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The purpose of this paper is to propose a method for performance assessment of organizations based on integrated approach of knowledge management and safety management using data envelopment analysis, and the proposed model is then applied in the car industry in Isfahan province to be checked. Therefore, deficiencies can be highlighted and possible strategies can be evolved to improve the performance.

As data envelopment analysis is a robust mathematical tool, it has been used to evaluate organizational performance. For discovering the organizational performance of knowledge management and safety management by data envelopment analysis (DEA), the first step is to specify proper criteria. To this end, in this method, the indices in both approaches of knowledge management and safety management were identified. Then, inputs and outputs were specified. Knowledge management and safety management were determined as input indices, and customer satisfaction and accident indicators were the output indices. It is noteworthy that each output index was used one time. In the next stage, performance of organizations was assessed based on both determined approaches and via data envelopment analysis. Finally, the organizations were ranked.

The suggested method was implemented in the car industry in the Isfahan province. The obtained results disclosed that among 12 decision-making units, 4 units are efficient when customer satisfaction is the output and 5 units are efficient when accidents indices are the output. In ranking with customer satisfaction as the output, Sepahan Atlas Pump Company was ranked first via super efficiency method, data envelopment analysis and similarity to ideal solution. In ranking with accidents as the output, Sepahan Atlas Pump Company ranked first via strong efficiency method and Sanatgar Company ranked first via data envelopment analysis and similarity to ideal solution.

Knowledge has been recognized as one of the valuable resources, and knowledge management would greatly effect improvement of job quality. Knowledge level increase is led by better performance and less errors. Consequently, it can enhance the organizational health and safety. There are some studies which have been conducted on safety management or knowledge management performance analysis. The organizational performance evaluation based on integrated approach of knowledge management and safety management is an important issue which is less considered in theory and practice. Thus, the authors have proposed a method which is able to evaluate the organization based on this integrated approach with functional indices, which resulted in accurate results, and finally, ranking can show the organization status to determine proper strategies.

The purpose of this paper is to improve the recent models for the evaluation of the efficiency of decision making units (DMUs) comprising a network structure with undesirable intermediate measures and fuzzy data.

In this paper a three-stage network structure model with desirable and undesirable data is presented and is solved as linear triangular fuzzy planning problems.

A new three stage network data envelopment analysis (DEA) model is established to evaluate the efficiency of industries with undesirable and desirable indicators in fuzzy environment.

The implication of this study is to evaluate the furniture services and the chipboard industries of wood lumber as a three-stage process.

In some cases, DMUs include two or multi-stage process (series or parallel) operating with a structure called a network DEA. Also, in the real world problems, the data are often presented imprecisely. Additionally, the intermediate measures under the real-world conditions include desirable and undesirable data. These mentioned indexes show the value of the proposed model.

We adopt a super slack-based measurement (SBM) data envelopment analysis (DEA) model to estimate the efficiency of five biggest freight rail operators in Russia, which are included in the top 30 freight rail operators in terms of two dimensions – financial and operational efficiency during 2013–2017. The result shows that the private companies characterized by high financial and operational efficiency, while the Rossiiskye Zheleznye Dorogi (RZD) subsidiaries characterized by sufficiently low financial and operational efficiency scores. And the result also presents that operational efficiency score of operators handling universal rolling stock is higher than financial efficiency scores. In contrast, financial efficiency scores of operators handling special rolling stock is higher than operational efficiency scores. rail freight operators in addition to a special rolling stock park should have a universal rolling stock park for higher profitability. State-owned companies and its subsidiary operate inefficiently in the midst of a market economy in Russia. Rail freight operators for a higher level of financial efficiency should be transferred to the private sector.

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 purpose of this study is to propose a model to evaluate the performance of organizational units considering intellectual capital (IC) and employee loyalty approach applying principal component analysis and data envelopment analysis (PCA-DEA) method.

Organization units are considered as decision-making units, IC components including human capital (HC), structural capital (SC) and customer capital are inputs and employee loyalty is output. The principal component analysis was used to converts inputs and outputs into the independent variables. As a return to scale is variable, a modified envelopment input-oriented BCC model applied to obtain the efficiency of organization units. Also, all units of organization are ranked. Eventually, sensitivity analysis performed to show how input variables influence on output variable.

Operation, design and construction, production planning, internal affairs, quality control and security were recognized as efficient units. Also, units of operation, internal affairs and quality control ranked first to third, and the human resource unit earned the last rank. In addition, results of sensitivity analysis on input variables showed that the order of impact intensity is: customer capital, HC and SC, respectively.

Existence a framework for the development of human resource strategies and prioritization in the allocation of organizational resources to improve the performance of the organization considering human resources is vital. Most of the previous studies, just have examined the impact of IC on different dimensions of organizational performance. Meanwhile, evaluating the performance of IC with employee loyalty approach, using PCA-DEA simultaneously can evaluate and measure the impact of IC on the performance of the organization and its units regarding employee loyalty, which has a significant impact on improving the organization’s level of IC and human resource management.

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.

The main purpose of this paper is to propose a new approach to determine the project activities weight factors using data envelopment analysis. Afterward, the model is applied in Mobarkeh Steel Company as a case study. Accordingly, the project schedule and plans can be written on the basis of the gained weight factors.

This study proposed an approach to determine the weights of activities using Data Envelopment Analysis. This approach consists of four phases. In the first phase, project activities are extracted based on the work breakdown structure. In the second phase, the parameters affecting the importance of activities are determined through a review of the related literature and based on the experts' opinions. In the third phase, the proper data envelopment analysis model is chosen and the inputs and outputs are signified. Then, the activities' weights are determined based on the efficiency numbers. Finally, the model is solved for the case of Isfahan Mobarakeh Steel Company.

The proposed method aimed to calculate the project activities weight factor. Thus, influential parameters on project activities importance include activity duration, activity cost, activity importance which includes successors and predecessors, activity difficulty which includes skill related (education and experience), safety, communication rate, intellectual effort, physical effort, unfavorable work conditions and work related hazards, have been recognized. Then, Projects' data were extracted from the organizational expert's opinions and recorded data in documents. Thereupon, applying DEA, the activities weight factor were calculated based on the efficiency numbers. The results show that the model is applicable and has promising benefits in real-world problems.

Planning is one the most fundamental steps of project management. The ever-growing business environment demands for more complex projects with larger number of activities wants more efficient project managers. Organizational resources are limited; therefore, activities planning is a critical from the perspectives of both managers and researchers. Knowing the importance of the activities can help to manage activities more efficient and to allocate time, budget, cost and other resources more accurate. Different elements such as cost, time, complexity, and difficulty can affect the activity weight factor. In this study, the proposed approach aims to determine the weights of activities using Data Envelopment Analysis.

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 purpose of this research paper is to prove the superiority of a two‐stage data envelopment analysis compared to a one‐stage approach in measuring a football club's efficiency. Moreover it provides best practice benchmarks for the research sample which supports football officials to orient themselves to the right clubs.

A non‐parametric two‐stage data envelopment analysis for the seasons 2006/07 to 2008/09 is introduced to measure the efficiency of English Premier League football clubs from an off‐field and an on‐field perspective. The results are compared with those of the traditional one‐stage data envelopment analysis approach to identify insufficiencies of the latter.

The results show evidence that different conclusions derive from either the one‐ or the two‐stage approach with the threat of potential misinterpretations in the case of the former. Furthermore, this study provides football clubs with information to focus on specific efficiency‐enhancing strategies at the individual stages of the production process and therefore acts as a supportive tool for the football club officials for setting corrective actions if inefficiencies are identified.

The present article provides a foundation for future studies in other football leagues as well as for an intertemporal analysis which evaluates the efficiency of a club on a yearly basis.

This is the first paper that introduces a two‐stage data envelopment analysis approach in football research. It has proven that it can identify sources of inefficiencies more accurately than a one‐stage data envelopment analysis and provides football officials with valuable information about their club.

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.