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This paper aims to promote the higher quality development of high-tech enterprises in China. While science and technology have greatly promoted human civilization, resources have been excessively consumed and the environment has been sharply polluted. Therefore, it is particularly important for current enterprises to make use of scientific and technological innovation to maximize the benefits of mankind, minimize the loss of nature, and promote the sustainable development of our country.

By using DEA-Banker-Charnes-Cooper (BCC) model and DEA-Malmquist model, this paper comprehensively examines the innovation efficiency of high-tech enterprises from both static and dynamic perspectives, and conducts a provincial comparative study with the panel data of ten representative provinces from 2011 to 2020.

The research findings are as follows: the rapid number increase of high-tech enterprises in most provinces (cities) is accompanied by an ineffective input–output efficiency; the quality of high-tech enterprises needs to comprehensively examine both input–output efficiency and total factor productivity; and there is not a positive correlation between element investment and innovation performance.

Because the DEA model used in this paper assumes that the improvement direction of invalid units is to ensure that the input ratio of various production factors remains unchanged but sometimes the proportion of scientific and technological activities personnel and the total research and development investment is not constant. In the future, the nonradial DEA model can be considered for further research. Due to historical data statistics, more provinces, cities and longer panel data are difficult to obtain. The samples studied in this paper mainly refer to the provinces and cities that ranked first in the number of national high-tech enterprises in 2020. Limited by the number of samples, DEA analysis failed to select more input and output indicators. In the future, with the accumulation of statistical data, the existing efficiency analysis will be further optimized.

Aiming at the misunderstanding of emphasizing quantity and neglecting quality in the cultivation of high-tech enterprises, this paper comprehensively uses DEA-BCC model and DEA Malmquist index decomposition method to make a comprehensive comparative study on the development of high-tech enterprises in ten representative provinces (cities) from two aspects of static efficiency evaluation and dynamic efficiency evaluation.

This chapter provides a survey of alternative methodologies for measuring and comparing productivity and efficiency of airlines, and reviews representative empirical studies. The survey shows the apparent shift from index procedures and traditional OLS estimation of production and cost functions to stochastic frontier methods and Data Envelopment Analysis (DEA) methods over the past three decades. Most of the airline productivity and efficiency studies over the last decade adopt some variant of DEA methods. Researchers in the 1980s and 1990s were mostly interested in the effects of deregulation and liberalization on airline productivity and efficiency as well as the effects of ownership and governance structure. Since the 2000s, however, studies tend to focus on how business models and management strategies affect the performance of airlines. Environmental efficiency now becomes an important area of airline productivity and efficiency studies, focusing on CO₂ emission as a negative or undesirable output. Despite the fact that quality of service is an important aspect of airline business, limited attempts have been made to incorporate quality of service in productivity and efficiency analysis.

Due to its high leverage nature, a bank suffers vitally from the credit risk it inherently bears. As a result, managing credit is the ultimate responsibility of a bank. In this chapter, we examine how efficiently banks manage their credit risk via a powerful tool used widely in the decision/management science area called data envelopment analysis (DEA). Among various existing versions, our DEA is a two-stage, dynamic model that captures how each bank performs relative to its peer banks in terms of value creation and credit risk control. Using data from the largest 22 banks in the United States over the period of 1996 till 2013, we have identified leading banks such as First Bank systems and Bank of New York Mellon before and after mergers and acquisitions, respectively. With the goal of preventing financial crises such as the one that occurred in 2008, a conceptual model of credit risk reduction and management (CRR&M) is proposed in the final section of this study. Discussions on strategy formulations at both the individual bank level and the national level are provided. With the help of our two-stage DEA-based decision support systems and CRR&M-driven strategies, policy/decision-makers in a banking sector can identify improvement opportunities regarding value creation and risk mitigation. The effective tool and procedures presented in this work will help banks worldwide manage the unknown and become more resilient to potential credit crises in the 21st century.

As state funding for public higher education has declined, there is a rising demand for accountability. Past studies have relied on indicator ratios to look at the relationship between funding and performance measures. This approach has some inherent problems that make it difficult to identify inefficiencies. This chapter will study efficiency in state systems of higher education by applying data envelopment analysis (DEA). DEA methodology converts multiple variables into a single comprehensive measure of performance efficiency and has the ability to perform benchmarking for the purpose of establishing performance goals. The advantages of DEA modeling will be shown by comparing results with those from a recent study of higher education finance based on publicly available data. DEA is shown to be feasible and implementable for studying state systems of higher education, and provides useful information in identifying “best practice” state systems and guidance for improvement. The value of DEA modeling to state policy makers and education researchers is discussed.

As cable and satellite industry undergoes transformation in the 21st century with the onslaught of innovation-driven changes, it is important to know which company is doing better and which company is falling behind. This study compares the relative performance of eight cable companies using three factors: operating expense for every dollar of operating revenue, earnings before interest, taxes, depreciation, and amortization, and return on assets. We also evaluate the performance of each firm against itself for the period 2010–2013 to see if they show improvement or deterioration in operating efficiency.

Data Envelopment Analysis (DEA) is a nonparametric mathematical programming technique used to measure the relative efficiency of the production organization's operations. This paper presents the theoretical measures of the railway systems, along with the bootstrap DEA analysis. A DEA model is applied to evaluate the relative efficiency of railway operations of 29 UIC (Union Internationale des Chemins de fer) countries, based on the data obtained from the International UIC publications. The bootstrap DEA analysis provides information (bias estimates) on the sensitivity of the DEA efficiency index to the sampling variations. The model results are analyzed and evaluated in terms of their relative operational performance efficiency. The model results facilitate an organization's decision-making by providing valuable information.

This study develops a multidimensional framework using data envelopment analysis (DEA) as a benchmarking tool to assess the performance of the commercial banks in India. Using the DEA approach, this study compares the relative performance of 35 banks against one another with 8 variables as the benchmark parameters. This study finds that most of the banks are consistently performing well over a period from 2005 to 2009. The study also shows the areas in which inefficient banks are lagging behind and how they can improve their performance to bring them at par with the efficient commercial banks.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

This chapter examines the impact of recent airline consolidations in the United States on the technical efficiencies of the airlines involved. Data envelopment analysis (DEA) is used to assess the efficiencies, and the consolidations examined are those that occurred among major network carriers between 2005 and 2013. The airline production process is conceptualized as the transformation of labor, fuel, and fleet-wide seating capacity into available seat-miles, or, under an alternative model specification, into user value, as measured by the airline’s operating revenue. Efficiency is conceptualized in terms of minimizing the airline’s usage of the three inputs, given its output level. The analysis seeks to determine whether the airlines that consolidated were more efficient, post-consolidation, than they were prior to consolidation, compared to airlines that did not enter into consolidations. Although there are limitations owing to the small number of airlines in the dataset, the chapter finds no evidence that the consolidations enhanced the efficiencies of the airlines involved, relative to the efficiencies of the airlines that did not enter into consolidations.

A stroke is a serious, life-threatening condition that occurs when the blood supply to a part of the brain is cut off. The earlier a stroke is treated, the less damage is likely to occur. One of the methods that can lead to faster treatment is timely and accurate prediction and diagnosis. This paper aims to compare the binary integer programming-data envelopment analysis (BIP-DEA) model and the logistic regression (LR) model for diagnosing and predicting the occurrence of stroke in Iran.

In this study, two algorithms of the BIP-DEA and LR methods were introduced and key risk factors leading to stroke were extracted.

The study population consisted of 2,100 samples (patients) divided into six subsamples of different sizes. The classification table of each algorithm showed that the BIP-DEA model had more reliable results than the LR for the small data size. After running each algorithm, the BIP-DEA and LR algorithms identified eight and five factors as more effective risk factors and causes of stroke, respectively. Finally, predictive models using the important risk factors were proposed.

The main objective of this study is to provide the integrated BIP-DEA algorithm as a fast, easy and suitable tool for evaluation and prediction. In fact, the BIP-DEA algorithm can be used as an alternative tool to the LR model when the sample size is small. These algorithms can be used in various fields, including the health-care industry, to predict and prevent various diseases before the patient’s condition becomes more dangerous.