Blog 72: DO NETWORKS MATTER? A retrospective on the potential applications of social network analysis

Social Network Analysis (SNA) is arguably the latest trend and buzzword among social science practitioners. With its strong theoretical and empirical rooting in sociology and graph theories, it could answer many research questions within a system. It is no secret that modern social media channels such as Facebook and LinkedIn have their theoretical backing from SNA. In this blog post, Subash S P and Sreeran V try to highlight some of the potential applications of SNA in agricultural sciences.

CONTEXT

Information is the new gold, it is the new oil. Anyone who controls information has access to great wealth and power. (From Killswitch: The battle to control the Internet (2005) directed by Ali Akbarazadeh) In today’s world, almost everyone leads a networked life; from simple, direct communication networks based on our acquaintances and relationships, to internet- mediated social networks which link human beings virtually across the globe. These networks are playing a crucial role in deciding the kind of knowledge one can have, the resources one can access, the opportunities one can explore and the extent and nature of contacts one can create. Can we tap into the potential information embedded in these networks and relationships, in case of agriculture?

Yes!! We surely can, if we apply Social Network Analysis (SNA). SNA is a methodology to map and qualify actors (nodes) and their relationships within a network. It allows for accounting the “flow”. This flow can be a resource (e.g. commodity or information); a service (credit or input); or a relation (kinship or friendship). Mapping and quantifying this information can yield potential benefits to a social scientist as it gives rich inputs about the position of a farmer in his/her social network. Invariably, it decides his/her access to various resources and information.

Initially SNA was used in the fields of sociology, psychology and anthropology. With the advancement in graph theory (mathematics) and computing knowledge, SNA tools have been developed to map and quantify networks. The network perspective is becoming a key approach in social and biological sciences (Borgatti and Li, 2009). In this blog post, we provide insights into the application and scope of SNA in agricultural research and extension.

EVOLUTION OF SNA 

The origin of SNA dates back to ancient Greeks, but major developments occurred in the 1930s. Figure 1 depicts the lineage of SNA. For further details about the history of SNA, please read the book (Scott, 2000). SNA, in its current form, is an amalgamation of socio- metric technique and graph theory. It has evolved through various phases and, over time, has turned into a data analysis technique with wider applications.

Figure 1. Lineage of Social Network Analysis (Scott, 2000).

APPLICATION OF SNA IN SOCIAL SCIENCES 

SNA is more about the social relations and interactions among people in a group rather than about the individual actor as followed by most of the other social science techniques. This focus of the interactions is more pragmatic as it can answer many difficult research questions on the capabilities and resource access of the actors within a system. Just as the location of a building in a city decides its access and potential, the position of an actor in a system can predict the possible resources and capabilities that he could access and benefit from. It could even answer the cognitive aspects of behaviour including the learning or adoption behaviour of the actors which is shaped by the interaction with other actors or their influence within the system. The rate of technology adoption within a system is dependent upon many social factors, for instance, influence of neighbours, cliques, relatives, progressive farmers and reference groups. This argument is empirically proven as it is supported by many research studies in various contexts.

One of the major imperfections of the diffusion of innovation model of the Rogers (2003) was the individual blame bias. Simply stated, this argument says that for technology rejection/discontinuance decisions, it is always the individual who is blamed rather than the system of which he is a part. In other words, if the shoe does not fit there is something wrong with one’s foot! This is because a social science researcher always takes an individual actor as the unit of his study. Whereas, in reality, there may be several other reasons, such as resistance from his social system or its inefficiencies which prevent an actor from adopting an innovation or continuing its use. This can be known only if we study the social system, particularly the social network of the concerned actor. This is what a SNA should try to figure out.

Further, SNA is applicable wherever there is a flow of something or where connections can be established among the units of a network. This something can be a resource (a commodity such as milk) or a service (credit) or even a relationship (information dependency). It has potential applications in various research themes: value chain analysis (Lazzarini, 2001; Borgatti and Li, 2009), technology adoption studies (Matsuskhe, 2008; Magnan et al., 2015) and impact analysis (Ekboir et al., 2011).

UTILITY OF SNA IN MAPPING VARIOUS NETWORKS: A FEW EXAMPLES 

Ekboir et al. (2011) used SNA to monitor changes in a research network to understand the process, innovation, opportunities and challenges. A network of 92 researchers depicting 624 collaborators in 302 organisations for the CGIAR Research programme on Roots, Tubers and Bananas (RTB) was mapped (see Figure 2). This helped in understanding how the programme is moving in its impact pathway, its partnerships, collaborations and interactions. This is important in framing strategic management and adaptive measures.

Figure 2. Intermediation (betweenness) of CGAIR centres (Ekboir et al., 2011) 

Note: Nodes colour-coded by centre affiliation: yellow = Biodiversity, black = CIAT, pink = CIP, red = IITA, grey = other centres. 

Lazzarini et al. (2001) introduced the concept of netchain to depict the interrelationship between horizontal and vertical networks in a value chain. Netchain is name given to horizontal ties between firms with layers of vertical ties (see Figure 3).

Figure 3. Netchain (Lazzarini et al., 2001) 

Magnan et al. (2015) used gender-disaggregated social network data from Uttar Pradesh to test the gender-specific network effects on demand for laser land levelling (see Error! Reference source not found.).

Figure 4: Gender-specific social network 

They found that though the factors determining male and female networks are similar, there is little overlap between them. The study also provided some evidence of female network effects on household technology demand and suggested leveraging female networks for extensive dissemination of technology. The study has also emphasised that small farmers mainly rely on social networks for information; hence, public and private efforts should be relayed using social networks for transmission of technology to a large number of farmers.

Thuo et al. (2013) used SNA to visualise patterns of groundnut farmers’ networks with regard to information sources, productivity support and local group affiliations. Their main concern was to understand the role of strong and weak ties in enhancing the productivity of groundnut farmers by providing them the requisite information. Network mapping demonstrated the flow of information on groundnut from a variety of sources, including the strong and weak ties. It also revealed that the network structures can vary considerably even among farmers in similar geographic regions producing similar crops. The network map is shown in Figure 5.

Figure 5: Groundnut productivity information flow network (Thuo et al., 2013)

CONCLUSIONS

Social network analysis is an emerging field of science which our social scientists can vigorously pursue for designing future studies. The field is rich with theoretical contribution from various disciplines blended with the potential possibilities of visualisation of networks and quantification of various parameters grounded on the graph theory. Though researchers like Spielman (2010) underscored the usefulness of this research technique in agriculture in developing countries, particularly for innovation system studies, network- based studies are still in their infancy in the Indian context. Though the same set of theories and applications are highly useful and widely used in case of biological sciences (mapping of genes and interaction effects), we also emphasised the application of SNA only in social sciences. Application of SNA is surely going to be a game changer for the fields of agricultural extension and economics, and will make great research impacts in the coming days.

References 

Borgatti, S., & Li, X. (2009). On Social Network Analysis in a Supply Chain Context. Journal of Supply Chain Management. 45(2): 1–17.

Ekboir, J., Canto, G. B., & Sette, C. (2011). Monitoring the composition and evolution of the research networks of the CGIAR Research Program on Roots, Tubers and Bananas (RTB). Available online on http://www.cgiar-ilac.org/files/ilac_report_research_networks_rtb_0.pdf. Accessed on 11-07-2015. Landherr, A., Friedl, B., & Heidemann, J. (2010). A Critical Review of Centrality Measures in Social Networks. Business & Information Systems Engineering, 2(6), 371–385. doi:10.1007/s12599-010- 0127-3

Magnan, N., Spielman, D. J., & Lybbert, T. J. (2015). Information Networks among Women and Men and the Demand for an Agricultural Technology in India. IFPRI Discussion paper 01411.

Matuschke, I. (2008). Evaluating the impact of social networks in rural innovation systems: An overview. IFPRI Discussion Paper, (November), 26.

MarThuo, Alexandra A. Bell, Boris E. Bravo-Ureta, David K. Okello, Evelyn Nasambu Okoko, Nelson L. Kidula, C. Michael Deom & Naveen Puppala (2013) Social Network Structures among Groundnut Farmers, The Journal of Agricultural Education and Extension,19:4, 339-359, DOI: 10.1080/1389224X.2012.757244

Scott, J.P. 2000. Social Network Analysis. Sage Publications India Pvt. Ltd, New Delhi.

Spielman, D. J., Davis, K., Negash, M., & Ayele, G. (2010). Rural innovation systems and networks: findings from a study of Ethiopian smallholders. Agriculture and Human Values, 28(2), 195–212. http://doi.org/10.1007/s10460-010-9273-y

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