Barriers and strategies for building information modelling implementation: a comparative study between New Zealand and China

Abstract

Building Information Modelling (BIM) is a sharing platform that can present a parametric 3 D model with various project information in the form of a digital display. In recent years, BIM adoption has become increasing globally as the Architectural, Engineering, Construction (AEC) industry has recognised its benefits. Meanwhile, many challenges of BIM adoption in different countries have been well documented. To address the gap in literature, this study examines the differences and similarities of BIM adoption between New Zealand and China. A questionnaire was conducted across the two countries to investigate the barriers and strategies for the implementation of BIM. Data from 146 respondents was collected in New Zealand and China. The result shows that there is a difference in the perception of Knowledge Barrier, Technology Barrier, Internal Strategy and External Strategy (Legal/Technology viewpoint) between New Zealand professionals and Chinese professionals. The differences identified offer important implications for government agencies to promote BIM implementation and for BIM service providers to better target the end-users.

Keywords:

• Building information modeling
• barriers
• strategies
• New Zealand
• China

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 The Chi-squared test is a statistical hypothesis test used for categorical variables. This test is used to assess the probability of association or independence between two categorical variables. The null hypothesis for this test is that there is no relationship between the two variables. As such, p-value lower than a chosen significance level indicates the existences of a relationship (Zibran 2007).

2 The Kaiser-Meyer-Olkin (KMO) Test and Bartlett’s Test of Sphericity are a traditional test used to assess if data are suitable for data reduction technique such as Factor Analysis. KMO can range from 0 to 1 and the accepted rule of thumb is to have a KMO greater than 0.7 while the p-value of from Bartlett’s Test of Sphericity must be lower than a chosen significance level to have suitable data (Watkins 2018).