Unsupervised machine learning for project stakeholder classification: Benefits and limitations - Costanza Mariani; Yuliya Navrotska; Mauro Mancini (Project Leadership and Society (PLAS), 2023)
The literature has shown that an accurate classification of project stakeholders allows for more comprehensive planning of their management strategies. The most used classification methods have limitations stemming from using a small number of stakeholder attributes thus returning high-level and imprecise classification results. This work investigates the potential benefits and limitations of adopting unsupervised machine learning clustering as an alternative method to automatically recognize stakeholder groups. The paper demonstrates the application of a PAM algorithm for project stakeholder classification, employing qualitative and quantitative data collected from a real project in an IT Italian company. The results show that the use of unsupervised clustering leads to a more granular and detailed stakeholder grouping that enables the design of better refined and customized stakeholder management strategies. Furthermore, the results of the paper demonstrate that the use of this methodology, when data is taken from a structured dataset, reduces the degree of subjectivity in classification, promoting a data-driven approach to project stakeholder management.
Keywords: 650; Projects; Stakeholder management; Unsupervised machine learning