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Interpretive Structural Model Based Factor Analysis of BIM Adoption in Chinese Construction Organizations

Author

Listed:
  • Guofeng Ma

    (School of Economics and Management, Tongji University, Shanghai 200092, China)

  • Jianyao Jia

    (School of Economics and Management, Tongji University, Shanghai 200092, China)

  • Jiyong Ding

    (Institute of Engineering Management, Business School of Hohai University, Nanjing 211100, China)

  • Shanshan Shang

    (School of Business and Management, Shanghai International Studies University, Shanghai 201620, China)

  • Shan Jiang

    (School of Economics and Management, Tongji University, Shanghai 200092, China)

Abstract

The adoption of Building Information Modeling (BIM) will definitely improve the efficiency and quality of the AEC (architecture, engineering and construction) industry. However, many factors need to be improved before BIM adoption. Based on the interaction between institutions and technology, factors affecting BIM adoption in AEC organizations, within the context of China, are identified and analyzed. Firstly, 21 factors are identified by literature research. Then, an interpretive structural model (ISM) technique is used to establish a hierarchical structure, and matrix impacts cross-reference multiplication applied to a classification (MICMAC) is used for factor classification. The results indicate that corporate/project leadership and software functionality are the two fundamental factors. What’s more, the dynamic mechanism has gradually changed from top-down to a combination of top-down and bottom-up.

Suggested Citation

  • Guofeng Ma & Jianyao Jia & Jiyong Ding & Shanshan Shang & Shan Jiang, 2019. "Interpretive Structural Model Based Factor Analysis of BIM Adoption in Chinese Construction Organizations," Sustainability, MDPI, vol. 11(7), pages 1-16, April.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:7:p:1982-:d:219562
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    References listed on IDEAS

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    3. Pavel Pelikan, 2003. "Bringing institutions into evolutionary economics: another view with links to changes in physical and social technologies," Journal of Evolutionary Economics, Springer, vol. 13(3), pages 237-258, August.
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    Cited by:

    1. Jiangang Shi & Kaifeng Duan & Quanwei Xu & Jiajia Li, 2020. "Analysis of Super-Gentrification Dynamic Factors Using Interpretative Structure Modeling," Land, MDPI, vol. 9(2), pages 1-16, February.
    2. Shelly Gupta & Sanjay Dhingra, 2022. "Modeling the key factors influencing the adoption of mobile financial services: an interpretive structural modeling approach," Journal of Financial Services Marketing, Palgrave Macmillan, vol. 27(2), pages 96-110, June.
    3. Yongliang Deng & Jinyun Li & Qiuting Wu & Shuangshuang Pei & Na Xu & Guodong Ni, 2020. "Using Network Theory to Explore BIM Application Barriers for BIM Sustainable Development in China," Sustainability, MDPI, vol. 12(8), pages 1-22, April.
    4. Seda Tan & Gulden Gumusburun Ayalp & Muhammed Zubeyr Tel & Merve Serter & Yusuf Berkay Metinal, 2022. "Modeling the Critical Success Factors for BIM Implementation in Developing Countries: Sampling the Turkish AEC Industry," Sustainability, MDPI, vol. 14(15), pages 1-28, August.
    5. Na Dong & Yanting Fu & Feng Xiong & Lujie Li & Yibin Ao & Igor Martek, 2019. "Sustainable Construction Project Management (SCPM) Evaluation—A Case Study of the Guangzhou Metro Line-7, PR China," Sustainability, MDPI, vol. 11(20), pages 1-17, October.
    6. Margarida Pimentel & Amílcar Arantes & Carlos Oliveira Cruz, 2022. "Barriers to the Adoption of Reverse Logistics in the Construction Industry: A Combined ISM and MICMAC Approach," Sustainability, MDPI, vol. 14(23), pages 1-21, November.

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