Thanks to the stakeholder network analysis, estimations regarding the power and influence of certain stakeholder groups can be made. For each stakeholder group, five different centrality analyses, i.e., degree centrality, closeness centrality, betweenness centrality and eigenvector centrality, were performed. In all these analyses, the group ‘Administration’ shows the highest degree of centrality. According to Hannemann and Riddle (2005), this strongly indicates that stakeholders belonging to this group are the most influential. In addition, the groups ‘Transport company’ and ‘Planning & research’ show a quite high degree of centrality, especially when looking specifically at the ‘betweenness centrality’. This measurement of power relations refers to a stakeholder’s position within the network. A stakeholder’s belonging to one of these three most important groups of ‘Administration’, ‘Transport company’ and ‘Planning & research’ can, therefore, be seen as an indicator of pivotal stakeholders. As many connections between other stakeholders run through them, their role is considered as especially important for the existence of the network.
Somewhat different is the interpretation regarding the stakeholders of the group ‘Associations & NGO’. While they show a high significance in the ‘degree centrality’, i.e., the number of ties, and ‘eigenvector centrality’, i.e., how many connections there are to the central stakeholders, their results in the ‘betweenness centrality’, i.e., how many indirect connections run through them, is quite low. Hence, these stakeholders have contacts with many other stakeholders but are overall not deeply involved in the stakeholder network. Therefore, it is expected that they have a less influential role for the function of the network. The two remaining stakeholder groups ‘Industry’ and ‘Political party’ are the stakeholders with the least influence and importance within the stakeholder network. This may be due to the fact that these stakeholders mostly appear as providers of input and as cooperation partners in certain processes.
Participatory stakeholder research can be helpful to classify stakeholders according to their power in order to develop targeted approaches and strategies and to involve the actors in the most efficient way. In addition, it needs to be considered to whom the addressed development has the highest importance. Prell et al. (2009) point out that top-ranked stakeholders are often prioritised in participatory projects, which may lead to partially neglecting weaker stakeholders. The empowerment of those weaker stakeholders, however, can account for substantial improvements as well, depending on their position in the network. Of course, a stakeholder could be pivotal from the network structure perspective, though not too relevant from the traditional participatory stakeholder analysis perspective.
The performed stakeholder network analysis also helped to test a hypothesis. As expected, the official institutions such as the Office of Mobility of the Canton Basel-Stadt are very much at the centre of the network and therefore possess a quite high influence within processes related to commuting. As discussed above, the stakeholder network can be described in its overall qualitative characteristics revealing the possible influence of stakeholders (-categories) according to their centrality and stakeholder clusters. This may answer the questions where respective measures need to be set in order to make commuting more sustainable. However, it does not reveal the views, opinions and experiences of the individual stakeholders and, therefore, does not answer to the question, how these measures should be designed. This issue was addressed in the online stakeholder survey where stakeholders were surveyed regarding their view on trends, innovations, supporting factors or barriers and their experiences within stakeholder processes. The survey was issued in all three of the Smart Commuting case study areas, which made it possible to compare these areas and verify the universality of the obtained results.
Hanneman, R. A. & Riddle, M. (2005). Introduction to social network methods. Riverside, CA: University of California, Riverside. Online textbook. Available at: http://faculty.ucr.edu/~hanneman/nettext/ (Accessed: 14.9.2018)
Prell, C., Hubacek, K., & Reed, M. (2009). Stakeholder analysis and social network analysis in natural resource management. Society and Natural Resources, 22(6), 501-518.