Deviation from Standards and Performance in Knowledge-Intensive Processes: Evidence from the Process of Selling Customized IT Solutions

Mikhail Monashev, Michal Krčál, Jan Mendling

Publication: Chapter in book/Conference proceedingContribution to conference proceedings

Abstract

Standardization has been shown to be a reliable method of reducing unpredictability and consequently improving the performance of routine processes. However, it is surprising that the literature on knowledge-intensive processes (KiPs) rarely discusses this option or portrays such processes as inherently unsuitable for standardization. This presents the question of whether and to what extent standardization and following standards benefit KiPs. In this paper, we report findings from a case study on the impact of deviations from standards on the sales process of an IT service provider. Each instance of the sales process is a new project which involves a series of tasks characterized by different degrees of knowledge intensity. The findings are based on two data sources: (i) process documentation, and (ii) semi-structured interviews with managers and process participants. We applied the constructivist grounded theory method in the analysis of these materials. Our analysis yielded a series of propositions that characterize the benefits and issues that deviations from standards may bring to KiPs and the circumstances under which they are likely to materialize. Our study implies that deviations from standards mostly undermine the performance of KiPs unless they are initiated internally by process actors when standards are not sufficiently robust.
Original languageEnglish
Title of host publicationBusiness Process Management
Subtitle of host publication21st International Conference, BPM 2023, Utrecht, The Netherlands, September 11–15, 2023, Proceedings
EditorsChiara Di Francescomarino, Andrea Burattin, Christian Janiesch, Shazia Sadiq
Place of PublicationCham
PublisherSpringer
Pages430–446
ISBN (Electronic)978-3-031-41620-0
ISBN (Print)978-3-031-41619-4
DOIs
Publication statusPublished - 2023

Publication series

SeriesLecture Notes in Computer Science
Volume14159
ISSN0302-9743

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