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    Diffusion of Building Information Modeling Functions in the Construction Industry

    Source: Journal of Management in Engineering:;2018:;Volume ( 034 ):;issue: 002
    Author:
    Gholizadeh Pouya;Esmaeili Behzad;Goodrum Paul
    DOI: 10.1061/(ASCE)ME.1943-5479.0000589
    Publisher: American Society of Civil Engineers
    Abstract: While advances in information and communication technologies (ICTs) have enabled architects, engineers, and contractors to reduce project time and cost and to improve quality, the diffusion of ICTs has typically been very slow in the construction industry. However, building information modeling (BIM) has overcome this diffusion challenge and has revolutionized such common practices as shifting the design process from two-dimensional (2D) drafting toward three-dimensional (3D) design, automated quantity takeoffs, and clash detection; such success marks the relevance of this technology for studying how technological innovations spread throughout the construction industry. Although some studies have explored BIM adoption, investigations using diffusion models to study the diffusion patterns of different BIM functions (e.g., 3D visualization, clash detection, energy modeling) are missing from the current literature. To fill this gap, this study uses reliable innovation diffusion models to describe the diffusion patterns of various BIM functions in the U.S. construction industry. Specifically, this study developed and sent a comprehensive questionnaire to 3,17 owner representatives, architects, and project managers; 118 individuals responded to the survey, of whom 81 are BIM users. The three most widely adopted BIM functions were 3D visualization, clash detection, and constructability analysis; the least frequently implemented functions were code validation; material tracking, delivery, and management; facility management; and energy analysis. Using these responses, this study analyzed the diffusion patterns of the BIM functions using four innovation diffusion models: internal (logistic), external, Bass, and Gompertz. Using such measures as Akaike’s information criterion (AIC) and variants of it, this study identified the Bass model as having the highest explanatory power for diffusion and determined that internal factors (e.g., imitation and bandwagon pressure) have the most influence on adoption rates of BIM functions. Furthermore, the diffusion models showed that the shop-drawing process and facility management functions had the lowest saturation rate in 214 (below 5%) but have a greater potential for being adopted in coming years. The findings of this study advance knowledge for both researchers and practitioners regarding the adoption process of different BIM functions and provide a theoretical basis for understanding the diffusion patterns of ICT innovations in the construction industry.
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      Diffusion of Building Information Modeling Functions in the Construction Industry

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    contributor authorGholizadeh Pouya;Esmaeili Behzad;Goodrum Paul
    date accessioned2019-02-26T07:30:35Z
    date available2019-02-26T07:30:35Z
    date issued2018
    identifier other%28ASCE%29ME.1943-5479.0000589.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4247472
    description abstractWhile advances in information and communication technologies (ICTs) have enabled architects, engineers, and contractors to reduce project time and cost and to improve quality, the diffusion of ICTs has typically been very slow in the construction industry. However, building information modeling (BIM) has overcome this diffusion challenge and has revolutionized such common practices as shifting the design process from two-dimensional (2D) drafting toward three-dimensional (3D) design, automated quantity takeoffs, and clash detection; such success marks the relevance of this technology for studying how technological innovations spread throughout the construction industry. Although some studies have explored BIM adoption, investigations using diffusion models to study the diffusion patterns of different BIM functions (e.g., 3D visualization, clash detection, energy modeling) are missing from the current literature. To fill this gap, this study uses reliable innovation diffusion models to describe the diffusion patterns of various BIM functions in the U.S. construction industry. Specifically, this study developed and sent a comprehensive questionnaire to 3,17 owner representatives, architects, and project managers; 118 individuals responded to the survey, of whom 81 are BIM users. The three most widely adopted BIM functions were 3D visualization, clash detection, and constructability analysis; the least frequently implemented functions were code validation; material tracking, delivery, and management; facility management; and energy analysis. Using these responses, this study analyzed the diffusion patterns of the BIM functions using four innovation diffusion models: internal (logistic), external, Bass, and Gompertz. Using such measures as Akaike’s information criterion (AIC) and variants of it, this study identified the Bass model as having the highest explanatory power for diffusion and determined that internal factors (e.g., imitation and bandwagon pressure) have the most influence on adoption rates of BIM functions. Furthermore, the diffusion models showed that the shop-drawing process and facility management functions had the lowest saturation rate in 214 (below 5%) but have a greater potential for being adopted in coming years. The findings of this study advance knowledge for both researchers and practitioners regarding the adoption process of different BIM functions and provide a theoretical basis for understanding the diffusion patterns of ICT innovations in the construction industry.
    publisherAmerican Society of Civil Engineers
    titleDiffusion of Building Information Modeling Functions in the Construction Industry
    typeJournal Paper
    journal volume34
    journal issue2
    journal titleJournal of Management in Engineering
    identifier doi10.1061/(ASCE)ME.1943-5479.0000589
    page4017060
    treeJournal of Management in Engineering:;2018:;Volume ( 034 ):;issue: 002
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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