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.
Harr, Michael Dominic and Seufert, Sarah, "Selecting Appropriate Process Models for IT Projects: Towards a Tool-Supported Decision Approach" (2023). Wirtschaftsinformatik 2023 Proceedings. 101.