| Title | Analyzing the causal dynamics of circular-economy drivers in SMES using interpretive structural modeling |
|---|---|
| ID_Doc | 1459 |
| Authors | Oliveira, PSPC; Ferreira, FAF; Dabic, M; Ferreira, JJM; Ferreira, NCMQF |
| Title | Analyzing the causal dynamics of circular-economy drivers in SMES using interpretive structural modeling |
| Year | 2024 |
| Published | |
| Abstract | 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 processoriented 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. |
| https://doi.org/10.1016/j.eneco.2024.107842 |
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