Project teams in the Architecture, Engineering, and Construction (AEC) industry are typically temporary and highly complex, multi-team systems. They require smooth coordination and integration of ideas while numerous individuals interact in a complex social network structure at sub-team and project team boundaries within and outside of their disciplines and organizations. With this motivation, a trans-disciplinary team of engineering, construction management, computer science, education, social networks, organizational psychology, and economics experts will develop a research model of intelligent social network interventions. By augmenting human cognition and the functioning of multi-team systems in real-world AEC and student teams, this model will enable individuals to develop the skills needed for future of work in complex social systems, and provide short and long-term economic and social benefits via improvements in student outcomes, individuals' skills, and project outcomes. The successful completion of the project will offer a practical system, equipping individuals and organizations with sufficient means to facilitate multi-team coordination and project effectiveness. AEC project teams have long-term social, economic, and environmental impacts through their built environment products and so, it is critical for workers to develop knowledge and skills that support highly interdependent work contributions in complex social and task structures. The results from this project will have a significant positive impact in the productivity of AEC workers that immediately take part in project teams, and will extend to a broad range of workforce via improvements in built environments. It will contribute to the science of organizations, engineering, and R&D teams across industries that employ complex multi-team systems now and in the future. New learning modules for project-based teaching and learning that incorporate intelligent social network interventions will be developed and disseminated through an outreach website to help train future workers. This is an advancement in the use of technology to sensitize humans on how teams work and continuously improve their skills for improved project performance, individual learning, and future of work.
While social network analysis research has been carried out from various perspectives, little has been done to derive "actionable" insights and use these insights as intervention to improve communication, especially from the context of work. This forms the basis for "dynamic (social) network rewiring" based not only on human behavior but also the work context, i.e., the goals of the work, via multiple cycles alternating between examining and intervening the network for behavior and context. To achieve these goals, the researcher team will use immediate and machine/deep learning enabled social network interventions to help individuals develop the skills needed for future of work and facilitate short and long-term economic and social benefits. The trans-disciplinary research team has formulated a longitudinal, comparative research design involving real-world AEC teams as well as classroom, student-team test-beds, where equal numbers of cases are to receive manual, machine learning bolstered, and no social network interventions. Complementing the recent network intervention studies, this project focuses on complex and temporary multi-team systems. Student teams in the study design will contribute to the understanding of smaller, intra-organizational, sub-team dynamics in multi-team systems and emergence of tomorrow's authentic workers and teams. The design will use multi-modal graph neural models to automate recognition of poor team functioning metrics so that problems can be diagnosed and interventions can be facilitated via augmentation of human cognition for multi-team coordination. The design can accumulate knowledge obtained from past learning and adapt it for future learning, even in new domains.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
