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A Predictive Analysis on Emerging Technology Utilization in Industrialized Construction in the United States and China

Authors :
Bing Qi
Shuyu Qian
Aaron Costin
Source :
Algorithms, Vol 13, Iss 8, p 180 (2020)
Publication Year :
2020
Publisher :
MDPI AG, 2020.

Abstract

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.

Details

Language :
English
ISSN :
13080180 and 19994893
Volume :
13
Issue :
8
Database :
Directory of Open Access Journals
Journal :
Algorithms
Publication Type :
Academic Journal
Accession number :
edsdoj.2455206d588c4414a9ff176104bf122b
Document Type :
article
Full Text :
https://doi.org/10.3390/a13080180