Preface

Data envelopment analysis (DEA) is a methodology for data-oriented analytics. Over the years, we have seen new and novel developments and applications of DEA in a variety of areas. In this INFOR special issue, we present theoretical and empirical research on ‘DEA and its applications in operations’. This special issue aims to compile state-of-the-art research papers spanning models, theory, empirical studies, applications and case studies on DEA. These contributions provide methodology advances, new and valuable insights, and implications to the practice of DEA for performance evaluation and benchmarking. We are pleased to publish the first part of the special issue which consists of five articles. The success of every country is a function of the many economic activities that drive that country. Comparing economic activities can present a challenge, because it covers such a wide range of activities including manufacturing, financial services, tourism and others. While tools like DEA have been used to evaluate and compare the performance of similar economic activities such as schools, banks, etc., such a tool has not been applied in settings where highly dissimilar activities are present. The paper by Avil es-Sacoto, Cook, G€ uemes-Castorena and Villarreal-Gonzalez, ‘Setting Goals for Economic Activities in Mexico’, develops a variant of the DEA methodology and applies it to the broad spectrum of the economic activities in Mexico. In ‘A Hybrid Data Envelopment Analysis Approach to Analyze College Graduation Rate at Higher Education Institutions’, Chen, Chen and Oztekin propose and implement a hybrid DEA methodology for analysing college graduation rate for higher education institutions (HEIs). The novel methodology involves the utilization of cross-industry standard process for data mining (CRISP-DM) technique to determine the rank order of the important determinative variables of college graduation and an integrated method composed of an input-oriented bounded-and-discrete-data DEA model and contextdependent DEA model to measure the performance of college students. This methodology is flexibly applicable in other settings and would hypothetically provide efficiency in complex decision-making processes of HEIs. In DEA literature, existing studies on returns to scale (RTS) are all based on the traditional definition of RTS in economics and assume that multiple inputs or outputs change in the same proportion, which is the starting point to determine the qualitative and quantitative features of RTS of decision-making units. However, for more complex products, such as the scientific research in institutes, changes of inputs or outputs are often not in proportion. The paper ‘Estimating Directional Returns to Scale in DEA’ by Yang and Liu extends the definitions of RTS and scale elasticity to directional RTS and directional scale elasticity in the DEA framework and estimates them using DEA models. In ‘An Additive Super-efficiency DEA Approach to Measuring Regional Environmental Performance in China’, Zhang develops an additive super-efficiency DEA approach to measuring environmental performance. Compared to infeasibility of the existing super-