Machine learning techniques to model the impact of relational communication on distributed team effectiveness

社会科学家急于了解分布式团队的关系方面，但在这种情况下对人际沟通的审查一直受到可用于分析的统计技术的限制。该项目利用机器学习领域的最新进展来研究分布式或虚拟组中的关系通信流，分析通信对有效性的影响，从而促进对虚拟组成员之间复杂的相互依赖的理解。PI假设，团队成员之间的依赖关系是理解影响分布式团队成功(或失败)的过程的关键。该项目包括理论形成和完善的两步方法，结合大规模观测数据和实验研究。研究小组将首先从开源软件开发项目中提取和分析公开可用的数据，以开发基于成员之间沟通关系模式的有效性模型。他们将利用这一分析的结果来制定有针对性的假设进行实证评估，并进行实验室实验来检验这些假设。该项目的结果应该能更全面地理解虚拟群体的人际沟通和绩效，包括洞察群体成员之间的依赖关系以及这些依赖关系对沟通和有效性的影响。这些结果应该具有实际意义。此外，该项目将修改和扩展最先进的机器学习工具，其方式应该对其他社会科学调查有用。

Social scientists are anxious to understand the relational aspects of distributed teams, but examination of interpersonal communication in such settings has been limited by the statistical techniques available for analysis. This project exploits recent advances in the field of machine learning to study relational communication flow in distributed or virtual groups, analyze the impact of communicationon effectiveness, and, as a result, to advance understanding of the complex interdependencies among virtual-group members. The PIs posit that it is the dependencies among team members that hold the key to understanding the processes that impact the success (or failure) of distributed teams.The project involves a two-step approach to theory formation and refinement, combining large-scale observational data and experimental studies. The research team will first extract and analyze publicly available data from open-source software development projects to develop models of effectiveness based on relational patterns of communication among members. They will use the results of this analysis to develop targeted hypotheses for empirical evaluation and conduct laboratory experiments testing these.The results of this project should provide a more comprehensive understanding of interpersonal communication and performance of virtual groups, including insights into the dependencies among group members and the influence of these dependencies on both communication and effectiveness. These results should have practical implications. Further, the project will modify and extend state-of-the-art machine learning tools in a way that should be useful for other social science investigations.