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Evaluating the Digital Transformation Potential in Pre-Construction for Sustainable Practices Using Structural Equation Modeling

Author

Listed:
  • Khalid K. Naji

    (Civil and Environmental Engineering Department, College of Engineering, Qatar University, Doha 2713, Qatar)

  • Murat Gunduz

    (Civil and Environmental Engineering Department, College of Engineering, Qatar University, Doha 2713, Qatar)

  • Fahid Al-Henzab

    (Engineering Management Department, College of Engineering, Qatar University, Doha 2713, Qatar)

Abstract

This study presents the development of a comprehensive model for evaluating the level of readiness of buildings for digital transformation during the pre-construction phase. The proposed model utilizes structural equation modeling (SEM) and includes a full list of key factors for achieving success. This tool is designed to support industry stakeholders in assessing operational efficiency in terms of digital transformation readiness in the pre-construction phase (DTRPC) and analyze the effectiveness and limitations of DTRPC across various management levels. Key success factors were identified through interviews with experts and a review of the relevant literature. These variables were then validated through two rounds of the 8 Delphi technique, which included the input of 13 highly qualified experts. Finally, an online questionnaire was disseminated to industry professionals, who assessed the factors’ relative levels of significance. Questionnaire responses were collected from a sample of 300 individuals from different professional fields. SEM was then used to quantitatively analyze the relationships between the various components of the DTRPC success factors. The goal was to determine the impact of each construct on the overall level of readiness. The model underwent a thorough evaluation to determine its strength and stability across several parameters, including accuracy, conformity to multivariate normalcy, and reliability and validity. A hypothesis analysis was also conducted. The collected data were used to develop the proposed DTRPC model, consisting of 30 essential performance indicators grouped into four categories. The use of SEM uncovered a significant correlation between the operational indicators of these critical factors and the construct groups, as well as the influence of effective DTRPC constructs on overall project performance. This research expands the current knowledge by identifying important indications for evaluating the success of the DTRPC model and using them to create a comprehensive global SEM that can be used as a tool for measuring readiness at the pre-construction stage. This has the potential to provide essential assistance to organizations, project managers, and policymakers in making informed decisions.

Suggested Citation

  • Khalid K. Naji & Murat Gunduz & Fahid Al-Henzab, 2024. "Evaluating the Digital Transformation Potential in Pre-Construction for Sustainable Practices Using Structural Equation Modeling," Sustainability, MDPI, vol. 16(17), pages 1-33, August.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:17:p:7323-:d:1464069
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    References listed on IDEAS

    as
    1. Hamad Almujibah, 2023. "Assessment of Building Information Modeling (BIM) as a Time and Cost-Saving Construction Management Tool: Evidence from Two-Story Villas in Jeddah," Sustainability, MDPI, vol. 15(9), pages 1-26, April.
    2. Zaheer Allam & Ayyoob Sharifi & Simon Elias Bibri & David Sydney Jones & John Krogstie, 2022. "The Metaverse as a Virtual Form of Smart Cities: Opportunities and Challenges for Environmental, Economic, and Social Sustainability in Urban Futures," Post-Print hal-03997438, HAL.
    3. Tatsiana Haponava & Saad Al‐Jibouri, 2009. "Identifying key performance indicators for use in control of pre‐project stage process in construction," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 58(2), pages 160-173, January.
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    6. Ping, Robert Jr., 2004. "On assuring valid measures for theoretical models using survey data," Journal of Business Research, Elsevier, vol. 57(2), pages 125-141, February.
    7. Gao, Shengyi & Mokhtarian, Patricia L. & Johnston, Robert A., 2008. "Non-normality of Data in Structural Equation Models," University of California Transportation Center, Working Papers qt7nf8f0v7, University of California Transportation Center.
    8. Chen, Rui & Meng, Qiang & Yu, Jiayi Joey, 2023. "Optimal government incentives to improve the new technology adoption: Subsidizing infrastructure investment or usage?," Omega, Elsevier, vol. 114(C).
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