Abstract
Construction project managers (CPMs) are highly in demand in the construction industry, but this has not yet comprehensively been studied from a demand-side perspective. This study investigated the real-world demand for CPMs’ competences by mining the big data of job advertisements. Using a large data set of 243,521 job advertisements that covered nearly the whole online job market for CPMs in China over 1 year, we tracked the demands for CPM competences at the industry level. A structural topic model (STM), a text-mining method, was used to analyze the descriptions of the CPM competences requirements to identify the major competences dimensions emphasized in the advertisements. The results suggest that there are eight major competences dimensions that recruiters expect from CPMs. Compared with relatively smaller firms, larger firms place more emphasis on construction site management, project objective monitoring, and organizational management capability. Compared with public firms, private firms prefer coordination and communication, external stakeholder management, and organizational management capability. Cluster analysis of job advertisements further suggested that there are five major types of CPMs, each of which specializes in only a few competences instead of being competent in all aspects. These findings revealed the real-world demands for CPMs and provided theoretical implications for developing a CPM competences framework from a realistic demand-side perspective. The findings also can be utilized to assist construction firms in formulating more-informed recruiting strategies and to help practitioners benchmark their own competences with industry demands.
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Data Availability Statement
Some or all data, models, or code generated or used during the study (raw text data and codes) are available from the corresponding author by request.
Acknowledgments
This study was funded by the National Natural Science Foundation of China (General Programs 51779124, 51479100, and 51379104) and Grant Nos. 2015-KY-5 and 2013-KY-5 from the State Key Laboratory of Hydroscience and Engineering. The authors also thank the MIT Center for Collective Intelligence for supporting this study.
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Journal of Construction Engineering and Management
Volume 146 • Issue 8 • August 2020
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©2020 American Society of Civil Engineers.
History
Received: Jul 4, 2019
Accepted: Jan 17, 2020
Published online: May 18, 2020
Published in print: Aug 1, 2020
Discussion open until: Oct 18, 2020
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