IDEAS home Printed from https://ideas.repec.org/a/hin/complx/9496731.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/8503/2018/9496731.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/8503/2018/9496731.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2018/9496731?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Liu, Pei-chen Barry & Hansen, Mark & Mukherjee, Avijit, 2008. "Scenario-based air traffic flow management: From theory to practice," Transportation Research Part B: Methodological, Elsevier, vol. 42(7-8), pages 685-702, August.
    2. Hélène Syed Zwick & S. Ali Shah Syed, 2017. "The polarization impact of the crisis on the Eurozone labour markets: a hierarchical cluster analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 24(7), pages 472-476, April.
    3. Goethner, Maximilian & Hornuf, Lars & Regner, Tobias, 2021. "Protecting investors in equity crowdfunding: An empirical analysis of the small investor protection act," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    4. Cees H. Elzinga & Aart C. Liefbroer, 2007. "De-standardization of Family-Life Trajectories of Young Adults: A Cross-National Comparison Using Sequence Analysis," European Journal of Population, Springer;European Association for Population Studies, vol. 23(3), pages 225-250, October.
    5. Almohri, Haidar & Chinnam, Ratna Babu & Colosimo, Mark, 2019. "Data-driven analytics for benchmarking and optimizing the performance of automotive dealerships," International Journal of Production Economics, Elsevier, vol. 213(C), pages 69-80.
    6. Ludovic-Alexandre Vidal & Franck Marle & Mathieu Dernis, 2021. "Modeling and Estimating Host Country Values in International Projects to Facilitate In-Country Value Creation," Sustainability, MDPI, vol. 13(10), pages 1-21, May.
    7. Michele Cincera, 2005. "Firms' productivity growth and R&D spillovers: An analysis of alternative technological proximity measures," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 14(8), pages 657-682.
    8. Pennings, J.S.J. & van Kranenburg, H.L. & Hagedoorn, J., 2005. "Past, present and future of the telecommunications industry," Research Memorandum 016, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    9. Balakrishnan, P. V. (Sundar) & Cooper, Martha C. & Jacob, Varghese S. & Lewis, Phillip A., 1996. "Comparative performance of the FSCL neural net and K-means algorithm for market segmentation," European Journal of Operational Research, Elsevier, vol. 93(2), pages 346-357, September.
    10. Anis Hoayek & Didier Rullière, 2024. "Assessing clustering methods using Shannon's entropy," Post-Print hal-03812055, HAL.
    11. Li-Xuan Qin & Steven G. Self, 2006. "The Clustering of Regression Models Method with Applications in Gene Expression Data," Biometrics, The International Biometric Society, vol. 62(2), pages 526-533, June.
    12. Rozkrut Dominik, 2014. "Measuring Eco-Innovation: Towards Better Policies to Support Green Growth," Folia Oeconomica Stetinensia, Sciendo, vol. 14(1), pages 137-148, June.
    13. Verworn, Birgit, 2009. "A structural equation model of the impact of the "fuzzy front end" on the success of new product development," Research Policy, Elsevier, vol. 38(10), pages 1571-1581, December.
    14. Caroline Méjean & Pauline Macouillard & Sandrine Péneau & Camille Lassale & Serge Hercberg & Katia Castetbon, 2014. "Association of Perception of Front-of-Pack Labels with Dietary, Lifestyle and Health Characteristics," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-11, March.
    15. Li, Pai-Ling & Chiou, Jeng-Min, 2011. "Identifying cluster number for subspace projected functional data clustering," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2090-2103, June.
    16. Natalia Soledad Kruger, 2011. "La segmentación educativa en Argentina: exploración empírica en base a PISA 2009," Investigaciones de Economía de la Educación volume 6, in: Antonio Caparrós Ruiz (ed.), Investigaciones de Economía de la Educación 6, edition 1, volume 6, chapter 8, pages 135-155, Asociación de Economía de la Educación.
    17. Lukas Burs & Ellen Roemer & Stefan Worm & Andrea Masini, 2020. "Are They All Equal? Uncovering Adopter Groups of Battery Electric Vehicles," Sustainability, MDPI, vol. 12(7), pages 1-16, April.
    18. Luis García-González & Ángel Abós & Sergio Diloy-Peña & Alexander Gil-Arias & Javier Sevil-Serrano, 2020. "Can a Hybrid Sport Education/Teaching Games for Understanding Volleyball Unit Be More Effective in Less Motivated Students? An Examination into a Set of Motivation-Related Variables," Sustainability, MDPI, vol. 12(15), pages 1-16, July.
    19. Hyeri Choi & Min Jae Park, 2019. "Evaluating the Efficiency of Governmental Excellence for Social Progress: Focusing on Low- and Lower-Middle-Income Countries," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 141(1), pages 111-130, January.
    20. Koltcov, Sergei, 2018. "Application of Rényi and Tsallis entropies to topic modeling optimization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1192-1204.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:complx:9496731. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.