Joe Zhu

Bio: Joe Zhu is an academic researcher from Worcester Polytechnic Institute. The author has contributed to research in topics: Data envelopment analysis & Efficient frontier. The author has an hindex of 1, co-authored 1 publications receiving 7 citations.

DEA Based Benchmarking Models

Abstract: Data envelopment analysis (DEA) is a methodology for identifying the efficient or best-practice frontier of decision making units (DMUs). It is required that all DMUs under consideration be evaluated against each other in a same pool. Adding or deleting an inefficient DMU does not alter the efficient frontier and the efficiencies of the existing DMUs. The inefficiency scores change only if the efficient frontier is altered. Benchmarking is the process of comparing a DMU’s performance to the best practices formed by a set of DMUs. DEA is also called “balanced benchmarking”, because DEA considers multiple performance metrics in a single model. Under such a notion, the best practices are the benchmarks identified by DEA. However, in a more general sense, best practices do not have to be identified by DEA—they can be existing “standards”. This chapter presents two DEA-based benchmarking approaches where one set of DMUs is compared (or benchmarked) against another. One approach is called “context-dependent” DEA where a set of DMUs is evaluated against a particular evaluation context. Each evaluation context represents an efficient frontier composed by DMUs in a specific performance level. The context-dependent DEA measures the attractiveness and the progress when DMUs exhibiting poorer and better performance are chosen as the evaluation context, respectively. The other approach consists of a fixed benchmark model and a variable benchmark model where each (new) DMU is evaluated against a set of given benchmarks (standards).

Measuring the efficiency of hospitals: a fully-ranking DEA–FAHP approach

Abstract: The goal of this study is to present a DEA-based fuzzy multi-criteria decision making model for firms in the health care industry in order to enhance their business performance. The study demonstrates a real-life use of the proposed model, mainly designed for hospitals. Data envelopment analysis enhanced with fuzzy analytic hierarchy process are collectively utilized to quantify the data and structure the model in decision-making. The juxtaposition of the two methods is used to compile a ranked list of multiple proxies containing diverse input and output variables which occur in two stages. This hybrid model provides several benefits, one of which is the ability to make the most appropriate decision considering the value of the weights determined by the data from the hybrid model.

Evaluation of Corporate Websites and Their Influence on the Performance of Olive Oil Companies

Abstract: Spain is among the largest producers of organic olive in the world. Yet the Spanish organic olive oil sector faces a major commercial problem due to an internal demand that is too small to match the volume of supply. Factors that explain this problem include the scarcity and scattered nature of points of sale, the lack of information available to consumers, and the very large gulf in the price between organic and nonorganic olive oil. To address these problems, the literature highlights the key commercial role of information and communication technologies (ICTs). The corporate website is a core element around which the company’s e-commerce activity revolves. The goal of this study is to confirm the relationship between business efficiency, measured using data envelopment analysis (DEA), and the quality of the corporate website, measured using the extended Model of Internet Commerce Adoption (eMICA). Although this analysis did not identify a direct relationship between these two variables, fuzzy-set Qualitative Comparative Analysis (fsQCA) revealed that combinations of elements related to corporate website quality (interactivity and processing), organizational, and structural factors (size of firm and outsourcing of ICT management) can have a direct effect on organizational performance, measured in terms of economic efficiency.

Bankruptcy Prediction with the Use of Data Envelopment Analysis: An Empirical Study of Slovak Businesses

Abstract: The paper deals with methods of predicting bankruptcy of a business with the aim of choosing a prediction method which will have exact results. Existing bankruptcy prediction models are a suitable tool for predicting the financial difficulties of businesses. However, such tools are based on strictly defined financial indicators. Therefore, the Data Envelopment Analysis (DEA) method has been applied, as it allows for the free choice of financial indicators. The research sample consisted of 343 businesses active in the heating industry in Slovakia. Analysed businesses have a significant relatively stable position in the given industry. The research was based on several studies which also used the DEA method to predict future financial difficulties and bankruptcies of studied businesses. The estimation accuracy of the Additive DEA model (ADD model) was compared with the Logit model to determine the reliability of the DEA method. Also, an optimal cut-off point for the ADD model and Logit model was determined. The main conclusion is that the DEA method is a suitable alternative for predicting the failure of the analysed sample of businesses. In contrast to the Logit model, its results are independent of any assumptions. The paper identified the key indicators of the future success of businesses in the analysed sample. These results can help businesses to improve their financial health and competitiveness.

Agri-food companies in the social media: a comparison of organic and non-organic firms

Abstract: Organic agri-food products in Spain face major commercial problems in the home market as a result of consumers’ lack of information about this type of product and difficulties in accessing it, and ...

Comparison of the Results of a Data Envelopment Analysis Model and Logit Model in Assessing Business Financial Health

Abstract: This paper focuses on business financial health evaluation with the use of selected mathematical and statistical methods. The issue of financial health assessment and prediction of business failure is a widely discussed topic across various industries in Slovakia and abroad. The aim of this paper was to formulate a data envelopment analysis (DEA) model and to verify the estimation accuracy of this model in comparison with the logit model. The research was carried out on a sample of companies operating in the field of heat supply in Slovakia. For this sample of businesses, we selected appropriate financial indicators as determinants of bankruptcy. The indicators were selected using related empirical studies, a univariate logit model, and a correlation matrix. In this paper, we applied two main models: the BCC DEA model, processed in DEAFrontier software; and the logit model, processed in Statistica software. We compared the estimation accuracy of the constructed models using error type I and error type II. The main conclusion of the paper is that the DEA method is a suitable alternative in assessing the financial health of businesses from the analyzed sample. In contrast to the logit model, the results of this method are independent of any assumptions.