基于人工智能的逆向物流改善循环经济绩效:发展中国家的视角

Subhodeep Mukherjee, Ramji Nagariya, K. Mathiyazhagan, Manish Mohan Baral, M.R. Pavithra, Andrea Appolloni
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引用次数: 0

摘要

目的逆向物流服务旨在将货物从消费点运送到终端,以获取价值或妥善处理产品和材料。基于人工智能(AI)的逆向物流将帮助微型和中小型企业(MSMEs)充分回收和再利用企业中的材料。本研究旨在衡量采用基于人工智能的逆向物流来提高循环经济(CE)绩效的情况。在本研究中,我们采用基于自然资源的观点和技术、组织和环境框架理论,提出了十项假设。数据收集自 363 家印度中小微企业,因为它们是印度经济的支柱,而且中小微企业需要进行数字化转型。研究结果在提出的十个假设中,九个被接受,一个被拒绝。结果显示,相对优势(RA)、信任(TR)、高层管理支持(TMS)、环境法规、行业活力(ID)、兼容性、技术准备度和政府支持(GS)与基于人工智能的逆向物流采用呈正相关。基于人工智能的逆向物流与行政首长的绩效呈正相关。中介分析结果显示,RA、TR、TMS 和技术准备度是互补中介。实践意义 该研究为行政首长绩效和基于人工智能的逆向物流文献做出了贡献。该研究将帮助管理者了解基于人工智能的逆向物流对提高中小微企业的行政首长绩效的重要性。该研究将有助于企业减少碳足迹,实现可持续发展目标。原创性/价值很少有研究关注消费电子绩效,但没有研究衡量了采用基于人工智能的逆向物流来提高中小微企业的消费电子绩效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial intelligence-based reverse logistics for improving circular economy performance: a developing country perspective
PurposeReverse 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/approachIn 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.FindingsNine 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 implicationsThe 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/valueFew studies focused on CE performance, but none measured the adoption of AI-based reverse logistics to enhance MSMEs’ CE performance.
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