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Creating Actionable and Insightful Knowledge Applying Graph-Centrality Metrics to Measure Project Collaborative Performance

Author

Listed:
  • Marco Nunes

    (Project Management Department at Tetra Pak, Wilhelm-Bergner-Straße 9c, 21509 Glinde, Germany)

  • Jelena Bagnjuk

    (Project Management Department, University Medical Center Eppendorf, Martinistraße 52, 20251 Hamburg, Germany)

  • António Abreu

    (Department of Mechanical Engineering, Polytechnic Institute of Lisbon, 1959-007 Lisbon, Portugal
    CTS Uninova, 2829-516 Caparica, Portugal)

  • Edgar Cardoso

    (Senior Data Analyst at Deutsche Bank, AG 1 Great Winchester Street, London EC2N 2DB, UK)

  • Joana Smith

    (Supply Chain Management Department at Borgwarner, 3000 University Drive, Auburn Hills, MI 48326, USA)

  • Célia Saraiva

    (Department of Informatic Engineering, UTAD-IST, Quinta de Prados, 5000-801 Vila Real, Portugal)

Abstract

Tools and techniques supported by math and statistics are often used by organizations to measure performance. These usually measure an employees’ traits and states performance. However, the third type of data usually neglected by organizations, known as relational data, can provide unique and actionable insights regarding the root causes of individual and collective performance. Relational data are best captured through the application of graph-based theory due to its ability to be easily understood and quantitatively measured, while mirroring how employees interact between them as they perform work-related tasks or activities. In this work, we propose a set of graph-based centrality metrics to measure relational data in projects by analyzing the five most voted relational dimensions ((1) communication, (2) internal and external collaboration, (3) know-how exchange and informal power, (4) team-set variability, and (5) teamwork performance), in a survey conducted to 700 international project stakeholders in eight business sectors. The aim of this research is to tackle two issues in projects: First, to understand in a quantitative way how the project’s relational data may correlate with project outputs and outcomes, and second, to create unique and actionable knowledge to help mitigate the increasing project failure rates. A case study illustrates the step-by-step application of the developed graph-based metrics as well as its benefits and limitations.

Suggested Citation

  • Marco Nunes & Jelena Bagnjuk & António Abreu & Edgar Cardoso & Joana Smith & Célia Saraiva, 2022. "Creating Actionable and Insightful Knowledge Applying Graph-Centrality Metrics to Measure Project Collaborative Performance," Sustainability, MDPI, vol. 14(8), pages 1-25, April.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:8:p:4592-:d:792032
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    References listed on IDEAS

    as
    1. Marco Nunes & António Abreu & Célia Saraiva, 2021. "Identifying Project Corporate Behavioral Risks to Support Long-Term Sustainable Cooperative Partnerships," Sustainability, MDPI, vol. 13(11), pages 1-27, June.
    2. Marco Nunes & António Abreu, 2020. "Managing Open Innovation Project Risks Based on a Social Network Analysis Perspective," Sustainability, MDPI, vol. 12(8), pages 1-31, April.
    3. Marco Nunes & Jelena Bagnjuk & António Abreu & Célia Saraiva & Edgar Nunes & Helena Viana, 2022. "Achieving Competitive Sustainable Advantages (CSAs) by Applying a Heuristic-Collaborative Risk Model," Sustainability, MDPI, vol. 14(6), pages 1-24, March.
    4. Marco Nunes & António Abreu, 2020. "Applying Social Network Analysis to Identify Project Critical Success Factors," Sustainability, MDPI, vol. 12(4), pages 1-32, February.
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    Cited by:

    1. Marco Nunes & António Abreu & Jelena Bagnjuk & Edgar Nunes & Célia Saraiva, 2022. "A Strategic Process to Manage Collaborative Risks in Supply Chain Networks (SCN) to Improve Resilience and Sustainability," Sustainability, MDPI, vol. 14(9), pages 1-33, April.

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