Exploring the influence of circular economy on big data analytics and supply chain resilience nexus: A structural equation modeling approach

Samia Islam , Sanjida Hassan , Sourav Hossain , Tazim Ahmed , Chitra Lekha Karmaker , A.B.M. Mainul Bari
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Abstract

The recent pandemic, geopolitical instability, and unforeseen crises have profoundly disrupted global supply chains, igniting a critical need to reevaluate and enhance supply chain resilience strategies. In response, researchers have extensively explored the role of big data analytics in bolstering supply chain resilience during the post-pandemic and war-time recovery phase. Again, to reduce dependency on finite resources, minimize waste, and create adaptive, closed-loop systems, the role of circular economy has also been analyzed widely. However, the interaction between these two, such as how big data analytics impacts supply chain resilience within the framework of circular economy, is still underexplored. Hence, this study aims to explore the mediating role of circular economy in the relationship between big data analytics and supply chain resilience, especially focusing on the readymade garments manufacturing sector of an emerging economy like Bangladesh. A second-order hierarchical component model has been developed and tested using partial least squares structural equation modeling to achieve this. The first-order constructs and measurement items were sourced from an extensive literature review and insights from experts in the Bangladeshi readymade garments sector. The study demonstrates that circular economy significantly plays the role of a partial mediator in the relationship between big data analytics and supply chain resilience. This indicates that while big data analytics directly enhances supply chain resilience, its effectiveness is further amplified when integrated with circular economy practices. The study’s findings offer valuable insights for industry managers and policymakers, enabling them to utilize big data analytics in order to align industrial practices with circular economy principles and bolster supply chain resilience to maintain and improve ecological and socio-economic sustainability.
探讨循环经济对大数据分析和供应链弹性关系的影响:结构方程建模方法
最近的大流行、地缘政治不稳定和不可预见的危机严重扰乱了全球供应链,迫切需要重新评估和加强供应链复原力战略。为此,研究人员广泛探索了大数据分析在大流行后和战时恢复阶段增强供应链弹性方面的作用。同样,为了减少对有限资源的依赖,最大限度地减少浪费,并创建自适应的闭环系统,循环经济的作用也被广泛分析。然而,这两者之间的相互作用,如大数据分析如何影响循环经济框架下的供应链弹性,仍未得到充分探索。因此,本研究旨在探讨循环经济在大数据分析与供应链弹性之间关系中的中介作用,特别关注孟加拉国等新兴经济体的成衣制造业。二阶层次组件模型已经开发和测试使用偏最小二乘结构方程模型来实现这一点。一阶结构和测量项目来源于广泛的文献综述和孟加拉国成衣行业专家的见解。研究表明,在大数据分析与供应链弹性的关系中,循环经济显著地发挥了部分中介作用。这表明,虽然大数据分析直接增强了供应链的弹性,但当与循环经济实践相结合时,其有效性将进一步增强。该研究的发现为行业管理者和政策制定者提供了宝贵的见解,使他们能够利用大数据分析,使工业实践与循环经济原则保持一致,并增强供应链的弹性,以维持和改善生态和社会经济的可持续性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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