Assessing the Impact of Factors Affecting Productivity UsingStochastic Data Envelopment Analysis
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{'SignBlockName': 'William Bainbridge', 'PO_EMAI': 'wbainbri@nsf.gov', 'PO_PHON': '7032927470'}
Multiple measures or the use of several ratios to evaluate performance          almost always results in conflicting performance measures because the           organization being evaluated may appear to be performing better than            average on certain indicators and poorer on others.  In addition                business unit managers who are evaluated in terms of achieving multiple         objectives are known to be concerned with the inequitable application           of multiple measures because such measures do not account for factors           which affect but are not under the control of the managers. The Data            Envelopment Analysis (DEA) was designed to measure productivity                 relative to the best practice available and it accommodates multiple            measures of performance (inputs and output) and adjusts for factors not         under the control of management.  This research seeks to develop a              stochastic data envelopment analysis method and explore the statistical         attributes of an extended stochastic DEA.                                                                                                                       The proposed research will be useful for empirical research and other           applied work in production frontier estimation because it seeks to              develop a model, (a) employing a flexible nonparametric form                    incorporating the impact of multiple input, multiple output                     correspondences; (b) incorporating the impact of both organizational            inefficiency and rand factors; and (c) providing a basis for testing            hypotheses within a flexible non parametric framework.
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{'Code': '1321', 'Text': 'Decision, Risk & Mgmt Sci'}
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