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Selecting Appropriate Process Models for IT Projects: Towards a Tool-Supported Decision Approach

Author

Listed:
  • Michael Dominic Harr

    (University of Duisburg-Essen)

  • Sarah Seufert

    (University of Duisburg-Essen)

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.

Suggested Citation

  • Michael Dominic Harr & Sarah Seufert, 2025. "Selecting Appropriate Process Models for IT Projects: Towards a Tool-Supported Decision Approach," Lecture Notes in Information Systems and Organization,, Springer.
    • Handle: RePEc:spr:lnichp:978-3-031-80119-8_28
    DOI: 10.1007/978-3-031-80119-8_28
    as

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