The emergence of building information modelling (BIM) has led to the need for pre-qualification and selection of organisations capable of working within a BIM environment. Several criteria have been proposed for the assessment of an organisation’s BIM capability during the pre-qualification and selection phase of projects. However, no studies have sought to empirically establish whether organisations selected on the basis of such criteria have actually been the most successful at delivering BIM on projects. The purpose of this paper is to address the aforementioned gap through a comparison of predicted BIM capability and post-selection performance.
BIM capability of firms in a case study was predicted using 28 BIM pre-qualification and selection criteria, prioritised based on their perceived contribution to BIM delivery success from a survey of practitioners on BIM-enabled projects. The comparison of predicted BIM capability and post-selection performance was, on the other hand, achieved through the application of the Technique to Order Preference by Similarity to Ideal Solution and fuzzy sets theory (Fuzzy-TOPSIS).
Findings underscore the reliability of the 28 BIM pre-qualification and selection criteria as well as the priority weightings proposed for their use in predicting BIM capability and likelihood of performance. The findings have highlighted the importance of criteria related as previous BIM use experience as well as information processing maturity as critical indicators of the capability of organisations, particularly design firms.
Overall, the findings highlight the need for prioritisation of BIM pre-qualification and selection criteria on the basis of their actual contribution to delivery success from post-selection evaluation of performance.
Mahamadu, A.-M., Manu, P., Mahdjoubi, L., Booth, C., Aigbavboa, C. and Abanda, F.H. (2019), "The importance of BIM capability assessment: An evaluation of post-selection performance of organisations on construction projects", Engineering, Construction and Architectural Management, Vol. 27 No. 1, pp. 24-48. https://doi.org/10.1108/ECAM-09-2018-0357
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