Abstract

As one of the emerging digital technologies, building information modeling (BIM) has been increasingly used in the architecture, engineering, and construction (AEC) industry of many countries. One of the core benefits associated with BIM is facilitating collaboration among project teams, thereby improving information sharing and project performance by providing BIM-based construction networks (BbCNs). Nevertheless, it is still challenging to ensure an effective collaboration process in BIM-enabled projects. Several studies have investigated the factors that influence collaboration in BIM-enabled projects; however, most of these studies focus on technical matters of BIM, while the perspectives of socio-organization and processes have received less attention and are still in the conceptual stage, especially for the role of people management. Thus, an exploratory study was proposed to explore how key factors influence the professionals’ willingness to collaborate in BIM-enabled projects. A total of 10 hypotheses were established based on literature review and expert verification. Following that, a questionnaire survey was administered in China to solicit the opinions of 273 BIM professionals for data collection. The hypotheses were then examined on the basis of the predictive capacity of regression analysis (binary logistic regression), with the findings validated using neural network analysis (multilayer perceptron). It was found that seven independent variables have a statistically significant influence on professionals’ willingness to collaborate in BIM-enabled projects, namely, professional knowledge, skill, training, investment, BIM tools, BIM ownership, and standards and regulations. Among them, the variable of professional knowledge was ranked as the most influential factor. While extending the existing knowledge of literature, the findings of this study also deliver insights for stakeholders by enhancing their understanding of the BIM collaboration process and its influence factors.

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Data Availability Statement

Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

This research work is financially supported by RDF-17-02-60 and RDF-16-01-24 projects of Xi’an Jiaotong–Liverpool University.

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Journal of Management in Engineering
Volume 38Issue 4July 2022

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Received: Sep 30, 2021
Accepted: Feb 25, 2022
Published online: Apr 29, 2022
Published in print: Jul 1, 2022
Discussion open until: Sep 29, 2022

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Ph.D. Student, Dept. of Civil Engineering, Xi’an Jiaotong–Liverpool Univ., 111 Ren’ai Rd., Suzhou Industrial Park, Suzhou 215123, PR China; Ph.D. Student, Dept. of Civil Engineering and Industrial Design, Univ. of Liverpool, Liverpool L69 3BX, UK. ORCID: https://orcid.org/0000-0003-2420-5521. Email: [email protected]
Associate Professor, Dept. of Civil Engineering, Xi’an Jiaotong–Liverpool Univ., 111 Ren’ai Rd., Suzhou Industrial Park, Suzhou 215123, PR China. ORCID: https://orcid.org/0000-0003-2465-1767. Email: [email protected]
Assisstant Professor, Dept. of Civil Engineering, Xi’an Jiaotong–Liverpool Univ., 111 Ren’ai Rd., Suzhou Industrial Park, Suzhou 215123, PR China (corresponding author). ORCID: https://orcid.org/0000-0002-9157-2236. Email: [email protected]
Shuting Guo [email protected]
Senior Engineer, Saiwill Education & Technology, 6 Huixin’nanli Rd., Chaoyang District, Beijing 10000, PR China. Email: [email protected]

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