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Promoting student commitment to BIM in construction education

Published on Apr 23, 2019in Engineering, Construction and Architectural Management
· DOI :10.1108/ECAM-04-2018-0173
Oluwole Alfred Olatunji10
Estimated H-index: 10
(Curtin University)
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Abstract
Purpose Industry uptake of digital modeling is improving. Recent evidence suggests building information modeling (BIM) is the commercial reality of today’s construction education, and the way of the future that has truly begun. Amongst the significance of this is BIM’s potential to revolutionize the industry. The purpose of this paper is to use the learning experiences of undergraduate students in two construction management subjects involving quantity measurement and cost estimation to explore students’ motivation toward BIM education. In particular, the study investigates decision factors underlying students’ selection of software for information management in a modeling environment. Design/methodology/approach A total of 674 undergraduate students from the same institution were surveyed, out of which 153 responses were retrieved. The data provide insights into decisions taken by students while metamorphosing traditional processes into BIM-driven outcomes, in the form of commercial estimates of a real life project reported through a bill of quantities. Some 29 decision factors were analyzed. These include prior training, flair for creativity, ease of use, economic reasons, learning outcomes, on-going technical support and support infrastructure. Reductionist methods involving factor analysis and Cronbach’s α reliability estimate procedures were used to investigate the most important decision factors from amongst the decision factors analyzed. Findings Results show all the 29 decision factors are statistically significant. Access to on-going support, the mandatory requirement to use a particular tool to complete an assessment task and the requirement to use the tool for job duties are the most significant decision factors. Vendors’ persuasion and the capability of the tool to achieve better outcomes than others are least significant. Statistical correlations between the decision factors were obtained. They all suggest near-absolute correlations. Practical implications The practical implications of these findings are vital. They help to unravel factors that promote students’ interest in BIM education, and contribute toward the development of software selection models that are relevant to professionals and incipient businesses. Originality/value Future studies on decision analysis can be built on the findings also. In particular, the decision factors help in developing creative cognitive solutions to BIM adoption issues. They also help on the challenge posed by the constraints of knowledge diffusion within and across project teams, and in designing tools that meet the requirements of non-design disciplines who also play vital roles in the BIM project environment.
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References53
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#1Rongrong Yu (University of Newcastle)H-Index: 4
#2John S. Gero (UNCC: University of North Carolina at Charlotte)H-Index: 46
#1Rongrong Yu (University of Newcastle)H-Index: 4
#2Oluwole Alfred Olatunji (Curtin University)H-Index: 10
Last.Abiola AkanmuH-Index: 6
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