This study surveys the data envelopment analysis (DEA) literature by applying a citation-based approach. The main goals are to find a set of papers playing the central role in DEA development and to discover the latest active DEA subareas. A directional network is constructed based on citation relationships among academic papers. After assigning an importance index to each link in the citation network, main DEA development paths emerge. We examine various types of main paths, including local main path, global main path, and multiple main paths. The analysis result suggests, as expected, that Charnes et al. (1978) [Charnes A, Cooper WW, Rhodes E. Measuring the efficiency of decision making units. European Journal of Operational Research 1978; 2(6): 429–444] is the most influential DEA paper. The five most active DEA subareas in recent years are identified; among them the “two-stage contextual factor evaluation framework” is relatively more active. Aside from the main path analysis, we summarize basic statistics on DEA journals and researchers. A growth curve analysis hints that the DEA literature’s size will eventually grow to at least double the size of the existing literature.

Data envelopment analysis 1978-2010: A citation-based literature survey

Network data envelopment analysis: A review

Data envelopment analysis (DEA) is a method for measuring the efficiency of peer decision making units (DMUs). An important area of development in recent years has been devoted to applications wherein DMUs represent two-stage or network processes. One particular subset of such processes is those in which all the outputs from the first stage are the only inputs to the second stage. The current paper reviews these models and establishes relations among various approaches. We show that all the existing approaches can be categorized as using either Stackelberg (leader-follower), or cooperative game concepts. Future perspectives and challenges are discussed.

Measuring performance of two-stage network structures by DEA: A review and future perspective

We model the performance of DMUs (decision-making units) using a two-stage network model. In the first stage of production DMUs use inputs to produce an intermediate output that becomes an input to a second stage where final outputs are produced. Previous black box DEA models allowed for non-radial scaling of outputs and inputs and accounted for slacks in the constraints that define the technology. We extend these models and build a performance measure that accounts for a network structure of production. We use our method to estimate the performance of Japanese banks, which use labor, physical capital, and financial equity capital in a first stage to produce an intermediate output of deposits. In the second stage, those deposits become an input in the production of loans and securities investments. The network estimates reveal greater bank inefficiency than do the estimates that treat the bank production process as a black box with all production taking place in a single stage.

Ning Li

Papers

Additive efficiency decomposition in two-stage DEA