Two-stage production modeling of large U.S. banks: A DEA-neural network approach

Kwon, He‐Boong; Lee, Jooh

doi:10.1016/j.eswa.2015.04.062

Cited by 87 publications

(44 citation statements)

References 57 publications

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“…2 for a flow chart of a typical two-stage analysis (e.g., Rangan et al 1988;Elyasiani and Mehdian 1990;Aly et al 1990;Favero and Papi 1995;Miller and Noulas 1996;Bhattacharyya et al 1997;Chen 1998;Chu and Lim 1998;Barr et al 1994;Barr and Siems 1997;Pasiouras 2008;Wanke and Barros 2014;Kwon and Lee 2015;Du et al 2018). Note however that the efficiency scores obtained with a two-stage analysis would still be environmentally-biased, because the inputs and outputs used in the first stage are not adjusted for environment.…”

Section: Landscape Of Research On Efficiency Assessment In Bankingmentioning

confidence: 99%

“…The first category consists of single country focused studies and covers US Banks (Rangan et al 1988;Ferrier and Lovell 1990;Mehdian 1990, 1992;Aly et al 1990;Yue 1992;Miller and Noulas 1996;Kwon and Lee 2015), UK Banks (Drake 2001;Webb 2003;Webb et al 2010;Tanna et al 2011), Italian Banks (Favero and Papi 1995), Turkish Banks (Zaim 1995;Kutlar et al 2017), Japanese Banks (Fukuyama 1993;Liu and Tone 2008), Taiwanese Banks (Chen 1998;Liu 2018), Hong Kong Banks (Drake et al 2006), Singaporean Banks (Chu and Lim 1998), Indian Banks (Bhattacharyya et al 1997), Mozambique Banks (Wanke et al 2016), and Korean Banks (Lee et al 2017). The second category consists of multi-country focused studies and covers banks in several countries such as US, Australian, New Zealand, Austrian, Spanish, German, UK, Italian, Belgian, French, Danish, Luxembourg, Dutch, and Portuguese Banks (e.g., Pastor et al 1997;Pastor 2002;Lozano-Vivas et al 2002;Casu and Molyneux 2003;Pasiouras 2008;Avkiran 2009).…”

Section: Landscape Of Research On Efficiency Assessment In Bankingmentioning

confidence: 99%

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Assessing efficiency profiles of UK commercial banks: a DEA analysis with regression-based feedback

Ouenniche

Carrales

2018

Ann Oper Res

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Data envelopment analysis (DEA) has witnessed increasing popularity in banking studies since 1985. In this paper, we propose a new DEA-based analysis framework with a regression-based feedback mechanism, where regression analysis provides DEA with feedback that informs about the relevance of the inputs and the outputs chosen by the analyst. Unlike previous studies, the DEA models used within the proposed framework could use both inputs and outputs, only inputs, or only outputs. So far, the UK banking sector remains relatively under researched despite its crucial importance to the UK economy. We use the proposed framework to address several research questions related to both the efficiency of the UK commercial banking sector and DEA analyses with and without regression-based feedback. Empirical results suggest that, on average, the commercial banks operating in the UK-whether domestic or foreign-are yet to achieve acceptable levels of overall technical efficiency, pure technical efficiency, and scale efficiency. On the other hand, DEA analyses with and without a linear regression-based feedback mechanism seem to provide consistent findings; however, in general DEA analyses without feedback tend to over-or under-estimate efficiency scores depending on the orientation of the analyses. Furthermore, in general, a linear regression-based feedback mechanism proves effective at improving discrimination in DEA analyses unless the initial choice of inputs and outputs is well informed.Keywords Data envelopment analysis · Efficiency · UK commercial banks · DEA models without explicit inputs · DEA models without explicit outputs

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Section: Landscape Of Research On Efficiency Assessment In Bankingmentioning

confidence: 99%

Section: Landscape Of Research On Efficiency Assessment In Bankingmentioning

confidence: 99%

Assessing efficiency profiles of UK commercial banks: a DEA analysis with regression-based feedback

Ouenniche

Carrales

2018

Ann Oper Res

View full text Add to dashboard Cite

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“…Esmaeili and Horri (2014) used the fuzzy data envelopment analysis (DEA) techniques for evaluating and ranking the efficiency of online customer satisfaction index in eight economic banks in Iran. Kwon and Lee (2015) proposed a DEA-BPNN approach to model a two-stage production process. The proposed model enhanced a two-stage DEA by adding predictive power through artificial neural network.…”

Section: Application Of Dea Methods In Banking Industriesmentioning

confidence: 99%

A Hybrid Approach Based on Multi-Criteria Satisfaction Analysis (MUSA) and a Network Data Envelopment Analysis (NDEA) to Evaluate Efficiency of Customer Services in Bank Branches

Khalili‐Damghani¹,

Taghavifard²,

Karbaschi³

2015

Industrial Engineering and Management Systems

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A hybrid procedure based on multi-Criteria Satisfaction Analysis (MUSA) and a Network Data Envelopment Analysis (NDEA) is proposed to evaluate the relative efficiency of customer services in bank branches. First, a three-stage process including sub-processes such as customer expectations, customer satisfaction, and customer loyalty, is defined to model the banking customer services. Then, fulfillment of customer expectations, customer loyalty level, and the customer satisfaction degree are measured and quantified through a multi-dimensional questionnaire based on customers' perceptions analysis and MUSA method, respectively. The customer services scores and the other criteria such as mean of employee evaluation score, operation costs, assets, deposits, loans, number of accounts are considered in network three-stage DEA model. The proposed NDEA model is formed based on multipliers perspective, outputoriented, and constant return to scale assumptions. The proposed NDEA model quantifies and assesses the total efficiency of main process and assigns the efficiency to customer expectations, customer satisfactions, and customer loyalties sub-processes in bank branches. The whole procedure is applied on 30 bank branches in IRAN. The proposed approach can be used in other organizations such as airports, airline agencies, urban transportation systems, railway organizations, chain stores, chain restaurants, public libraries, and entertainment centers.

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“…It has a wide application and has been used to evaluate efficiency in many fields, such as financial sector [17][18][19][20], police force performance [21], resource allocation [22,23], environmental efficiency [24,25], or electricity sector [26,27].…”

Section: Theoretical Backgroundsmentioning

confidence: 99%

Assessing the Efficiency of Public Universities through DEA. A Case Study

2017

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This paper presents the results of an efficiency study of Colombian public universities in 2012, conducted using the methodology of Data Envelopment Analysis (DEA) and the models CCR, BCC and SBM under output orientation. The main objective is to determine technical, pure technical, scale and mix efficiencies using data acquired from the Ministry of National Education. An analysis of the results shows the extent to which outputs of inefficient Higher Education Institutions (HEIs) could be improved and the possible cause of this inefficiency. The universities were also ranked using a Pareto efficient cross-efficiency model and a study was made of changes to overall productivity between 2011 and 2012. The results showed Tolima, Caldas and UNAD to be the best-performing universities, with Universidad del Pacífico as the worst performer.Malmquist index was applied to analyze the change in productivity from

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Two-stage production modeling of large U.S. banks: A DEA-neural network approach

Cited by 87 publications

References 57 publications

Assessing efficiency profiles of UK commercial banks: a DEA analysis with regression-based feedback

Assessing efficiency profiles of UK commercial banks: a DEA analysis with regression-based feedback

A Hybrid Approach Based on Multi-Criteria Satisfaction Analysis (MUSA) and a Network Data Envelopment Analysis (NDEA) to Evaluate Efficiency of Customer Services in Bank Branches

Assessing the Efficiency of Public Universities through DEA. A Case Study

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