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Aramali, V, Gibson, G E, El Asmar, M and Sanboskani, H (2023) Novel earned value management system maturity framework and its relation to project performance. Journal of Construction Engineering and Management, 149(06).
Arora, J, Kar, S and Jha, K N (2023) Assessment of the significance of identified attributes affecting the rebar-fixing productivity using multiple regression. Journal of Construction Engineering and Management, 149(06).
Asadi, R, Rotimi, J O B and Wilkinson, S (2023) Analyzing underlying factors of rework in generating contractual claims in construction projects. Journal of Construction Engineering and Management, 149(06).
Hsu, C L, Wang, J T and Huang, Y H (2023) A functional element-naming approach for BIM elements in building envelope systems. Journal of Construction Engineering and Management, 149(06).
Karamoozian, A, Wu, D and Luo, C (2023) Green supplier selection in the construction industry using a novel fuzzy decision-making approach. Journal of Construction Engineering and Management, 149(06).
Long, W, Wang, X, Liang, Y and Ye, K (2023) Characteristics of collusive practices in bidding: Mixed methods study in China. Journal of Construction Engineering and Management, 149(06).
Nguyen, T Q, Yeoh, J K W and Angelia, N (2023) Predicting percent plan complete through time series analysis. Journal of Construction Engineering and Management, 149(06).
Rathnayake, A and Middleton, C (2023) Systematic review of the literature on construction productivity. Journal of Construction Engineering and Management, 149(06).
- Type: Journal Article
- Keywords: construction; factors; measurement; productivity; review
- ISBN/ISSN: 0733-9364
- URL: http://doi.org/10.1061/JCEMD4.COENG-13045
- Abstract:
Productivity, defined as output per input, is a key performance indicator. Construction is one of the least productive industries, and in this paper we explore how productivity measurement can be improved. The findings are based on a review of more than 100 studies. First, we summarize the indicators and data sources used to measure productivity at the macro (economic) and micro (more detailed) levels. Then we explore industry-level productivity trends. Contrary to popular belief, US construction productivity has not declined or stagnated over time. Also, the US has consistently outperformed the UK, Germany, and France. Next, we identify the most critical factors affecting construction productivity. These are related to labor, equipment and technology, construction site, schedule, supervisors, and materials. We also discuss new technologies that can help improve productivity: off-site construction, building information modeling, material tracking technologies, and automated productivity monitoring. Finally, we discuss challenges in this area: the unsuitability of labor productivity as an indicator, issues with activity-level productivity analysis, the disconnect between macro and micro levels, and the lack of micro-level productivity databases.
Tao, Y, Hu, H, Xu, F and Zhang, Z (2023) Ergonomic risk assessment of construction workers and projects based on fuzzy Bayesian network and D-S evidence theory. Journal of Construction Engineering and Management, 149(06).
Tarekegn Gurmu, A (2023) Preconstruction phase management practices enhancing labor productivity in multistory building projects. Journal of Construction Engineering and Management, 149(06).
Tatari, A (2023) Simulating cost risks for prefabricated construction in developing countries using Bayesian networks. Journal of Construction Engineering and Management, 149(06).
Wang, Y, Chen, L and Zhuang, J (2023) Pricing of credit default swaps from the perspective of credit enhancement in PPP projects. Journal of Construction Engineering and Management, 149(06).
Zhang, W, Zhang, H and Yu, L (2023) Collaborative planning for stacking and installation of prefabricated building components regarding crane-collision avoidance. Journal of Construction Engineering and Management, 149(06).