Revisiting dual-role factors in data envelopment analysis: Derivation and implications

Wen-Chih Chen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Data Envelopment Analysis (DEA) is a mathematical programming method to evaluate relative performance. Typical DEA studies consider a production process transforming inputs to outputs. In some cases, however, some factors can be both inputs and outputs simultaneously and are termed dual-role factors. For example, research funding can be an input that strengthens a university's academic performance and the actual funds can be an output. This article investigates the problem of how to incorporate dual-role factors in DEA. Rather than proposing an ad hoc evaluation model directly, this article considers the concept of "joint technology," two individual production processes acting in common by summarizing the intuitive thinking. The efficiency evaluation models, based on variant assumptions, thus can be axiomatically derived, validated, and extended. How to determine the input/output tendency of a dual-role factor based on the evaluating results is shown and explained from different aspects. It is concluded that the tendency is a property on the projected boundary, not the data point itself.

Original languageEnglish
Pages (from-to)653-663
Number of pages11
JournalIIE Transactions (Institute of Industrial Engineers)
Volume46
Issue number7
DOIs
StatePublished - 3 Jul 2014

Keywords

  • Data envelopment analysis
  • dual-role factor
  • efficiency

Fingerprint Dive into the research topics of 'Revisiting dual-role factors in data envelopment analysis: Derivation and implications'. Together they form a unique fingerprint.

Cite this