Conducting social network analysis

according to information on the outcomes of experiments from every other actor. In contrast, net-work models assume that information on new technologies is not freely available between actors but shared through social networks (Conley and Udry 2001). From a social network perspective, innova-tion adopinnova-tion is explained in terms of network exposure: the proporinnova-tion of a node’s contacts that have already adopted the innovation, assuming that the likelihood of adoption increases with the number of adopters in a node’s personal network (Valente 2005). Exposure can be operationalized either from a connectionist or a structuralist viewpoint. From a connectionist stance, network expo-sure relates to social influence in terms of transmission of information, persuasion, or direct pres-sure. From this point of view, the likelihood of adoption can be measured in terms of the number of direct ties to alters who have already adopted. From a structuralist stance, network exposure relates to social influence, social comparison or competition. From this point of view, the likelihood of adop-tion is measured in terms of structural equivalence (Valente 2005).

While diffusion theory focuses on how innovations spread throughout a social system (Rogers 2003), social learning theory provides deeper insights into the social process of knowledge acquisition and creation (Glückler et al. 2017). Social learning theory conceives of learning as a relational process that is embedded in the relationships and interactions between people (Elkjaer 2000; Genilo 2007).

In this sense, the capacity for learning depends on the relational infrastructure as a source of knowledge and advice. Multiple network features have been found to be positively related to social learning and innovation, including tie quantity and quality (e.g. strong / weak ties), network struc-ture (e.g. density, centralization), actor position (e.g. centrality, brokerage). For innovation to materi-alize, it has been argued, the right mix of structural features is needed (Newman and Dale 2005) as well as, in particular, boundary-spanning individuals connecting between core and periphery

(Bodin and Crona 2009; Klerkx et al. 2010; Glückler et al. 2017). Besides structural features emphasis is placed on the social aspects of learning. Learning is considered not as merely the transmission of knowledge, but as a social relationship between the seeker of advice and the provider of advice. For example, advice-seeking behavior is determined by social status and interrelated with collaboration, or friendship (Glückler et al. 2017). Accordingly, innovation transfer through social networks, is not only a matter of efficient network structure, but also a matter of motivated and skilled actors able to span boundaries between subgroups in order to mobilize knowledge, power, and resources

(Moore and Westley 2011).

When conducting SNA, it should be noted, that – in general – social networks do not exist as such. The definition of a social network is a theoretical act, and hence can only provide an approximation of the more complex social system under study (Butts 2009; Hennig et al. 2012). As a theoretical construct, social networks – unlike social groups – do not have natural boundaries and do not necessarily have to be connected. Who or what is defined as nodes and what is defined as ties depends on the research question and explanatory theory (Butts 2009; Borgatti and Halgin 2011).

There is no standardized way of carrying out network analyses. According to Hennig et al. (2012), there are specific conceptual decisions that need to be addressed before empirical network research is conducted. This includes the definition of a) the dependent / independent variable, b) the level of analysis, c) the type of network, d) the relations of interest (Hennig et al. 2012).

a) Dependent / independent variable: in a first step, dependent and independent variables need to be specified. If the question is how network structure affects social behavior, the network is the independent /explanatory variable. If the question is how and why people are linked in a specific way, then the network is the dependent variable.

b) Level of analysis: furthermore, the level of aggregation needs to be decided upon. This requires the decision as to whether nodes represent individuals, or rather aggregates, such as house-holds, social groups, or organizations.

c) Network type: depending on the scope of interest, two different approaches to network as-sessment can be applied:

• Socio-centric approaches capture the internal structure of interactions between members of a unit of analysis. Such complete networks entail all direct and indirect relations be-tween all members of a defined population. Complete networks can be differentiated into one-mode networks, and two-mode networks. One-mode networks represent specific substantive connections between a single set of actors. Two-mode networks consist of two distinct sets of actors (e.g. persons and organizations) or, more generally, two distinct sets of units (e.g. people and social events) and the relations between these two (e.g. affili-ation with an organizaffili-ation / participaffili-ation of people in social events). The assessment of complete networks requires the a-priori definition of a limited number of network mem-bers and the systematic assessment of all possible ties between them. Depending on net-work size, this can be a time and resource-intensive endeavor, unless secondary infor-mation about interactions is already available.

• Ego-centric approaches capture the social embeddedness of particular actors. Ego net-works describe the direct interactions between individuals (egos) and their social envi-ronment (alters), whereas personal networks, in addition, also account for the structure of an ego’s social environment, i.e. the relations between alters. Unlike complete net-works, ego networks are applicable in contexts in which alters are unknown, and hence are suitable for mass representative surveys. While most empirical work has been con-ducted on either complete or ego networks, in recent years, a growing number of studies are applying hybrid approaches, in order to add “openness” to socio-centric approaches and “structure” to ego-centric approaches.

d) Relations of interest: finally, the relations of interest need to be defined. Connections between actors can be distinguished by content and form. Content refers to the substantive type or rela-tion (e.g. friendship or exchange of informarela-tion), while the form refers to the properties of the connection, which exists independently from its content (e.g. its strength or frequency).

In general, there are two distinct strategies for analyzing network structure. The “relational” or “so-cial cohesion” approach focuses on connectivity between actors, either direct or indirect (Emirbayer

and Goodwin 1994). Conceptually, relational approaches draw on the “flow model”, conceiving of social networks as conduits of flows (Borgatti and Halgin 2011). Measures of interest from a rela-tional perspective are, for example, measures of network connectivity, fragmentation, and different forms of centrality (Hanneman and Riddle).

In contrast the “positional approach” focuses on the patterns of relations that define an actor’s posi-tion relative to all other actors in the network – that is, the “posiposi-tion” or “role” that an actor / or a set of actors occupy within a social system (Emirbayer and Goodwin 1994). Positional approaches build on the bonding rather than on the distributing function of networks. Measures include “structural equivalence” or “substitutability”, which are analyzed with the help of sophisticated methods of ma-trix clustering (block models) (Scott 2011).

Based on theorems of graph theory a broad range of mathematical procedures can be applied to ana-lyze formal network properties (Wasserman and Faust 1994; Scott 2013). Descriptive network measures build on concepts, such as adjacency, reachability, distance, connectivity, and embed-dedness. These concepts are basically defined on the dyad level, but can be aggregated on a higher level , i.e. the actor level, the sub-group level, and the network level (Hennig et al. 2012) . For an overview of selected measures relevant in the context of this study see Box 5.

Box 5: Selected network measures (adapted from Hanneman and Riddle (2005))

Centrality describes the location of particular actors in terms of how close they are to the center action in a network. The most popular centrality measures are:

• Degree centrality refers to the number of direct ties an actor possesses. In directed net-works in-degree measures the number of ingoing ties, whereas out-degree measures the number of outgoing ties. The underlying assumption is that actors with many ties are in an advantaged position, because they have alternative ways of satisfying needs and are able to access more resources. High in-degree can be indicative of resource access or prestige. Actors with many outgoing ties are supposed to be influential actors.

• Closeness centrality emphasizes the distance between an actor and all others in the net-work or, in other words, how close one actor is to all the others, and hence is an indica-tor of the influence an acindica-tor has on other acindica-tors in the network.

• Betweenness centrality explores the extent to which an actor is located on the shortest possible paths (geodesics) between other actors in the network, and hence is a suitable measure of brokerage and the power to control flows between subgroups of a network.

• While centrality focuses only on direct ties an ego has to its alters and can be applied for the study of ego networks and complete networks alike (Marsden 2002), closeness and betweenness centrality require data on the structure of relations between a defined set of actors (complete networks), and therefore are not applied in the context of this study.

Degree centralization measures the variance in actors’ degree in a given network expressed as a percentage of the variance in actors’ degree in a perfectly centralized network of the same size (i.e. a star network). In networks of high degree centralization, degree is unequally distributed among actors, implying that positional advantages are unequally distributed. Centralization can be also calculated for other centrality measures.

Density expresses the extent of dyadic connection in a network. Density is the ration of the number of ties in a given network to the maximum possible number of ties in a network of the same size. In networks of high density information and resources are circulated more rapidly and equally, while a network of low density might be indicative of limited flow of resources and information. High density, however, might not always be advantageous, in particular in net-works with closed sub-groups where access to novel information is lacking.

Nodes with highest:

degree centrality closeness centrality betweenness centrality

a) high centralization b) de-centralized c) low centralization