Knowledge Intensive Team Case Study

Knowledge intensive team (KIT) [1-4] can solve complex and vague problems and enhance adaptability and innovation ability of enterprises. It has become an im-portant organizational form of enterprises and has gradually become the academic focus.

KIT generally deals with knowledge intensive tasks, which require strong expertize, independent thinking and collaborating, rather than routine tasks with fixed workflow and strong structure [5]. The level of knowledge, autonomous behavior and interac-tion among team members, as well as task difficulty make the KIT a complex adap-tive system (CAS) with openness and autonomy [6-8]. Task allocation scheme basi-cally refers to assigning tasks to team members according to certain strategies, and different

The classical assignment problem mainly concentrates on assignment algorithms. It was first discovered in 1940s, and it mainly uses linear pro-gramming in operational research to solve the problem of using existing resources to complete fixed tasks [13,14]. With the continuous increasing constraints imposed on task allocation, the cost of solving the problem got higher. Inspired by Sagar [15], a plethora of intelligent optimization algorithms [16,17] emerged in new task allocation problems. After the 90s, with the rapid development of agent technology, the task allocation problem gradually shift to focus MAS. Compared with the traditional task allocation problem, the task allocation in MAS is closer to reality and more humane. Loads of agent-based task allocation methods, including extended contract network protocol [18], self-organization [19,20] etc. have greatly promoted the development of task allocation theory. Recently, the task allocation problem has gradually changed from static to dynamic. Sighn, A. J. et al. have designed algorithms to dy-namically measure the individual utility and team utility of task allocation [21]. Chi-nese researchers also proposed a centralized dynamic task assignment model based on the Markoff decision process, which is suitable for small scale systems to

Features of this study are: (1) Knowledge exchange among team members is an important prerequi-site for team to complete tasks efficiently. We study the impact of different TASs on team performance considering the knowledge exchange as a core. (2) Different TASs generate different ways of knowledge exchange, and members dissimilarly make adaptive adjustments, which will differently impact team performance. This series of processes is complexly adaptive. (3) The “non-exclusive” knowledge resource signifi-cantly differentiates from the traditional “exclusive” resources such as material, capi-tal and so on. Knowledge resources are not consumed in time of using whereas they may be innovated and proliferated due to