A performance management methodology for collaborative design and construction project teams

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Economists have correlated astounding performance (i.e., productivity) differences among thousands of firms in the global manufacturing industry. They find that better management practices correlate with better performance. However, no such structural link is known in the Architecture, Engineering and Construction (AEC) industry. Project teams generally do not have an explicit performance management method, a culture of frequent and systematic performance tracking, or sufficient data to enable statistical analysis. Current AEC practice includes ad-hoc tracking and judgment-based evaluation, which limit the delivery of increasingly complex projects with high predictability of performance outcomes. My research goal was to determine any extent to which, for collaborative AEC project teams, there is a similar correlation as in manufacturing between management method and at least one measure of project performance, i.e., client satisfaction. This work contributes a theory of Metric-Based Performance Feedback Methodology (called MetPerforma) to the AEC literature. MetPerforma helps teams to develop candidate metrics, frequently track and report performance on shared feedback dashboards to enable transparent and social feedback, and statistically analyze metric relationships to understand how to attain more predictable outcomes. In addition to client satisfaction, MetPerforma includes project performance metrics pertaining to areas of project quality, cost, schedule, organizational effectiveness and client (i.e., building owner or owner's representative) satisfaction, all critical to project success of collaborative teams. I tested MetPerforma using quasi-experimental time-series interventions on five longitudinal case studies with two clients: one in healthcare and the other in theme park development. I posited three general criteria for an effective methodology: (1) use is sustained by project teams, (2) use improves predictability of client satisfaction (measured by client satisfaction volatility), and (3) use enables descriptive statistics to provide valuable insights to project teams. Empirically, MetPerforma intervention on the case study projects addressed these three criteria respectively: (1) it resulted in three years of weekly quantitative and social feedback across five project teams, (2) it reduced client satisfaction volatility across five projects, and (3) it enabled detection of various robust metric-to-metric relationships. I interpret these test findings as evidence of power in the domain of collaborative design and construction projects given the MetPerforma results from five projects over a period of six (6) to twelve (12) months of implementation. MetPerforma is theoretically novel because it further elaborates management theory researched by economists (i.e., MetPerforma, as an explicit method, has more specificity than the definition of 'good' performance management practice), was effectively tested in a different empirical setting (i.e., longitudinally tested by five collaborative AEC project teams versus manufacturing firms), and validated at a different level of analysis (i.e., identification of predictors of outcome versus simply higher outcome). I claim evidence of generality given that MetPerforma results were replicated across five projects with characteristic heterogeneity (i.e., project delivery type, client team, project phase, team composition). Practically, this research shows that application of the theory of MetPerforma enables teams to tactically manage project performance to achieve continuous improvement and ultimately improve project performance outcomes. MetPerforma contributes to performance management theory in the domain of collaborative design and construction projects. This research calls for further exploration in the causal relationships between metrics and between metrics and project management practices and for automation and integration with existing project management tools.


Type of resource text
Form electronic; electronic resource; remote
Extent 1 online resource.
Publication date 2015
Issuance monographic
Language English


Associated with Li, Wendy Xiaowei
Associated with Stanford University, Department of Civil and Environmental Engineering.
Primary advisor Fischer, Martin, 1960 July 11-
Thesis advisor Fischer, Martin, 1960 July 11-
Thesis advisor Kunz, John
Thesis advisor Lepech, Michael
Advisor Kunz, John
Advisor Lepech, Michael


Genre Theses

Bibliographic information

Statement of responsibility Wendy Xiaowei Li.
Note Submitted to the Department of Civil and Environmental Engineering.
Thesis Thesis (Ph.D.)--Stanford University, 2015.
Location electronic resource

Access conditions

© 2015 by Wendy Xiaowei Li
This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).

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