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Predicting the performance of MSMEs: a hybrid DEA-machine learning approach by Sabri Boubaker; Tu D. Q. Le; Thanh Ngo; Riadh Manita is a Engineering article available to read on EtoBox.

What is Predicting the performance of MSMEs: a hybrid DEA-machine learning approach about?

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 (2010MSMEs ( -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 l

Who reads Predicting the performance of MSMEs: a hybrid DEA-machine learning approach?

Author

Sabri Boubaker; Tu D. Q. Le; Thanh Ngo; Riadh Manita

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Engineering (Social Sciences)