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Structural Equation Modeling in Technology Adoption and Use in the Construction Industry: A Scientometric Analysis and Qualitative Review

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  • Chuyou Fu

    (School of Public Administration and Humanities, Dalian Maritime University, Dalian 116026, China)

  • Jun Wang

    (School of Management Engineering, Qingdao University of Technology, Qingdao 266520, China)

  • Ziyi Qu

    (School of Management Engineering, Qingdao University of Technology, Qingdao 266520, China)

  • Martin Skitmore

    (Faculty of Society and Design, Bond University, Robina, QLD 4226, Australia)

  • Jiaxin Yi

    (Yishui Campus, Linyi University, Linyi 276400, China)

  • Zhengjie Sun

    (School of Accounting, Southwestern University of Finance and Economics, Chengdu 611130, China)

  • Jianli Chen

    (Department of Civil Engineering, University of Utah, Salt Lake City, UT 84112, USA)

Abstract

Considering the emergence and adoption of various innovative technologies, the construction industry has undergone transformation into a more secure, highly efficient, and ecologically sustainable landscape. An increasing number of studies uses the structural equation modeling (SEM) method to explore the dynamics of technology adoption and use within the construction sector. Previous studies have mainly focused on qualitative analysis using the SEM method to analyze technology adoption and usage in the construction industry. This study, however, distinguishes itself from previous research by focusing on the SEM method itself and conducting a systematic analysis using scientometric methods. Based on a total of 140 relevant journal articles, this study adopts a scientometric analysis approach to conduct a holistic review encompassing sources, researchers, keywords, and highly cited documents. The research findings are as follows: (1) the primary focus of the current research topics is on BIM technology; (2) most studies employ cross-sectional SEM instead of longitudinal SEM; (3) there is a deficiency in the theoretical foundation for designing SEM in current research; and (4) the selection of either reflective or formative measures lacks sufficient rigor. Qualitative analysis is used to examine prevailing issues in research design and address the intricate technicalities and potential challenges inherent in the SEM method. Three research gaps and future directions are presented: diversifying regions of study and research topics, incorporating theoretical support for research design, and carefully choosing reflective or formative measures. The findings provide a comprehensive roadmap and valuable reference for future research in this field.

Suggested Citation

  • Chuyou Fu & Jun Wang & Ziyi Qu & Martin Skitmore & Jiaxin Yi & Zhengjie Sun & Jianli Chen, 2024. "Structural Equation Modeling in Technology Adoption and Use in the Construction Industry: A Scientometric Analysis and Qualitative Review," Sustainability, MDPI, vol. 16(9), pages 1-23, May.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:9:p:3824-:d:1387882
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    References listed on IDEAS

    as
    1. Ibukun O. Famakin & Idris Othman & Ahmed Farouk Kineber & Ayodeji Emmanuel Oke & Oludolapo Ibrahim Olanrewaju & Mohammed Magdy Hamed & Taiwo Matthew Olayemi, 2023. "Building Information Modeling Execution Drivers for Sustainable Building Developments," Sustainability, MDPI, vol. 15(4), pages 1-18, February.
    2. Hair, Joe F. & Howard, Matt C. & Nitzl, Christian, 2020. "Assessing measurement model quality in PLS-SEM using confirmatory composite analysis," Journal of Business Research, Elsevier, vol. 109(C), pages 101-110.
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    4. Ludo Waltman & Nees Eck, 2013. "A smart local moving algorithm for large-scale modularity-based community detection," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 86(11), pages 1-14, November.
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    6. Cakraningrat Kencana Murti & Fadhilah Muslim, 2023. "Relationship between Functions, Drivers, Barriers, and Strategies of Building Information Modelling (BIM) and Sustainable Construction Criteria: Indonesia Construction Industry," Sustainability, MDPI, vol. 15(6), pages 1-27, March.
    7. Jarvis, Cheryl Burke & MacKenzie, Scott B & Podsakoff, Philip M, 2003. "A Critical Review of Construct Indicators and Measurement Model Misspecification in Marketing and Consumer Research," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 30(2), pages 199-218, September.
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    9. Dash, Ganesh & Paul, Justin, 2021. "CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
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