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Export Reference (APA) 
Oliveira, P. S. P. C., Ferreira, F. A. F., Dabić, M., Ferreira, J. J. M. & Ferreira, N. C. M. Q. F. (2024). Analyzing the causal dynamics of circular-economy drivers in SMES using interpretive structural modeling. Energy Economics. 138
Export Reference (IEEE) 
P. S. Oliveira et al.,  "Analyzing the causal dynamics of circular-economy drivers in SMES using interpretive structural modeling", in Energy Economics, vol. 138, 2024
Export BibTeX 
@article{oliveira2024_1764946513615,
	author = "Oliveira, P. S. P. C. and Ferreira, F. A. F. and Dabić, M. and Ferreira, J. J. M. and Ferreira, N. C. M. Q. F.",
	title = "Analyzing the causal dynamics of circular-economy drivers in SMES using interpretive structural modeling",
	journal = "Energy Economics",
	year = "2024",
	volume = "138",
	number = "",
	doi = "10.1016/j.eneco.2024.107842",
	url = "https://www.sciencedirect.com/journal/energy-economics"
}
Export RIS 
TY  - JOUR
TI  - Analyzing the causal dynamics of circular-economy drivers in SMES using interpretive structural modeling
T2  - Energy Economics
VL  - 138
AU  - Oliveira, P. S. P. C.
AU  - Ferreira, F. A. F.
AU  - Dabić, M.
AU  - Ferreira, J. J. M.
AU  - Ferreira, N. C. M. Q. F.
PY  - 2024
SN  - 0140-9883
DO  - 10.1016/j.eneco.2024.107842
UR  - https://www.sciencedirect.com/journal/energy-economics
AB  - The circular economy has emerged as a crucial way for companies to achieve their sustainability goals. Numerous businesses, especially small and medium-sized enterprises (SMEs), are integrating circular-economy projects into their operations. However, this undertaking presents multiple challenges as many managers must grapple with constraints in resources and expertise. This study’s primary objective is to develop a process-oriented decision-making system designed to deal with complex circular-economy scenarios. The proposed analysis system can help SMEs identify the driving forces behind circular-economy principles and evaluate the intricate connections between these determinants, using a unique combination of multiple criteria decision analysis methods (i.e., cognitive mapping, and interpretive structural modeling). Collaborative sessions involving circular-economy experts were instrumental in refining the analysis system, and in-depth discussions with other specialists from the International Labor Organization further enriched this decision-support system. The findings include that circular-economy drivers can be grouped into five clusters: products, processes, policies/regulations, attitudes/behaviors, and communication/awareness. This structured breakdown provides SMEs with the tools to comprehend and address the pivotal factors that shape circular-economy initiatives. This pioneering study thus produced a comprehensive decision-making model attuned to the intricacies of the circular economy while highlighting the benefits of collaborative endeavors involving industry experts and global decision makers.
ER  -