TY - JOUR
T1 - Identifying the Perception Differences of Emerging Construction-Related Technologies between Industry and Academia to Enable High Levels of Collaboration
AU - Jang, Youjin
AU - Kim, Kinam
AU - Leite, Fernanda
AU - Ayer, Steven
AU - Cho, Yong K.
N1 - Publisher Copyright:
© 2021 American Society of Civil Engineers.
PY - 2021/10/1
Y1 - 2021/10/1
N2 - Emerging technologies play an essential role in improving overall productivity in the construction industry. However, this field has historically been criticized for the slow adoption of these innovative tools. To promote emerging technology adoption, exchanging knowledge and practices through academia and industry collaboration is essential. This paper analyzes the perception differences between academics and construction industry practitioners with regard to 17 emerging technologies through survey data and identifies potential technologies for high levels of collaboration from an academic perspective. The survey was designed to obtain responses about technological use, interest, maturity, implementations, applications, benefits, and barriers. Three technologies, three-dimensional (3D) printing; artificial intelligence (AI), neural networks (NNs), and deep learning (DL); and smart materials, were identified as having the highest potential for enabling collaboration. The findings from this study will allow academic researchers to strategically develop research directions that align with the needs reported by industry practitioners to sustain research funding and foster the adoption of innovative technologies in the construction fields.
AB - Emerging technologies play an essential role in improving overall productivity in the construction industry. However, this field has historically been criticized for the slow adoption of these innovative tools. To promote emerging technology adoption, exchanging knowledge and practices through academia and industry collaboration is essential. This paper analyzes the perception differences between academics and construction industry practitioners with regard to 17 emerging technologies through survey data and identifies potential technologies for high levels of collaboration from an academic perspective. The survey was designed to obtain responses about technological use, interest, maturity, implementations, applications, benefits, and barriers. Three technologies, three-dimensional (3D) printing; artificial intelligence (AI), neural networks (NNs), and deep learning (DL); and smart materials, were identified as having the highest potential for enabling collaboration. The findings from this study will allow academic researchers to strategically develop research directions that align with the needs reported by industry practitioners to sustain research funding and foster the adoption of innovative technologies in the construction fields.
KW - Academia-industry collaboration
KW - Construction industry
KW - Emerging technologies
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U2 - 10.1061/(ASCE)CO.1943-7862.0002156
DO - 10.1061/(ASCE)CO.1943-7862.0002156
M3 - Article
AN - SCOPUS:85111429655
SN - 0733-9364
VL - 147
JO - Journal of Construction Engineering and Management
JF - Journal of Construction Engineering and Management
IS - 10
M1 - 06021004
ER -