Project teams with diversified interests and talents allocate their limited resources according to a shared form of a plan to constitute temporary alliances in Architectural, Engineering, and Construction (AEC) industry. These resources, or goods, service, and knowledge, are transferred across technologically separable interfaces and those activities for which firms provide less costly management can be organized within productive teams. The dynamic nature of acquiring and processing information, contractual and organizational relationships, technology adoption and uncertain environments affect the collaboration dynamics which lead to determining overall performance of the projects. However, despite the critical role of these ‘softer’ social dynamics, they have yet to be comprehensively examined through the lens of transaction cost (TC) theory in existing literature, especially amid profound technological and environmental shifts.This dissertation examines these dynamics and the potential of generative AI (GAI), specifically attention-based language models, within inter-organizational project networks to uncover tangible lessons learned for improving project collaboration. It outlines the steps from data acquisition to analysis, focusing on multi-level communication dynamics and performance-related documents from a complex healthcare project and a medium-complexity mixed-use project in Michigan, USA. The study integrates quantitative and qualitative datasets, including emails, semi-structured interviews, surveys, construction documents, and interorganizational meeting recordings. Social Network Analysis (SNA) guides the investigation of communication networks, while language models are assessed for classification and communication-related tasks. Programming languages Python and R facilitate data cleaning, statistical tests, and visualizations, with code snippets provided in the appendix for replication. After the introductory section, the second chapter (1) investigates how complex relationships from task descriptions can be categorized and tracked, comparing current machine learning methods and fine-tuned open-source language models with limited training data, considering real-world AEC industry mindset. (2) The third chapter explores how times of disruption (ToD) affect communication dynamics among interorganizational project networks through a COVID-19 case study. (3) The fourth chapter explores text complexity metrics, absorptive capacity (ACAP) measured through tier and role based characteristics, and GAI’s role in AEC communications, analyzing how project parties perceive its effectiveness in refining emails and RFIs to reduce TCs. Key findings highlight the value of GAI and strategic communication: (1) Fine-tuned language models can outperform current machine learning methods in categorizing complex tasks, especially when provided with detailed descriptions that reveal underlying social dynamics. (2) Balancing direct and indirect communication can enhance information flow between central project members and distant parties, particularly subcontractors with high specificity, during ToD. TC Theory highlights the need for adaptive, tier-specific management, and flexibility, especially from Tier 1 leaders, to sustain network stability and project continuity. (3) GAI’s editing capabilities can effectively tailor text complexity and readability to facilitate project communication, provided appropriability safeguards private information. (4) Overall, a cohesive strategy of strategic communication, adaptability, and generative technologies can optimize project outcomes by reducing knowledge transfer frictions and harnessing stakeholders’ authentic strengths.
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