Building Performance Models from Expert Knowledge

Improving management control of knowledge-based organizations motivates building performance management models (PMM) of causally related, key success factors (KSF). This study elicits knowledge maps of KSF from field experts. These knowledge maps are layered to create the foundation of the organization's PMM. The study elicits causal knowledge from experts who through their experience, training, etc. have encoded relational or causal knowledge about complex systems; that is, they understand how things fit and work together, although they might not have articulated that knowledge. Converting experts' tacit causal knowledge into organizational capability or explicit knowledge should a) perpetuate that knowledge in the organization, b) enable improved training of less experienced employees, and c) lead to deployment of improved systems (e.g., PMM). Because no single method for eliciting mental models or knowledge maps dominates the literature, the study uses multiple methods and overlays their results to build a comprehensive causal model. This study reports the results of a field study to build the foundation of a PMM in a clinical department of a large hospital. The study uses three qualitative methods to elicit mental models of KSF and their interactions from key clinical program administrators, physicians, and nurses. The motivation of the present study is to report the results of (1) tapping the causal knowledge of individual experts in the field and (2) triangulating multiple methods of qualitative data analysis. The alternative method of building a PMM by using archival data-mining is rejected for reasons of (1) limited archival time-series data, (2) limited scope of archival data, (3) myopic focus on conveniently available data, and (4) inability to screen out spurious relations. Because these limitations are generally present in knowledge-intensive organizations, this study's approach can have general application.