Drivers of Performance Information Use: Systematic Literature Review and Directions for Future Research

ABSTRACT

The use of performance information in decision-making is a management behavior that has received much attention in public administration research and practice. This article seeks to contribute to a better understanding of this behavior. It conducts a systematic review of 25 recently published empirical studies that have examined drivers of performance information use. Analyzing these studies, which were selected on the basis of their definition of purposeful data use, the article identifies factors that have repeatedly shown a positive impact: measurement system maturity, stakeholder involvement, leadership support, support capacity, innovative culture, and goal clarity. This systematic analysis also uncovers less conclusive variables; findings which are highly relevant for future studies. Based on the review, the article suggests directions for further research endeavors, including theoretical and methodological propositions.

KEYWORDS::

• literature review
• performance information
• purposeful use

Notes

The terms factor, variable, determinant, and driver are used interchangeably in this article. It was not necessary to differentiate them to achieve the purposes of this discussion.

In the first step, 28 studies were selected. Due to the missing disclosure of all used operationalizations or the use of rather idiosyncratic explanation factors, in the second step three studies were excluded from the sample. In order to avoid overrepresentation of singular data sets, only one regression model per publication (if several had been presented) was included in the review. The review always focused on the model that took the most control variables into account and where the dependent variable came closest to the presented understanding of purposeful information use.

Since some studies only reported either the R² or the adjusted R², my estimation took both indicators into account. In cases where both measures were reported, their mean was used.

All vote counts were weighted using the following formula:

${\displaystyle {\mathrm{VoteCount}}_{\text{weighted}}=1-\frac{1}{\sqrt{n}}.}$

For example, regarding the first variable, “stakeholder involvement,” the table shows nine counts (large-N samples and single-case studies counted equally) in support of a positive effect at a significance level of at least 0.1 (2 + 7 counts). Similarly, the last column shows that there are also 8.43 positive counts if one assigns weights to every study’s sample.

Additional information

Alexander Kroll is an Assistant Professor of Public Administration at Florida International University. He is interested in public management topics, such as performance management, strategy, leadership, and motivation. His research on performance information use has been published in American Review of Public Administration, International Public Management Journal, and Public Administration Review.