Building Performance Models from Expert Knowledge

41 Pages Posted: 12 May 2003

See all articles by Margaret A. Abernethy

Margaret A. Abernethy

University of Melbourne, Department of Accounting

Malcolm Horne

Monash Medical Centre - Faculty of Medicine, Nursing Health Sciences; Southern Health

Anne M. Lillis

University of Melbourne

Mary A. Malina

University of Colorado at Denver - Business School

Frank H. Selto

University of Colorado, Boulder

Abstract

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.

Keywords: causal knowledge, qualitative method, knowledge map, performance measurement

JEL Classification: M40, M46

Suggested Citation

Abernethy, Margaret A. and Horne, Malcolm and Lillis, Anne and Malina, Mary A. and Selto, Frank H., Building Performance Models from Expert Knowledge. Available at SSRN: https://ssrn.com/abstract=403220 or http://dx.doi.org/10.2139/ssrn.403220

Margaret A. Abernethy

University of Melbourne, Department of Accounting ( email )

Victoria
Melbourne, Victoria 3010 3010
Australia
+61 3 8344 7655 (Phone)
+61 3 9349 2397 (Fax)

Malcolm Horne

Monash Medical Centre - Faculty of Medicine, Nursing Health Sciences ( email )

Wellington Road
Clayton
Victoria 3800
Australia
+61 3 9594 5521 (Phone)

Southern Health

246 Clayton road
Clayton
Victoria, 3168
Australia

Anne Lillis

University of Melbourne ( email )

Victoria
Parkville, Victoria 3010
Australia

Mary A. Malina

University of Colorado at Denver - Business School ( email )

1250 14th St.
Denver, CO 80204
United States

Frank H. Selto (Contact Author)

University of Colorado, Boulder ( email )

419 UCB
Boulder, CO 80309-0419
United States

Register to save articles to
your library

Register

Paper statistics

Downloads
548
Abstract Views
3,084
rank
48,623
PlumX Metrics