基于SNA的工程项目组织网络建设研究

应用建筑信息模型(BIM)的项目有利于项目团队成员之间的信息交换，减少团队内部的冲突和分歧，并在项目成本、三维可视化、施工顺序问题、冲突解决方面具有显著效果。研究考察了BIM项目的社交网络特征，通过分析BIM项目的参与方信息流动特点，从而揭示项目的组织网络特征，然后使用项目绩效作为度量筛选出针对BIM项目而言最优的社交网络结构，最终提出了BIM项目信息流建设的相关意见。研究引入关键绩效指标(KPI)以衡量项目绩效，使用密度、平均度数、直径和平均路径长度等社交网络指标揭示不同BIM项目的网络特征，最后揭示了项目社交网络与KPI之间存在显著相关性。结果表明，高度紧密的组间社会网络更有利于项目绩效的提升，有效提升组间沟通质量是建立最优社交网络的必要条件。然而对于网络的平均度数指标应当辩证性看待，高平均度数代表该网络的沟通能力极佳，但从权力量化角度来看也意味着可能存在高度集权的节点。

Research on Construction of Engineering Project Organization Network Based on SNA

Building information modeling (BIM) projects are conducive to the exchange of information between project team members, reduces conflicts and disagreements within the team, and have significant effects in project cost, three-dimensional visualization, construction sequence issues and conflict resolution. The research examines the social network characteristics of BIM projects, reveals the organizational network characteristics of the project by analyzing the information flow characteristics of the participants in the BIM project. Then we uses project performance as a measure to filter out the optimal social network structure for the BIM project, and provides relevant opinions on the construction of BIM project information flow. The study introduced key performance indicators (KPIs) to measure project performance, used social network indicators such as density, average degree, diameter, and average path length to reveal the network characteristics of different BIM projects, and finally revealed a significant correlation between project social networks and KPIs. The result shows that a highly tight social network between groups is more conducive to the improvement of project performance, and effective improvement of the quality of communication between groups is a necessary condition for establishing an optimal social network. However, the average degree index of the network should be viewed dialectically. A high average degree indicates that the network has excellent communication capabilities, but from the perspective of power quantification, it also means that there may be highly centralized nodes.