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Fahmy, A, Hassan, T, Bassioni, H and McCaffer, R (2019) Dynamic scheduling model for the construction industry. Built Environment Project and Asset Management, 10(03), 313–30.

Geekiyanage, D and Ramachandra, T (2020) Nexus between running costs and building characteristics of commercial buildings: hedonic regression modelling. Built Environment Project and Asset Management, 10(03), 389–406.

Ling, F Y, Zhang, Z and Wong, W T (2020) How personality traits influence management styles of construction project managers. Built Environment Project and Asset Management, 10(03), 453–68.

Mathar, H, Assaf, S, Hassanain, M A, Abdallah, A and Sayed, A M (2020) Critical success factors for large building construction projects. Built Environment Project and Asset Management, 10(03), 349–67.

Mwesigwa, R, Nabwami, R, Mayengo, J and Basulira, G (2020) Contractual completeness as a cornerstone to stakeholder management in public private partnership projects in Uganda. Built Environment Project and Asset Management, 10(03), 469–84.

Shojaei, P and bolvardizadeh, A (2020) Rough MCDM model for green supplier selection in Iran: a case of university construction project. Built Environment Project and Asset Management, 10(03), 437–52.

• Type: Journal Article
• Keywords: Green supplier; Construction projects; Rough AHP; Rough TOPSIS; University; Iran;
• ISBN/ISSN: 2044-124X
• URL: https://doi.org/10.1108/BEPAM-11-2019-0117
• Abstract:
  The construction industry has a significant function in improving the quality of life in the urban environment; meanwhile, greening the supply chain is becoming a seriously pressing issue in the construction industry. This paper seeks to select green suppliers in construction projects implemented at Iranian state universities via multicriteria decision-making (MCDM) models through rough set theory. A mixed methodology design was conducted through a literature review of studies concerned with green suppliers to identify the related criteria and the rough MCDM techniques. As such, 15 criteria were finalized through content validity ratio (introduced by Lawshe, 1975). The weights of the criteria were calculated through the rough AHP and the suppliers were prioritized using the rough TOPSIS to contribute to the Construction Department. The study proposed a hierarchical structure of the decision process for green supplier selection in construction projects. According to the weighting results, environmental awareness, green social responsibility and the environmental management system were the most important criteria. Because the scope of the study was limited to state universities and the methods worked according to the experts' views, the results should be generalized with more caution. The validity of the results should be examined by applying the model to similar contexts. The study conceptualizes green supplier selection in construction projects at state universities. Furthermore, the method used makes it possible to deal with the uncertainty arising from experts' limited awareness of only part of the problem rather than the whole system under investigation.

Shooshtarian, S, Lingard, H and Wong, P S (2020) Using the cost of construction work to trigger legislative duties for WHS: the Australian experience. Built Environment Project and Asset Management, 10(03), 369–87.

Vilventhan, A and Rajadurai, R (2019) 4D Bridge Information Modelling for management of bridge projects: a case study from India. Built Environment Project and Asset Management, 10(03), 423–35.

Wuni, I Y and Shen, G Q (2020) Stakeholder management in prefabricated prefinished volumetric construction projects: benchmarking the key result areas. Built Environment Project and Asset Management, 10(03), 407–21.

Yap, J B H and Chow, I N (2020) Investigating the managerial ‘‘nuts and bolts’’ for the construction industry. Built Environment Project and Asset Management, 10(03), 331–48.