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Performance Measures for Managerial Decision Making: Performance Measurement Synergies in Multi-Attribute Performance Measurement Systems

机译:管理决策的绩效测量:多属性绩效测量系统中的绩效测量协同作用

摘要

This research tests for correlation between corporate performance and use of financial measures, nonfinancial measures, and number of balanced scorecard categories used. Literature notes a preference for managing by nonfinancial measures because financial measures are lagging indicators, but little empirical evidence is available on the relationship between nonfinancial measures and financial performance, and few companies are found to realize the benefits of nonfinancial measurements. The balanced scorecard has been studied to find the impact of diversity of performance measures, and anecdotal improvements have been reported, but there is a paucity of empirical evidence regarding how the use of a balanced scorecard impacts organizational performance. These issues are investigated in this research with a web based survey distributed to a sample of publicly traded companies using a systematic selection process based on randomly selected numbers generated for each 3-digit NAICS category. The dependent variable is a rank of high, medium or low performance based on 12-month rolling average stock price comparisons from January 2005 to January 2009. These averages are analyzed as a percent change for each company, with performance standardized by 3-digit NAICS category to eliminate cross industry variance in performance ranking. Kruskal-Wallis one-way ANOVA is used to test for correlation. High performers show greatest utilization of both financial and nonfinancial measures, followed by medium performers, with low performers utilizing both measures the least. Nonfinancial performance measures are more correlated to firm value than financial measures with the high performersu27 mean score for nonfinancial measures being higher than for financial measures. By contrast, medium and low performers exhibit the opposite: higher mean scores for financial measures than for nonfinancial measures [p ≤ 0.05 for nonfinancial measures and p ≤ 0.1 for financial measures]. Correlation is found to be borderline significant (p = 0.06) for the number of balanced scorecard categories used with high performers utilizing the highest number of categories and low performers utilizing the lowest number of categories [p = 0.009 with inclusion of two respondents reporting no usage of balanced scorecard categories].
机译:这项研究测试了公司绩效与财务指标,非财务指标和平衡计分卡类别数量之间的相关性。文献指出,由于财务指标是滞后的指标,因此倾向于采用非财务指标进行管理,但是关于非财务指标与财务绩效之间关系的经验证据很少,而且很少发现公司能够实现非财务指标的收益。对平衡计分卡进行了研究,以发现绩效指标多样性的影响,并且据传有传闻性改进,但是关于平衡计分卡的使用如何影响组织绩效的经验证据很少。在本研究中,通过使用基于系统选择过程的网络调查将这些问题分发给一些上市公司样本,这些系统选择过程是基于为每个3位数字的NAICS类别生成的随机选择的数字进行的。因变量是根据2005年1月至2009年1月的12个月滚动平均股价比较得出的高,中或低绩效排名。这些平均值被分析为每个公司的变化百分比,其绩效由3位数字的NAICS标准化类别,以消除绩效排名中的跨行业差异。 Kruskal-Wallis单向方差分析用于检验相关性。绩效较高的人对财务和非财务指标的利用最大,其次是绩效中等的人,绩效低的人对财务和非财务指标的利用最少。非财务绩效衡量指标与企业价值之间的相关性高于财务衡量指标,非财务衡量指标的高绩效平均得分高于财务衡量指标。相比之下,中,低绩效者则相反:财务指标的平均得分高于非财务指标[非财务指标的p≤0.05,财务指标的p≤0.1]。对于使用最高类别的高绩效者和使用最低类别的低绩效者,平衡得分卡类别的数量之间的相关性被认为是显着的(p = 0.06)[p = 0.009,其中包括两个报告称未使用的受访者平衡计分卡类别]。

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    Fowke Robert Andrew;

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