Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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Start date: 2023Subject: search.filters.subject.Data envelopment analysis (DEA)

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    Shadow economy and energy efficiency: utilising goal programming for sustainability assessment
    (Springer Science+Business Media, LLC, 2025-08-07) Alharbi SS; Boubaker S; Ngo T; Yuen MK
    This paper combined different methods of operations research, goal programming, and unsupervised machine learning into a single framework to examine energy efficiency across the globe. Using the latest data from 131 countries in 2017, our empirical findings reveal different patterns of energy efficiency among countries and country groups under both the meta-frontier and group-frontiers. We found an inequality in production technology for many countries, which made it difficult for them to improve their energy efficiency. Importantly, our analysis also reveals that the size of the shadow economy has a small but negative impact on energy efficiency. Consequently, we suggest that governments should (i) pay more attention to the shadow economy, (ii) increase investments in education and human capital, and (iii) strengthen their institutions.
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    Predicting the performance of MSMEs: a hybrid DEA-machine learning approach
    (Springer Science+Business Media, LLC, 2023-02-14) Boubaker S; Le TDQ; Ngo T; Manita R
    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.