4.6 Article

Stakeholder engagement variability across public, private and public-private partnership projects: A data-driven network-based analysis

Journal

PLOS ONE
Volume 18, Issue 1, Pages -

Publisher

PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0279916

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Stakeholder engagement is a crucial factor affecting project outcomes, but there is a lack of empirical evidence on the differences in stakeholder engagement patterns between public, private, and public-private partnership (PPP) projects. This study uses social network research methods to capture and compare these engagement structures quantitatively. The findings reveal significant differences in network size, edge number, density, and betweenness centralization across the three types of projects. Additionally, the density varies significantly between 'within budget' and cost overrun projects for private and PPP projects. The study highlights the importance of network data and analytical techniques in managing relationships in complex project ecosystems.
Stakeholder engagement is generally considered one of the most pertinent factors impacting project outcomes. However, there is lacking empirical evidence documenting patterns of stakeholder engagement and their potential differences between public, private and public-private partnership (PPP) projects. This study leverages social network research methods to capture and quantitively compare these engagement structures. Stakeholder network data were collected by an online questionnaire from 17 public, 30 private and 9 PPP projects. A series of network-based analyses were subsequently applied to the data at both the level of individual project stakeholders and entire project stakeholder ecologies. A statistically significant difference (p<0.05) exists among the network-level measures of network size, edge number, density and betweenness centralization across the three project types. Among these four network measures, the density varies significantly (p<0.05) between 'within budget' and cost overrun projects for the private and PPP projects. The top-5 stakeholder lists with respect to three node-level centrality values reveal distinctive differences across the three project types. To further interpret the data, exponential random graph models were also applied to determine the most statistically prevalent network motifs within each type of project. Again, statistically significant differences were found across these three project types. The findings consistently point to structural differences in patterns of stakeholder engagement across the public and private domain and illustrate the applicability of network data and analytical techniques to monitoring and managing complex webs of relationships among actors who affect and are affected by diverse types of projects.

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