Journal Articles

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

Browse

Subject: search.filters.subject.Efficiency

Search Results

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    From Efficiency Analyses to Policy Implications: a Multilevel Hierarchical Linear Model Approach
    (Taylor and Francis Group, 2021-09-25) Dao TTT; Mai XTT; Ngo T; Le T; Ho H
    This paper examines the key factors that influenced the cost efficiency of 7,633 Vietnamese manufacturing firms during 2010–2016 via a hierarchical linear modelling (HLM) approach. The main reason for using HLM in this case is that observations in the same group may not be independent from each other (e.g. firms operate within the same city), and some variables may not vary across those observations. Although most of the findings are consistent with previous studies, the statistical power of our HLM model is higher than that of the traditional single-level analysis, suggesting that HLM can provide better analytical insights. The results further indicate a case for cities or provinces pursuing different policies aimed at improving the performance of their local firms.
  • Thumbnail Image
    Item
    Decomposing productivity and efficiency of Western Australian grain producers
    (Western Agricultural Economics Association, 2013) Tozer PR; Villano R
    We provide empirical evidence to decompose productivity growth of a group of producers into technical change and efficiency measures at the farm level. Using four years of farm-level data from forty-five grain producers in the low- to medium-rainfall zone of Western Australia, we decompose productivity numbers to analyze total factor productivity. The results show that producers are generally technical, mix, and scale efficient, but the results for input and output mix efficiencies vary. The outcomes for input mix efficiency suggest that producers face some rigidity in their production decisions. In contrast, output mix efficiency suggests that most producers adjust their output mixes to account for different seasonal conditions and enterprise mixes.
  • Thumbnail Image
    Item
    Impact of gender and governance on microfinance efficiency
    (Elsevier, 2018-03) Bibi UB; Ozer-Balli H; Matthews CM; Tripe DT
    This study examines the efficiency of South Asian microfinance institutions (MFIs) using Data Envelopment Analysis. Bias corrected efficiency estimates for the individual MFIs are regressed on a set of explanatory variables (including governance and gender) employing the double bootstrap truncated regression approach (Simar & Wilson, 2007) and panel data regression. First stage results suggest that South Asian MFIs are more financially efficient than socially efficient. More precisely, we find that these MFIs are technically inefficient but scale efficient, and that there was some improvement in financial efficiency over time. The relatively low average efficiency scores show that there is quite a bit of variation in microfinance efficiency. Second stage regression reveals that female loan officers are positive determinants of MFIs’ efficiency. We find a strong association between a MFI’s governance and its financial and social efficiency.