[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121403-en":3,"doc-seo-121403-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121403,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Predicting the performance of MSMEs - a hybrid DEA-machine learning approach","Micro, small and medium enterprises (MSMEs) shape employment across the world, making performance understanding and improvement a high-priority challenge. The study investigates how big-data analytics, especially machine learning, can deliver faster and more reliable performance assessment. It introduces a hybrid framework that estimates a common set of weights for data envelopment analysis, enabling measurement evaluation and ranking while addressing non-convexity issues. Experiments on over 5400 Vietnamese MSMEs (2010–2016) show machine learning models outperform econometric methods in efficiency and accuracy.","Annals of Operations Research (2025) 350:555–577 [https://doi.org/10.1007/s10479-023-05230-8](https://doi.org/10.1007/s10479-023-05230-8)  \nORIGINAL RESEARCH  \nPredicting the performance of MSMEs: a hybrid DEA-machine learning approach  \nSabri Boubaker1,2,3 · Tu D. Q. Le4,5 · Thanh Ngo6,7 · Riadh Manita8  \nAccepted: 2 February 2023 / Published online: 14 February 2023 © The Author(s) 2023  \nAbstract  \nMicro, small and medium enterprises (MSMEs) dominate the business landscape and create more than half of employment worldwide. How we can apply big data analytical tools such as machine learning to examine the performance of MSMEs has become an important question to provide quicker results and recommend better and more reliable solutions that improve performance. This paper proposes a novel method for estimating a common set of weights (CSW) based on regression analysis for data envelopment analysis (DEA) as an important analytical and operational research technique, which (i) allows for measurement evaluations and ranking comparisons of the MSMEs, and (ii) helps overcome the time-consuming non-convexity issues of other CSW DEA methodologies. Our hybrid approach used several econometric and machine learning techniques (such as Tobit, least absolute shrinkage and selection operator, and Random Forest regression) to empirically explain and predict the performance of more than 5400 Vietnamese MSMEs (2010–2016), and showed that the machine learning techniques are more efficient and accurate than the econometric ones. Our study, therefore, sheds new light on the two-stage DEA literature, especially in terms of predicting performance in the era of big data to strengthen the role of analytics in business and management.  \nKeywords Machine learning (ML) · Common set of weights (CSW) · Data envelopment analysis (DEA) · Micro, small, and medium enterprise (MSME) · Efficiency  \nJEL Classification C61 · D24 · L60  \nB  \n1  \n2  \n3  \n4  \n5  \n6  \n7  \n8  \nThanh Ngo  \n[T.ngo@massey.ac.nz](T.ngo@massey.ac.nz)  \nEM Normandie Business School, Métis Lab, Paris, France International School, Vietnam National University, Hanoi, Vietnam Swansea University, Swansea, United Kingdom  \nUniversity of Economics & Law, Ho Chi Minh City, Vietnam Vietnam National University, Ho Chi Minh City, Vietnam  \nSchool of Aviation, Massey University, Palmerston North, New Zealand VNU University of Economics & Business, Hanoi, Vietnam  \nNEOMA Business School, Mont-Saint-Aignan, France  \n1 Introduction  \nMicro, Small and Medium Enterprises (MSMEs) play a key role in the global economy, accounting for about 90% of firms and creating more than 50% of employment worldwide (Ayyagari et al., 2003; IFC, 2012) . In developing countries such as Vietnam, most MSMEs operate in the manufacturing sector (CIEM, 2016; GSO, 2016; Rand & Tarp, 2020), contributing to about 36% of the national value-added (OECD, 2021) . It is therefore important to understand how efficiently MSMEs are operating and, especially, how to improve their performance. In the manufacturing sector, MSMEs are at the crossroads of technological advancement and operational excellence, where optimisation, Industry 4.0, and big data analysis are the buzzwords making the rounds (Schoenherr & Speier-Pero, 2015) . A key research question arising from this situation is how to apply big data analytical tools such as machine learning (ML) to examine the performance of MSMEs, not only in terms of providing quicker results (regarding big data) but also in terms of recommending better and reliable solutions for improving their performance. Despite the growing body of literature on the application of analytics to solving operational problems (Kamble et al., 2020; Manimuthu et al., 2021; Wamba et al., 2017), research on MSMEs, especially in the manufacturing sector in developing countries, is still limited.  \nData envelopment analysis (DEA) is a popular non-parametric tool for measuring efficiency and performance in various fields such as ba","cbCaiolLauF992SX","https://ap.wps.com/l/cbCaiolLauF992SX","pdf",552525,1,23,"English","en",105,"# Abstract\n# Introduction\n## Role of MSMEs and the need for performance improvement\n## DEA and efficiency measurement\n## Common set of weights and computational challenges","[{\"question\":\"What problem does the paper address about MSME performance evaluation?\",\"answer\":\"It addresses how to apply big-data analytics such as machine learning to evaluate MSME performance more quickly and reliably, especially for recommending better solutions.\"},{\"question\":\"How does the proposed hybrid approach relate to DEA and common set of weights?\",\"answer\":\"The method estimates a common set of weights using regression analysis within data envelopment analysis, supporting consistent measurement evaluation and ranking across MSMEs.\"},{\"question\":\"What datasets and results are used to validate the approach?\",\"answer\":\"The paper uses data from more than 5400 Vietnamese MSMEs during 2010–2016 and finds that machine learning techniques are more efficient and accurate than econometric approaches.\"}]","Predicting the performance of MSMEs - a hybrid DEA-machine learning approach | PDF",1785735517,58,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"predicting-the-performance-of-msmes-a-hybrid-dea-machine-learning-approach","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-the-performance-of-msmes-a-hybrid-dea-machine-learning-approach/121403/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper address about MSME performance evaluation?","Question",{"text":75,"@type":76},"It addresses how to apply big-data analytics such as machine learning to evaluate MSME performance more quickly and reliably, especially for recommending better solutions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed hybrid approach relate to DEA and common set of weights?",{"text":80,"@type":76},"The method estimates a common set of weights using regression analysis within data envelopment analysis, supporting consistent measurement evaluation and ranking across MSMEs.",{"name":82,"@type":73,"acceptedAnswer":83},"What datasets and results are used to validate the approach?",{"text":84,"@type":76},"The paper uses data from more than 5400 Vietnamese MSMEs during 2010–2016 and finds that machine learning techniques are more efficient and accurate than econometric approaches.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]