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Application of Bayesian structural equation modeling in construction and demolition waste management studies: Development of an extended theory of planned behavior

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Listed:
  • Nur Anisah Mohamed
  • Ayed R A Alanzi
  • Azlinna Noor Azizan
  • Suzana Ariff Azizan
  • Nadia Samsudin
  • Hashem Salarzadeh Jenatabadi

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.

Suggested Citation

  • Nur Anisah Mohamed & Ayed R A Alanzi & Azlinna Noor Azizan & Suzana Ariff Azizan & Nadia Samsudin & Hashem Salarzadeh Jenatabadi, 2024. "Application of Bayesian structural equation modeling in construction and demolition waste management studies: Development of an extended theory of planned behavior," PLOS ONE, Public Library of Science, vol. 19(1), pages 1-20, January.
    • Handle: RePEc:plo:pone00:0290376
    DOI: 10.1371/journal.pone.0290376
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    References listed on IDEAS

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    1. Jaime A. Mesa & Carlos Fúquene-Retamoso & Aníbal Maury-Ramírez, 2021. "Life Cycle Assessment on Construction and Demolition Waste: A Systematic Literature Review," Sustainability, MDPI, vol. 13(14), pages 1-22, July.
    2. Hashem Salarzadeh Jenatabadi & Sedigheh Moghavvemi & Che Wan Jasimah Bt Wan Mohamed Radzi & Parastoo Babashamsi & Mohammad Arashi, 2017. "Testing students’ e-learning via Facebook through Bayesian structural equation modeling," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-19, September.
    3. Li, Jingru & Tam, Vivian W.Y. & Zuo, Jian & Zhu, Jiaolan, 2015. "Designers’ attitude and behaviour towards construction waste minimization by design: A study in Shenzhen, China," Resources, Conservation & Recycling, Elsevier, vol. 105(PA), pages 29-35.
    4. Bo Yu & Jiayuan Wang & Ying Liao & Huanyu Wu & Aslan B. Wong, 2021. "Determinants Affecting Purchase Willingness of Contractors towards Construction and Demolition Waste Recycling Products: An Empirical Study in Shenzhen, China," IJERPH, MDPI, vol. 18(9), pages 1-20, April.
    5. Segars, A. H., 1997. "Assessing the unidimensionality of measurement: a paradigm and illustration within the context of information systems research," Omega, Elsevier, vol. 25(1), pages 107-121, February.
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