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86 CHAPTER 3. NOISE REDUCTION EFFECTIVENESS
leaving ample room for rival explanations. In chapter 2 of this thesis, the support for
the noise-reduction explanation is based on the empirically significant relation between
environmental uncertainty (i.e., a driver for noise) and the extent to which organizations
rely on RPE. However, other explanations in the literature explain the relation between
environmental uncertainty and RPE. For example, Holmstrom (1982) argues that RPE
can be used as a means to pit employees or organizational parts against each other to
create a sense of internal rivalry that increases the efficiency of the individual employees.
Additionally, Gibbons & Murphy (1990:33-S) show that RPE is used to facilitate organizational
learning. Both of the presented applications can be especially useful in a highly
uncertain environment, where environmental turbulence demands that a firm exhibits its
optimal performance to survive. As a result, the analyses in chapter 2 cannot empirically
distinguish between the noise-reduction explanation and the two alternative explanations.
To provide a more direct analysis of the noise-reduction explanation of RPE theory, this
chapter analyses the effectiveness of the noise-reduction properties of RPE. This analysis
assesses whether organizational reliance on RPE actually reduces the noise levels in the
performance evaluation of employees. For the empirical analysis, I use a SEM model to
analyse survey data of 325 business unit managers in the Netherlands. Overall, the results
are consistent with the findings of the analytical agency literature and the conclusions of
chapter 2. The analyses support the main claim of RPE theory (i.e., RPE can reduce the
amount of noise in an agent’s performance evaluation). This finding is important because
it further validates the noise-reduction explanation on RPE.
The evidence for the noise-reduction claim is robust over two operationalizations of RPE
use. Similar to chapter 2, this chapter relies on both a broad, unspecific measure of RPE
use and on a measure that focuses on the explicit application of RPE to performance
targets. Both measures yield qualitatively and quantitatively similar results. The SEM
models explain 11-13% of the variance in the dependent variable ‘noise in the performance
evaluation’. Additionally, both models indicate an adequate fit on a number of goodnessof-fit
indicators, as suggested by Kline (2005).
However, the models do have important limitations. These limitations lie in the measurement
of the dependent variable. Firstly, the dependent variable noise in the performance
evaluation is measured with a single item construct. Using single-item measures increases
measurement error in the estimation and potentially reduces the validity of the model and
the findings. Although pre-testing and robustness checks support the convergent validity
of the single-item construct, the use of single-item constructs remains less than perfect.
Secondly, the responses on the noise-measure may be biased to the extent that weaker performance
managers may attribute negative performance to exogenous factors more than
high performing managers. This potential bias may introduce some noise in my noise measure.
Despite these limitations, I argue that the current study furthers our confidence in
the cornerstone explanation of RPE theory. Because of more direct evidence on the implied
causal mechanism underlying RPE theory, we can be more certain about the effectiveness
of RPE in reducing noise in the performance evaluation.