Title
Measuring Performance in Knowledge-intensive Processes.
Abstract
Knowledge-intensive Processes (KIPs) are processes whose execution is heavily dependent on knowledge workers performing various interconnected knowledge-intensive decision-making tasks. 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 thereon 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 have been applied to a case study of a real organization in Brazil to manage the performance of an Incident Troubleshooting Process within an ICT (Information and Communications Technology) Outsourcing Company.
Year
DOI
Venue
2019
10.1145/3289180
ACM Trans. Internet Techn.
Keywords
Field
DocType
Process performance indicators, knowledge-intensive processes, performance measure
Troubleshooting,Ontology,Performance indicator,Research question,Computer science,Implicit knowledge,Outsourcing,Information and Communications Technology,Performance management,Distributed computing,Process management
Journal
Volume
Issue
ISSN
19
1
1533-5399
Citations 
PageRank 
References 
0
0.34
28
Authors
7