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Early in 2014, the Dutch railway system spiralled out of control after traffic management was confronted with the decision to take four double switches and two rail tracks out of service. A lack of coordination between the responsible teams resulted in the decision to stop all traffic in one of the busiest parts of the network during the rush hour. In this study we aim to understand why the teams in the Dutch railway system were not able to adopt a coordinated approach to reschedule rail services. To answer this question, we used a mixed-method approach by combining dynamic network analysis (DNA) with sensemaking. Our analyses show that a diverging framing of the situation accumulated over time, leading to inconsistent actions, incorrect assumptions and a lack of effective communication. Informal and indirect communication spurred uncertainty and promoted negative emotions, which eventually resulted in a conflict between the actors. We discuss the difficulties of managing ambiguous events in multi-team systems.

Social Network Analysis (SNA) can be used to investigate complex social systems. SNA is typically applied as a quantitative method, which has important limitations. First, quantitative methods are capable of capturing the form of relationships (e.g. strength and frequency), but they are less suitable for capturing the content of relationships (e.g. interests and motivations). Second, while complex social systems are highly dynamic, the representations that SNA creates of such systems are often static. These limitations can be overcome by balancing a quantita- tive approach to SNA with a qualitative approach. In the article two different approaches that seek this balance are demonstrated. The illustrations show that in this combination quantitative SNA is most useful for revealing system-level patterns, but that a deeper understanding of the mechanisms that produce these patterns is more easily achieved through the interpretation of qualitative data.

This article develops an explanation for the emergence of metropolitan governance in the Amsterdam metropolitan region. A coevolutionary framework is deployed to analyze how the governance system developed between 1982 and 2009. Data was gathered from 43 policy docu- ments, over 150 newspaper articles and 14 semi-structured interviews. The analysis focuses on the workings of selection pressures and the adaptive processes. It shows that metropolitan gov- ernance evolves in a punctuated fashion when fitness with the environment is lost and when the governance system's ability to absorb selection pressures has eroded.