Mohamed, Nur Anisah and Alanzi, Ayed R. A. and Azizan, Azlinna Noor and Azizan, Suzana Ariff and Samsudin, Nadia and Jenatabadi, Hashem Salarzadeh (2024) Application of Bayesian structural equation modeling in construction and demolition waste management studies: Development of an extended theory of planned behavior. PLoS ONE, 19 (1). ISSN 1932-6203, DOI https://doi.org/10.1371/journal.pone.0290376.
Full text not available from this repository.Abstract
Sustainable construction and demolition waste management relies heavily on the attitudes and actions of its constituents; nevertheless, deep analysis for introducing the best estimator is rarely attained. The main objective of this study is to perform a comparison analysis among different approaches of Structural Equation Modeling (SEM) in Construction and Demolition Waste Management (C&DWM) modeling based on an Extended Theory of Planned Behaviour (Extended TPB). The introduced research model includes twelve latent variables, six independent variables, one mediator, three control variables, and one dependent variable. Maximum likelihood (ML), partial least square (PLS), and Bayesian estimators were considered in this study. The output of SEM with the Bayesian estimator was 85.8%, and among effectiveness of six main variables on C&DWM Behavioral (Depenmalaydent variables), five of them have significant relations. Meanwhile, the variation based on SEM with ML estimator was equal to 78.2%, and four correlations with dependent variable have significant relationship. At the conclusion, the R-square of SEM with the PLS estimator was equivalent to 73.4% and three correlations with the dependent variable had significant relationships. At the same time, the values of the three statistical indices include root mean square error (RMSE), mean absolute percentage error (MPE), and mean absolute error (MSE) with involving Bayesian estimator are lower than both ML and PLS estimators. Therefore, compared to both PLS and ML, the predicted values of the Bayesian estimator are closer to the observed values. The lower values of MPE, RMSE, and MSE and the higher values of R-square will generate better goodness of fit for SEM with a Bayesian estimator. Moreover, the SEM with a Bayesian estimator revealed better data fit than both the PLS and ML estimators. The pattern shows that the relationship between research variables can change with different estimators. Hence, researchers using the SEM technique must carefully consider the primary estimator for their data analysis. The precaution is necessary because higher error means different regression coefficients in the research model.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | CONTRACTORS CONSTRUCTION; DETERMINANTS; CHINA; DESIGN; BIASES |
| Subjects: | H Social Sciences > HB Economic Theory Q Science > QA Mathematics T Technology > TD Environmental technology. Sanitary engineering |
| Divisions: | Faculty of Science > Institute of Mathematical Sciences Faculty of Science > Department of Science and Technology Studies |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 14 Jun 2024 03:07 |
| Last Modified: | 14 Jun 2024 03:07 |
| URI: | http://eprints.um.edu.my/id/eprint/44164 |
Actions (login required)
 |
View Item |
