Measuring Performance in Knowledge-intensive Processes

Abstract

Knowledge-intensive Processes (KIPs) can be defined as a type of process that comprises sequences of activities based on intensive acquisition, sharing, storage, and (re)use of knowledge, whereby the amount of value added to the organization depends on the knowledge of the actors involved. Among other characteristics, KIPs are usually non-repeatable, collaboration-oriented, unpredictable, and, in many cases, driven by implicit knowledge, derived from the capabilities and previous experiences of participants. Despite the growing body of research focused on understanding KIPs and on proposing systems to support these KIPs, the research question on how to define performance measures in this context remains open. In this article, we address this issue with a proposal to enable the performance management of KIPs. Our approach comprises an ontology that allows us to define process performance indicators (PPIs) in the context of KIPs, and a methodology that builds on the ontology and the concepts of lead and lag indicators to provide process participants with actionable guidelines that help them conduct the KIP in a way that fulfills a set of performance goals. Both the ontology and the methodology were applied to a case study of a real organization in Brazil to manage the performance of an Incident management process within a communication technology outsourcing company. The insights provided by our approach were considered highly valuable by the company.