[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126609-en":3,"doc-seo-126609-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126609,687207020761,"Patrick","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Developing Capabilities for Supply Chain Resilience in a Post-COVID World - A machine learning-based thematic analysis","This study examines the past, present, and future of Supply Chain Resilience (SCR) research in the context of COVID-19. A corpus of 1717 papers is classified into 11 thematic clusters and then validated through supervised machine learning. The clusters are analyzed within COVID-19 to identify three associated capabilities—interconnectedness, transformability, and sharing—on which firms should focus. Results guide managers and scholars developing future SCR research for maximum impact.","University of Birmingham  \nDeveloping Capabilities for Supply Chain Resilience in a Post-COVID World  \nLi, Dun; Zhi, Bangdong; Schoenherr, Tobias; Wang, Xiaojun  \nDOI:  \n10.1080/24725854.2023.2176951  \nLicense:  \nCreative Commons: Attribution-NonCommercial-NoDerivs (CC BY-NC-ND)  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nCitation for published version (Harvard):  \nLi, D, Zhi, B, Schoenherr, T & Wang, X 2023, 'Developing Capabilities for Supply Chain Resilience in a PostCOVID World: A Machine Learning based Thematic Analysis', IISE Transactions , vol. 55, no. 12, pp.  \n1256–1276. [https://doi.org/10.1080/24725854.2023.2176951](https://doi.org/10.1080/24725854.2023.2176951)  \nLink to publication on Research at Birmingham portal  \nGeneral rights  \nUnless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. The express permission of the copyright holder must be obtained for any use of this material other than for purposes permitted by law.  \n•Users may freely distribute the URL that is used to identify this publication.  \n•Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research.  \n•User may use extracts from the document in line with the concept of ‘fair dealing’ under the Copyright, Designs and Patents Act 1988 (?)  \n•Users may not further distribute the material nor use it for the purposes of commercial gain.  \nWhere a licence is displayed above, please note the terms and conditions of the licence govern your use of this document.  \nWhen citing, please reference the published version.  \nTake down policy  \nWhile the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive.  \nIf you believe that this is the case for this document, [please contact UBIRA@lists.bham.ac.uk](please contact UBIRA@lists.bham.ac.uk) providing details and we will remove access to the work immediately and investigate.  \nDownload date: 04. Aug. 2026  \nIISE Transactions  \nISSN: (Print) (Online) Journal homepage: [https://www.tandfonline.com/loi/uiie21](https://www.tandfonline.com/loi/uiie21)  \nDeveloping capabilities for supply chain resilience in a post-COVID world: A machine learning-based thematic analysis  \nDun Li, Bangdong Zhi, Tobias Schoenherr & Xiaojun Wang  \nTo cite this article: Dun Li, Bangdong Zhi, Tobias Schoenherr & Xiaojun Wang  \n(2023) Developing capabilities for supply chain resilience in a post-COVID world: A machine learning-based thematic analysis, IISE Transactions, 55:12, 1256-1276, DOI:  \n10. 1080/24725854 .2023.2176951  \nTo link to this article: [https://doi.org/10.1080/24725854.2023.2176951](https://doi.org/10.1080/24725854.2023.2176951)  \nCopyright © 2023 The Author(s) . Published with license by Taylor & Francis Group, LLC  \n View supplementary material   \n\n|  Published online: 28 Mar 2023. |\n| --- |\n|  Article views: 2155 |\n|  View Crossmark data |\n\n\n|  Submit your article to this journal  |\n| --- |\n|  View related articles  |\n|  Citing articles: 2 View citing articles  |\n\nFull Terms & Conditions of access and use can be found at [https://www.tandfonline.com/action/journalInformation?journalCode=uiie21](https://www.tandfonline.com/action/journalInformation?journalCode=uiie21)  \nIISE TRANSACTIONS  \n2023, VOL. 55, NO. 12, 1256–1276  \n[https://doi.org/10.1080/24725854.2023.2176951](https://doi.org/10.1080/24725854.2023.2176951)  \nDeveloping capabilities for supply chain resilience in a post-COVID world: A machine learning-based thematic analysis  \nDun Lia, Bangdong Zhib , Tobias Schoenherrc , and Xiaojun Wangd  \naManagement School, Guizhou University, China; bBusiness School, University of Bristol, Bristol, UK; cBroad College of Business, Michigan ","cbCaiqJH1PRyrVrz","https://ap.wps.com/l/cbCaiqJH1PRyrVrz","pdf",4534600,2,1,23,"English","en",105,"# Introduction\n## Supply chain disruptions during COVID-19\n## Resilience research scope and approach\n# Method and thematic clustering\n## Paper classification into thematic clusters\n## Supervised machine learning verification\n# Findings and COVID-19-linked capabilities\n## Interconnectedness\n## Transformability\n## Sharing\n# Implications","[{\"question\":\"How many SCR papers are analyzed and how are they processed?\",\"answer\":\"The study classifies 1717 papers in the supply chain resilience field into 11 thematic clusters. The clustering is subsequently verified using a supervised machine learning approach.\"},{\"question\":\"What three capabilities does the research identify for building a more resilient supply chain?\",\"answer\":\"The analysis identifies interconnectedness, transformability, and sharing as capabilities associated with the thematic clusters. Firms are encouraged to focus on these capabilities in the post-COVID world.\"},{\"question\":\"What is the practical value of the study’s insights?\",\"answer\":\"The derived insights support practicing managers and also help scholars design future SCR research projects with greater impact. The guidance is grounded in the COVID-19 context and the validated thematic structure.\"}]","Developing Capabilities for Supply Chain Resilience in a Post-COVID World - A machine learning-based thematic analysis | PDF",1785933726,58,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"developing-capabilities-for-supply-chain-resilience-in-a-post-covid-world-a-machine-learning-based-thematic-analysis","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/developing-capabilities-for-supply-chain-resilience-in-a-post-covid-world-a-machine-learning-based-thematic-analysis/126609/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"How many SCR papers are analyzed and how are they processed?","Question",{"text":76,"@type":77},"The study classifies 1717 papers in the supply chain resilience field into 11 thematic clusters. The clustering is subsequently verified using a supervised machine learning approach.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What three capabilities does the research identify for building a more resilient supply chain?",{"text":81,"@type":77},"The analysis identifies interconnectedness, transformability, and sharing as capabilities associated with the thematic clusters. Firms are encouraged to focus on these capabilities in the post-COVID world.",{"name":83,"@type":74,"acceptedAnswer":84},"What is the practical value of the study’s insights?",{"text":85,"@type":77},"The derived insights support practicing managers and also help scholars design future SCR research projects with greater impact. 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