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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.

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.

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.

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.

This paper aims to analyze the economic-financial performance (EFP) and value creation (VC) in the Brazilian construction industry.

Based on the theories of strategy and finance, a quantitative-qualitative, descriptive and explanatory and applied study was carried out, contrasting the performance of the Direcional company and the civil construction industry – both listed on the Brazilian Stock Exchange and the Over-the-Counter Market (B3)

The analysis of the EFP in the Brazilian construction industry shows that EZTEC, Helbor, Trisul and Direcional were the companies with the best EFP in the period. The analysis of the Economic Value Added (EVA®, henceforth EVA), as a VC metric and basis for assessing the relative technical efficiency score by Data Envelopment Analysis (DEA®, henceforth DEA), revealed that the companies Direcional, EZTEC, MRV and CR2 were considered efficient throughout the period covered. The multicriteria methodology for empirical testing of the EFP and VC allowed not only contrasts Direcional's results with the other companies of the construction industry but also offered a complementary tool for comparative analysis of enterprises of different sizes, structures and realities.

Regardless of any contextual limitations, from a theoretical point of view, the research not only helps fill the research gap aforementioned but also expands knowledge on the topic and demonstrates how this multi-criteria methodology (integrating DEA and EVA) can be used to assess EFP and VC in addition to traditional tools. However, this new approach evaluates, at the same time, corporate and sectorial effectiveness by contrasting the efficiency and efficacy (simultaneously) in the generation of performance and value of a company in relation to the industry.

Significant implications for managerial practice could be noted by offering a tool to improve company performance and creating a competitive benchmarking process for analysts, investors, managers, financing agencies, shareholders, policymakers and business owners, as well as organizations and sectors in similar situations – who need to assess the EFP and VC holistically and improve their decision-making processes.

The uniqueness and innovation of this research come from the original multi-criteria methodology developed, applied and validated for analysis of EFP and VC. This methodology was operationalized through DEA applied to the companies' EVA, making it possible to compare corporate results and those of the whole industry in a balanced way – an unexplored issue in the literature, especially in emerging economies, opening several avenues for future research.

Este artículo tiene como objetivo analizar el desempeño económico-financiero (DEF) y la creación de valor (CV) en la industria de la construcción brasileña.

Con base en las teorías de estrategia y finanzas, se realizó un estudio cuanti-cualitativo, descriptivo, explicativo y aplicado, contrastando el desempeño de la empresa Direcional y de la industria de la construcción civil, ambas cotizadas en la Bolsa y Mercado Extrabursátil Brasileña (B3).

El análisis de la DEF en la industria de la construcción brasileña muestra que EZTEC, Helbor, Trisul y la Direcional fueron las empresas con el mejor desempeño en el período. El análisis del Valor Económico Agregado (en adelante EVA), como métrica de CV y base para evaluar el puntaje de eficiencia técnica relativa mediante Análisis Envolvente de Datos (en adelante DEA), reveló que las empresas Direcional, EZTEC, MRV y la CR2 se consideraron eficientes durante todo el período cubierto. La metodología multicriterio para pruebas empíricas de la DEF y CV permitió no sólo contrastar los resultados de la Direcional con los de otras empresas del sector de la construcción, sino que también ofreció una herramienta complementaria para el análisis comparativo de empresas de diferentes tamaños, estructuras y realidades.

La singularidad y la innovación de esta investigación provienen de la metodología original multicriterio desarrollada, aplicada y validada para el análisis de DEF y CV. Esta metodología fue operacionalizada a través de DEA aplicado al EVA de las empresas, permitiendo comparar los resultados corporativos y los de toda la industria de manera equilibrada – un tema inexplorado en la literatura, especialmente en las economías emergentes, abriendo varias vías para futuras investigaciones.

Independientemente de las limitaciones contextuales, desde un punto de vista teórico, la investigación no solo ayuda a llenar el vacío mencionado anteriormente, sino que también amplía el conocimiento sobre el tema y demuestra cómo esta metodología multicriterio (integrando DEA y EVA) puede utilizarse para evaluar el DEF y CV además de las herramientas tradicionales. Sin embargo, este nuevo enfoque evalúa, al mismo tiempo, la efectividad corporativa y sectorial contrastando la eficiencia y eficacia (simultáneamente) en la generación de desempeño y valor de una empresa en relación con la industria.

Se podrían observar implicaciones significativas para la práctica gerencial al ofrecer una herramienta para mejorar el desempeño de la empresa y crear un proceso de evaluación comparativa competitivo para analistas, inversionistas, gerentes, agencias financieras, accionistas, formuladores de políticas y propietarios de negocios, así como organizaciones y sectores en situaciones similares, que necesitan evaluar el DEF y el CV de manera integral y mejorar sus procesos de toma de decisiones.

This study aims to evaluate the efficiency of the three telecommunications companies in Saudi Arabia: Saudi Telecom Company (STC), Mobily and Zain over the period of 2010–2019. This evaluation is a step toward improving the performance of the Saudi telecommunications sector.

Three multicriteria decision-making (MCDM) techniques were used to calculate technical efficiency. These techniques include the traditional data envelopment analysis (DEA), window DEA and analytical hierarchy process (AHP). The three inputs used were total assets, operating expenses and capital expenditures, whereas the two outputs were sales revenue and total stockholders’ equity.

STC was ranked first using the three techniques, followed by Zain, and then Mobily. According to the DEA window analysis, these three companies were all efficient only in 2012. The efficiency was high in the initial years, 2010–2013, when it was above 0.90, and it dropped below 0.90 in the subsequent years, 2014–2019. In addition, the efficiency of STC remained high, with an average of 0.990. However, the average efficiencies of Zain and Mobily during this period were 0.807 and 0.804, respectively.

This is the first study to use the three MCDM techniques to evaluate the performance of telecommunications providers. The results show that window DEA is better than the other two techniques at evaluating performance over time, as it has a higher discrimination power than either the traditional DEA or AHP.

The purpose of this study is to estimate inputs (outputs) and flexible measures when outputs (inputs) are changed provided that the relative efficiency values remain without change.

A novel inverse data envelopment analysis (DEA) approach with flexible measures is proposed in this research to assess inputs (outputs) and flexible measures when outputs (inputs) are perturbed on condition that the relative efficiency scores remain unchanged. Furthermore, flexible inverse DEA approaches proposed in this study are used for a numerical example from the literature and an application of Iranian banking industry to clarify and validate them.

The findings show that including flexible measures into the investigation effects on the changes of performance measures estimated and leads to more reasonable achievements.

The traditional inverse DEA models usually investigate the changes of some determinate input-output factors for the changes of other given input-output indicators assuming that the efficiency values are preserved. However, there are situations that the changes of performance measures should be tackled while some measures, called flexible measures, can play either input or output roles. Accordingly, inverse DEA optimization models with flexible measures are rendered in this paper to address these issues.

This study aims to investigate environmental efficiency based on energy change by using energy-related or nonenergy-related variables by reckoning with months and years as decision-making units (DMUs) for a hospital under radial and nonradial models.

The non-oriented slack-based measures (SBM)-data envelopment analysis (DEA) model considering desirable and undesirable outputs has been embraced in this study, where its obtained results were compared with the results of other DEA models are output-oriented SBM-DEA and Banker, Charnes, & Cooper-DEA. For this purpose, this research has used a data set covering the 2012–2018 period for a reference hospital, which includes energy-related and nonenergy-related variables.

The results demonstrate that environmental efficiency based on energy reached the highest level in the winter months, whereas the summer months have the lowest efficiency values arising from the increasing electricity consumption due to high cooling needs. According to results of the non-oriented SBM model, the month with the highest efficiency in all periods is January with a 0.936 average efficiency score, the lowest month is August with a 0.406 value.

This paper differs from other studies related to energy and environmental efficiencies in the literature with some aspects. First, to the best of the authors’ knowledge, this study is the first one that takes into account time periods (months and years) as (DMUs for a single organization. Second, this study investigates environmental nonefficiencies, which are derived from energy uses and factors affecting energy use.

Zakat is an important Islamic economic instrument that plays significant role in Sustainable Development Goals. Accordingly, Zakat Institutions must manage zakat in a proper and efficient manner. This study aims to examine the efficiency of Zakat Institutions based on their clusters which are government, business and social organizations.

This study uses three quantitative methods: data envelopment analysis (DEA), free disposal hull and super-efficiency DEA. The analytical method is based on production approach, variable return to scale assumption and output orientation. The sample consists of 14 Zakat Institutions from three clusters: Zakat Institutions managed by government, Zakat Institutions managed by corporation and Zakat Institution managed by social organizations.

The results revealed that all of three techniques culminate the same ranking order of efficiency. Zakat Institution managed by the government is the most efficient Zakat Institution, with the average value of 0.87 by using three approaches combined. Meanwhile, Zakat Institutions owned by company and social institutions cluster are in second and third position, with the average value of 0.65 and 0.4, respectively, based on the results of the three approaches. This study contends that the level of efficiency of Zakat Institutions may be supported by clusters (affiliations) in their management.

This study’s limitation is the inadequacy of the required data. Nonetheless, this study provides insights to improve the efficiency of Zakat Institutions based on their clusters. Zakat Institutions in each cluster can improve their efficiency by optimizing inputs to produce multiple outputs.

This study enhances research on the efficiency of Zakat Institutions using three methods to assess the consistency and strength of Zakat Institutions’ efficiency values. In addition, this study examines the efficiency level of Zakat Institutions based on their clusters.

This paper aims to focus on the assessment of a domestic property's energy performance status by a domestic energy assessor (DEA), to ascertain the possible underlying reasons for variability in the results of Energy Performance Certificates (EPCs). By variability, the authors mean discrepancies in assessment between different DEAs on similar properties. This is important because the uses for the EPC have been extended beyond their original function as an asset rating system, to include themes encompassing building policy decisions, building performance and the distribution of incentives and grants. Consequently, inaccuracies in EPC reporting will have a greater impact than may have been the case at the outset.

A case study approach involving the conducting of semi-structured interviews with 20 practicing DEAs was carried out, with transcribed recordings of the interview material subjected to thematic analysis. This formed part of a wider mixed methods study.

The results identify a wide range of underlying reasons for variability driven by issues in both practice and process, including conflicts of interests, the EPC auditing process, the default inputting of missing data by RdSAP where information may not be available/discoverable by the DEA, the quality and perception of EPCs and DEA training and experience.

The sample size of 20 is by definition limiting, and it is possible that different results would have been obtained from a different sample. Although thematic saturation from the analysis of the responses on the key question of whether EPCs are considered variable does mitigate this. The respondents were all in possession of five years or more experience and of carrying out EPCs for different purposes. Less experienced DEAs may inevitably have responded to questions differently. The thematic analysis gives the researcher control over the presentation of the results, and it is noted that this creates a potential for bias. The researcher is immersed in the world of construction and property, with regular contact with DEAs and EPCs, which may influence the perspective of the results.

The research identifies risks to the accuracy of EPCs. To this end, and with the specific research findings in mind, this research may be of interest to construction professionals with respect to EPC practice and procurement, to the Accrediting Bodies who audit EPCs, to the creators of RdSAP with respect to automated EPC inputs, to academics either at face value or for use in further research and to policy makers who may wish to consider RdSAP data in future with qualifiers or margins of error, or may even look to review the EPC as the instrument of choice for some applications.

There is much literature analysing the shortcomings and nuances of RdSAP results, and the software model that generates the EPC, but only very limited literature extending the discussion about RdSAP to its operator: the DEA. At the time of writing, there is no literature focusing directly on the DEA and its role within the EPC production process. Their role is more important now, given the expanding use of EPCs, and increased reliance on EPC data.