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Analysis of factors of willingness to adopt intelligent construction technology in highway construction enterprises
This study aims to investigate the factors that influence the willingness of highway construction enterprises in China to adopt intelligent construction technology. Based on the existing literature, a TOSE framework was proposed, and four dimensions and 15 hypothesized influencing factors were ident...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10630408/ https://www.ncbi.nlm.nih.gov/pubmed/37935804 http://dx.doi.org/10.1038/s41598-023-46241-6 |
Sumario: | This study aims to investigate the factors that influence the willingness of highway construction enterprises in China to adopt intelligent construction technology. Based on the existing literature, a TOSE framework was proposed, and four dimensions and 15 hypothesized influencing factors were identified through expert interviews. By using a combination of PLS-SEM and ANN, 513 survey data were analyzed to determine the linear and non-linear relationships of the influencing factors on the willingness to adopt. The results showed that all 14 hypothesized factors had varying degrees of positive or negative effects on the willingness to adopt, except for organizational culture, which was found to have no significant impact. Specifically, technology cost was found to be the most influential negative factor, while market demand and organizational structure were the most influential positive factors. The findings of this study have important reference value for decision makers and participants in highway construction enterprises, as well as other construction companies when considering the adoption of smart construction technologies. The originality of this research lies in the novel application of the TOSE framework to investigate smart construction technology adoption, and the combined use of PLS-SEM and ANN to examine both linear and nonlinear relationships between variables for the first time. |
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