Art der Publikation: Beitrag in Zeitschrift

Toward AI-Enabled Process Model Selection for IT Projects: A Self-Enforcing Network Approach

Autor(en):

Syfuß, Dustin; Harr, Michael Dominic; Seufert, Sarah; Schütte, Reinhard

Titel der Zeitschrift:

Computers

Jahrgang (Veröffentlichung):

15 (2026)

Heftnummer:

10

Seiten:

1-70

ISSN:

2073-431X

Sprache:

English

Schlagworte:

IT project management; process model; project management methodology; IT project; decision-making; artificial intelligence; self-enforcing network; contingency theory

Digital Object Identifier (DOI):

doi:10.3390/computers15100689

Link zum Volltext:

https://doi.org/10.3390/computers15100689

Zitation:

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Kurzfassung

The selection of an appropriate process model is a critical determinant of information technology (IT) project success. However, existing decision models insufficiently consider hybrid approaches and show limited practical adoption. Based on a systematic literature review of 35 publications, this article reveals five recurring deficits of analyzed decision models: coarse decision granularity, few alternatives, few criteria, absent criterion weighting, and limited empirical validation and practical implementation. Drawing on contingency theory, we develop and implement an artificial intelligence (AI) enabled decision model within a self-enforcing network. The model covers 17 process models, considers 82 project-specific, project-management-specific, team-related, and organizational criteria, and applies explicit criterion weighting. Two online surveys with project management experts yielded 20 retrospectively characterized IT projects for retrospective validation. For representative IT projects, the first recommendation matches the adopted overarching IT project management approach in 81.25% of cases, whereas the agreement at the level of concrete process models remains lower. Across all comparison metrics, it performs at least as well as the most comprehensive existing decision model, while activating agile process models more strongly. The article contributes a transparent and reproducible decision aid that helps narrow the gap between IT project management research and practice.