Purpose This paper analyzes the effect of circular economy practices on firm performance for a circular supply chain and explores the moderating role that big-data-driven supply chain plays within these relationships. Design/methodology/approach This study uses data collected through an online survey distributed to managers of 378 Italian firms that have adopted circular economy principles. The data are processed using multiple regression analysis. Findings The results indicate that the three categories of circular economy practices investigated – namely circular economy supply chain management design, circular economy supply chain relationship management and circular economy HR management – play a crucial role in enhancing firm performance from a circular economy perspective. A big-data-driven supply chain acts as a moderator of the relationship between circular economy HR management and firm performance for a circular economy supply chain. Originality/value This study makes a number of original contributions to research on circular economy practices in a big-data-driven supply chain and provides useful insights for practitioners. First, it answers the call to capture digital transformation trends and to extend research on sustainability in supply chain management. Second, it enhances the literature by investigating the relationships between three different kinds of circular economy supply chain practices and firm performance. Finally, it clarifies the moderating role of big data in making decisions and implementing circular supply chain solutions to achieve better environmental, social and economic benefits.

Del Giudice, M., Chierici, R., Mazzucchelli, A., Fiano, F. (2020). Supply chain management in the era of circular economy: the moderating effect of big data. THE INTERNATIONAL JOURNAL OF LOGISTICS MANAGEMENT [10.1108/IJLM-03-2020-0119].

Supply chain management in the era of circular economy: the moderating effect of big data

Del Giudice M.;Chierici R.
;
Mazzucchelli A.;
2020

Abstract

Purpose This paper analyzes the effect of circular economy practices on firm performance for a circular supply chain and explores the moderating role that big-data-driven supply chain plays within these relationships. Design/methodology/approach This study uses data collected through an online survey distributed to managers of 378 Italian firms that have adopted circular economy principles. The data are processed using multiple regression analysis. Findings The results indicate that the three categories of circular economy practices investigated – namely circular economy supply chain management design, circular economy supply chain relationship management and circular economy HR management – play a crucial role in enhancing firm performance from a circular economy perspective. A big-data-driven supply chain acts as a moderator of the relationship between circular economy HR management and firm performance for a circular economy supply chain. Originality/value This study makes a number of original contributions to research on circular economy practices in a big-data-driven supply chain and provides useful insights for practitioners. First, it answers the call to capture digital transformation trends and to extend research on sustainability in supply chain management. Second, it enhances the literature by investigating the relationships between three different kinds of circular economy supply chain practices and firm performance. Finally, it clarifies the moderating role of big data in making decisions and implementing circular supply chain solutions to achieve better environmental, social and economic benefits.
Articolo in rivista - Articolo scientifico
Big data; Circular economy; Supply chain management design; Supply chain relationship management
English
2020
open
Del Giudice, M., Chierici, R., Mazzucchelli, A., Fiano, F. (2020). Supply chain management in the era of circular economy: the moderating effect of big data. THE INTERNATIONAL JOURNAL OF LOGISTICS MANAGEMENT [10.1108/IJLM-03-2020-0119].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/284920
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