Multi-factor performance measure model with an application to Fortune 500 companies

The paper develops tools for reconciling diverse measures which characterize the ®nancial performance of the Fortune 500 companies. The technology of data envelopment analysis (DEA) is employed to determine a multi-factor ®nancial performance model which inherently recognizes tradeo€s among various ®nancial measures. This study o€ers an alternative perspective and characterization on the performance of the Fortune 500 companies. It is shown that the top-ranked companies by revenue do not necessarily have top-ranked performance viewed as being multidimensional. Only about 3% companies were operating on the best-practice frontier. Substantial technical and scale ineciencies are found. Decreasing returns to scale (DRS) are uncovered among the relatively large (revenue-top-ranked) companies. The study of congestion shows that a reduction in current levels of employees, assets and equity may actually increase revenue and pro®t levels. Factor-speci®c measures, within the framework of multidimensional measure, are developed to further study the performance of companies and industries. The performance of best-practice frontier companies is analyzed by constructing reference-share measures which indicate the role each frontier company plays in evaluating non-frontier companies. Finally, the reliability of the best-practice frontier is examined. Ó 2000 Elsevier Science B.V. All rights reserved.