[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121369-en":3,"doc-seo-121369-105":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},121369,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Predicting the performance of MSMEs - a hybrid DEA-machine learning approach","Micro, small and medium enterprises (MSMEs) are central to job creation worldwide, making reliable performance evaluation and forecasting a key management need. This study integrates machine learning with data envelopment analysis (DEA) by proposing a hybrid framework that estimates a common set of weights via regression to support measurement and ranking. Using econometric and ML methods including Tobit, LASSO, and Random Forest on over 5,400 Vietnamese MSMEs (2010–2016), the paper finds ML models outperform econometric alternatives, strengthening two-stage DEA in the big data era.","Annals of Operations Research  \n[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 © 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 3  \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 banking, healthcare, and aviation (Adler et al., 2","cbCaiby7BVvs5vo3","https://ap.wps.com/l/cbCaiby7BVvs5vo3","pdf",563809,1,23,"English","en",105,"# Abstract\n# Keywords\n# Introduction\n## Role of MSMEs and need for performance prediction\n## DEA for efficiency measurement and benchmarking\n## Limitations of DEA and motivation for CSW\n## Two-stage DEA and environmental factors","[{\"question\":\"What problem does the paper address for MSMEs?\",\"answer\":\"It addresses how to use big data analytics and machine learning to examine MSME performance and provide quicker, more reliable improvement recommendations.\"},{\"question\":\"How does the hybrid approach combine DEA with machine learning?\",\"answer\":\"It estimates a common set of weights (CSW) using regression to enable consistent DEA-based measurement and ranking, then applies multiple econometric and machine learning techniques to explain and predict performance.\"},{\"question\":\"Which methods were used to predict the performance of Vietnamese MSMEs, and what was the result?\",\"answer\":\"The study uses Tobit, LASSO, and Random Forest regression on 5,400+ Vietnamese MSMEs (2010–2016), showing machine learning techniques are more efficient and accurate than the econometric ones.\"}]","Predicting the performance of MSMEs - a hybrid DEA-machine learning approach | PDF",1785735292,58,{"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/121369/",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 for MSMEs?","Question",{"text":75,"@type":76},"It addresses how to use big data analytics and machine learning to examine MSME performance and provide quicker, more reliable improvement recommendations.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the hybrid approach combine DEA with machine learning?",{"text":80,"@type":76},"It estimates a common set of weights (CSW) using regression to enable consistent DEA-based measurement and ranking, then applies multiple econometric and machine learning techniques to explain and predict performance.",{"name":82,"@type":73,"acceptedAnswer":83},"Which methods were used to predict the performance of Vietnamese MSMEs, and what was the result?",{"text":84,"@type":76},"The study uses Tobit, LASSO, and Random Forest regression on 5,400+ Vietnamese MSMEs (2010–2016), showing machine learning techniques are more efficient and accurate than the econometric ones.","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"]