An integrated model for predicting the probability of adoption of green building in South Africa

Purpose: The purpose of this paper is to examine the predictable effect of economic and non-economic factors regarded as the most important to stimulate stakeholders’ behavioural intentions to adopt green building. Design/methodology/approach: The primary data was collected from 106 green building accredited professionals in both the public and private sectors registered with the Green Building Council of South Africa. The data analysis techniques adopted include descriptive and inferential statistics, namely, factor analysis and logistic regression model (LRM). Findings: The LRM results revealed five predictors and two control variables made a unique statistically significant contribution to the model. The strongest predictor to enhance the intention to adopt green building was a financial benefit (FB), recording an odds ratio of 9.1, which indicates that the likelihood to adopt is approximately 9.1 times more if FBs is evident. Practical implications: It is anticipated that the most significant facilitators/enablers identified by built environment stakeholders will create an enabling environment to enhance the adoption of green building. Originality/value: This research has contributed to the existing knowledge by developing a decision support model. The decision support model provides predictive indicators for clients, consultants and contractors to harness their resources and identify significant parameters to improve their decision-making in adopting green building.