دانلود رایگان مقاله اندازه گیری عملکرد به طور تئوری و عمل

ABSTRACT

This paper builds on principles and techniques developed in measurement science, as currently understood in physical sciences and engineering, to improve the theory and practice of performance measurement. To do so, it firstly discusses three fundamental positions on measurement, characterized as metaphysical, anti-metaphysical and relativistic. Subsequently, it lays the foundations of a pragmatic epistemology of measurement in both physical and social sciences. Finally, these insights are integrated through the examination of possible advances in both the theory and practice of performance measurement in organizations.

6. Toward a pragmatic view of performance measurement

In physical sciences and engineering the metaphysical paradigm has been largely superseded by the representational one, which, in turn, is increasingly complemented by emphasis on the role of models and, therefore, by the relativistic paradigm, moderated by the pragmatic instances analyzed above. The adoption of a pragmatic perspective in PM research and practice requires a critical re-conceptualization. First of all, the adoption of a model-based view, as opposed to a truth-based view, has substantial implications on the measurement process and on the interpretation of its results. From a model-based point of view, measurement is regarded as a knowledge-based process, rather than a purely empirical determination. The object whose property is measured is assumed to exist in the empirical world, but it is acknowledged that the data collected about that object results from an interpretation process, i.e., an explicit or implicit model, which belongs to the symbolic/informational realm. As a consequence, the measurement procedure must be defined, and the system under measurement designed and set up by considering the context and the goals for which the measurement itself is being undertaken. Furthermore, particular care has to be exerted when utilizing any data or information in a context other than that in which it was meant, as this has substantial implications on the drivers, purposes and uses of PM (Behn, 2003; Hatry, 1999). This is particularly significantin benchmarking exercises or compilation of league tables in both private and public sector contexts (Ammons, 1999; Goldstein and Spiegelhalter, 1996). Equally, from a model-based point of view, it would be nonsensical to state that a performance indicator is either ‘good’ or ‘bad’ in absolute terms. Rather, on the bases of its goals and other relevant factors (e.g., cost, quality), an indicator could be deemed to be adequate or inadequateto-purpose. For example, an indicator that considers the average time an organization spends to produce a quote might be appropriate to monitor the organization’s responsiveness to customers. This does not equate to saying that the average difference between date of verbal confirmation of receipt of quote by customers and the date of first contact by customers is the ‘right measure’ for responsiveness to customers. In fact, the calculation of variance in performance could also be informative. However, a simple average might be appropriate for monitoring purposes, whereas richer information would be necessary to support process improvement.