). Data envelopment analysis (DEA) is a "data-oriented" approach for evaluating the performance of a set of entities called decision-making units (DMUs) whose performance is categorized by multiple metrics. These performance metrics are classified or termed as inputs and outputs under DEA. Although DEA has a strong link to production theory in economics, the tool is also used for benchmarking in operations management, where a set of measures is selected to benchmark the performance of manufacturing and service operations. In the circumstance of benchmarking, the efficient DMUs, as defined by DEA, may not necessarily form a "production frontier," but rather lead to a "best-practice frontier" (Cook, Tone, and Zhu, 2014).Over the years, we have seen a variety of DEA empirical applications. This handbook aims to compile state-of-the-art empirical studies and applications using DEA. It includes a collection of 18 chapters written by DEA experts.Chapter 1, by Chen, Gregoriou, and Rouah, examines the performance of chief executive officers (CEOs) of US banks and thrifts. The authors find evidence that best-practice CEOs who have a DEA efficiency score of one are rewarded with higher compensation compared to underperforming CEOs who have a DEA efficiency score greater than one. They also find DEA efficiency score to be a highly significant predictor of CEO compensation.Chapter 2, by Yu and Chen, is dedicated to describe the network operational structure of transportation organizations and the relative network data envelopment analysis model. Route-based performance evaluation, environmental factors, undesirable outputs, and multi-activity framework are incorporated into their application. v Chapter 3, by Hu and Chang, demonstrates how to use different types of DEA models to compute the total-factor energy efficiency scores with an application to energy efficiency.Chapter 4, by Growitsch, Jamasb, Müller, and Wissner, explores the impact of incorporating customers' willingness to pay for service quality in benchmarking models on cost efficiency of distribution networks.Chapter 5, by Volz, provides a brief review of previous applications of DEA to the professional baseball industry followed by two detailed applications to Major League Baseball.Chapter 6, by Cummins and Xie, examines efficiency and productivity of US property-liability (P-L) insurers using DEA. The authors estimate pure technical, scale, cost, revenue, and profit efficiency over the period 1993-2011. Insurers' adjacent year total-factor productivity changes, and their contributing factors are also investigated.Chapter 7, by Premachandra, Zhu, Watson, and Galagedera, presents a two-stage network DEA model that decomposes the overall efficiency of a decision-making unit into two components and demonstrates its applicability by assessing the relative performance of 66 large mutual fund families in the USA over the period 1993-2008. Chapter 8, by Basso and Funari, presents a comprehensive review of the literature of DEA models for the performance assessment o...
