A multi-method approach to building causal performance maps from expert knowledge

This approach demonstrates the use of cognitive mapping to extract tacit knowledge from employees in knowledge-intensive organizations and the extensive array of performance-relevant variables that arises from such mapping, and the potential to use the resulting causal performance map as a comprehensive, articulated basis for developing a performance measurement system.

Abstract

This paper describes a multi-method approach to building the foundations of a causal performance measurement model. Such models have received considerable attention in the management accounting literature in recent years. Conventional models, such as the balanced scorecard commence with the strategic understanding of top management which is then translated into operational measures at lower levels. In contrast, this study proposes methods of performance mapping that draw on the knowledge of experts who control core-operating tasks. Causal knowledge is elicited from individuals who through their experience and training 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. Because no single method for eliciting causal performance maps dominates the literature, the study triangulates three methods of deriving a map of causally linked key success factors (KSFs)—a computerized analysis, an ethnographic analysis and an interactive mapping by expert participants. The study's primary contribution is the development and illustration of an approach to building performance models in management control settings where expert knowledge workers perform complex processes, the outcomes of which are difficult to quantify. The study's secondary contribution is the triangulation of multiple qualitative methods to enhance the validity of performance model development. This approach demonstrates (1) the use of cognitive mapping to extract tacit knowledge from employees in knowledge-intensive organizations; (2) the extensive array of performance-relevant variables that arises from such mapping, and (3) the potential to use the resulting causal performance map as a comprehensive, articulated basis for developing a performance measurement system. The approach used in this study for developing a causal performance map is adaptable to management control of other knowledge-intensive organizations.