ONTO UNDERSTANDING DIFFUSION MECHANISMS IN AN ADAPTIVE NETWORK SETTING

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ONTO UNDERSTANDING DIFFUSION MECHANISMS IN AN ADAPTIVE

NETWORK SETTING

The case of mainstreaming Disaster Risk Reduction on the Island of Dordrecht, The Netherlands

Myrthe Wolsink

The Joint Master’s Programme in International Humanitarian Action (90 ECTS)

NOHA Network on Humanitarian Action

First supervisor: Prof. dr. A.J. (Andrej Janko) Zwitter

Second supervisor: Dr. M. (Meik) Nowak

Rijksuniversiteit Groningen

Ruhruniversität Bochum

Words: 24.700

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This thesis is submitted for obtaining the Joint Master’s Degree in International Humanitarian Action of the

NOHA Network on Humanitarian Action. By submitting the thesis, the author certifies that she is the sole

author of the thesis and that the thesis contains no material derived from the published or unpublished work

of others except where due reference has been made in the text.

©2018

Myrthe Wolsink

ALL RIGHTS RESERVED

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Abstract

This thesis revolves around the act of mainstreaming Disaster Risk Reduction (DRR) and

endeavours to answer the question “How does information diffusion among stakeholders

operating in one policy network both result in the mainstreaming of DRR measurements and

in network adaptiveness? To answer this question, this thesis unravelled the information

exchange and resultant policy development of the network of the Island of Dordrecht, the

epitome of a low-lying city in the Netherlands, vulnerable to water, that has mainstreamed

DRR over a period of sixteen years and via explicit undertakings in five significant water

projects. This thesis is qualitative in nature and uses a joining of two methods, Process

Tracing and Social Network Analysis. Both conjoined in what is here called a Process Traced

Network Analysis. Key results shed light on Adaptive Governance, Network Governance,

policy and norm diffusion and the information exchange of the network of Dordrecht. They

show how the adaptive network properties of the municipality helped shift from a joint

governance landscape with disjointed responsibilities to a multi-layer governance landscape

with shared responsibilities. Moreover, showing a concomitant development from ‘methods

for information exchange’ to explicitly conceptualized mainstreaming instruments for

managerial consolidation. The conclusion synthesizes what type of actors participate in the

network, what type of information is adopted and used, what type of alliances they collaborate

in, what multi-level governance and international engagement is present in the network, and

where power lies within the network. Altogether leading to an understanding of how the

municipality mainstreamed DRR. And as part of the discussion, the debates of Adaptive

Governance and Network Governance are integrated with diffusion theory by coining the

concept of irregular diffusion: a reconceptualization of diffusion in a more nowadays context

of adaptive networks.

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Acknowledgements

This thesis is made possible through the help and support of six people.

First and foremost, I am extending my thankfulness to Professor Zwitter for his continually

swift responses, feedback, patience and persistent and kind support during the period of

writing this thesis that played out differently than I was expecting.

I also extend my sincere thankfulness to Elene Herman-Pletiougina, who has made it possible

for me to finalize this thesis with a more favourable timetable, who has always expressed her

confidence in me, and who has showed great enthusiasm for the new developments in life that

delayed the finalization of this thesis.

Also Rik Heinen from the municipality of Dordrecht receives my thankfulness for providing

me relevant information, always answering my impromptu questions, partaking in an

interview that in the end wasn’t used and putting me in touch with other relevant participants

of the network.

Doctor Meik Nowak deserves my thankfulness for brainstorming with me via Skype, for

providing feedback on research proposals and simply for showing greater involvement than is

required from a second supervisor.

I also thank Berry Gersonius from UNESCO-IHE and Dordrecht municipality for swiftly

replying to my request and partaking in an interview that unfortunately wasn’t used.

And lastly, my deepest thankfulness goes out to Jeroen who showed his support while I

enrolled the joint master’s programme, who drove me back and forth to Groningen, Germany

and Belgium whenever necessary, who listened to my ideas for writing this thesis and

supported me tremendously throughout the challenging time when I was writing this thesis.

Myrthe Wolsink

14 March 2018

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Inhoudsopgave

Abstract ... 1

Acknowledgements ... 2

List of tables and figures ... 4

List of abbreviations and acronyms ... 6

List of network partners ... 7

I. Introduction ... 14

1.1 Research topic... 14

1.2 Relevance ... 14

1.3 Research question and objectives ... 17

II. State of the art – conceptual definitions ... 18

2.1 Theoretical approach and argument ... 18

2.2 Disaster Risk Reduction ... 18

2.3 Adaptive Governance ... 19

2.4 Network Governance ... 21

2.5 Mainstreaming ... 25

2.6 Policy Diffusion ... 26

2.7 Norm Diffusion ... 27

III. Research design ... 28

3.1 Research paradigm ... 28

3.2 Process Tracing ... 29

3.4 Social Network Analysis ... 30

3.6 Process Traced Network Analysis ... 32

3.7 Operationalization ... 32

3.8 Operational definitions ... 33

IV. The low-lying Island of Dordrecht ... 34

4.1 A history of peat soil subsidence, floods and land reclamation ... 34

4.2 Present day features... 36

4.3 State of the art: inductive learning and the Self-Reliant Island of Dordrecht ... 39

V. Data results ... 40

5.1 Overall policy development on the Island of Dordrecht ... 40

5.2 Adaptive governance on the Island of Dordrecht ... 46

5.3 Network governance on the Island of Dordrecht... 55

5.4 Policy and norm diffusion on the Island of Dordrecht ... 61

5.5 The Dordrecht network information exchange ... 67

VI. Conclusion ... 78

6.1 Conclusion ... 78

VII. Discussion ... 82

7.1 Validity, reliability and methodological limitations ... 82

7.2 A reconceptualization of diffusion ... 83

7.3 Irregular Diffusion ... 85

7.4 Suggestions for further research... 85

Bibliography ... 87

Appendix I. Framework for the dataset ... 96

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List of tables and figures

Table 1. Conceptual definitions in relation to operational definitions………....…27

Table 2. The predefined six variables with indicators for measurement………..………..28

Table 3. Categorization of used information………....57

Table 4. Randomized synthesis of mentioned documents with mentioned type of adoption of all projects………....63

Table 5. Activities of the participatory and communicative approach………..…………..65

Table 6. Activities of low-key consolidation……….65

Table 7. Various methods for information exchange……….…….65

Table 8. Methods for information exchange showing a greater focus on collaboration……….………66

Table 9. Methods for information exchange showing professionalization of collaboration………..….67

Figure 1. Historic developments since the St. Elisabeth flood showing land reclamation.……...……..29

Figure 2. “1953: “Narrow escape because of dike breaches inneighbouring dike ring area.”…….…29

Figure 3. Pictures of the historic harbour area of the City of Dordrecht………...30

Figure 4. Drierivierenpunt Dordrecht………30

Figure 5. The enclosed Island of Dordrecht………...………..30

Figure 6. Location of Dordrecht within the red delta……….…………....31

Figure 7. Position in lowlands of the Netherlands……….………….31

Figure 8. Bulkhead system of the Voorstraat………31

Figure 9. Separate bulkhead system of the Voorstraat……….………..31

Figure 10. Situation January 2012………..………...32

Figure 11. A not uncommon image in Dordrecht………..………..32

Figure 12. Timeline of key projects of the Dordrecht municipality………..33

Figure 13. Overview of overall policy development………...………..35

Figure 14. Sociogram depicting only governance actors and visualizing horizontal and vertical interdependence………...40

Figure 15. Development of vertical and horizontal interdependence………..…43

Figure 16. Sociogram of the City Water Plan actors to depict multiplicity of knowledge…………...44

Figure 17. Sociogram of UFM actors to depict multiplicity of knowledge……….45

Figure 18. Sociogram of MARE actors to depict multiplicity of knowledge………..……46

Figure 19. Sociogram of CAMIMO actors to depict multiplicity of knowledge………..…..47

Figure 20. Sociogram of MIRT & Self-Reliant Island actors to depict multiplicity of knowledge………48

Figure 21. Sociogram showing all involved actors of the network and depicting the networked and network-making power………...………..……50

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Figure 24. Graph showing used information of MARE………..…………58 Figure 25. Graph showing used information of CAMINO……….……59 Figure 26. Graph showing used information of MIRT & Self Reliant Island……….…...…59 Figure 27. Graph showing the overall use of information in all the involved projects…………...…….61 Figure 28. Timeline showing the development from methods for information exchange to

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List of abbreviations and acronyms

1

AG:

Adaptive Governance

ATP-O:

Adaptation Tipping Point - Opportunity

BEGIN:

Blue Green Infrastructure through Social Innovation project

BGI:

Blue Green Infrastructure

CAMINO:

Climate Adaptation Mainstreaming through Innovation project

CPT:

Climate Proofing Toolbox

C2C:

City-to-city [learning]

DRM:

Disaster Risk Management

DRR:

Disaster Risk Reduction

FRM:

Flood Risk Management

LAA:

Learning and Action Alliance

MARE:

Managing Responses to changing flood risk project

MIRT:

Multi Annual Programs Infrastructure, Environment and Transport project

PPP:

Public-Private Partnership

SNA:

Social Network Analysis

UFM:

Urban Flood Management project

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List of network partners

Albert Schweitzer hospital

NL: Albert Schweitzer Ziekenhuis

AnO (literally translated: ‘contracting after research’) team – innovation programme PSIbouw NL:Aanbesteding na Onderzoek-project (AnO) van het innovatieprogramma Proces- en Systeeminnovatie in de Bouwsector

Association Nature and Environment NL: Stichting Natuur en Milieu

Bax & Willems Consulting Venturing

NL: Bax & Willems Consulting Venturing

Bjerknes centre for climate research, Bergen, Norway BL: Bjerknes centre for climate research

Care facility Zorggroep Crabbehoff NL: Zorggroep Crabbehoff

City of Hannover NL: Hannover

City of Bergen NL: Bergen

Cost Action C22, EU Framework Programme Horizon 2020, European Commission NL: Cost Action C22

Delta platform Center of Expertise Delta Technology NL: Deltaplatform

Deltares (formerly WL Delft Hydraulics) NL: Deltares

Dike reeve - Water board de Groote Waard. NL: Dijkgraaf

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Directorate General for Environment and Water - Ministry of Infrastructure and Environment NL:Directoraat General Ruimte en Water

Dura Vermeer Group

NL: Dura Vermeer Groep

Environment Agency, UK

NL: Enrivonment Agency

Europan Europe, contest for challenging urban spatial questions NL: Europan Europe

Erasmus University Rotterdam NL: Erasmus Universiteit

Evides (drinking water) NL: Evides

Expert water managers (non-municipality) NL: Experts van buiten de gemeente

Fire brigade

NL: Brandweer Nederland

Flemish Environment Agency

NL: Vlaamse Milieumaatschappij

Fire Brigade expert – Security Region South Holland South NL: Brandweer van Veiligheidregio Zuid-Holland Zuid

HKV Consultants

NL: HKV consultants

Human Environment and Transport Inspectorate - Ministry of Infrastructure and Water Management NL: Inspectie Leefomgeving en Transport

H + N + S landscape architects

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Interest Group for Foundation Issues

NL: Belangenvereniging Funderingsgedupeerden Nederland

Interest groups nature and environment.

NL: belangenverenigingen voor natuur en milieu Interest groups recreation and leisure

NL: belangenvereniging voor recreatie

Interest groups recreational or sports fishing

NL: belangenvereniging sport en recreatie visserij

Interreg IVB North Sea Programme 2007-2013 - European Regional Development Fund, European Commission. NL: Interreg IVB KPN (telecommunication) NL: KPN Krimpenerwaard municipality NL: Gemeente Krimpenerwaard

Leibniz University of Hannover NL: Leibniz University

Mental health facility Parkhuis: NL: Het Parkhuis

Ministry of Infrastructure and Environment.

NL: Ministerie van Infrastructuur en Milieu

Ministry of Justice and Safety

NL: Ministerie van Justitie en Veiligheid

Medical Relief Organization of the Region

NL: Geneeskundige Hulpverleningsorganisatie in de Regio

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NL: Landelijk Operationaal Coordinatie Centrum van de Nationaal Coordinator Terrorismebestrijding en Veiligheid

National Red Cross NL: Rode Kruis

Nederlandse Spoorwegen (Dutch railway company). NL: Nederlandse Spoorwegen

Netherlands Enterprise Agency, Ministry of Economic Affairs and Climate Policy: NL: Rijksdienst voor Ondernemend Nederland

Network of Provincial environmental federations. NL: Stichting Provinciale Milieufederaties.

Office of The Royal Netherlands Sea Rescue Institute NL: Nederlands Landelijk Bureau Reddingsbrigade.

Police force NL: Politie

Project Team of the Strategic Agenda for Water and Evacuation NL: Projectteam Water en Evacuatie

Province South Holland

NL: Provincie Zuid Holland

Residents

NL: bewoners

Rijkswaterstaat, Ministry of Infrastructure and Water Management NL: Rijkswaterstaat

Rotherham Metropolitan Borough Council NL: Rotherham

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Royal Association for Plants and Gardening skills

NL: Koninklijke Maatschappij voor Plant en Tuinkunde

Sanitas-Water research and consultancy for public health in urban water management

NL: Sanitas-Water, onderzoeks- en adviesbureau voor gezondheidsaspecten rondom stedelijk Water

Seattle

NL: Seattle

Security Council

NL: Veiligheidsberaad

Security Region Hollands Midden

NL: Veiligheidsregio Hollands Midden

Security Region South Holland South

NL: Veiligheidsregio Zuid-Holland Zuid

Security Region Zeeland

NL: Veiligheidsregio Zeeland

Sheffield City Council NL: Sheffield

South Holland Environment Agency

NL: Omgevingsdienst Zuid-Holland Zuid

Stedin & Tennet (electricity) NL: Stedin en Tennet

STOWA, Foundation for Applied Science in Water issues NL: Stichting Toegepast Onderzoek Waterproblematiek

Sustainability factory Dordrecht

NL: Duurzaamheidsfabriek Dordrecht

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NL: Technische Universiteit Hamburg

The Royal Netherlands Army

NL: Nederlandse krijgsmacht

Trivire housing corporation (formerly Progrez). NL: Trivire (voormalig Progrez)

Triple Bridge knowledge and network organization NL: Triple Bridge kennis en network-organisatie

Twynstra Gudde organizational advice agency

NL: Twynstra Gudde Organisatieadviesbureau

UNESCO IHE Institute for Water Education NL: IHE-Delft

University of Applied Science Zwolle NL: Hogeschool Zwolle

University of Sheffield, Pennine Water Group NL: University of Sheffield

University of Utrecht

NL: Universiteit Utrecht

Verkerk Group, electro-technical advice and innovation

NL: Verkerk Groep, elektrotechnisch dienstverlener voor innovatie met visie

VMB automation

NL: VMB automatisering

Vrije Universiteit Brussel

NL: Vrije Universiteit Brussel

Wagenbouw, housing specialist

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Water Board de Groote Waard

NL: Waterschap de Groote Waard

Water Board Hollandse Delta (formerly Water Board Hollandse Eilanden en Waarden). NL: Waterschap Hollandse Delta

Water Board Hollands Noorderkwartier

NL: Waterschap Hollandse Noorderkwartier

Water Governance Centre

NL: Water Governance Centre

Welldra consultancy in the water sector

NL: Welldra advies en procesbegeleiding in de watersector

Witteveen + Bos, consultancy and engineering services NL: Witteveen + Bos advies en ingenieursdiensten

Yulius mental health facility NL: Yulius

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I. Introduction

1.1 Research topic

This thesis revolves around the action of mainstreaming Disaster Risk Reduction (DRR). It

endeavours to unveil information exchange and respective mainstreaming instruments among

stakeholders involved in projects of the municipality of the low-lying Island of Dordrecht,

The Netherlands, which can alternatively be called the City of Dordrecht or when speaking of

its local governance the Dordrecht municipality. With this, it aims to arrive at an

understanding of how certain mainstreaming instruments and information exchange led to

actual integration of DRR measurements in operating procedures of both public and private

actors, and to potentially serve as an example of actual mainstreaming. And with this it aims

to shed new light on modern-day diffusion, leading to a potential reconceptualization of

diffusion that takes place in an adaptive network setting.

In doing so, mainstreaming is operationalised into the exchange of information and

instruments used to accommodate such information exchange, which is further conceptualized

into diffusion. And the system is considered the network of stakeholders that participated (and

participate) in the projects that were carried out during a sixteen-year inductive

knowledge-driven trajectory of the Dordrecht municipality. Hence, in formal, this thesis aims to study

information diffusion among said stakeholders so as to explore how the Dordrecht

municipality actually mainstreamed DRR. Here, DRR measurements are those measurements

tailored to mitigate urban flood risks.

The diffusion is probed on two things. One, how the diffusion has led to the integration of

DRR measurements into operating procedures of partners within the network of the City of

Dordrecht. In other words, to answer the plain and simple question: what kind of diffusion has

proven to work in mainstreaming DRR? With that question, the kind of diffusion is assessed

on six predefined variables. Two, if and how the network engenders and sustains network

adaptiveness through its information exchange. With this, it is assessed if and how

adaptiveness is engendered and sustained by Adaptive Governance and Network Governance.

1.2 Relevance

The relevance of this thesis is twofold, it contributes to both theory and the improvement of

information exchange within the DRR profession. Looking at Disaster Risk studies, a variety

of methodological approaches and numerous disciplinary and theoretical perspectives have

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been applied to explore disaster risk, disaster impact and risk reduction (Peek, 2016).

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_{Head to}

head to the reality of natural hazards and their impact, the global DRR scholarship explores

the entire life cycle of disasters. But whilst the scholarship is broad and comprehensive,

comparatively fewer studies revolve around the transboundary socio-political space

surrounding DRR and the endorsement of DRR within that setting. But when they do,

transboundary interconnectedness sustained by functional information sharing and use of

knowledge and the functions of system adaptiveness hold sway (Chmutina and Bosher, 2015;

LaTrobe and Davis, 2005; DeTombe, 2001; Haasnoot et al., 2013; Geurts and Joldersma,

2001; Djalante et al., 2013; Djalante, Holley and Thomella, 2012; Swanson et al., 2010;

Walker, Rahman and Cave, 2001; Pahl-Wostl, 2009 ), with the latter supposedly facilitated by

what is called the concept of Adaptive Governance.

Adaptiveness and Adaptive Governance are the most referred to frameworks for the

management of complex environmental issues, such as DRR (Djalante et al., 2013; Djalante,

Holley and Thomella, 2012). And as Adaptive Governance (AG) is assumed to increase

system adaptiveness to future uncertainties and complexities also the mainstreaming of DRR

is advised to be carried out within an Adaptive Governance framework as then it is

undertaken across multiple sectors and scales (governance levels) (Djalante et al., 2013;

UNISDR, 2017a; Chmutina and Bosher, 2015; Trobe and Davis, 2005). At the same time,

whereas the AG scholarship has provided insights into the framing of transnational

governance revolving management of environmental issues, it hardly stretched its boundaries

to integrate adaptive policy theory. And with that also policy (and norm) diffusion theory is

still far removed from current AG scholarship, even while the diffusion of information is the

means and the end of the act of mainstreaming, which is what most of DRR on a policy level

pivots around.

On a different note but somewhat similar is the newly coined concept of Network

Governance. As mentioned, whereas Adaptive Governance finds its footing in relation to the

management of complex environmental issues, Network Governance stems, for obvious

reasons, from Network Theory. Network theory, as meant by the Network Society (Castells,

1996, 1989), and Network Governance, as meant by the relational power in networks

Hazenberg and Zwitter, 2017), are in and by themselves responses to the effects of

globalization on politics and inherent contemporary ‘new’ strands of governance theory

2_{For Disaster risk see for instance Turner et al., 2003; Bull-Kamanga et al., 2003; Taubenböck et al, 2008;}

Adger, 2006; Alwang, Siegel, and Jorgensen, 2001; Bakir and Boduroglu, 2002; Peduzza, 2006; Pelling, 2003; Wisner, 2004; Rashed and Weeks, 2003. For Disaster impact see for instance Lynham, Noy and Page, 2017; Cole et al., 2017; Okuyama and Santos, 2014; Biswas et al., 2015; Shaw, 2006. And for DRR see for instance Twigg 2015; Twigg, 2004; Berkes, Colding and Folke, Eds., 2003; Gunderson, 1999; Wisner, Gaillard and Kelman, 2012; Young, 1989; Djalante et al., 2013; Chmutina and Bosher, 2015; LaTrobe and Davis, 2005.

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(Rhodes, 1996; Kooiman, 1999; Dunsire, 1990; Levi-Faur, 2012; Peters, 1994; Lobel, 2012;

Börzel and Risse, 2010). Both concepts are relevant and related for the good reason that

adaptiveness is seemingly essential for dealing with complex environmental issues, and this in

and by itself requires a certain type of government (i.e. Adaptive Governance), all the while

networks have become the setting for governance to take place in (i.e. Network Governance).

Consequently, when it comes to DRR, both concepts are intertwined to the extent they cannot

be identified separately nor function separately. One could say that Adaptive Governance is a

key feature of a certain type of Network Governance: the governance of a policy network that

exists by the imperative of mainstreaming DRR, such as the network of the City of Dordrecht

that with several changing alliances and over a span of sixteen years has carried out five

noteworthy projects that incrementally mainstreamed DRR:

The City Water Plan 2002-2007

Urban Flood Management 2006-2008 (UFM)

MAnaging REsponses to changing flood risk 2009-2012 (MARE)

Climate Adaptation Mainstreaming through Innovation 2013-2015 (CAMINO)

Multi annual Programme Infrastructure, Environment and Transport (MIRT) and the resultant

Self-Reliant Island strategy (2014-2018 and ongoing).

What becomes clear, however, is that whereas Adaptive Governance just doesn’t bridge with

diffusion theory, Network theory does tie with diffusion theory, although to a very meagre

extent as well. A few book-worthy studies about norm diffusion in regional organisations

(Hollis, 2015) and the international system (Ring, 2014) - both which touch on the existence

of different types of networks and network structures in relation to diffusion -, a few smaller

writings about norm diffusion within ASEAN (Reumann, 2017), and network emulation and

theory-driven learning in international diffusion of public sector downsizing (Lee and

Strange, 2006) touch on both diffusion and network theory. But the scholarship about

diffusion in a network society is of extremely modest proportions.

With this thesis, an attempt is made to integrate Adaptive Governance and Network

Governance theory with diffusion theory. Examinations of information exchange and

mainstreaming instruments among practitioners participating in such a network provide an

illuminating kick-start to the integration of diffusion theory with Adaptive Governance and

Network Governance theory, and potentially proves a worthy and perhaps more hands-on

contribution to all three scholarships with a reconceptualised understanding of diffusion in an

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On a more practical note, said examinations also provide insights upon which the action of

mainstreaming can be improved, providing the potential for more efficiency in information

exchange and acceleration of learning processes. In other words, if information exchange and

mainstreaming instruments of such projects are studied, information exchange might improve

based on new insights, leading to learning processes that on their turn lead to greater

efficiency of operating procedures within the network. This eventually contributes to more

interconnectedness and coherence among partners within the network and potentially outside

the network as a good example is the best sermon.

1.3 Research question and objectives

This thesis endeavours to study information diffusion and mainstreaming instruments among

stakeholders that participated in five big projects of the Dordrecht municipality that were

carried out to manage water and mitigate urban flood risk. It aims to identify two aspects of

diffusion, which are the nature of the information that was exchanged and the kind of

convergence that has taken place. It aims to identify two features of system adaptiveness as

expressed by the concept of Adaptive Governance, which are polycentrism and participation

and collaboration (Djalante et al., 2013). And it aims to identify the features of networked or

networking-making power as a feature of Network Governance (Hazenberg and Zwitter,

2017). With the nature of the information, it is established on what type of information the

network thrives, which can be either policy diffusion, norm diffusion, both or other types of

information diffusion. The concept of polycentrism is measured by the verticality or

horizontality of information exchange: the vertical interdependence with bottom-up and

top-down movement of information across governance levels or the interdependence on one

governance level. And participation and collaboration is measured by the multiplicity of

knowledge, that is, if different ideas and opinions regarding DRR are not only valued but also

integrated into the operating procedures of stakeholders within the network. With Process

Tracing the developments overtime are tracked. And with sociograms

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_{and analyses of the}

partners that contributed to each project, a Social Network Analysis is done (SNA), which

leads to an understanding of how information is mainstreamed over and among a number of

stakeholders.

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To arrive at the above observations, the research question of this thesis is:

How does information diffusion among stakeholders operating in one policy network both

result in the mainstreaming of DRR measurements and in network adaptiveness?

Added by two sub-questions that both translate into six variables with several indicators:

First, how information diffusion has led to the integration of DRR measurements into

operating procedures of partners within the network?

Second, if and how the network engenders and sustains system adaptiveness through its

information exchange?

II. State of the art – conceptual definitions

2.1 Theoretical approach and argument

In explaining the theoretical approach of this thesis and embed the theoretical argument that is

behind this thesis in associated literature, the concepts of Disaster Risk Reduction, Adaptive

Governance, Network Governance, Mainstreaming, Policy Diffusion and lastly Norm

Diffusion are set out. In terms of ‘getting to know what one likes to know’ and measuring the

right things, the act of mainstreaming translates into diffusion theory. And said adaptive

setting translates into Adaptive and Network Governance theory. And, as mentioned, the

argument for this thesis is embedded in the lack of integration of these debates leading to a

possibly outdated understanding of diffusion in nowadays ‘networked societies’ (Castells,

1996; Hazenberg and Zwitter, 2017; Rhodes, 1996; Kooiman, 1999; Dunsire, 1990). For a

proper understanding of information exchange within a large-scale policy network, these

debates have to be integrated, at least to the extent that an updated and hands-on

understanding of diffusion is established by which this thesis is able to properly describe

mainstreaming mechanisms of DRR norms and policies, as translated into DRR

measurements, in the network of the Dordrecht municipality.

2.2 Disaster Risk Reduction

In literature about natural hazards, vulnerability and disasters, technical terms, conceptual

definitions and operational terms abound. A few remarkable concerns relate to that. To start,

many people working in aid and development find the technical jargon off-putting. And as

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mentioned by Twigg (2015, p.3), the word ‘disaster’ appeals to images of emergency relief

which often leads to DRR being viewed as solely an aspect of humanitarian aid whereas it

should also be pivotal to the contents of development work too. Also, a 2012 study explained

that there is no consensus among stakeholders as how to define disaster risk reduction

(Hagelsteen and Becker, 2013; Eriksson and Gustavsson, 2007). According to Hagelsteen and

Becker, the same terms are defined in different ways by different organizations (2013). And

as explained by Thywissen, this might result in a “Babylonian Confusion of terminology”

(Thywissen, 2006 in Hagelsteen and Becker, 2013). So, Disaster Risk Reduction has various

definitions in technical literature and in practice. Notwithstanding, certain terms are used

more regularly (Twigg, 2015, p.3).

The UNISDR, which is considered a DRR norm and policy entrepreneur (Hollis, 2015),

defines Disaster Risk Reduction as the process to “minimise vulnerabilities and disaster risk,

to avoid (prevention), or to limit (mitigation and preparedness) the adverse impacts of

hazards” (UNISDR, 2004). And by The Humanitarian Policy Network it is recognized as “the

development and application of policies, strategies and practices to reduce vulnerabilities and

disaster risks throughout society” (Twigg, 2015, p.6). It covers policy, strategic, institutional

and operational issues, describing “a very broad-based approach to the causes of disasters and

dealing with their consequences” (Twigg, 2015, p.9).

The term ‘disaster risk management’ (DRM) is often coined in the same context as it refers to

“a systematic approach to identifying, assessing and reducing risks” (Twigg, 2015, p.9). In

practice, both terms are sometimes used quite loosely with very similar meanings (Twigg,

2015, p.9). This translates back to what was stated earlier, that the same terms are defined

differently depending on the organization that is applying them: capacity building, resilience

building, capacity, capacity development, and/or adaptive capacity all share a similar

connotation. Supported by the fact that there also seem to be gaps between guidelines given

by available theory and how capacity development for disaster risk reduction is done in

practice (Hagelsteen and Becker, 2013, p.5), in this thesis it is recognizes that, despite

theoretical advancement, conceptual definitions of DRR should be handled loosely.

2.3 Adaptive Governance

Adaptive Governance is an emergent framework for the management of complex

environmental issues. It is the ‘human context’ for management of complex ecosystems

(Dietz et al. 2003; Folke et al., 2005). In Gunderson and Light’s study of governance in the

everglades ecosystem (2002), to define Adaptive Governance they refer to the definition of

Brunner et al. (2005). They describe Adaptive Governance as:

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“Operating in a situation where the science is contextual, knowledge is incomplete, multiple

ways of knowing and understanding are present, policy is implemented to deal with modest

steps and unintended consequences and decision making is both top-down (although

fragmented) and bottom-up” (Gunderson and Light, 2002, p.325).

As such, adaptive governance is aimed at integrating science, policy and decision making in

systems that “assume and manage for change, rather than against change” (Gunderson et al.,

1995). Adaptive Governance deals with the complex human interactions that have been

obstacles to the implementation of adaptive management (Lee, 1993; Walters, 1997;

Gunderson, 1999). Djalante et al., (2013) add that Adaptive Governance is “characterised by

notions of governance that are more flexible and innovative and that encourage learning to

better manage uncertainties and system complexities” (Brunner et al. 2005; Dietz et al.

2003; Folke et al. 2005). According to them, four key characteristics can significantly

influence disaster resilience. These are polycentric and multi-layer institutions, participation

and collaboration, self-organization and networks, and learning and innovation (Djalante,

Holley and Thomalla, 2011). They suggest that while other characteristics of governance also

contribute to resilience, these four are highly relevant to building disaster resilience and

enhancing system adaptiveness.

Djalante, Holley and Thomalla (2011) argue that polycentric institutions influence the

capacity to manage resilience, due to the existence and inclusion of different organizations at

different scales, which in their words allows for “a better matching of organizational and

ecological scales” (Djalante et al., 2013 referring to Folke et al. 2005), “an improved fit

between knowledge and action” (Djalante et al., 2013 referring to Lebel et al. 2006), “and the

moderation of vertical interplay” (Djalante et al., 2013 referring to Young 2002).

Additionally, as they put it, “participation and collaboration can improve effectiveness and

efficiency and reduce uncertainties in managing environmental problems” (Djalante et al.,

2013 referring to Lane and Robinson, 2009). Self-organization and networks are important to

build resilience especially at community level (Kendra and Wachtendorf 2003).4 The process

of ‘social learning’ (Lave and Wenger 1991; Scholz and Stiftel 2005) enhances resilience by

providing access to knowledge (Ostrom 2010; Pahl-Wostl 2009) and platforms for

coordination, negotiation and knowledge sharing (Thomalla and Larsen 2010). The process of

4_{Additionally, suggested by Djalante et al., 2013; agency (Larsen et al., 2011), collective action (Ireland and} Thomalla, 2011) and social capital (Adger, 2003; Pelling and High, 2005) are all important in positively

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inter-organizational learning during emergency situations and stress can lead to innovation

(Comfort and Kapucu 2006; Comfort et al. 2004).

Further to the notion of Adaptive Governance are Adaptive Policies. Adaptive Policy theory

assumes that even the best models cannot accurately predict the details of future system

behaviour. Policy-making is about the future. And in Adaptive Policy theory it is assumed

that we cannot just identify policies by simply examining the future that would follow from

the implementation of each possible policy and picking the one that produced the most

favourable outcomes (Walker, Rahman and Cave, 2001, p.282). Hence, the classical approach

of choosing a policy based on the outcomes from a best estimate model is no longer credible.

Instead, policies that comprise sequential combinations of policy options become more

convenient. Adaptive Policies therefore include contingency plans as well as a specification

of conditions under which the entire policy should be reconsidered. The policies themselves

are, therefore, designed to be incremental, adaptive, and conditional (Walker, Rahman and

Cave, 2001, p. 284).

2.4 Network Governance

Looking beyond the Adaptive Governance scholarship one embarks on a journey through

myriad other scholarships that have delved into the globalizing politics that toward the end of

the second millennium are characterized by more complexity and ‘glocalized’ structures in

which the global is modified by its contact with the local (Robertson, 1992, p. 173-4). These

politics are causing a development that marks a reconceptualization of governance theory in

which a concomitant paradigm shift from a centralized government model deriving from

Statism to different models of governance is made. In new governance theory, governance is

coined as structures, processes mechanisms and strategies (Levi-Faur, 2012), as ‘new’

governance without a State that marks Statism as the governance theory of yesterday (Börzel

and Risse, 2010; Lobel, 2012), as governance as a ‘Socio-Hybernetic system’ with an

increasingly differentiated political system in which policies are not the product of actions by

central government but the product of interaction with local governments, hence in which

‘systems thinking’ and the communication of information in systems, bureaucracies, and

markets is pivotal (Rhodes, 1996; Kooiman, 1999; Dunsire, 1990), as governance as

Self-organizing Networks (Rhodes 1996), and descriptively as ‘Mode II’ governance as

generically indicating the above specified switch (Hazenberg and Zwitter, 2017).

From various disciplines, globalization theorists have in different ways but along similar lines

modelled how the juxtaposition between local and global has become an important force in

world politics. Increased connectivity and interdependency are recognized as essential

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features of that globalization process. And, aside from the collapse of Soviet Statism and the

restructuring and catalysing of global capitalism, it is sure that the computer, internet and

resultant media have made unfathomable contributions to what we now know is the

‘information age’ and the informational society (Appelrouth and Edles, 2008, pp. 345-350;

790). Many scholars made contributions, and a few were able to extrapolate their

understandings of global society into newly recognized theories.

One of the major voices in globalization theory, Arjun Appadurai, assumes an increasingly

borderless global economy whilst at the same time claiming that the world has become a

single system with a range of complex subsystems. He proposes that for exploring the

complex global economy one has to look at the relationship between five dimensions of

global cultural flow: ethnoscapes, mediascapes, technoscapes, finanscapes, and ideoscapes

(1990, p. 296). The added suffic ‘scape’ is indicating that the relations between the scapes are

not objective. They reflect the perception of actors which are “deeply perspectival constructs,

inflected very much by historical, linguistic and political situatedness of different sorts of

actors: nation-states, multinationals, diasporic communities, as well as sub-national groupings

and movements, and even intimate face-to-face groups, such as villages, neighbourhoods, and

families.” (p. 296). What stands out in his theory of the ‘Global Cultural Economy’ is the

individual actor becoming intertwined with larger formations, which can be referred to as

‘scapes of global cultural flows’ that intersect, overlap and influence one another. And that

among those ‘flows’ three stand out in explicit regard to the information age: the technoscape,

mediascape and ideoscape. Though with different dimensions, these scapes suggest that

territorial relations have become much more fluid, they moreover suggest that “as individuals

we are members of several networks at any given time” (Howard, 2011).

For Manual Castells, the network society is directly related to the information and

communication of our present-day societies. He departs from his conviction that information

networks have created a new urban space called ‘the informational city’, as a distinctive and

further stage in the capitalist mode of production (Castells, 1989). Resultantly, connectivity

has expanded the city to a global scale and created “a network society” (1996, p. 60) Adding

that “…the informational, global economy is organized around command and control centres

able to coordinate, innovate, and manage the intertwined activities of networks of firms”

(1996, p. 409).

5

_{Extrapolating this into one of his chief axioms that the “new information}

5_{By which he also refers to Hall (1995, pp. 3-32), for an overview of current transformations of spatial forms and processes} at the global level, to Daniels (1993) for new insights in the service industry, and to Norman (1993) for information flow that

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technologies are integrating the world in global networks of instrumentality” (1996, p.22), he

arrives at the theoretical basis of his concept of the network society.

Both Castells and Appadurai argue that the state has had the key role of keeping an economy

and a culture unified through politics but that these fixed and territorial relations have become

much more fluid. Essentially, power relationships are now largely defined by and through

structures of communication (Howard, 2011). Castells relies in his explanation of network

power on the proposition of Grewal to theorize globalization through network analysis

(Castells, 2011, p. 774). That way, network power is shaped by the standards that enable

global coordination (Grewal, 2008, p. 5). However, contrary to nation-state ‘old’ government,

according to Castells, in networked societies, there are power relationships at work in new

forms and with new kinds of actors (Castells, 2008, p. 776). Network power is seen as the

relational capacity to impose an actor’s will over another actor’s will on the basis of the

structural capacity of domination embedded in the institutions of society (and hence

networks) (p.775). It is both the ability to project power over existing networks and the ability

to construct new networks. Following this logic, Castells demonstrates four types of power

that are exercised through networks:

1. Networking Power: the power of the actors and organizations included in the networks that

constitute the core of the global network society over human collectives and individuals who

are not included in these global networks.

2. Network Power: the power resulting from the standards required to coordinate social

interaction in the networks. In this case, power is exercised not by exclusion from the

networks but by the imposition of the rules of inclusion.

3. Networked Power: the power of social actors over other social actors in the network. The

forms and processes of networked power are specific to each network.  

4. Network-making Power: the power to program specific networks according to the interests

and values of the programmers, and the power to switch different networks following the

strategic alliances between the dominant actors of various networks.  

Managing networks of social relations and media distribution is one of the most important

tasks for contemporary political authority (Howard, 2011). And as Castells suggests, more

subtle, complex and negotiated systems of power enforcement must be established (2008, p.

777). To arrive at a definition of governance in this particular setting and take Castells notions

of power a step further, this thesis relies on one of the most recent writings about governance

in light of the emergence of Big Data. Whereas Big Data lies outside the scope of this thesis, a

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few parallels can be drawn in how the emergence of Big Data and increased need for proper

Disaster Risk Reduction governance change the requirements for governance. Hazenberg and

Zwitter (2017) have distilled two effects on policy-making and governance mechanisms,

which in their article apply to the effects of Big Data but that similarly apply to the effects of

the increased need for Disaster Risk Reduction. Those stipulated effects of the ‘transformative

nature’ of Big Data - ánd the need for DRR - on policy-making and its governance

mechanisms are:

1. New actors have entered both the national and international stage of policy making and

governance mechanisms.

2. Those changes in governance roles and relationships affect power distributions.

As with the emergence of Big Data (Hazenberg & Zwitter, 2017), within the network of the

Dordrecht municipality (and networks alike), new actors have entered the processes of policy

creation. Ranging from private sector companies; NGOs; international financial institutions;

civil society (organisations); science and technology research institutions; organisations and

networks to universities; local government departments; individual parliamentarians; regional

organisations; UN organisations and networks and platforms all over (UNISDR, 2017c), they

all and altogether, depending on their specific expertise and/or function in relation to other

partners and in cross-reference with a certain undertaking at that very moment, take up

governance roles among and with each other. What makes for a ‘governance role’ or

‘governance actor’ has thus changed significantly since what we now call ‘old’ governance

theory. And this, in effect, has changed power relations. Hence, Hazenberg and Zwitter

suggest that in networks power is to be seen as relational. Building on Dahl’s (1957)

conceptualization of power, they suggest that when power is conceived as a relation, then

power exists in “coordination and the ability of an actor to command that coordination”

(Hazenberg and Zwitter, 2017).

In their networked approach to governance, in their terms ‘Mode III’ governance or Network

Governance, the relational nature of power - rather than solely a hierarchical ór horizontal

conceptualisation of power - requires fluidity in governance tasks and distribution thereof to

be variable per relation and in relation to other actors (p. 19). Network Governance is

essentialized in the following words, which together and without making restatements are

accepted and in this thesis used as the conceptual definition of Network Governance:

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“In different roles and relationships multiple actors can possess each of the novel network

power. Network governance requires governance mechanisms that distribute tasks and

obligations independent of who an actor is but according to the specific type of power an

actor exerts over others in a specific policy domain. Networked governance thus does not

presuppose a certain delineation between actors as pre-given but that actors’ rights,

obligations, and regulatory authorities change depending on the function and role they assume

in relation to other actors. The two most crucial new forms of power are the power to

constitute and reprogram networks (network-making power) and to connect and ensure

corporation within networks (networked-power)” (Hazenberg and Zwitter, 2017, p.19).

2.5 Mainstreaming

As mentioned, in this thesis, mainstreaming is operationalized into information exchange and

the mainstreaming instruments that accommodate this exchange of information. When the

word mainstreaming is semantically dissected into a verb and a noun, respectively something

is ‘to cause to go or to be taken’ (verb) ‘into the mainstream’, in which ‘the mainstream’

(noun) is the main force/group/tendency. In other words, mainstreaming is here understood as

to integrate something that is as yet not integrated into normalcy. Or, to ‘normalize’

something. In order to do so, social constructs (ideas, opinions, thoughts) need to transfer

from one person to another, or from one institution or country to another. This means that, in

any case, a social construct needs to be dispersed, it needs to be scattered. In essence, the

social construct needs to be diffused.

Mainstreaming is understood as diffusion. And logically this happens via the exchange of

information and learning processes. Of course information exchange is crucial for the creation

of social coordination (Hazenberg and Zwitter, 2017), but this is especially emphasized in –

logically – diffusion theory (Starke, 2013; Maggetti and Gilardi, 2016; Shipan and Volden,

2006; Gilardi 2012; Hollis, 2015), network theory (Castells, 1996; Lewis, 2011; Gibson,

2012), in the method of Social Network Analysis (also logical) (Scott, 2017; Hanneman and

Riddle, 2011; Borgatti and Lopez-Kidwell, 2011; Goyal, 2011; De Nooy, Mrvar and Batagelj,

2011), participatory policy analysis theory (again, logical) (Geurts and Joldersma, 2001;

DeLeon, 2014; De Oliveira, 2017), and in Adaptive Governance theory (again, also logical)

(Djalante et al., 2013; Doremus, 2011; Scholz and Stiftel, 2005; Karkkainen, 1989). In this

thesis, therefore, mainstreaming is conceptualized into policy diffusion and norm diffusion.

Information exchange and the instruments used to accommodate this information exchange

are the elements observed within that conceptual space.

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2.6 Policy Diffusion

When referring to policies within a transnational context, it is a rather logical step to look at

policy diffusion too. However, this is where one suddenly finds out that moving from

Adaptive Governance theory to adaptive policy theory is a cakewalk, whereas moving from

adaptive policy theory to diffusion theory is a hurdle over a fairly illogical gap in literature.

Diffusion theory is clear about some ways and mechanisms in which diffusion might take

place – among countries, within countries, between states and world society, and also within

networks (Gilardi, 2012; Hollis, 2015; Ring, 2014), and with coercion, competition,

emulation and learning (Ring, 2014) – but it isn’t clear about diffusion mechanisms in

adaptive settings.

The first systematic analyses of policy diffusion in the US were published in the first half of

the 20th century in the context of American federalism. (McVoy, 1940 in Gilardi 2012, p.3).

Goes to show that diffusion theories often refer to interdependency between states and that

“policy decisions in a given country are systematically conditioned by prior policy choices

made in other countries” (Simmons, Dobbin and Garrett, 2006, p.787). But diffusion can also

take place within countries, among a wide range of public and private actors. And it can lead

to the spread of all kinds of things, from specific instruments, standards, and institutions, both

public and private, to broad policy models, ideational frameworks, and institutional settings.

So basically, diffusion is just the process that leads to the pattern of adoption (Gilardi, 2012,

p.3). It is “any process where prior adoption of a trait or practice in a population alters the

probability of adoption for remaining non-adopters” (Strang, 1991, p.325).

To date, as mentioned, a couple of diffusion mechanisms are recognized. Diffusion is said to

take place in four mechanisms: coercion, competition, emulation, and learning (Ring, 2014,

p.33-41). Coercion is the imposition of a policy by powerful international organizations or

countries (Gilardi, 2012). It builds on “the ability of one actor to make another actor do

something it would not do on its own accord” (Ring, 2014). Competition means that countries

influence one another because they try to attract economic resources (Gilardi, 2012). Ring

suggests it occurs “when actors who want something have to invest in reforms before they are

able to capture it.” It only exists if there is a limited supply of a good, and there are incentives

to compete against other actors who also desire that good (Ring, 2014). Learning means that

the experience of other countries can supply useful information on the likely consequences of

a policy (Gilardi, 2012). Learning requires that a state has specific goals that they internally

need (Ring, 2014). Emulation means that the normative and socially constructed

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ranging from individuals to states, may be much more likely to look towards esteemed

individuals or states and towards those who and which are most like themselves to gain

information about how they should deal with something. They explain that “when goals are

ambiguous, or when the environment creates symbolic uncertainty, organizations may model

themselves on other organizations” (1983, p.151). Elkins and Simmons (2005, p.43) suggest

“It is conceivable that information cascades will produce convergence toward one policy

choice even in situations in which actors know nothing other than who has adopted what

policy.”

In competition, states are motivated by rivalries with other states of similar status. In

emulation, status becomes important in that states with a lower status look to global and

regional leaders (Ring, 2014). Be as it may, diffusion is seen as the process that leads to

convergence (Gilardi, 2012). But one might ask himself, what if the diffusion process is

required to be a continuous process with continuous convergence (Komoo et al., 2011;

Trujillo and Baas, 2014; Matsuoka and Shaw, 2013), as is required for effective Disaster Risk

Reduction? How might this change existing understandings of diffusion?

2.7 Norm Diffusion

Weyland (2007) unpacked the concept of ‘policy’ by drawing a distinction between loose

templates (principles) and concrete models (policies):

“A principle is a general guideline for designing programs or institutions. Such a maxim

provides a broad orientation for policy-makers that encompasses several specific design

options. It charts an overall direction but not a specific course of action. By contrast, a model

is one specific option from the menu offered by a policy principle; a model […] prescribes a

coherent, integrated way of organizing a policy program or designing an institution”

(Weyland, 2007, p.18 in Gilardi, 2012, p.8).

Following Weyland’s distinction between loose templates and concrete policies, a somewhat

similar distinction can be made between policies and norms. Norms evoke an identity or role

that is appropriate in and to a specific situation. With norms, the logic of appropriateness

plays a role and “organizations conform to what is societally defined as appropriate and

efficient, largely disregarding the actual impact on organizational performance" (Tolbert and

Zucker, 1983, p.26). This mechanism requires that political actors shift from the ‘logic of

consequences’ to the ‘logic of appropriateness’ (Checkel, 2005).

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“collective expectations about proper behaviour for a given identity, then the diffusion of

norms is when these collective forms of appropriate behaviour are communicated through

certain channels over time among the members of a social system” (Hollis, 2015, p.92).

According to Gilardi, norm diffusion can also be understood as emulation. Relying on

Finnemore and Sikkink (1998), he reiterates that norm dynamics follow a three-stage process

of norm emergence, cascade, and internalization. During ‘norm emergence’, new rules of

suitable behaviour are promoted by considered norm entrepreneurs, with the occasional

support of organizational platforms. When there is a sufficient number of states that have

been convinced to adopt the new norm - about one third of the potential number of adopters -

a critical point is reached that pushes norm dynamics into their second stage: the norm

cascade. By then, norms are promoted in a socialization process that rewards conformity and

punishes non-compliance (Gilardi, 2012). And if the socialization process is strong enough,

norms may become so deeply accepted that they end up being taken for granted as the only

appropriate type of behaviour: the ‘internalization stage’ (Finnemore and Sikkink, 1998).

III. Research design

This thesis abides by a twofold methodology of a joining of Process Tracing and Social

Network Analysis: Process Traced Network Analysis. This model derives from the fact that,

in this thesis, the aim is to explore and understand the process of diffusion in an adaptive

network setting (Creswell, 2008). It utilizes qualitative data from secondary source analyses

and inductive reasoning, which both attribute meaning to the sociograms that stem from

Social Network Analysis and are established with Gephi software. This thesis doesn’t use a

priori hypotheses. It is exploratory in nature, a theory-generation method that uses inductive

data structuring that leads to inductive process development: ‘theory in process’. And it

eventually ‘models’ its understandings of the network information exchange into what it

thinks is reflecting the network information exchange as much as possible.

3.1 Research paradigm

Ontology and epistemology both shape the approach to theory and the methods that are

utilized in this thesis (Stoker and Marsh, 2002). Determining the ontological approach comes

first because it deals with the very nature of what constitutes reality. It reflects the position of

the author’s view about the nature of the world (Stoker and Marsh, 2002). Second comes

epistemology, which reflects the author’s view of what we can know about the world, it is the

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study of how we know things (Bernard, 2006). It refers “to the claims or assumptions made

about the ways in which it is possible to gain knowledge of reality” (Blaikie, 1993).

On a continuum from essentialism to relativism, the ontological approach stems from a

position that leans toward the relativist ends. On the one hand, the author claims there is a

‘real’ world that is independent of our knowledge of it and that can be explained, reflecting

the essentialist element of what the author conceives is reality. On the flipside, the author

believes that there are “essential differences of ‘being’ that provide the foundation upon

which social life is built” (Stoker and Marsh, 2002), reflecting the relativist element of what

the author conceives is reality. This means that the author thinks that any knowledge people

have is mediated by the concepts we use to analyse it (Quine, 1961). That way, experience

and knowledge are interpreted, both while and for the use of everyday life and for studying

everyday life. This opens up space for the author’s opinion that many if not most things that

exist have a ‘truth’ to them that is locally or regionally embedded, perhaps indeed

‘constructed’, but that is no less susceptible to being studied through a scientific lens.

Epistemologically speaking, this thesis aims to explain as part of understanding. And here,

again, the perspectives of Quine apply. Quine claimed that the knowledge we as human

beings have is mediated by the concepts we use to analyse it. So, it is impossible to classify or

describe experiences without interpreting them (Quine, 1961 in Stoker and Marsh, 2002). And

this also means that theory and experiment are not simply separable, rather “…theory affects

the facts we focus on and how we interpret them. And this may also affect the conclusions we

draw if the ‘facts’ appear to falsify the theory” (Stoker and Marsh, 2002 in referring to Quine,

1961). With Quine’s theories, the author recognizes the interdependence of theory and

observation, and engages with a post-positivist epistemology in which positivist and

interpretivist traditions have begun to dissolve (Sanders, 2002). This thesis will grasp the

network information exchange as objective as possible, yet it will also take into account the

double hermeneutic (Smith and Osborn, 2003) that comes with the authors believes: it is only

through believing the explanation that the author can comprehend them. Moreover, eventually

this study will ‘model’ the results, albeit to the end of staying as close to the objective reality

as possible.

3.2 Process Tracing

As suggested by Collier, “process tracing is an analytical tool for drawing descriptive and

causal inference from diagnostic pieces of evidence, often understood as a temporal sequence

of events or phenomena” (2011, p.824). Among things, it can be used to identify novel

political and social phenomena and systematically describe them. It can as well be used to

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