Alignment between intensity of risk and level of collaboration in partnered architecture, engineering and construction projects : a quantitative approach to test impacts on project performance outcomes

Risk is a typical characteristic of Architecture, Engineering, and Construction (AEC) projects due to their inherent complexities. The intensity of such risk is influenced by factors such as the dynamic nature of project elements (e.g., fragmented multi-disciplinary project teams), interactions among these elements, and lack of clear project goals. Project management theory and practice both endorse that as the intensity of risk in a project increases, a higher level of collaboration among the multi-disciplinary project teams is desirable in order to achieve optimal project performance outcomes.To facilitate higher or improved level of collaboration among project teams, the AEC industry utilizes innovative project delivery methods (e.g., IPD), technologies (e.g., BIM), and practices (e.g., Lean Construction). Project Partnering is one such project delivery practice; adopting which, two or more organizations commit to harboring an environment of collaboration (e.g., effective communication, shared vision, goal alignment, trust) in a structured approach, with the intention of achieving optimum shared project performance goals (e.g., reduced costs, delays).Although both researchers and practitioners commonly recognize that collaboration is an effective risk management strategy, a theoretical gap exists in providing empirical reinforcement supporting this assertion. A part of this gap is due to the lack of a structured framework for investigating collaboration. Partnering fulfills this shortcoming and provides a structured framework to study collaboration analytically. Thus, partnered AEC projects are the focus and unit of analysis for this research.In addition, existing Partnering literature is largely qualitative and presents conceptual models, potential benefits to adopting Partnering, barriers to its adoption, critical success factors, and performance measurement and evaluation methods. Quantitative research in this domain is limited and has studied projects either coming largely from a single source of ownership (e.g., DOTs) or a particular project type (e.g., horizontal infrastructure projects) at a time. There is a need for evidence-based quantitative research that identifies specific factors linked to partnered-project success using data from a large and diversified sample of projects.Thus, the need for an empirical assessment of the association (denoted by the variable fit) between risk (specifically its intensity) and level of collaboration (via partnering practices in this study), and its impact on project performance prompted the undertaking of this study. Due to lack of pre-defined terminology to capture the association between risk intensity and partnering level, it was denoted in this study via an introduction of the variable 'fit'. The goal of the study is to investigate partnered AEC projects for the impact of the fit between their intensity of risk and adopted partnering level on their performance outcomes (e.g., cost, schedule). Accordingly, this study aims to answer the following research question: 'In partnered AEC projects, does the fit between risk intensity and level of partnering correlate with performance outcomes?' The hypothesis developed by the researcher is that 'In a partnered project, better the fit between the intensity of risk and adopted partnering level, better is its performance.' This study used an archival data-set containing details of 127 partnered projects from the United States completed between 2010 and 2018. Literature study followed by an exploratory data analysis was conducted to develop models to determine constructs of interest - risk intensity, partnering level and performance evaluation metrics, from AEC project characteristics. The models were validated via a survey; and are one of the outcomes of this study. Further, via content analysis, quantitative measures of these constructs were systematically coded from project details in the data set.The hypothesis of this study was tested separately for different performance outcome metrics (e.g., cost growth, schedule growth) via a quantitative approach of unsupervised statistical learning tests such as the Kruskal-Wallis test and the Dunn Test for comparison of performance metric data as samples across the fit categories. The deliverable of this study are tools or models for risk intensity assessment and simultaneous determination of recommended level of partnering. The study was able to contribute to the body of knowledge of risk management via collaboration by providing empirical reinforcement to the association or lack of thereof between risk, collaboration and performance. Lastly, this study provides guidelines for best practices in Partnering contributing to effective risk management on AEC projects.