Sample

Our analyses are based on the 2014 database on smart grid projects compiled by the Joint Research Centre (JRC) of the European Union (Covrig et al., 2014). The 2014 JRC database is based on different sources with an online-survey as its backbone, in which data on projects is self-reported (typically by their leaders) and double-checked for consistency and accuracy by JRC staff. The database includes smart grid projects in Europe between 2002 and January 2014.

We performed a quality check on the existing data and made several changes. Duplicates were removed and inconsistencies (e.g. due to different spellings, languages or abbreviations) adapted. We also found some projects with no or more than one project leader. In this case we searched on project websites or other web-sources to identify the primary leader for each project. From project websites and the JRC online- database we also added project classifications in terms of content with seven overlapping categories: Smart Network Management, Integration of Distributed Energy Resources, Integration of Large Scale Renewable Energy Systems, Aggregation (Demand Response, Virtual Power Plant), Smart Customer/Smart Home, Electric Vehicles and Vehicle2Grid Applications, and finally Smart Meters (only if they are part of a wider Smart Grid project). Finally, we limited our analysis to the period of 2002 to 2012 because entries for 2013 and 2014 were incomplete.

The JRC data distinguishes between research & development (R&D) and demonstration & deployment (D&D) projects. The definition of R&D projects is in

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accordance with the Frascati Manual (OECD, 2002) and includes three activities: basic research, applied research and experimental development. Demonstration projects, in contrast, are “designed to test the performance of a technology in different operational environments, through to full market trials in which the technology is used in customer installations (Brown and Hendry, 2009)” (Covrig et al., 2014; p. 20). In our analysis, we use this distinction to explore different TIS functions (cf. Bergek et al., 2008): R&D projects are assigned to the function of ‘knowledge development’, while D&D projects are assigned to ‘entrepreneurial experimentation’. This is consistent with the indicators developed by Gosens et al., (2015) that proposed R&D projects as an indicator for knowledge development, and demonstration pilots, studies and field trials as the indicators for entrepreneurial experimentation.

With regard to the actors involved in smart grid projects, different information attributes are available. For our interest in the spatial network characteristics, the country of origin of the actors is particularly important. Moreover, we use the information that is available is about different types of actors. The JRC database distinguishes 10 categories, which we aggregated as depicted in the table below.

Table 3-1. Actor types (compiled based on Covirg et al., 2014)

JRC Database Aggregation Remarks

Association Association Intermediary actors that represent specific interests

Manufacturer/ Engineering services/

Contractors/ Operators/ Manager company Manufacturers Industry actors that are involved in technology development IT company and Telecom ICT Actors from the information and

telecommunication sector Municipalities/ Public Authority/ Government Public Public actors

University/ Research centre/ Consultancy University Universities and similar actors that concentrate on knowledge creation Distribution system operator Utilities Actors from the electricity supply sector Transmission System Operator

Generation company

Energy company/ Utility company/ Energy retailer/ Electricity Service provider

Other Other Other actors

3.3.2.2 Network construction

There are several options when constructing networks from data on 2-mode or bipartite networks. These include assumptions about i) the type of network, ii) who is connected, iii) the lifetime of ties and nodes and iv) inclusion of single actor projects.

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i) Our database provides information about how actors are affiliated with projects. This generates a bipartite graph with two types of nodes, actors and projects. To apply SNA tools, bipartite (2-mode) graphs need to be transferred into 1-mode projections. In our case, we create a network of actors (nodes) that are connected by projects (ties).

ii) Who is connected: Our data includes information about the project leaders. This opens up two options (based on different assumptions) for the creation of the network (Breschi and Cusmano, 2004). First, it can be assumed that all partners in a project are equally in contact with each other, which leads to completely connected subgraph (clique) for each project. Second, it can be assumed that the leader has a central role in the project and acts as an intermediary, i.e. all information passes through the leader. This results in a star network. Both assumptions are strong and equally plausible and can be applied for addressing specific questions. We checked numerous project websites and in most cases found particular emphasis on the role of the leader, which is why we decided to work with the star network assumption for analyzing collaborations. On the other hand, path dependency requires considering involvements in projects over time, rather than collaboration with the project leader; therefore, the clique network assumption is used for path-dependency analysis.

iii) Considering the life-time of the ties, there are two basic options. We can either assume that the ties between actors only last for the duration of the project (network based on running projects) or that they continue even after the project has finished until the duration of analysis ends. The latter is based on the assumption that collaboration and knowledge exchange between actors continues even if they are not any longer connected by a formal project. In this case, the network increases with every new project (cumulative network). Below we look into data for the cumulative network over specific periods of analysis.

iv) Inclusion of single actor projects: 47% of the projects in our database involve just one actor. As we wanted to include them in our analysis as a benchmark for comparing spatial characteristics, we assigned each single actor a tie to itself with the consequence that these (national) ties are counted when determining the nationalization index (see below).

72 3.3.2.3 Determination of three periods

Our analysis encompasses a period of 11 years (2002-2012). Earlier research based on the same data has already pointed to qualitative changes during this period, i.e. a shift in the type of projects from R&D to D&D projects (Colak et al., 2015). Aligned with the revised IAD framework, institutions at the European level can be considered exogenous to the action arena, and the IAD framework operationalizes these institutions as specific events influencing interactions and the action arena. Towards this end, we compiled a list of major events related to smart grids in Europe in areas such as regulation, public funding of research, coordination of development and industry activities. We identified two points in time, when – in our view – important and qualitatively new events came together. From this, we derived three periods for analysis.

3.3.3 A revised framework for analyzing spatial diversity of network