Recommendation of Project Management Practices: A Contribution to Hybrid Models

• Published in: IEEE transactions on engineering management Vol. 69; no. 6; pp. 3558 - 3571
• Main Authors: Bianchi, Michael J., Conforto, Edivandro C., Rebentisch, Eric, Amaral, Daniel C., Rezende, Solange O., de Padua, Renan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Association rules; Cluster analysis; Customization; Data mining; hybrid models; Product customization; Product development; Project management; Recommender systems; Software development; Waterfalls
• ISSN: 0018-9391; 1558-0040
• DOI: 10.1109/TEM.2021.3101179

Dealing with an uncertain and dynamic environment when facing multiple and fast-paced challenges forces professionals to adopt agile practices in different environments, resulting in the use of hybrid project management models. However, identifying the right practice to adopt can be challenging, given the variety of project types and environmental factors. In this article, a recommendation method that would allow for the identification of patterns of project management practices for different environments-using an agility indicator-is proposed. The proposed method is tested with a dataset of 856 projects. A cluster analysis is applied to divide the projects into three groups according to environmental characteristics, called scenarios: waterfall, agile, and hybrid. Then, we apply the association rule technique for each group separately, identifying specific patterns of practice for each group. Through a comparative analysis, we verify the consistency between the recommendations of the proposed method for each scenario and the literature on project management. The results indicate the feasibility of the proposed method, thus opening up new research opportunities for hybrid models that can be customized for different projects. This article can help project management professionals apply the agile method beyond its use in software development and improve the process of combining project management practices. We also suggest directions for new research to advance the knowledge of useful decision support tools for hybrid model customization.