Publications
With our publications we cover the most diverse research areas that arise in the field of man, task and technology. In addition to traditional Business Information Systems topics such as knowledge management and business process management, you will also find articles on current topics such as blended learning, cloud computing or smart grids. Use this overview to get an impression of the range and possibilities of research in Business Information Systems at the University of Duisburg-Essen.
Type of Publication: Article in Journal
Selecting appropriate process models for IT projects: Towards a tool-supported decision approach
- Author(s):
- Harr, Michael; Seufert, Sarah
- Title of Journal:
- Wirtschaftsinformatik (WI) 2023 Proceedings
- Publication Date:
- 2023
- Language:
- EN
- Keywords:
- IT Project Management, Process Models, Decision Model, Self-Enforcing Network, Contingency Theory.
- Fulltext:
- Selecting appropriate process models for IT projects: Towards a tool-supported decision approach (868 KB)
- Link to complete version:
- https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1100&context=wi2023
- Talk associated with this publication:
- Wirtschaftsinformatik Conference (2023)
- Citation:
- Download BibTeX
Abstract
The appropriate selection of suitable process models plays an important role for IT project success. To aid in decision-making, IT project management literature offers a plethora of decision models for selecting suitable process models, however, hybrid process models are often neglected and adoption in practice is low or non-existent. To address this challenge, we draw on contingency theory to develop and implement a tool-supported decision model for the selection and evaluation of appropriate process models for IT projects, thereby leveraging artificial intelligence and machine learning in the context of a self-enforcing network. Our model provides an objective tool to assess process model suitability. Results from a conducted online survey with project management experts indicate high validity. Therefore, we contribute to the field of IT project management by expanding AI-based decision models for selecting and evaluating process models through extending the range of covered models and implementing inherent weighting of criteria.