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The purpose of this paper is to present a case study showing how the selection of performance indices affects performance results and the evaluation of a firm's performance.

This paper employs a data envelopment analysis (DEA) framework using four kinds of performance indices selection, which include basic input/output items, balance scorecard (BSC) indices, balanced scorecard with risk management, and traditional financial indices, to evaluate banking operations.

Shows that a DEA‐based evaluation of performance produces a similar view of the firm's well‐being as does an analysis of financial indices; however, a BSC‐based evaluation produces a different assessment.

This study was based on the following assumptions: first, when organizational units achieve technical efficiency, they will improve their organizational performance. Secondly, the inputs and outputs selected for the data envelopment analysis provided an indicator of the changes of bank's technical efficiency over the six‐year period.

This research was based on the data envelopment analysis approach to find different performance efficiency to apply four performance indicator selections, which include basic inputs/outputs items, balanced scorecard indices, balanced scorecard with risk management, and traditional financial indices, to evaluate bank operation.

Combines the balanced scorecard concept with data envelopment analysis measurements (model information) to generate measures of technical efficiency for a Taiwanese bank. It shows how comparisons can be made within and across companies on the basis of balanced scorecard measures.

While market theorists have devoted a great deal of effort to the conceptualization of service quality, the practical guidance available to service providers continues to be very limited. Utilizing the emerging role of a new marketing entity, the retail electric service provider, as an illustration, the article discusses how data envelopment analysis might be used to analyze service quality at the retail service level. Specific dimensions thought to influence consumers’ perceptions of the quality of retail electric energy services are identified, and the potential use of data envelopment analysis as a diagnostic tool for effective management of service quality by retail electric service providers is demonstrated. Generalization to different types of service providers is suggested. Empirical studies to develop practical guidance along this line of analysis are encouraged.

The long tradition in the public sector of introducing decision-making tools that then fail to live up to expectations has fueled a debate over the proper role of government. This paper suggests that the debate over government productivity may be misplaced. Public productivity may be hindered as a result of inappropriate use of decisionmaking tools for the allocation of resources. Thus, this paper argues for an enlarged repertoire of decision-making techniques. In particular, data envelopment analysis (DEA) is presented as an alternative often more appropriate than such commonly used techniques as cost-benefit ratio and regression analyses.

This paper aims to evaluate commercial bank branches' performance in dynamic and competitive conditions where decision-making units (DMUs) seek a greater proportion of shared resources as it happens in the real world. By introducing the concepts of cross-shared and serial-shared resources, the authors have emphasized the role of evaluation results of past periods on branches' total efficiency.

In this study, a new mixed-integer data envelopment analysis (MI-DEA) model has been proposed to evaluate the performance of a dynamic network in the presence of cross-shared and serial-shared resources.

The proposed model helps bank managers to find the source of inefficiencies and establish a connection between the results of the periodic performance of the DMUs and the distribution of serial and cross-shared resources. The results show that the weighting coefficients of the periods do not significantly affect the overall efficiency of commercial bank branches, unlike desirable and undesirable intermediates.

This paper presents the following factors: (1) A new mixed-integer network data envelopment analysis model is developed under dynamic competitive conditions. (2) For the first time in DEA models, the concept of cross-shared resources is proposed to consider shared resources between DMUs. (3) All controllable, uncontrollable, desirable and undesirable outputs in the model are considered with the possibility to transfer to the next periods. (4) A case study is given for the performance evaluation of 38 branches of an Iranian commercial bank from 2016 to 2020.

Data envelopment analysis (DEA) and window analysis are used to follow the changes in Australian trading banks' pure technical efficiency, scale efficiency, and the nature of returns to scale. The main findings indicate declining average efficiency scores until 1991, followed by a steady rise thereafter. Pure technical inefficiency emerges as a greater source of inefficiency than scale inefficiency. Overall, regional banks exhibit increasing returns to scale and major trading banks exhibit decreasing returns to scale. Also worthy of note is the mixed size of banks operating at optimal returns to scale.

The importance of health care is growing worldwide, and the health sector receives a good proportion of GDP. As health care costs are increasing, it is important to assess the performance of health care facilities across countries. Therefore, this paper aims to examine the effectiveness of data envelopment analysis, which is one of the most important techniques currently available to model and measure the efficiency of national health care systems across the world.

In this paper, the effectiveness of the operation of 180 countries is evaluated using data envelopment analysis (DEA). The countries selected are the members of the World Health Organization. Efficiency measures are calculated with respect to two groups of indicators, namely health status and health service coverage. In the health status group, four outputs representing mortality rates and one input representing the total expenditure on health is used in the analysis. In the health service coverage group, two outputs representing immunisation coverage and one input representing the total expenditure on health are used in the analysis.

Using data for the year 2008 from World Health Statistics (WHO), 45 countries are found to be efficient.

The value of the paper lies in the fact that it is one of the few studies to examine the robustness of data envelopment analysis to evaluate the performance of health care systems across countries.

This study aims to empirically evaluate the performance of commercial banks operating in India.

The efficiency of the commercial banks is evaluated using the data envelopment analysis (DEA) approach. We measure the technical, pure technical and scale efficiency of the sampled conventional banks using the input-oriented model. We employed an extended DEA window analysis approach based on a panel sample of 47 banks in the Indian scenario. The period of study is from 2009 to 2018.

The results obtained from CRS and VRS measures envisage that Indian banks have failed to manage their inputs efficiently and convert them into outputs. It implies that Indian banks do not operate at an optimum level. Moreover, the results show that public banks exhibit superior efficiency scores followed by private and foreign banks. Apart from the aggregate sector level, we also investigate the performance of Indian banks at the individual level for in-depth analysis. The individual bank-level analysis reports that the public sector banks (PSBs) are the most efficient followed by foreign banks, whereas, the least efficient are the private banks.

The findings of our study have implications for government, financial institutions and policymakers to access the verve and flexibility of the Indian banking system. The government should consider restructuring inefficient banks to enhance overall performance. This can be considered by improvement in managerial efficiency, efficient allocation of scarce resources and appropriate scale of operation. However, the findings of the study should be interpreted in light of the period of study for the banks being operational (as we filter out banks that ceased to exist) in India and empirical methods employed. The results may vary if alternative measures are used.

The present paper investigates the efficiency of the Indian banking sector employing the Data Envelopment Window Analysis (DEWA) technique. To the best of our knowledge, the present study is perhaps the first one to employ the DEWA measure on the Indian banking industry to gauge their performance over time.

Proper performance measurement is an important issue in library operational management. A data envelopment analysis (DEA) model is applied to evaluate the relative operational efficiency of 25 U.S. private research-university library members of the Association of Research Libraries (ARL). Operations of each library decision-making unit are considered as a production process using four resource input and four service output variables. The model results are analyzed and compared with the efficient group and a peer group by using a t-test. The model provides decision-makers with more accurate information to implement better library services with appropriate resource allocation.

The chapter examines a comprehensive review of cross-disciplinary literature in the domain of supply chain forecasting during research period 1991–2017, with the primary aim of exploring the growth of literature from operational to demand centric forecasting and decision making in service supply chain systems. A noted list of 15,000 articles from journals and search results are used from academic databases (viz. Science Direct, Web of Sciences). Out of various content analysis techniques (Seuring & Gold, 2012), latent sementic analysis (LSA) is used as a content analysis tool (Wei, Yang, & Lin, 2008; Kundu et al., 2015). The reason for adoption of LSA over existing bibliometric techniques is to use the combination of text analysis and mining method to formulate latent factors. LSA creates the scientific grounding to understand the trends. Using LSA, Understanding future research trends will assist researchers in the area of service supply chain forecasting. The study will be beneficial for practitioners of the strategic and operational aspects of service supply chain decision making. The chapter incorporates four sections. The first section describes the introduction to service supply chain management and research development in this domain. The second section describes usage of LSA for current study. The third section describes the finding and results. The fourth and final sections conclude the chapter with a brief discussion on research findings, its limitations, and the implications for future research. The outcomes of analysis presented in this chapter also provide opportunities for researchers/professionals to position their future service supply chain research and/or implementation strategies.

Demands for accountability in education are not a new phenomenon, however, they have increased significantly in the recent past and have encompassed not only educational outcomes but also efficiency. In this study, ratio measures, similar to those recommended by the GASB, were compared to measures of relative efficiency determined through the use of data envelopment analysis (DEA). The consistency of the two approaches in distinguishing between relatively efficient and inefficient school districts was examined. It was found that compared to the DEA approach, the ratio measures, may be unable to provide reliable information for educational decision making.