Integración de las herramientas de innovación

This section describes the process by which the desktop research and interview data are analysed to address the research questions, using coding and stakeholder matrix analysis.

The method of stakeholder analysis is the completion of a bespoke series of matrices, building on the Actor-Centered Institutionalism framework concepts. The matrices are completed through a coding process. All documentation – interviews as well as written documents - relevant to each case study is reviewed and coded using four types of codes. The codes arise from the ACI framework, and are grouped as follows:

1. Type 1: Self-identified: Actor A thinks X of Actor A, e.g.:

o A1A : A describes its own capabilities and resources

o A2A: A describes its own orientations about the DHC network o A3A: A describes its own preferences for the DHC network o A4A: A describes its own marking shaping strategy for DHC o A5A: A describes its own regulation of DHC

o A6A: A describes its own market incentives for DHC

o A7A : A describes its own market capacity building around DHC 2. Type 2: Other identified: Actor X thinks X of Actor B, e.g.:

o A1B: A describes what it understands about B’s capabilities and resources o B2C: B describes what it understands about C’s orientations towards the DHC

network

o D3A: D describes what it understands about A’s preferences towards the DHC network.

3. Type 3: Statements of fact: describing process, naming of actors involved, and methods of communication or collaboration. Three codes were used:

o 20: A neutral description of the process of establishing the DHC network.

o 21: A reference to the actors involved in the policy or decision making about the DHC network

o 22: A description of communication or collaboration methods in establishing the DHC network.

4. Type 4: Other commentary on the case study by actors not directly involved in delivery of the DHC network (journalists, other research, post-hoc perceptions, etc). This third party commentary was coded ‘23’.

Coding is an iterative process with the interviews and desktop research, whereby actors identified in code 21 are then investigated and documentation associated with them added into the coding process. Coding is undertaken using Atlas TI version 5.6.3. These codes enable the completion of the stakeholder analysis matrices. Appendix C contains a list of all codes used in each case study and the full stakeholder analysis matrices.

Stakeholder analysis matrices

Four stakeholder analysis matrices are used to define the set of actors and the actor constellation and to inform the understanding of the mode of interaction. One 'self-identified actor' analysis matrix summarises the capabilities, orientations, and preferences of all the actors involved in one case study, as identified by each actor. Essentially, this matrix is a summary of what each actor thinks of itself. This was completed with codes A1A, A2A, and A3A, as Table 14 illustrates.

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Actor Capabilities Orientation Preferences

e.g. local

A second 'collective actor' analysis matrix represents the same information about each actor, but instead of summarising what each actor thinks of itself, it summarises the perception of each actor by all other actors. Using the coding process, it groups responses from all actors and from the desktop research, so that each specific actor is described in a collective way by all the other actors. The codes for this matrix have greater variation, examples such as: A1B, B1A, D3F, etc.

This collectively identified matrix highlights conflict in perceptions among actors in terms of their perception of other actors, and also identifies where consensus exists. This collective actor matrix begins to build a picture of the actor constellations in each case study. The research is careful to note changes in perceptions of other actors, recognizing that actor constellations are not static but evolve over time, through the effect of negotiations or interactions. Table 15 provides an example of a collective actor matrix; the full matrices are included in Appendix C.

Actor Capabilities Orientation Preferences

e.g. Government Typical code LPA1GEM; B1GEM LPA2GEM; F2GEM LPA3GEM; H3GEM e.g. local

e.g. policy department e.g. state goals and interfere in new development Typical code GEM1LPA; C1LPA GEM2LPA; D2LPA GEM3LPA; F3LPA

Table 15 : Example collective actor matrix

Actors and actor constellations are analysed in detail through a further two matrices which describe the market interventions of the actor set. These strategy matrices map the 'potential' and the ‘actual’ strategies for each actor against four categories of market intervention identified by Allmendinger and Tiesdell, as discussed in Chapter 2. The self-identified strategy matrix defines how each actor perceives their potential options given institutional constraints and powers, and what roles or policy influences that actor enables. This employs codes such as A4A, A5A, A6A, A7A, etc as described above. The collective strategy matrix summarises what all actors perceive of other actors potential and actual interventions, using codes which referenced other actors, for example A4F, C5B, D6A, E7Z, etc. Again, this 'what everyone thinks of everyone else' matrix highlights areas of conflict or consensus in perception of others.

The ++ indicators represent a scale of regulatory options as identified by the researcher: 0 (none), + (minor), ++ (medium), and +++ (significant). To illustrate, in the collective strategy matrix below in Table 17, Actor B has a minor market shaping role (e.g. participating in policy development) but a significant potential incentive role (e.g. providing infrastructure grants).

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Potential Actual Potential Actual Potential Actual Potential Actual

Actor A ++ + +++ + 0 0 +++ 0 studies, these matrices help the research understand if perceived and actual strategies – or the gap between perceived and actual - are a causal factor in the delivery of DHC.

Full code lists and detailed analysis matrices for each case study can be found in Appendix C.

ACI Framework Established Through Mode of interaction - negotiated Selection of case studies

Confirmed by desktop reviews Interviews

Collective strategy analysis Relational characteristics Interviews

Comparative analysis DHC delivered or not delivered Selection of case studies

Table 18 : Research analysis methods in summary

79 Evaluation and research narrative

The final step in the research methodology is to reflect on the stakeholder analysis and strategy analysis in light of the research questions. This is done at both a case study level and in a comparative, cross-case study level. For each case study, the research evaluates how the components of ACI result in the policy outcomes, using the matrices to ‘get to grips’ with what causes the success or failure of DHC as an environmental policy goal. This case evaluation considers the eight case study questions identified in Chapter 4.

1. Who are the primary actors?

2. How are the actors influenced by the institutional setting?

3. What are the actors' self-identified capabilities, orientations, and preferences?

4. How can the actor constellation be described?

5. How can the interactions be characterised?

6. Is this network governance?

7. What are the relational characteristics?

8. How can the role of planning be described?

These questions are addressed for each case study in Chapters 6 to 10 through a combination of description of the actors, narrative describing the outcome of the stakeholder analysis, direct quotes from actors and stakeholders interviewed, and the researcher's reflection on specific research case study questions. A narrative approach is considered appropriate given the breadth of information presented; it unlocks the story of the governance network without labouring the coding procedures. The coding procedures and stakeholder analysis matrices are detailed in Appendix C.

The answers to the eight case study questions enable the research to compare across the cases studies on how each component of ACI related to outcome of the attempt to establish a DHC network. Chapter 11 describes the comparative analysis which directly addresses the three main research questions.

Data Collection Processing Analysis Desktop Research

Semi-Structured Interviews

Transcription Notes

Coding of all texts

Reflection on Institutional Setting Self-identified Actor Analysis Collective Actor Analysis Strategy Analysis

Collective Strategy Analysis Reflection on Mode of Interaction Reflection on existence of governance network behaviours

Reflection on role of planning.

Comparative Analysis Table 19 : Summary of data collection, processing and analysis

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