Branch banking network assessment using DEA: A benchmarking analysis—A note

Efficiency of the Czech Banking Sector Employing the DEA Window Analysis Approach

Abstract: Measuring the efficiency of a national park is difficult due to, among other factors, the heterogeneity of resources supplied (e.g., budget, staffs) and outcomes expected (e.g., income, visitors’ flow). While this is an issue in protected area management, it has been approached successfully in other fields by using data envelopment analysis (DEA). DEA has a number of advantages over other techniques as it simultaneously uses multiple heterogeneous inputs and outputs to determine which projects are performing most efficiently, referred to as being at the efficiency frontier, when compared to others in the data set. This study therefore uses DEA for the evaluation of management efficiency in Old Oyo National Park for the period of 2001-2015. The results showed that the park was efficient for 11 and 13 years, respectively, in terms of its overall technical and pure technical efficiency with a mean scale efficiency of 97%. Also, the park operated at 80% of its productive scale size. These results, and the use of DEA, highlight both the success of using this technique in helping determine protected area efficiency and those factors to consider while allocating resources for new projects at the park.

PurposeThe paper aims to enrich previous findings for an emerging banking industry such as Vietnam, reporting the difference between the parametric and nonparametric methods when measuring cost efficiency. The purpose of the study is to assess the consistency in issuing policies to improve the cost efficiency of Vietnamese commercial banks.Design/methodology/approachThe cost efficiency of banks is assessed through the data envelopment analysis (DEA) and the stochastic frontier analysis (SFA). Next, five tests are conducted in succession to analyze the differences in cost efficiency measured by these two methods, including the distribution, the rankings, the identification of the best and worst banks, the time consistency and the determinants of efficiency frontier. The data are collected from the annual financial statements of Vietnamese banks during 2005–2017.FindingsThe results show that the cost efficiency obtained under the SFA models is more consistent than under the DEA models. However, the DEA-based efficiency scores are more similar in ranking order and stability over time. The inconsistency in efficiency characteristics under two different methods reminds policy makers and bank administrators to compare and select the appropriate efficiency frontier measure for each stage and specific economic conditions.Originality/valueThis paper shows the need to control for heterogeneity over banking groups and time as well as for random noise and outliers when measuring the cost efficiency.

Data envelopment analysis (DEA) is a method for measuring efficiency of peer decision-making units (DMUs). Conventional DEA evaluates the performance of each DMU using a set of most favourable weights. As a result, traditional DEA models can be considered methods for the analysis of the best relative efficiency or analysis of the optimistic efficiency. DEA efficient DMUs obtained from conventional DEA models create an efficient production frontier. Traditional DEA can be used to identify units with good performance in the most desirable scenarios. There is a similar approach that evaluates the performance indicators of each DMU using a set of most unfavourable weights. Accordingly, such models can be considered models for analysing the worst relative efficiency or pessimistic efficiency. This approach uses the inefficient production frontier for determining the worst relative efficiency that can be assigned to each DMU. DMUs lying on the inefficient production frontier are referred to as DEA inefficient while those neither on the efficient frontier nor on the inefficient frontier are declared DEA inefficient. It can be argued that both relative efficiencies should be considered simultaneously and any approach with only one of them would be biased. This paper proposed the integration of both efficiencies as an interval so that the overall performance score would belong to this interval. It was shown that efficiency interval provided more information than either of the two efficiencies, which was illustrated using two numerical examples.

This paper examines the strategic fit of mergers and acquisitions (M&A) in Mozambican banks, using a two‐stage data envelopment analysis (DEA) approach to compute the impact of contextual variables on efficiency scores and returns to scale of the resulting virtual merged banks. In an ‘M&A DEA’ model, different pairs of bidder and target companies are considered. In the first stage an M&A DEA model is used. Simplex regression is adopted in the second stage. The results reveal that ownership (public or foreign) impacts virtual efficiency levels. However, the findings also show that care must be exercised with M&A because the resultant banking organization could be oversized when foreign ownership is predominant, especially when public ownership is low. Given the relative sizes of the markets in terms of productive resources, M&A involving Mozambican banks can easily lead to decreasing returns to scale. Thus, a greater emphasis should be given to merging purely public banks with foreign ones and vice versa.

Purpose– The purpose of this paper is to measure the efficiencies of parallel subsystems with shared inputs/outputs. Each subsystem has not only a set of common inputs and outputs, but also some dedicated inputs and outputs as well as some shared inputs and outputs. A more general data envelopment analysis (DEA) approach is proposed to deal with this efficiency evaluation issue. Based on the proposed approach, mechanisms for shared inputs/outputs distribution and efficiency decomposition among sub-units are presented.Design/methodology/approach– To evaluate the efficiency of the parallel systems, this paper proposes a centralized DEA approach by assuming that the same input/output factor in a decision-making unit (DMU) has the same multiplier for all its sub-units. Furthermore, different proportions of shared inputs/outputs are imposed on sub-units within different DMUs in evaluating each DMU’s efficiency. The proposed approach is applied to evaluate the operational efficiencies of 18 railway firms in China.Findings– By using the proposed DEA approach, the efficiencies of the whole DMU and its sub-units can be measured at the same time, and the optimal allocation strategy of shared inputs/outputs can also be obtained. The proposed model is more reasonable and robust for measuring the operational performance of parallel systems with shared inputs and outputs. The efficiency of railway system in China is relatively low, and its inefficiency is largely caused by lower freight transportation performance. Great disparities among firms can be found in the passenger transportation efficiency and freight transportation efficiency.Research limitations/implications– This study develops the DEA model under the assumption of constant returns to scale, which can be directly extended to a situation with variable returns to scale.Practical implications– In this paper, the proposed approach is a more effective way to evaluate the efficiencies of parallel systems with shared inputs/outputs. With respect to the application, to improve the overall efficiency of China’s railway system, more efforts should be taken to improve its operational performance of freight transportation. Furthermore, firms’ disparities should also be considered when making these related policies.Originality/value– The proposed approach can evaluate the whole DMU and its sub-units at the same time. Considering simultaneously the common/dedicated/shared inputs/outputs, the proposed approach is more general than the existing approaches in the literature. In the described approach, the same type of input or output is assumed to have the same weight for all sub-units within one DMU. More importantly, the proposed model imposes different proportions of shared inputs/outputs on different DMUs’ sub-units when measuring the efficiency for each DMU.

Data envelopment analysis (DEA) is a mathematical method based on production theory and the principles of linear programming. It enables one to assess how efficiently a firm, organization, agency, or such other unit uses the resources available (inputs) to generate a set of outputs relative to other units in the data set. Recent papers by different writers present different applications of DEA in the transportation engineering domain. All of these papers are published in transportation journals. These papers are mainly aimed at addressing the transportation-related issues and thus do not focus too much on the DEA concept itself. It can be asserted that DEA is very likely to be used more and more in the transportation engineering domain. Given this, there is a need for the transportation professionals to fully understand the DEA concept. It is essential for such a community to identify cases where the application of this innovative and powerful method can be useful to help the decision-making process, to accurately apply DEA in a particular setting, to derive meaningful conclusions from the obtained results, and to acknowledge the limitations of DEA in certain cases so as to approach the results with caution. The purpose of this paper is to illustrate to the civil engineering, more specifically to the transportation engineering community the use of this powerful approach in performing comparative performance measurement. Within this context, this paper will address a transportation-related problem by using the DEA approach. Different from the other papers containing transportation-related DEA applications (as mentioned above), this paper will discuss, in detail, the steps that need to be taken to generate the DEA model and solve it.

The main aim of the study was to evaluate the efficiency of Islamic Azad University branches in East Azerbaijan using stochastic frontier analysis (SFA) and to compare the findings with the results of data envelopment analysis (DEA). In this respect, SFA and DEA techniques were applied to measure production efficiency of 28 branches of East Azerbaijan Islamic Azad University considering the following variables as either output or input: 3 variables as output (including the number of graduated students and students accepted for the further pursuit of their studies, as well as the number of articles, books, and research projects) and 6 variables as input (including the number of university faculty members, educational fields, current students, staff working in different sections of the university, and the total university campus in square meters as well as university costs). Estimation of DEA and obtaining of the units' efficiency indicated that the average efficiency of the universities under investigation were 0.66 and 0.80 in SFA and DEA techniques, respectively. In addition, efficiency distributions in SFA and DEA methods were tremendously different from each other so that in SFA method nearly 40% while in DEA method more than 60% of unit efficiency was between 0.8-1 intervals. The minimum level of estimated efficiency (0.14) was observed for Hadishahr using SFA method, and in DEA approach, the minimum level of efficiency belonged to Khoda Afarin branch (0.065).

In this paper are examined impact of share of non performing loans and cost to income ratio to banks’ profitability measured through rate of return of average assets in macedonian banking sector. For that aim, have been used date on quarter basis from 2010 until Q3 2019 which are grouped according to the size of assets of banks i.e large, medium and small. In the analysis have been used descriptive statistics and models for estimating multiple regression using the method of ordinary least squares. Results from the analysis show that share of non performing loans and cost to income ratio has significant statistical impact to rate of return on average assets. With increasing the share of non performing loans and with increasing the cost to income ratio, are decreasing the rate of return on average assets in all three groups of banks, and that is predominantly showed in group of large banks in macedonian banking sector. Thus, in traditional banking model is confirmed that the credit portfolio plays a dominant role in the operation of the banks. Loans are a major source of banks’ revenues, but at the same time they are also connected with highest risks. Problems in the credit portfolio are basic generators of problems, also in the other areas of banks’ operations and determine the profitable positions of the banks.

The aim of the present paper is to demonstrate the viability of using data envelopment analysis (DEA) in a regional context to evaluate entrepreneurial activities. DEA was used to assess regional entrepreneurship in Greece using individual measures of entrepreneurship as inputs and employment rates as outputs. In addition to point estimates, a bootstrap algorithm was used to produce bias-corrected metrics. In the light of the results of the study, the Greek regions perform differently in terms of converting entrepreneurial activity into job creation. Moreover, there is some evidence that unemployment may be a driver of entrepreneurship and thus negatively affects DEA-based inefficiency. The derived indicators can serve as diagnostic tools and can also be used for the design of various interventions at the regional level.

Performance assessment of major global cities by DEA and Malmquist index analysis

DEA approach for unified efficiency measurement: Assessment of Japanese fossil fuel power generation

European countries have been continuously under the pressure to improve public balances and efficiency of public spending. Economic crisis which started during 2007 weakened public finances at the state and local level in countries all over the world. In Croatia local government budgets are still below the pre-crisis level in many local government units. This paper empirically examines efficiency of public expenditures at the regional level. Performance has been investigated by developing a composite indicator of output. Spending efficiency at the regional level was analysed using Data Envelopment Analysis methodology. Results suggest that there are large differences at the regional level in using resources to provide public services. The results show that the local government units in the least efficient county should on average decrease their expenses by 55 percent, while achieving the same performance to become efficient.

PurposeThe purpose of this paper is to propose a benchmarking framework to evaluate the efficiency and effectiveness of the hotel industry, in a multi‐period context, with consideration of perishable traits and carry‐over activities. The sustained high performers in the case study are identified and their business strategies are discussed.Design/methodology/approachThe dynamic DEA (data envelopment analysis) approach is used to identify the multi‐period sustained high performers. The super‐efficiency DEA approach is employed to conduct a thorough ranking under an input‐output‐consumption structure. The supplementary analysis is further implemented to help elucidate the benchmarking results.FindingsIn total, nine out of 80 international tourist hotels in Taiwan during 2006‐2010 are identified as the sustained high performers. These hotels have diverged business strategies in terms of employees (intensive versus economical labor forces), products (room versus F&B (food and beverage) services), prices (extremely expensive versus very inexpensive room rates), guests (business versus tourism guests), and others (e.g. location advantages).Originality/valueThis study contributes to benchmarking literature and to hotel industry in several aspects. Compared with conventional single‐period benchmarking in efficiency aspect only, the proposed multi‐period benchmarking framework under input‐production‐consumption structure can take into account the carry‐over activities, account for perishable traits, provide more robust results, and add more useful information to facilitate the hotel managers to ameliorate the efficiency and effectiveness. The proposed framework should be readily applied to other service industries (e.g. transport).

Industry 4.0 (I4.0) technologies and relevant research initiatives have been at the focal point of sustainable industrial development initiatives. Adoption of these technologies require a maturity level to create sustainable economic, social, and environmental benefits to society. In this study, we investigated the I4.0 maturity in OECD countries. A two-phase methodology is proposed: principal component analysis (PCA) and data envelopment analysis (DEA). The main contribution of the study to the state-of-art is a statistically reliable analytical framework which yields I4.0 maturity score from relevant United Nations Sustainable Development Goals’ perspectives. Results indicate that the proposed two-phase method significantly reduces the potential multi-collinearity impacts on I4.0 maturity performance. Moreover, USA, Sweden, Finland, and Switzerland were found to the on the efficiency frontier in terms of I4.0 maturity whereas Turkey, Chile, Latvia, and Mexico were found to be in the lowest ranks which need substantial policy implementation to increase their digitalization efforts.