In this study, the data envelopment analysis (DEA), three-stage DEA (3SDEA) and artificial neural network (ANN) are employed to measure the technical efficiency of 29 semi-conductor firms in Taiwan. Estimated results show that there are significant differences in efficiency scores among DEA, 3SDEA and ANN analysis. The advanced setting of the three stages mechanism of DEA does show some changes...

Internet financial risk is not only directly related to the operation and development of the Internet financial system itself, but also has a very important impact on the country’s macroeconomic operation because of its rapid development speed and growing scale of development. As of February 2017, there were 2335 network loan platforms, among which 55 platforms for problem existed. The event, s...

As a non-parametric technique, Data Envelopment Analysis (DEA) evaluates the relative efficiency of peer decision making units (DMUs) that have multiple inputs and outputs. In many situations, DMUs have a two-stage structure, where * Corresponding author. E-mail address: [email protected] (A. Ashrafi) 1436 A. Ashrafi, A. B. Jaafar, L. S. Lee and M. R. Abu Bakar the first stage uses inputs to ...

In this paper we investigate whether technical efficiency is an important ex-ante predictor of business failure. We use samples of French textiles, wood, and R&D companies to obtain efficiency estimates for individual firms in each industry. These efficiency measures are derived from a directional technology distance function constructed empirically using non-parametric Data Envelopment Analysi...

data envelopment analysis (dea) was developed to help compare the relative performance of decision-making units. it is a non-parametric method for performing frontier analysis. it uses linear programming to estimate the efficiency of multiple decision-making units and it is commonly used in production, management and economics [3]. dea generates an efficiency score between 0 and 1 for each unit...

Data envelopment analysis (DEA), which is used to determine the efficiency of a decision-making unit (DMU), is able to recognize the amount of input congestion. Moreover, the relative importance of inputs and outputs can be incorporated into DEA models by weight restrictions. These restrictions or a priori weights are introduced by the decision maker and lead to changes in models and efficiency...

From 1980 to 1999, rural designated hospitals closed at a disproportionally high rate. In response to this emergent threat to healthcare access in rural settings, the Balanced Budget Act of 1997 made provisions for the creation of a new rural hospital--the critical access hospital (CAH). The conversion to CAH and the associated cost-based reimbursement scheme significantly slowed the closure ra...

Data envelopment analysis (DEA) measures relative efficiency among the decision making units (DMU) without considering noise in data. The least efficient DMU indicates that it is in the worst situation. In this paper, we measure efficiency of individual DMU whenever it losses the maximum output, and the efficiency of other DMUs is measured in the observed situation. This efficiency is the minim...

Data Envelopment Analysis (DEA) has become an established approach in the analysis of efficiency problems in both public and private sectors. The aim of this paper is to present and apply a newly developed, adjusted DEA model – emerging from a blend of a Distance Friction Minimization (DFM) and a Goals Achievement (GA) approach on the basis of the Charnes-Cooper-Rhodes (CCR) method – in order t...

Data envelopment analysis (DEA) is a widely used mathematical programming approach for comparing the input and output of a set of comparable decision-making units (DMUs) by evaluating their relative efficiency. The traditional DEA methods require accurate measurement of both the inputs and outputs. However, the real evaluation of the DMUs is often characterized by imprecision and uncertainty in...