境中的团队有影响，而且对分布式工作团队和在线社区等新兴环境也有影响。我们确定了导致最强交互记忆和最高团队绩效的沟通模式。因此，这项研究将产生团队成员和领导者可以用来提高团队绩效的信息。此外，开发TMS的行为测量将使研究人员能够在大型团队和地理分布的环境中分析TMS，这些环境在世界各地的组织和在线社区中正变得越来越普遍。综上所述，这些研究结果有可能提高我们对团队协作、团队组建、沟通网络和团队绩效的理解。本研究考察了沟通网络对交互记忆系统发展和团队绩效的影响。交互存储系统(TMS)是用于编码、存储和检索信息的集合系统。已经发现，与没有TM的团队相比，拥有良好TM的团队在各种任务中表现得更好。TMS被称为“谁知道什么”的系统，它通过团队成员之间的沟通和互动来实现，因为他们相互学习和依赖彼此的技能和知识。尽管TMS取决于沟通和互动，但关于通信网络如何影响群体的TMS的研究很少。通信网络限制或允许团队内部的通信，从而潜在地改变TMS的发展。我们的研究通过一系列研究，结合补充的实验室和档案方法，调查了促进经颅多普勒超声发展的通信网络的特征。首先，我们在实验中操纵团队中存在的通信网络，以检查网络特征对团队TMS和绩效的影响。我们考察了团队绩效的两个维度：错误数量和创造力。其次，因为在许多情况下，个体选择他们自己的网络和/或他们在网络中的位置，所以我们比较了实验者强加的网络和位置分配与参与者选择的网络和位置分配的影响。这使我们能够确定观察到的影响是由于通信网络本身还是由于参与者制定的网络。通过操纵网络，并对网络和网络中的位置进行随机分配，我们能够就网络特征对TMS发展和团队绩效的影响做出因果声明。第三，使用我们的实验数据，我们将开发和提炼一种新的不引人注目的TMS行为测量方法。这一措施将适用于使用在线网络进行大规模技术中介项目的真实团队的数据。在这一新的TMS衡量标准得到验证后，我们将研究它与大型在线团队的绩效之间的关系。

Teams are increasingly used in a variety of organizational contexts, including education, health care, government and for-profit firms. Information about how to increase their effectiveness promises to improve the performance and competitiveness of organizations in the U.S. and the well being of their members. Our proposed research has implications not only for teams in traditional work settings but also in new emerging settings such as distributed work teams and online communities. We identify the patterns of communication that lead to the strongest transactive memory and the highest team performance. Thus, the research will result in information that team members and leaders can use to improve their team's performance. In addition, the development of a behavioral measure of TMS would enable researchers to analyze TMS in large teams and geographically distributed contexts, which are becoming increasingly prevalent in organizations and online communities around the world. All together, the results have the potential to advance our understanding of team collaboration, team formation, communication networks and team performance.The research examines the effects of communication networks on transactive memory system development and team performance. A transactive memory system (TMS) is a collective system for encoding, storing and retrieving information. Teams with well-developed TMSs have been found to perform better across a variety of tasks compared to teams lacking TMSs. Known as systems of "who knows what," a TMS emerges through communications and interactions among team members as they learn and rely on each other's skills and knowledge. Although a TMS hinges on communication and interaction, very little research has been conducted on how communication networks affect a group's TMS. Communication networks constrain or enable communication within a team and thus potentially alter the development of TMS. Our research investigates the characteristics of communication networks that enhance the development of TMS in a series of studies incorporating complementary laboratory and archival methods. First, we manipulate the communication networks that exist within teams in an experiment to examine the effects of network characteristics on the team's TMS and performance. We investigate two dimensions of team performance: the number of errors and creativity. Second, because in many contexts individuals choose their own networks and/or their positions within the network, we compare the effects of networks and position assignments that are imposed by the experimenter to those that are chosen by participants. This enables us to determine if effects observed are due to the communication network itself or due to participants' enactment of the network. By manipulating the networks and using random assignment to networks and to positions within the networks, we are able to make causal statements about the effects of network characteristics on TMS development and team performance. Third, using data from our experiments, we will develop and refine a new unobtrusive, behavioral measure of TMS. This measure will be applied to data from real teams who use online networks for large-scale technology-mediated projects. Following the validation of this new TMS measure, we will examine how it relates to performance for large online teams.