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Exploring Project Complexity through Project Failure Factors: Analysis of Cluster Patterns Using Self-Organizing Maps

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  • Vicente Rodríguez Montequín
  • Joaquín Villanueva Balsera
  • Sonia María Cousillas Fernández
  • Francisco Ortega Fernández

Abstract

In the field of project management, complexity is closely related to project outcomes and hence project success and failure factors. Subjectivity is inherent to these concepts, which are also influenced by sectorial, cultural, and geographical differences. While theoretical frameworks to identify organizational complexity factors do exist, a thorough and multidimensional account of organizational complexity must take into account the behavior and interrelatedness of these factors. Our study is focused on analyzing the combinations of failure factors by means of self-organizing maps (SOM) and clustering techniques, thus getting different patterns about the project managers perception on influencing project failure causes and hence project complexity. The analysis is based on a survey conducted among project manager practitioners from all over the world to gather information on the degree of influence of different factors on the projects failure causes. The study is cross-sectorial. Behavioral patterns were found, concluding that in the sampled population there are five clearly differentiated groups (clusters) and at least three clear patterns of answers. The prevalent order of influence is project factors, organization related factors, project manager and team members factors, and external factors.

Suggested Citation

  • Vicente Rodríguez Montequín & Joaquín Villanueva Balsera & Sonia María Cousillas Fernández & Francisco Ortega Fernández, 2018. "Exploring Project Complexity through Project Failure Factors: Analysis of Cluster Patterns Using Self-Organizing Maps," Complexity, Hindawi, vol. 2018, pages 1-17, May.
    • Handle: RePEc:hin:complx:9496731
    DOI: 10.1155/2018/9496731
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    References listed on IDEAS

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    1. Dvir, Dov & Lechler, Thomas, 2004. "Plans are nothing, changing plans is everything: the impact of changes on project success," Research Policy, Elsevier, vol. 33(1), pages 1-15, January.
    2. Glenn Milligan & Martha Cooper, 1985. "An examination of procedures for determining the number of clusters in a data set," Psychometrika, Springer;The Psychometric Society, vol. 50(2), pages 159-179, June.
    3. Marle, Franck & Vidal, Ludovic-Alexandre & Bocquet, Jean-Claude, 2013. "Interactions-based risk clustering methodologies and algorithms for complex project management," International Journal of Production Economics, Elsevier, vol. 142(2), pages 225-234.
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