Determinants for the Goodness of Performance Measurement Systems: The Visibility of Performance

Determinants for the Goodness of Performance Measurement Systems: The Visibility of Performance

Tim Pidun (Technische Universität Dresden, Germany)
DOI: 10.4018/978-1-5225-1837-2.ch079
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Abstract

The supply of adequate information is one of the main functions of Performance Measurement Systems (PMS), but also one of its drawbacks and reason for failure. Not only the collection of indicators is crucial, but also the stakeholders' understanding of their meaning, purpose, and contextual embedding. Today, companies seek a PMS without a way to express the goodness of a solution, indicating its ability to deliver appropriate information and to address these demands. The goal of this chapter is to explore the mechanisms that drive information and knowledge supply in PMS in order to model a way to express this goodness. Using a grounded theory approach, a theory of visibility of performance is developed, featuring a catalog of determinants for the goodness of PMS. Companies can conveniently use them to assess their PMS and to improve the visibility of their performance.
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Introduction

In a BARC study on BPM (BARC, 2009), 80% of the enterprises claim the persistent need to improve their overall performance management related processes. Deloitte stated that 53% of all companies still complain that their measures are inappropriate to anticipate future developments (Deloitte, 2007). 21% of them are even unable to determine the actual state and health of their company, and in particular 59% of all companies (Deloitte, 2004) miss an appropriate tool support for analysis. To address this issue, various types of Performance Measurement Systems (PMS) are used. PMS are business information systems that are collecting, compiling, analyzing, and disseminating data and valuable information (Neely et al., 1997) on organizational performance. Individual requirements and preferences force companies to choose an appropriate solution from many different conceptual performance measurement approaches; from the customized visualization of some financial figures to highly adjustable and mature methodologies like the Balanced Scorecard (BSC).

The theory of administrative behavior by Simon (1959) explains this multitude of possibilities by the assumption that individuals are faced with multiple constraints when striving for the best information (the problem of bounded rationality) and therefor are using satisfying and sufficient information instead. This implies the impossibility of one optimum PMS solution that fits the needs of each stakeholder. Hence, it is of interest what drivers and determinants make a PMS beneficial and successful in order to deliver individual, appropriate, and sufficient performance information.

Hence, the goal of this investigation is to explore and expose the mechanisms that drive information and knowledge supply in PMS in order to assess their appropriateness for the organization.

There are quite a lot of PMS concepts, featuring their own mix of principal viewpoints, perspectives and measures. PMS originate from the domains of accounting and finance, the most prominent representatives being the Balanced Scorecard by Kaplan and Norton (1996) with four initial perspectives financial, customer, internal and learning and development, combined with the link to strategy and execution. Neely et al. (2002) propose the Performance Prism, adding regulatory requirements, partnering conditions, the competitive environment and the consideration of measuring intangible assets. Lynch and Cross introduce their Performance Pyramid (Lynch and Cross, 1992) incorporating views of the customer, the employee and the shareholder. Additional to these market leading systems, there are at least sixteen other systems available in the literature (Pidun and Felden, 2011), so in principle, there should be a dedicated concept or customizable system for every company.

Though, empirical research shows that many applications of PMS still fail. De Waal and Counet (2009) claim that 56% of all PMS projects are not successful at all. Horvarth et al. (2008) note that 80% of all companies missed a certain tangible benefit while using a BSC. Even 54% of all of these companies do it just with very guarded enthusiasm and not to its full extent.

Explicitly accepting that there cannot be a one size fits all solution, it seems to be very hard to find the appropriate PMS for an adequate information supply. So there is a need for a way to express the goodness of a PMS by aspects of appropriateness of performance information, thus delineating a certain visibility of performance that is driven by specific determinants.

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