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<p>The construction sector is rapidly evolving via digitisation, with cloud computing (CC) at its core; however, adoption faces many challenges. This study qualitatively evaluated the barriers to CC adoption in Saudi Arabia’s construction industry. A cross-sectional survey of 72 professionals from various construction firms was performed, and data were analysed using t-test, exploratory factor analysis (EFA), and partial least squares-structural equation modelling. PLS-SEM) and artificial neural networks (ANN). The t-test results revealed that the security (mean 4.11, SD = 0.84) connectivity issues (mean = 4.0 were the most significant challenges (<i>p</i> &lt; 0.05). EFA extracted three major components explaining 67.7% of the total variance: data security and regulatory compliance (43.7%), technical and organisational infrastructure (12.8%) and cost and expertise limitations (11.1%). Reliability tests indicated strong internal consistency (Cronbach’s α = 0.921). PLS-SEM analysis confirmed significant positive relationships between open-source software (OSS) and CC challenges (β = 0.169, <i>p</i> = 0.000), while ANN analysis achieved 100% prediction accuracy (R<sup>2</sup> = 1.00). The results highlight that addressing data security, technical capacity, and cost-related constraints is important to improving CC incorporation and advancing sustainability goals in Saudi Arabia’s construction industry. This study offers empirical evidence to inform strategic decisions and policy directions for digital transformation and sustainable development in the construction industry.</p>

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Identifying Challenges to Cloud Computing Adoption in Saudi Arabia’s Construction Industry: An Empirical Analysis Using Structural Equation Modelling and Artificial Neural Networks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月21日

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  • Naif Sultan Alaboud

摘要

The construction sector is rapidly evolving via digitisation, with cloud computing (CC) at its core; however, adoption faces many challenges. This study qualitatively evaluated the barriers to CC adoption in Saudi Arabia’s construction industry. A cross-sectional survey of 72 professionals from various construction firms was performed, and data were analysed using t-test, exploratory factor analysis (EFA), and partial least squares-structural equation modelling. PLS-SEM) and artificial neural networks (ANN). The t-test results revealed that the security (mean 4.11, SD = 0.84) connectivity issues (mean = 4.0 were the most significant challenges (p < 0.05). EFA extracted three major components explaining 67.7% of the total variance: data security and regulatory compliance (43.7%), technical and organisational infrastructure (12.8%) and cost and expertise limitations (11.1%). Reliability tests indicated strong internal consistency (Cronbach’s α = 0.921). PLS-SEM analysis confirmed significant positive relationships between open-source software (OSS) and CC challenges (β = 0.169, p = 0.000), while ANN analysis achieved 100% prediction accuracy (R2 = 1.00). The results highlight that addressing data security, technical capacity, and cost-related constraints is important to improving CC incorporation and advancing sustainability goals in Saudi Arabia’s construction industry. This study offers empirical evidence to inform strategic decisions and policy directions for digital transformation and sustainable development in the construction industry.