Search results

The purpose of this paper is to propose a modified model in multi-stage processes when there are intermediate measures between the stages and in this sense, the new efficiency scores are more accurate. Conventional data envelopment analysis (DEA) models disregard the internal structures of peer decision-making units (DMUs) in evaluating their relative efficiency. Such an approach would cause managers to lose important DMU information. Therefore, in multistage processes, traditional DEA models encounter problems when intermediate measures are used for efficiency evaluation.

In this study, two-stage additive integer-valued DEA models were proposed. Three models were proposed for measuring inefficiency slacks in each stage and in the system as a whole.

Three models were proposed for measuring inefficiency slacks in each stage and in the system as a whole.

The advantage of the proposed models for multi-stage systems is that they can accurately determine the stages with the greatest weaknesses/strengths. By introducing an applied case in the Iranian power industry, the paper demonstrated the applications and advantages of the proposed models.

This paper sets out to describe the results of a four‐year‐long study into the efficiency of Estonian central public libraries.

The study uses data envelopment analysis (DEA), a non‐parametric linear programming‐based tool to determine the relative efficiency of 20 central public libraries of Estonia. Relative efficiency shows the capacity of an organisation to provide the same output for less expenditure.

The study finds that 40 per cent of the libraries investigated used their resources effectively. Efficiency scores of the rest of the libraries varied from 0.74 to 0.98, with staff expenditure slack being the main inefficiency factor for half of inefficient libraries.

The results of this research enhance the diagnostic capacity for planning the future activities of Estonian central public libraries and offers insights into using DEA for efficiency measurement and decision‐making in public libraries in general.

The purpose of this paper is to compare efficiency scores from the benchmarking exercise with those of previous studies and to discuss the reasons behind diverging results.

This paper uses data envelopment analysis (DEA) on primary data of large container terminals.

The results differ strongly from those available in the literature. Causes for these differences are: public (secondary) data are not always accurate; different terminal types are compared; terminals of different scale are compared; and terminals are mixed with ports.

DEA may be appropriate for container terminal benchmarking, but only if better quality and additional input and output data can be obtained. In its application, the analysis should be controlled for terminal types.

Summary of the state of play in the use of DEA methodologies for comparing the efficiency of container terminals at ports.

Assessing the performance of medical laboratories plays an important role in the quality of health services. However, because of imprecise data, reliable results from laboratory performance cannot be obtained easily. The purpose of this paper is to illustrate the use of interval network data envelopment analysis (INDEA) based on sustainable development indicators under uncertainty.

In this study, each medical diagnostic laboratory is considered as a decision-making unit (DMU) and an INDEA model is used for calculating the efficiency of each medical diagnostic laboratory under imprecise inputs and outputs. The proposed model helps provide managers with effective performance scores for deficiencies and business improvements. The proposed model with realistic efficiency scores can help administrators manage their deficiencies and ultimately improve their business.

The results indicate that uncertainty can lead to changes in performance scores, rankings and performance classifications. Therefore, the use of DEA models under certainty can be potentially misleading.

The contribution of this study provides useful insights into the use of INDEA as a modeling tool to aid managerial decision-making in assessing efficiency of medical diagnostic laboratories based on sustainable development indicators under uncertainty.

The aim of the paper is to review some previous researches examining ICT efficiency and the impact of ICT on educational output/outcome as well as different conceptual and methodological issues related to performance measurement.

This paper adopts a non‐parametric methodology, i.e. data envelopment analysis (DEA) technique, and applies it to selected EU‐27 and OECD countries.

The empirical results of the varying levels of (output‐oriented) efficiency (under the VRSTE framework) show that Finland, Norway, Belgium and Korea are the most efficient countries in terms of their ICT sectors. In addition, the analysis also finds evidence that most of the countries under consideration hold great potential for increased efficiency in ICT and for improving their educational outputs and outcomes.

This is the first paper that investigates such a wide range of countries with DEA technique when analyzing efficiency of ICT sector from an educational perspective.

As the pressure to win and generate revenue and as the allegations of out-of-control spending continue to increase, there exists much interest in intercollegiate athletics. While researchers in the past have investigated specific issues related to athletics success, revenue generation, and graduation rates, no previous studies have attempted to evaluate these factors simultaneously. This chapter discusses the development of a data envelopment analysis (DEA) model aimed at measuring how efficient university athletic departments are in terms of the use of resources to achieve athletics success, generate revenue, and promote academic success and on-time graduation. Data from National Collegiate Athletic Association (NCAA) Division I Football Bowl Subdivision (FBS) universities are used to evaluate the relative efficiency of the institutions. The model identifies a series of “best-practice” universities which are used to calculate efficient target resource levels for inefficient institutions. The value of the proposed methodology to decision makers is discussed.

This paper aims to propose that more useful novel schemes could develop from a more principled examination and application of promising authentication features. Text passwords persist despite several decades of evidence of their security and usability challenges. It seems extremely unlikely that a single scheme will globally replace text passwords, suggesting that a diverse ecosystem of multiple authentication schemes designed for specific environments is needed. Authentication scheme research has thus far proceeded in an unstructured manner.

This paper presents the User-Centred Authentication Feature Framework, a conceptual framework that classifies the various features that knowledge-based authentication schemes may support. This framework can used by researchers when designing, comparing and innovating authentication schemes, as well as administrators and users, who can use the framework to identify desirable features in schemes available for selection.

This paper illustrates how the framework can be used by demonstrating its applicability to several authentication schemes, and by briefly discussing the development and user testing of two framework-inspired schemes: Persuasive Text Passwords and Cued Gaze-Points.

This framework is intended to support the increasingly diverse ecosystem of authentication schemes by providing authentication researchers, professionals and users with the increased ability to design, develop and select authentication schemes better suited for particular applications, environments and contexts.

A Nash bargaining game data envelopment analysis (NBG-DEA) model is proposed to measure the efficiency of dynamic multi-period network structures. This paper aims to propose NBG-DEA model to measure the performance of decision-making units with complicated network structures.

As the proposed NBG-DEA model is a non-linear mathematical programming, finding its global optimum solution is hard. Therefore, meta-heuristic algorithms are used to solve non-linear optimization problems. Fortunately, the NBG-DEA model optimizes the well-formed problem, so that it can be solved by different non-linear methods including meta-heuristic algorithms. Hence, a meta-heuristic algorithm, called particle swarm optimization (PSO) is proposed to solve the NBG-DEA model in this paper. The case study is Industrial Management Institute (IMI), which is a leading organization in providing consulting management, publication and educational services in Iran. The sub-processes of IMI are considered as players where their pay-off is defined as the efficiency of sub-processes. The network structure of IMI is studied during multiple periods.

The proposed NBG-DEA model is applied to measure the efficiency scores in the IMI case study. The solution found by the PSO algorithm, which is implemented in MATLAB software, is compared with that generated by a classic non-linear method called gradient descent implemented in LINGO software.

The experiments proved that suitable and feasible solutions could be found by solving the NBG-DEA model and shows that PSO algorithm solves this model in reasonable central process unit time.

The purpose of this paper is to examine the optimizing behavior of agricultural supply and marketing cooperatives in the US Midwestern states.

This paper uses firm-level data from CoBank, the primary lender to agricultural cooperatives, to evaluate optimizing behavior of 77 Midwestern agricultural marketing and supply cooperatives from 2014 through 2017. The study uses the data envelopment analysis (DEA) and the weak axioms of cost minimization and profit maximization to identify whether or not a cooperative is following a cost minimization and/or profit maximization objective.

In contrast to previous research, results provide stronger support for profit maximization as the behavioral objective of agricultural cooperatives instead of cost minimization, especially for larger cooperatives.

Traditional firm theory for investor-owned firms states that businesses seek to maximize profits. Agricultural cooperatives however, could aim to maximize profits so as to redistribute profits back to user-owners as patronage refunds. Or, they could minimize costs so as to lower cost of products sold and services provided to their user-owners. Given users-control the cooperative, knowing the primary objective of agricultural cooperatives will allow for better design of research and education programming.

Given recent changes and consolidation in the agricultural industry among farms and agricultural cooperatives, this study reexamines past work that evaluated the optimizing behavior of agricultural cooperatives. Changes in industry and the necessity to remain competitive from an agribusiness standpoint have resulted in an anticipated shift toward a profit maximizing objective. The value of this study is providing an evaluation framework, using a firm-level comprehensive dataset, that represents the cooperative system.