Digital Construction Technology and Job-Site Equipment Demonstration: Modelling Relationship Strategies for Technology Adoption
Received: 24 May 2019 / Revised: 21 June 2019 / Accepted: 25 June 2019 / Published: 29 June 2019
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The construction technology market is competitive and complicated, due to the high-risk of digital technology utilisation in construction projects and the conservative character of construction companies. This complexity affects the process of job-site technology dissemination and adoption in which construction companies make decisions
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The construction technology market is competitive and complicated, due to the high-risk of digital technology utilisation in construction projects and the conservative character of construction companies. This complexity affects the process of job-site technology dissemination and adoption in which construction companies make decisions to purchase and utilise the new technology. The complexity is one of the reasons that many new remote technologies, positioning and locating systems, lasers and drones, 3D printing, and robots are not widely adopted in the short term, despite vendors making determined efforts to overcome this. Three objectives are investigated in this paper: (i) to define criteria for examining patterns of vendors’ strategies to support technology adoption; (ii) to present fact-based evidence of different vendors’ demonstration methods; and (iii) to present examples of different technology groups based on their required strategies. This paper presents the results of a longitudinal investigation of the construction technology market, including patterns of technology demonstration and a conceptual model of classifying vendors and their technologies in construction market places. The model involves the three most important factors that distinguish technology exhibitors: Physical appearance, Interpersonal relationship and Technology demonstration. Data was collected from technology exhibitions, involving randomly selected vendors. This data was analysed using hierarchical and c-means clustering techniques. The hard-clustering techniques resulted in vendors being placed in five classes based on the elements of the PIT framework. Fuzzy analysis shows how these classes fit into an underlying strategy spectrum. Understanding the strategies used in each class enables new vendors to select their own dissemination strategies based on their own particular circumstances. The practical implication of this study is to present a set of dissemination strategies to new technology stakeholders involved in Industry 4.0. The identified patterns of technology vendor strategies and the novel conceptual model contribute to the body of knowledge in technology diffusion.