Project external environmental factors affecting project delivery systems selection
Abstract
Project delivery systems (PDSs) selection is crucial to construction project management success. The matching between construction projects and PDSs is hypersensitive to project external environment. Existing studies on selecting PDSs mainly focus on owner’s and project’s characteristics and attach less attention to project environmental factors. This study, therefore, aims to formally identify key project external environmental factors affecting PDSs selection using a data-driven approach. Key factors are summarized and identified through the granular computing method based on 61 Chinese project samples. Empirical results indicate that four factors including market competitiveness, technology accessibility, material availability, and regulatory impact are critical to PDSs selection. This study extended previous research findings on PDSs selection from a perspective of project external environments. Research conclusions can be used as references underpinning construction owners selecting appropriate PDSs considering project external environmental factors.
Keyword : project external environment, project delivery systems, granular computing, empirical study, key factors, Chinese projects
How to Cite
Liu, B., Xue, B., Huo, T., Shen, G., & Fu, M. (2019). Project external environmental factors affecting project delivery systems selection. Journal of Civil Engineering and Management, 25(3), 276-286. https://doi.org/10.3846/jcem.2019.7460
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Mostafavi, A., & Karamouz, M. (2010). Selecting appropriate project delivery system: Fuzzy approach with risk analysis. Journal of Construction Engineering and Management, 136(8), 923-930. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000190
Qiao, Z. R. (2011). Study on attributes reduction algorithm and its application based on Granular Computing (Thesis). Shanxi Normal University (in Chinese).
Shin, T. H., Chin, S., Yoon, S. W., & Kwon, S. W. (2011). A service-oriented integrated information framework for RFID/WSN-based intelligent construction supply chain management. Automation in Construction, 20(6), 706-715. https://doi.org/10.1016/j.autcon.2010.12.002
Wang, X., Liu, Y., Li, P., & Liu, J. (2017). Multi-granularity soft rough set and its application in multi-attribute decision making. Journal of Intelligent & Fuzzy Systems, 33(4), 2033-2045. https://doi.org/10.3233/JIFS-161498
Wei, G. W. (2011). Some generalized aggregating operators with linguistic information and their application to multiple attribute group decision making. Computers & Industrial Engineering, 61(1), 32-38. https://doi.org/10.1016/j.cie.2011.02.007
Yager, R. R. (2008). Intelligent social network analysis using granular computing. International Journal of Intelligent Systems, 23(11), 1197-1219. https://doi.org/10.1002/int.20314
Zadeh, L. A. (1997). Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems, 90(2), 111-127. https://doi.org/10.1016/S0165-0114(97)00077-8
Chen, Y. Q., Lu, H., Lu, W., & Zhang, N. (2010). Analysis of project delivery systems in Chinese construction industry with data envelopment analysis (DEA). Engineering, Construction and Architectural Management, 17(6), 598-614. https://doi.org/10.1108/09699981011090215
George, D., & Mallery, P. (2003). SPSS for windows step by step: A simple guide and reference. 11.0 update (4th ed.). Boston, USA: Allyn and Bacon.
Ibbs, C. W., Kwak, Y. H., Ng, T., & Odabasi, A. M. (2003). Project delivery systems and project change: Quantitative analysis. Journal of Construction Engineering and Management, 129(4), 382-387. https://doi.org/10.1061/(ASCE)0733-9364(2003)129:4(382)
Konchar, M., & Sanvido, V. (1998). Comparison of US project delivery systems. Journal of Construction Engineering and Management, 124(6), 435-444. https://doi.org/10.1061/(ASCE)0733-9364(1998)124:6(435)
Lam, E. W., Chan, A. P., & Chan, D. W. (2007). Benchmarking the performance of design-build projects: Development of project success index. Benchmarking: An International Journal, 14(5), 624-638. https://doi.org/10.1108/14635770710819290
Liu, B., Huo, T., Liang, Y., Sun, Y., & Hu, X. (2016). Key factors of project characteristics affecting project delivery system decision making in the Chinese construction industry: Case study using Chinese data based on rough set theory. Journal of Professional Issues in Engineering Education and Practice, 142(4), 05016003. https://doi.org/10.1061/(ASCE)EI.1943-5541.0000278
Liu, B., Huo, T., Liao, P. C., Yuan, J., Sun, J., & Hu, X. (2017). A special Partial Least Squares (PLS) path decision modeling for bid evaluation of large construction projects. KSCE Journal of Civil Engineering, 21(3), 579-592. https://doi.org/10.1007/s12205-016-0702-3
Liu, B., Huo, T., Liao, P., Gong, J., & Xue, B. (2015a). A group decision-making aggregation model for contractor selection in large scale construction projects based on two-stage partial least squares (PLS) path modeling. Group Decision and Negotiation, 24(5), 855-883. https://doi.org/10.1007/s10726-014-9418-2
Liu, B., Huo, T., Meng, J., Gong, J., Shen, Q., & Sun, T. (2015b). Identification of key contractor characteristic factors that affect project success under different project delivery systems: empirical analysis based on a group of data from China. Journal of Management in Engineering, 32(1), 05015003. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000388
Liu, B., Huo, T., Shen, Q., Yang, Z., Meng, J., & Xue, B. (2014). Which owner characteristics are key factors affecting project delivery system decision making? Empirical analysis based on the rough set theory. Journal of Management in Engineering, 31(4), 05014018. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000298
Liu, B., Huo, T., Wang, X., Shen, Q., & Chen, Y. (2013). The decision model of the intuitionistic fuzzy group bid evaluation for urban infrastructure projects considering social costs. Canadian Journal of Civil Engineering, 40(3), 263-273. https://doi.org/10.1139/cjce-2012-0283
Liu, F., Geng, H., & Zhang, Y. Q. (2005). Interactive fuzzy interval reasoning for smart web shopping. Applied Soft Computing, 5(4), 433-439. https://doi.org/10.1016/j.asoc.2004.10.003
Luu, D. T., Ng, S. T., & Chen, S. E. (2005). Formulating procurement selection criteria through case-based reasoning approach. Journal of Computing in Civil Engineering, 19(3), 269-276. https://doi.org/10.1061/(ASCE)0887-3801(2005)19:3(269)
Luu, D. T., Ng, S. T., & Eng Chen, S. (2003). Parameters governing the selection of procurement system-an empirical survey. Engineering, Construction and Architectural Management, 10(3), 209-218. https://doi.org/10.1108/09699980310478458
Luu, D. T., Ng, S. T., Chen, S. E., & Jefferies, M. (2006). A strategy for evaluating a fuzzy case-based construction procurement selection system. Advances in Engineering Software, 37(3), 159-171. https://doi.org/10.1016/j.advengsoft.2005.05.004
Mafakheri, F., Dai, L., Slezak, D., & Nasiri, F. (2007). Project delivery system selection under uncertainty: Multicriteria multilevel decision aid model. Journal of Management in Engineering, 23(4), 200-206. https://doi.org/10.1061/(ASCE)0742-597X(2007)23:4(200)
Mahdi, I. M., & Alreshaid, K. (2005). Decision support system for selecting the proper project delivery method using analytical hierarchy process (AHP). International Journal of Project Management, 23(7), 564-572. https://doi.org/10.1016/j.ijproman.2005.05.007
Ministry of Housing and Urban-Rural Development (MOHURD). (2003). Guidance on developing general contracting and project management enterprises.
Mostafavi, A., & Karamouz, M. (2010). Selecting appropriate project delivery system: Fuzzy approach with risk analysis. Journal of Construction Engineering and Management, 136(8), 923-930. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000190
Qiao, Z. R. (2011). Study on attributes reduction algorithm and its application based on Granular Computing (Thesis). Shanxi Normal University (in Chinese).
Shin, T. H., Chin, S., Yoon, S. W., & Kwon, S. W. (2011). A service-oriented integrated information framework for RFID/WSN-based intelligent construction supply chain management. Automation in Construction, 20(6), 706-715. https://doi.org/10.1016/j.autcon.2010.12.002
Wang, X., Liu, Y., Li, P., & Liu, J. (2017). Multi-granularity soft rough set and its application in multi-attribute decision making. Journal of Intelligent & Fuzzy Systems, 33(4), 2033-2045. https://doi.org/10.3233/JIFS-161498
Wei, G. W. (2011). Some generalized aggregating operators with linguistic information and their application to multiple attribute group decision making. Computers & Industrial Engineering, 61(1), 32-38. https://doi.org/10.1016/j.cie.2011.02.007
Yager, R. R. (2008). Intelligent social network analysis using granular computing. International Journal of Intelligent Systems, 23(11), 1197-1219. https://doi.org/10.1002/int.20314
Zadeh, L. A. (1997). Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems, 90(2), 111-127. https://doi.org/10.1016/S0165-0114(97)00077-8