The handbook's chapters are organized into two categories: i basic DEA models, concepts, and their extensions, and ii DEA applications. First edition contributors have returned to update their work. The second edition includes updated versions of selected first edition chapters.
Handbook on Data Envelopment Analysis
Table of Contents
About this book This handbook covers DEA topics that are extensively used and solidly based. Show all.
From the reviews: "This handbook provides a valuable overview of DEA models on the one hand and many DEA applications supplemented by software information on the other hand. Chapter 7 presents a two-stage network DEA model that decomposes the overall efficiency of a decision-making unit into two components.
Handbook on data envelopment analysis - Semantic Scholar
Chapter 8 presents a review of the literature of DEA models for the perfoemance assessment of mutual funds, and chapter 9 discusses the management strategies formulation of the international tourist hotel industry in Taiwan. Chapter 10 presents a novel use of the two-stage network DEA to evaluate sustainable product design performances. In chapter 11 authors highlight limitations of some DEA environmental efficiency models, and chapter 12 reviews applications of DEA in secondary and tertiary education.
Chapter 13 measures the relative performance of New York State school districts in the academic year. Chapter 14 provides an introductory prelude to chapters 15 and 16, which both provide detailed applications of DEA in marketing. Chapter 17 then shows how to decompose a new total factor productivity index that satisfies all economically-relevant axioms from index theory with an application to U.
Handbook on data envelopment analysis
Finally, chapter 18 presents a unique study that conducts a DEA research front analysis, applying a network clustering method to group the DEA literature over the period to It aims at reflecting the state-of-the-art as well as representing a milestone in DEA advancing. I found this handbook a valuable reference for researchers, graduate students, and consultant analysts.
However, it requires a relatively important degree of familiarity with the main DEA models and extensions to be used as an introductory door to this field. For this purpose for instance, for classroom use in undergraduate classes other references by the same authors are more appropriate Charnes et al.
In this scope, I found all the chapters on the first part of the book, covering methodological issues, quite interesting and useful, in particular those devoted to the incorporation of value judgments and sensitivity analysis in DEA models. The chapters exploring the links with statistics, devoted to the performance of bootstrap techniques and statistical tests based on efficiency scores, also unveil important research directions.
However, these chapters require from the reader a level of expertise on DEA models as well as other topics , which cannot be acquired in the handbook itself. The chapter dealing with the consideration of qualitative data is the only one where the links between DEA and multiple criteria decision making are briefly explored. This is a relevant research and application topic and it would have been useful to have a whole chapter devoted to it. Of course, it will be impossible to include chapters, or even mention, all the areas in which DEA applications have been reported in the literature.
Therefore, I believe a more judicious selection of material to be included should have been done to reflect state-of-the-art and relevance in DEA applications. The chapters included in the handbook are interesting and indeed lessons can be learned therein that can be replicated in studies in other areas.
However, the handbook would have benefited from the inclusion of chapters describing studies in other perhaps more relevant areas, such as, for instance, energy, agriculture, environment, or telecommunications. Also, there is some imbalance in the treatment of applications in the chapters in the second part of the handbook.