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Using data envelopment analysis to measure and improve organizational performance
Public Administration Review ( IF 8.144 ) Pub Date : 2023-05-29 , DOI: 10.1111/puar.13679
Thomas R. Sexton, Christine Pitocco, Herbert F. Lewis

Organizations are complex and have many goals while almost all analytical tools measure performance using only one goal. Thus, analysts often rely on multiple analytical tools to produce a bewildering array of performance measures that often lack internal consistency and a clear focus. In this article, we show how data envelopment analysis (DEA) builds a performance frontier (analogous to a production frontier) that measures organizational performance in the presence of multiple organizational measures. The DEA frontier produces target values for each organizational measure based on the observed performance of organizations in the comparison set. In addition, DEA provides factor performance levels for each performance measure for each organization and can detect circumstances in which an organization has a strong overall performance measure but still has weaknesses in one or more measures. We will illustrate this approach with applications to several examples using real data. Analyzing organizational performance data is critical in the organizational improvement process. The data must directly reflect the organization's goals and the analytical tools used must be appropriate. However, organizations are complex and have many goals while univariate analytical tools measure performance relative to only one goal. Thus, analysts often rely on multiple analytical tools to produce a collection of performance measures, sometimes resulting in a bewildering array of measures that lack focus.

中文翻译:

使用数据包络分析来衡量和提高组织绩效

组织很复杂,有很多目标,而几乎所有分析工具都仅使用一个目标来衡量绩效。因此,分析师经常依赖多种分析工具来得出一系列令人眼花缭乱的绩效衡量标准,而这些衡量标准往往缺乏内部一致性和明确的重点。在本文中,我们展示了数据包络分析 (DEA) 如何构建绩效前沿(类似于生产前沿),以在存在多种组织指标的情况下衡量组织绩效。DEA 前沿根据比较集中组织的观察绩效为每个组织衡量标准生成目标值。此外,DEA 为每个组织的每项绩效指标提供因子绩效水平,并可以检测组织具有较强的整体绩效指标但在一项或多项指标中仍存在弱点的情况。我们将通过使用真实数据的几个示例来说明这种方法。分析组织绩效数据对于组织改进过程至关重要。数据必须直接反映组织的目标,并且使用的分析工具必须适当。然而,组织很复杂并且有许多目标,而单变量分析工具仅衡量与一个目标相关的绩效。因此,分析师经常依赖多种分析工具来生成一系列绩效指标,有时会导致一系列令人眼花缭乱、缺乏重点的指标。
更新日期:2023-05-29
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