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Journal cover: Measuring Business Excellence

Measuring Business Excellence

ISSN: 1368-3047

Online from: 1997

Subject Area: Performance Management and Measurement

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Performance measurement in not-for-profit and public-sector organisations

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Title:Performance measurement in not-for-profit and public-sector organisations
Author(s):Malcolm Macpherson, (Malcolm Macpherson is an Independent Consultant and Veteran Quality Award Assessor. He edits a Web site dedicated to the US Malcolm Baldrige National Quality Award and its many international and local derivatives (at and publishes e-mail magazines on organisational excellence and leadership. He is an elected member of the Central Otago District Council, in the South Island of New Zealand.)
Citation:Malcolm Macpherson, (2001) "Performance measurement in not-for-profit and public-sector organisations", Measuring Business Excellence, Vol. 5 Iss: 2, pp.13 - 17
Keywords:Human resource management, Non-profit organizations, Performance measurement, Public sector
Article type:General review
DOI:10.1108/13683040110397220 (Permanent URL)
Publisher:MCB UP Ltd
Abstract:Measuring performance is increasingly important in not-for-profit and public sector organisations from those as large as the US federal government to the smallest volunteer group. Human resource metrics are the most relevant – spanning function, operations and strategy. Function measures include employee efficiency and effectiveness (turnover, sick leave, insurance and recruitment costs, for example). Operational measures include specifics like revenue per employee, as well as broad measures of effectiveness that link management to performance and returns on investment. Future-oriented strategic measures match capability against anticipated need, and are increasingly a key part of core planning activities. Barriers to effective measurement include fear (of retribution, variation and loss of control). Data may be gathered using top-down or bottom-up approaches. Issues to be considered when implementing a metrics methodology include linking outputs to outcomes, data quality, leading vs lagging indicators, indicator maturity, and imperfection.

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