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Predicting the performance of MSMEs: a hybrid DEA-machine learning approach
,
Tu D. Q. Le,
Thanh Ngo ,
Riadh Manita
Annals of Operations Research, Volume: 350, Issue: 2, Pages: 555 - 577
Swansea University Author:
Sabri Boubaker
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DOI (Published version): 10.1007/s10479-023-05230-8
Abstract
Micro, 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 recom...
| Published in: | Annals of Operations Research |
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| ISSN: | 0254-5330 1572-9338 |
| Published: |
Springer Nature
2025
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa70080 |
| Abstract: |
Micro, 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. |
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| Keywords: |
Machine learning (ML); Common set of weights (CSW); Data envelopment analysis (DEA); Micro, small, and medium enterprise (MSME); Efficiency |
| College: |
Faculty of Humanities and Social Sciences |
| Funders: |
Open Access funding enabled and organized by CAUL and its Member Institutions. |
| Issue: |
2 |
| Start Page: |
555 |
| End Page: |
577 |