{"title":"生成式人工智能的使用与可持续供应链绩效:基于实践的观点","authors":"Lixu Li , Wenwen Zhu , Lujie Chen , Yaoqi Liu","doi":"10.1016/j.tre.2024.103761","DOIUrl":null,"url":null,"abstract":"<div><p>The emergence of generative AI presents numerous potential solutions to address challenges in sustainable supply chain management (SCM). However, not all firms can effectively master the methods of using generative AI and realize potential benefits. To address this dilemma, we adopt a practice-based view (PBV) to examine generative AI usage’s effect on sustainable supply chain performance (SSCP). Analyzing survey data from 213 Chinese manufacturing firms, we identify a positive relationship between generative AI usage and SSCP. Moreover, two types of sustainable supply chain practices—green supply chain collaboration (GSCC) and circular economy implementation (CEI)——emerge as serial mediators connecting this relationship. We contribute to existing AI-enabled SCM research by elucidating the potential mediation mechanisms underlying the link between generative AI usage and SSCP. We also offer insightful implications for firms adapting to new norms in global SCM.</p></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":null,"pages":null},"PeriodicalIF":8.3000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Generative AI usage and sustainable supply chain performance: A practice-based view\",\"authors\":\"Lixu Li , Wenwen Zhu , Lujie Chen , Yaoqi Liu\",\"doi\":\"10.1016/j.tre.2024.103761\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The emergence of generative AI presents numerous potential solutions to address challenges in sustainable supply chain management (SCM). However, not all firms can effectively master the methods of using generative AI and realize potential benefits. To address this dilemma, we adopt a practice-based view (PBV) to examine generative AI usage’s effect on sustainable supply chain performance (SSCP). Analyzing survey data from 213 Chinese manufacturing firms, we identify a positive relationship between generative AI usage and SSCP. Moreover, two types of sustainable supply chain practices—green supply chain collaboration (GSCC) and circular economy implementation (CEI)——emerge as serial mediators connecting this relationship. We contribute to existing AI-enabled SCM research by elucidating the potential mediation mechanisms underlying the link between generative AI usage and SSCP. We also offer insightful implications for firms adapting to new norms in global SCM.</p></div>\",\"PeriodicalId\":49418,\"journal\":{\"name\":\"Transportation Research Part E-Logistics and Transportation Review\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":8.3000,\"publicationDate\":\"2024-09-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transportation Research Part E-Logistics and Transportation Review\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1366554524003521\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Research Part E-Logistics and Transportation Review","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1366554524003521","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ECONOMICS","Score":null,"Total":0}
Generative AI usage and sustainable supply chain performance: A practice-based view
The emergence of generative AI presents numerous potential solutions to address challenges in sustainable supply chain management (SCM). However, not all firms can effectively master the methods of using generative AI and realize potential benefits. To address this dilemma, we adopt a practice-based view (PBV) to examine generative AI usage’s effect on sustainable supply chain performance (SSCP). Analyzing survey data from 213 Chinese manufacturing firms, we identify a positive relationship between generative AI usage and SSCP. Moreover, two types of sustainable supply chain practices—green supply chain collaboration (GSCC) and circular economy implementation (CEI)——emerge as serial mediators connecting this relationship. We contribute to existing AI-enabled SCM research by elucidating the potential mediation mechanisms underlying the link between generative AI usage and SSCP. We also offer insightful implications for firms adapting to new norms in global SCM.
期刊介绍:
Transportation Research Part E: Logistics and Transportation Review is a reputable journal that publishes high-quality articles covering a wide range of topics in the field of logistics and transportation research. The journal welcomes submissions on various subjects, including transport economics, transport infrastructure and investment appraisal, evaluation of public policies related to transportation, empirical and analytical studies of logistics management practices and performance, logistics and operations models, and logistics and supply chain management.
Part E aims to provide informative and well-researched articles that contribute to the understanding and advancement of the field. The content of the journal is complementary to other prestigious journals in transportation research, such as Transportation Research Part A: Policy and Practice, Part B: Methodological, Part C: Emerging Technologies, Part D: Transport and Environment, and Part F: Traffic Psychology and Behaviour. Together, these journals form a comprehensive and cohesive reference for current research in transportation science.