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Carpenter, N and Bausman, D C (2016) Project Delivery Method Performance for Public School Construction: Design-Bid-Build versus CM at Risk. Journal of Construction Engineering and Management, 142(10).
Chang, C and Chen, S (2016) Transitional Public–Private Partnership Model in China: Contracting with Little Recourse to Contracts. Journal of Construction Engineering and Management, 142(10).
Chen, C, Wang, Q, Martek, I and Li, H (2016) International Market Selection Model for Large Chinese Contractors. Journal of Construction Engineering and Management, 142(10).
Choi, J O, O’Connor, J T and Kim, T W (2016) Recipes for Cost and Schedule Successes in Industrial Modular Projects: Qualitative Comparative Analysis. Journal of Construction Engineering and Management, 142(10).
Choi, K and Lee, H W (2016) Deconstructing the Construction Industry: A Spatiotemporal Clustering Approach to Profitability Modeling. Journal of Construction Engineering and Management, 142(10).
- Type: Journal Article
- Keywords: Construction industry; Productivity; Performance measurement; Cluster analysis; Economic census; Project planning and design;
- ISBN/ISSN: 0733-9364
- URL: https://doi.org/10.1061/(ASCE)CO.1943-7862.0001165
- Abstract:
In spite of the strong influence of the construction industry on the national health of the United States’ economy, very little research has specifically aimed at evaluating the key performance parameters and trends (KPPT) of the industry. Due to this knowledge gap, concerns have been constantly raised over lack of accurate measures of KPPT. To circumvent these challenges, this study investigates and models the macroeconomic KPPT of the industry through spatiotemporal clustering modeling. This study specifically aims to analyze the industry in 14 of its subsectors and subsequently, by 51 geographic spatial areas at a 15-year temporal scale. KPPT and their interdependence were firstly examined by utilizing the interpolated comprehensive U.S. economic census data. A hierarchical spatiotemporal clustering analysis was then performed to create predictive models that can reliably determine firm’s profitability as a function of the key parameters. Lastly, the robustness of the predictive models was tested by a cross-validation technique called the predicted error sum of square. This study yields a notable conclusion that three key performance parameters—labor productivity, gross margin, and labor wages—have steadily improved over the study period from 1992 to 2007. This study also reveals that labor productivity is the most critical factor; the states and subsectors with the highest productivity are the most profitable. This study should be of value to decision-makers when plotting a roadmap for future growth and rendering a strategic business decisions.
de Castro e Silva Neto, D, Cruz, C O, Rodrigues, F and Silva, P (2016) Bibliometric Analysis of PPP and PFI Literature: Overview of 25 Years of Research. Journal of Construction Engineering and Management, 142(10).
Duzkale, A K and Lucko, G (2016) Exposing Uncertainty in Bid Preparation of Steel Construction Cost Estimating: I. Conceptual Framework and Qualitative C-I-V-I-L Classification. Journal of Construction Engineering and Management, 142(10).
Duzkale, A K and Lucko, G (2016) Exposing Uncertainty in Bid Preparation of Steel Construction Cost Estimating: II. Comparative Analysis and Quantitative C-I-V-I-L Classification. Journal of Construction Engineering and Management, 142(10).
Gwak, H, Son, S, Park, Y and Lee, D (2016) Exact Time–Cost Tradeoff Analysis in Concurrency-Based Scheduling. Journal of Construction Engineering and Management, 142(10).
Harper, C M, Molenaar, K R and Cannon, J P (2016) Measuring Constructs of Relational Contracting in Construction Projects: The Owner’s Perspective. Journal of Construction Engineering and Management, 142(10).
Moret, Y and Einstein, H H (2016) Construction Cost and Duration Uncertainty Model: Application to High-Speed Rail Line Project. Journal of Construction Engineering and Management, 142(10).
Namian, M, Albert, A, Zuluaga, C M and Jaselskis, E J (2016) Improving Hazard-Recognition Performance and Safety Training Outcomes: Integrating Strategies for Training Transfer. Journal of Construction Engineering and Management, 142(10).
Poshdar, M, González, V A, Raftery, G M, Orozco, F, Romeo, J S and Forcael, E (2016) A Probabilistic-Based Method to Determine Optimum Size of Project Buffer in Construction Schedules. Journal of Construction Engineering and Management, 142(10).
Ramaji, I J and Memari, A M (2016) Product Architecture Model for Multistory Modular Buildings. Journal of Construction Engineering and Management, 142(10).
Salas, R and Hallowell, M (2016) Predictive Validity of Safety Leading Indicators: Empirical Assessment in the Oil and Gas Sector. Journal of Construction Engineering and Management, 142(10).
Sveikauskas, L, Rowe, S, Mildenberger, J, Price, J and Young, A (2016) Productivity Growth in Construction. Journal of Construction Engineering and Management, 142(10).