Artificial intelligence-based reverse logistics for improving circular economy performance: a developing country perspective

逆向物流 业务 调解 重新使用 循环经济 知识管理 相互依存 活力 中小企业 产业组织 结构方程建模 过程管理 运营管理 营销 计算机科学 供应链 经济 工程类 生态学 生物 物理 财务 量子力学 机器学习 政治学 法学 废物管理
作者
Subhodeep Mukherjee,Ramji Nagariya,K. Mathiyazhagan,Manish Mohan Baral,Pavithra Maddipetlolu Rajendran,Andrea Appolloni
出处
期刊:The International Journal of Logistics Management [Emerald Publishing Limited]
卷期号:35 (6): 1779-1806 被引量:31
标识
DOI:10.1108/ijlm-03-2023-0102
摘要

Purpose Reverse logistics services are designed to move goods from their point of consumption to an endpoint to capture value or properly dispose of products and materials. Artificial intelligence (AI)-based reverse logistics will help Micro, Small, and medium Enterprises (MSMEs) adequately recycle and reuse the materials in the firms. This research aims to measure the adoption of AI-based reverse logistics to improve circular economy (CE) performance. Design/methodology/approach In this study, we proposed ten hypotheses using the theory of natural resource-based view and technology, organizational and environmental framework. Data are collected from 363 Indian MSMEs as they are the backbone of the Indian economy, and there is a need for digital transformation in MSMEs. A structural equation modeling approach is applied to analyze and test the hypothesis. Findings Nine of the ten proposed hypotheses were accepted, and one was rejected. The results revealed that the relative advantage (RA), trust (TR), top management support (TMS), environmental regulations, industry dynamism (ID), compatibility, technology readiness and government support (GS) positively relate to AI-based reverse logistics adoption. AI-based reverse logistics indicated a positive relationship with CE performance. For mediation analysis, the results revealed that RA, TR, TMS and technological readiness are complementary mediation. Still, GS, ID, organizational flexibility, environmental uncertainty and technical capability have no mediation. Practical implications The study contributed to the CE performance and AI-based reverse logistics literature. The study will help managers understand the importance of AI-based reverse logistics for improving the performance of the CE in MSMEs. This study will help firms reduce their carbon footprint and achieve sustainable development goals. Originality/value Few studies focused on CE performance, but none measured the adoption of AI-based reverse logistics to enhance MSMEs’ CE performance.
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