Hong, Ying Civil (2018) Modelling and evaluating the adoption of building information modelling in Australian and Chinese small and medium-sized construction organisations. PhD thesis, University of New South Wales, Australia.
Abstract
Adoption rates of Building Information Modelling (BIM) within the construction industry are constantly on the rise, particularly with all the evidence that has supported its use for improved productivity and efficiency. However, there are limited studies focusing on assessing BIM adoption in SMEs and predicting BIM implementation performance. This thesis proposes models that analyse BIM adoption decision-making process, which are validated via Structural Equation Modelling (SEM) and SEM-based multi-group analysis. In addition, Artificial Neural Networks (ANNs) based classification is employed to predict the net costs of BIM implementation, while 2-stage stochastic programming is used to optimise organisation’s BIM implementation strategy. The results of validated BIM adoption models indicate that a successful implementation in practice requires adequate effort to assess implementation problems, establish knowledge support and engage staff in using BIM. Meanwhile, in order to achieve a better BIM implementation outcome, construction companies are suggested to undergo a rigorous evaluation of organisational competency and cost-benefit analysis of BIM adoption. Moreover, construction companies are not encouraged to fully integrate BIM in their operations across all applications without detailed analysis of resulting benefits and costs. Therefore, mandating BIM implementation LOD, as planned by some governments (for example, UK), may not necessarily lead to best implementation outcome. In fact, the results of this study show that, depending on the nature of the projects, a combination of BIM and non-BIM application may lead to a better outcome than full BIM implementation. In addition, the highlighted differences in BIM adoption models in China and Australia, warns against direct adoption of models developed in one country to another.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Uncontrolled Keywords: | SMEs; building information modelling; programming; Australia; China; productivity; structural equation modelling; artificial neural network; neural network |
| Index terms: | evidence, programming, China, artificial neural network, construction company, neural network, structural equation modelling, strategy, construction industry, implementation, modelling, building information modelling, construction organization, efficiency, Australia, productivity, decision-making process |
| Subjects: | Geography, programming, organization, evaluation and assessment methods, analytical methods, contractual arrangements, performance management, management, statistical analysis, information systems, industry analysis, decision analysis, artificial intelligence, modelling and simulation |
| Topics: | Digital Applications, Research Practice, Business Strategy, Quality Management, Procurement, Risk Management, Geographical Context, Engineering Principles |
| Descriptive scope: | 4 PCTA |
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