Macro BIM adoption: Comparative market analysis
- Publication Type:
- Journal Article
- Citation:
- Automation in Construction, 2017, 81 pp. 286 - 299
- Issue Date:
- 2017-09-01
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|---|---|---|---|---|---|
| 1-s2.0-S0926580517303205-main.pdf | Published Version | 1.57 MB |
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© 2017 Elsevier B.V. The adoption of Building Information Modelling (BIM) across markets is a pertinent topic for academic discourse and industry attention. This is evidenced by the unrelenting release of national BIM initiatives; new BIM protocols; and candidate international standards. This paper is the second part of an ongoing Macro BIM Adoption study: the first paper “Macro BIM Adoption: Conceptual Structures” (Succar and Kassem, 2015) introduced five conceptual models for assessing macro BIM adoption across markets and informing the development of BIM adoption policies. This second paper clarifies how these models are validated through capturing the input of 99 experts from 21 countries using a survey tool; highlights the commonalities and differences between sample countries with respect to BIM adoption; and introduces sample tools and templates for either developing or calibrating BIM adoption policies. Survey data collected indicate that all five conceptual models demonstrate high levels of ‘clarity’, ‘accuracy’ and ‘usefulness’, the three metrics measured. They also indicate (1) varying rates of BIM diffusion across countries with BIM capability near the lower-end of the spectrum; (2) varying levels of BIM maturity with - the mean of - most macro BIM components falling below the medium level; (3) varying diffusion dynamics across countries with the prevalence of the middle-out diffusion dynamic; (4) varying policy actions across countries with a predominance of the passive policy approach; and (5) varying distribution of diffusion responsibilities among player groups with no detectable dominant pattern across countries. The two papers provide an opportunity to improve our understanding of BIM adoption dynamics across countries. Future research can build upon the models and tools introduced to enable (a) an expansion of benchmarking data through surveying additional countries; (b) identifying BIM adoption changes in surveyed countries over time; (c) correlating changes in adoption rates/patterns with policy interventions; (d) identifying BIM policy variations within the same country; (e) establishing statistical correlations between the conceptual models; and (f) developing new tools to facilitate BIM policy development and encouraging BIM adoption.
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