In recent years, a growing amount of research has focused on improving the performance of industrialized construction using emerging technologies. It is still necessary to have an in-depth understanding of the industry practitioners’ perspectives on the application of emerging technologies. Thus, a well-designed survey was distributed to industry practitioners who have been involved in industrialized construction projects. Then, a set of data analysis methods were utilized on the collected data to address the proposed four specific research questions. Results indicate that 3D and nD models, sensing techniques, and business information models are the technologies with the highest current utilization level. Extended reality, additive manufacturing, and advanced data analytics are the technologies with the highest development potential. Project inputs (e.g., cost, time, and labor), as well as implementation cost and software constraints (e.g., capital costs, software upgrading, and compatibility), are the main factors that affect practitioners’ decisions to adopt emerging technologies in industrialized projects. Inter-group comparison results indicate that company background has little significant influence on practitioners’ perspectives, while personal career profiles can significantly affect practitioners’ perspectives. Significantly, by uncovering the suggestions and viewpoints of practitioners, this paper aligns academic research with industry needs, ultimately providing guidance on future research directions and applications.

Considering the increasing use of emerging technologies in industrialized construction in recent years, the primary objective of this article is to develop and validate predictive models to predict the emerging technology utilization level of industrialized construction industry practitioners. Our preliminary research results indicate that the company background and personal career profiles can significantly affect practitioners’ technology utilization level. Thus, our prediction model is based on four variables: company size, company type, working experience, and working position. The United States and China are selected as the case studies to validate the prediction model. First, a well-designed questionnaire survey is distributed to the industrialized construction industry practitioners from the two countries, which leads to 81 and 99 valid responses separately. Then, ordinal logistic regression is used to develop a set of models to predict the practitioners’ utilization level of the four main technology types. Finally, the external test dataset consisting of 16 cases indicates the prediction models have a high accuracy. The results also reflect some differences of the technology utilization status in the industrialized construction industry between the United States and China. The major contribution of this research is offering an efficient and accurate method to predict practitioners’ technology utilization level in industrialized construction. Significantly, the models are believed to have a wide application in promoting the emerging technologies in the actual industrialized construction.