Performance measurement for accountability in corporate governance: A data envelopment analysis approach

Ehsan H. Feroz, Sanjay Goel, Raymond L. Raab

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


Purpose The purpose of this paper is to show the applicability of data envelopment analysis (DEA) in arriving at an unbiased account of relative performance in a set of companies, using the pharmaceutical industry as an example. Design/methodology/approach A DEA-based income efficiency measure of business performance for the pharmaceutical industry is computed. The pharmaceutical industry, which includes many multinational corporations with complex governance problems, and the strategies that allowed firm efficiency rankings to change over time, over ten recent years, are analyzed. Findings The analyses indicate that the inclines and declines in DEA efficiency rankings are related to the strategic choices made by the upper management. Research limitations/implications The paper attempted to trace firm behavior post hoc to validate the DEA rankings. All relevant firm behavior may not have been captured; the paper only attempted to capture behavior reported in the respectable business press, which may introduce a bias. Practical implications The approach may be ideal to evaluate strategic managers (CEOs, general managers, and presidents) by board of directors, since it relates multiple performance indices to a meta-measure of performance. Another group of beneficiaries include sector financial analysts. The approach adds a new dimension to sector analysis, to compare specific industries and identify the relative rankings of firms on multiple performance indices. Originality/value The paper demonstrates the usefulness of DEA in performance governance measurement by applying it to the pharmaceuticals industry.

Original languageEnglish (US)
Pages (from-to)121-130
Number of pages10
JournalReview of Accounting and Finance
Issue number2
StatePublished - May 16 2008


  • Corporate governance
  • Data analysis
  • Manangement accountability
  • Performance measures
  • Pharmaceuticals industry


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