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

Marco Nunes, Jelena Bagnjuk, António Abreu, Edgar Cardoso, Joana Smith and Célia Saraiva
Additional contact information
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
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

Sustainability, 2022, vol. 14, issue 8, 1-25

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.

Keywords: project management; graph-centrality metrics; project outcome; project lifecycle; individual performance; collective performance; correlation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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