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Goel, A, Ganesh, L and Kaur, A (2020) Social sustainability considerations in construction project feasibility study: a stakeholder salience perspective. Engineering, Construction and Architectural Management, 27(07), 1429–59.

Karami, H and Olatunji, O A (2020) Critical overrun causations in marine projects. Engineering, Construction and Architectural Management, 27(07), 1579–94.

Li, G, Chen, C, Zhang, G and Martek, I (2019) Bid/no-bid decision factors for Chinese international contractors in international construction projects. Engineering, Construction and Architectural Management, 27(07), 1619–43.

Pablo, Z and London, K A (2020) Stable relationality and dynamic innovation: two models of collaboration in SME-driven offsite manufacturing supply chains in housing construction. Engineering, Construction and Architectural Management, 27(07), 1553–77.

Rostamnezhad, M, Nasirzadeh, F, Khanzadi, M, Jarban, M J and Ghayoumian, M (2020) Modeling social sustainability in construction projects by integrating system dynamics and fuzzy‐DEMATEL method: a case study of highway project. Engineering, Construction and Architectural Management, 27(07), 1595–618.

Wang, G, Xia, C and Cao, D (2020) State and determinants of inter-regional market entry practices in the Chinese construction industry: evidence from national quality award projects. Engineering, Construction and Architectural Management, 27(07), 1461–77.

Xue, J, Shen, G Q, Yang, R J, Zafar, I, Ekanayake, E, Lin, X and Darko, A (2020) Influence of formal and informal stakeholder relationship on megaproject performance: a case of China. Engineering, Construction and Architectural Management, 27(07), 1505–31.

Yap, J B H, Leong, W J and Skitmore, M (2020) Capitalising teamwork for enhancing project delivery and management in construction: empirical study in Malaysia. Engineering, Construction and Architectural Management, 27(07), 1479–503.

Yuan, F, Tang, M and Hong, J (2020) Efficiency estimation and reduction potential of the Chinese construction industry via SE-DEA and artificial neural network. Engineering, Construction and Architectural Management, 27(07), 1533–52.

• Type: Journal Article
• Keywords: Construction industry; Overall technical efficiency; Reduction potential; Super-efficiency data envelopment analysis (SE-DEA); Artificial neural network (ANN);
• ISBN/ISSN: 0969-9988
• URL: https://doi.org/10.1108/ECAM-10-2019-0564
• Abstract:
  The objective of this study is to evaluate the overall technical efficiency, labor efficiency, capital efficiency and equipment efficiency of 30 Chinese construction sectors to foster sustainable economic growth in the construction industry. This study employed the super-efficiency data envelopment analysis (SE-DEA) and artificial neural network model (ANN) to evaluate the industrial performance and improvement potential of the Chinese regional construction sectors from 2000 to 2017. Results showed that the overall technical and capital efficiencies displayed relatively stable patterns. Equipment efficiency presented a relatively huge fluctuation during the sample period. Meanwhile, labor, capital and equipment efficiencies could potentially improve in the next five years. A spatial examination of efficiencies implied that the economic level was still a major factor in determining the efficiency performance of the regional construction industry. Beijing, Shanghai and Zhejiang were consistently the leading regions with the best performance in all efficiencies. Shandong and Hubei were critical regions with respect to their large reduction potential of labor, capital and equipment. The study focused on the regional efficiency performance of the construction industry; however, it failed to further deeply discover the mechanism that captured the regional inefficiency. In addition, sample datasets used to predict might induce the accuracy of prediction results. Qualitative policy implications failed to regress the efficiency performance of the industrial policy variables. These limitations will be discussed in our further researches. Enhancing the overall performance of the Chinese construction industry should focus on regions located in the western areas. In comparison with labor and capital efficiencies, equipment efficiency should be given priority by eliminating outdated equipment and developing high technology in the construction industry. In addition, the setting of the national reduction responsibility system should be stratified to account for regional variations. The findings of this study can provide a systematic understanding for the current and future industry performance of the Chinese construction industry, which would help decision makers to customize appropriate strategies to improve the overall industrial performance with the consideration of regional differences.