Social Network Theory Applications to Supply Networks
A social network is a social structure made of nodes which are generally individuals or organizations. It indicates the ways in which they are connected through various social familiarities ranging from casual acquaintance to close familial bonds. The term was first coined in 1954 by J. A. Barnes (in: Class and Committees in a Norwegian Island Parish, "Human Relations"). The maximum size of social networks tends to be around 150 people and the average size around 124 (Hill and Dunbar, 2002).
Social network analysis (also sometimes called network theory) has emerged as a key technique in modern sociology, anthropology, Social Psychology and organizational studies, as well as a popular topic of speculation and study. Research in a number of academic fields have demonstrated that social networks operate on many levels, from families up to the level of nations, and play a critical role in determining the way problems are solved, organizations are run, and the degree to which individuals succeed in achieving their goals.
The shape of the social network helps determine a network's usefulness to its individuals. Smaller, tighter networks can be less useful to their members than networks with lots of loose connections (weak ties) to individuals outside the main network. More "open" networks, with many weak ties and social connections, are more likely to introduce new ideas and opportunities to their members than closed networks with many redundant ties. In other words, a group of friends who only do things with each other already share the same knowledge and opportunities. A group of individuals with connections to other social worlds is likely to have access to a wider range of information. It is better for individual success to have connections to a variety of networks rather than many connections within a single network. Similarly, individuals can exercise influence or act as brokers within their social networks by bridging two networks that are not directly linked (called filling social holes).
Social networks have also been used to examine how companies interact with each other, characterizing the many informal connections that link executives together, as well as associations and connections between individual employees at different companies. These networks provide ways for companies to gather information, deter competition, and even collude in setting prices or policies.
Power within organizations, for example, has been found to come more from the degree to which an individual within a network is at the center of many relationships than actual job title. Social networks also play a key role in hiring, in business success for firms, and in job performance. • Diffusion of innovations theory explores social networks and their role in influencing the spread of new ideas and practices. Change agents and opinion leaders often play major roles in spurring the adoption of innovations, although factors inherent to the innovations also play a role.
The so-called rule of 150, states that the size of a genuine social network is limited to about 150 members (sometimes called Dunbar's number). The rule arises from cross-cultural studies in sociology and especially anthropology of the maximum size of a village (in modern parlance most reasonably understood as an ecovillage). It is theorized in evolutionary psychology that the number may be some kind of limit of average human ability to recognize members and track emotional facts about all members of a group.
Degrees of Separation and the Global Social Network • The small world phenomenon is the hypothesis that the chain of social acquaintances required to connect one arbitrary person to another arbitrary person anywhere in the world is generally short. The concept gave rise to the famous phrase six degrees of separation after a 1967small world experiment by psychologist Stanley Milgram which found that two random US citizens were connected by at most, six acquaintances. Current internet experiments continue to explore this phenomenon, including the Ohio State Electronic Small World Project and Columbia's Small World Project. As of 2005, these experiments confirm that about five to seven degrees of separation are sufficient for connecting any two people through the internet.
Indices for Social Network Analysis Betweenness • Degree an individual lies between other individuals in the network; the extent to which a node is directly connected only to those other nodes that are not directly connected to each other; an intermediary; liaisons; bridges. Therefore, it's the number of people who a person is connected to indirectly through their direct links. See also Betweenness Closeness • The degree an individual is near all other individuals in a network (directly or indirectly). It reflects the ability to access information through the "grapevine" of network members. Thus, closeness is the inverse of the sum of the shortest distances between each individual and every other person in the network. See also Closeness Degree • The count of the number of ties to other actors in the network. See also degree (graph theory)
Indices for Social Network Analysis Eigenvector Centrality • Eigenvector centrality is a measure of the importance of a node in a network. It assigns relative scores to all nodes in the network based on the principle that connections to nodes having a high score contribute more to the score of the node in question. Clustering Coefficient • The clustering coefficient is a measure of the likelihood that two associates of a node are associates themselves. A higher clustering coefficient indicates a greater 'cliquishness'. Cohesion • Refers to the degree to which actors are connected directly to each other by cohesive bonds. Groups are identified as ‘cliques’ if every actor is directly tied to every other actor, or ‘social circles’ if there is less stringency of direct contact
Indices for Social Network Analysis ConstraintContagion Density • Individual-level density is the degree a respondent's ties know one another/ proportion of ties among an individual's nominees. Network or global-level density is the proportion of ties in a network relative to the total number possible (sparse versus dense networks). Integration • Group degree centralisation • A measure of group dispersion or how network links focus on a specific node or nodes. Radiality • Degree an individual’s network reaches out into the network and provides novel information and influence
Indices for Social Network Analysis Reach • The degree any member of a network can reach other members of the network. See also reach. Structural Equivalence • Refers to the extent to which actors have a common set of linkages to other actors in the system. The actors don’t need to have any ties to each other to be structurally equivalent. Structural Hole • Static holes that can be strategically filled by connecting one or more links to link together other points. Linked to ideas of social capital: if you link to two people who are not linked you can control their communication.
What is social capitalSocial capital is generally referred to as the set of trust, institutions, social norms, social networks, and organizations that shape the interactions of actors within a society and are an asset for the individual and collective production of well-being. At the macro level, social capital can affect the economic performance and the processes of economic growth and development.
SNT Measures • The oldest, and also simplest notion referring to quantitative aspects of social capital its volume or extensity. The (often implicit) theoretical argument is that bigger, larger, or simply more social capital is better social capital for individual goal attainment (Bourdieu, 1980; Burt, 1992), without specifically referring to (numbers of) relationships, resources, or the availability of any resources. • A second, more often used notion is that of diversity: because specific resources and relationships can be located and accessed more successfully when more differentiation is present in the network, this results in better social capital. More specifically, this notion has been applied to either the diversity of social resource collections (Erickson, 1996; Lin, 2001a) or the diversity of network relationships, as worded in hypotheses considering the presence of weak ties (Granovetter, 1973), structural holes (Burt, 1992), and other many other typical configurations in social network structures (Borgatti et al, 1998).
SNT Measures • A third class of morphological social capital characteristics that could be considered for measurement is based on specific resources present in networks. • The only social capital measure that has been used regularly in this fashion is `highest accessed prestige' from the Position Generator model (Lin & Dumin, 1986; Lin, Fu, and Hsung, 2001), based on the hypothesis that positive social capital results from accessing network members with high prestige (we will return to this model shortly). Identifying more of these specific (groups of) resources is one of the current aims of social capital research.
SNT Measures • The most comprehensive measurement instrument used to construct social capital measures is the exchange type Name Generator / interpreter (McCallister & Fischer, 1978). This method maps the ego-centered social network as a starting point for a subsequent social resource inventory. It can result in very detailed and informative social capital descriptions, both in terms of relationships and resources. The single `core'-network identifying name generating item `with whom do you talk about personal matters' stems from this approach, and has been widely used ever since (e.g. in the American General Social Survey, see Marsden, 1987). • A measurement method focusing more on the presence of social resources than relationships in networks is the Position Generator (Lin & Dumin, 1986; Lin, Fu, and Hsung, 2001). This method measures access through network members to certain occupations, that represent social resource collections based on job prestige in an hierarchically modeled society, following Lin's theories of social resources and social capital (Lin, 1982; 2001a). This instrument is more interview-friendly, and measures calculated from it are firmly rooted in theory. • Another more resource-oriented social capital measurement instrument is the Resource Generator (Snijders, 1999; Van der Gaag & Snijders, 2003b). This instrument asks about access to a fixed list of specific social resources, that each represent a vivid, concrete sub collection of social capital, together covering several domains of life. This instrument can be administered quickly, and result in easily interpretable representations of social capital, with more possibilities for use in goal specificity research.
In a recent network design book, Advanced IP Network Design, the authors define a well-designed topology as the basis of a well-behaved and stable network. They propose the idea that, “…three competing goals must be balanced for good network design: reducing hop count, reducing available paths, and increasing the number of failures the network can withstand” . Social network algorithms can assist in meeting all three of these goals. Reducing the hop count infers minimizing the average path length throughout the network. This can be done by maximizing the closeness of all nodes to each other. Reducing the available paths leads to minimizing the number of shortest paths between members in the network. Increasing the number of failures a network can withstand focuses on minimizing the centralization of the entire network. Social network models can model our computer networks and suggest link changes to form an effective topology that has a short average hop count, not too many paths, and just enough redundancy.
The leading hypothesis is that as social diversity increases, the level of exposure to a certain illness also increases. Thus the immune system is better prepared to defend itself against any future exposure to the sickness. However, the researchers have so far not been able to thoroughly support this hypothesis experimentally. What this research does show is another strong benefit of having high social diversity or social capital . • The results found by these researchers are quite surprising, “The magnitude of the health risk of being relatively isolated (socially) is comparable to the risks associated with cigarette smoking, high blood pressure and obesity and is robust even after controlling for these and other traditional risk factors” . It appears that cultural isolation can have a profound effect on physical well being. Their research has also shown that the development of mental illness is associated with the level of social contact a person has. Some researchers believe that this is due to the fact that people’s identities are tied to their social roles. By meeting role expectations, individuals are given the opportunity to enhance their self-esteem. They believe that these social roles provide a purpose to life. They imply that a sense of purpose is an integral component of psychological well being.
Research in social networks has also proven to provide great benefits to the field of marketing. Social networks and their patterns of relationships are a fundamental fact of market behavior and can be used effectively as a basis for marketing strategies. A major challenge facing marketing strategists is how to increase the effectiveness of social network based marketing strategies. In order to reach this goal marketing researchers and scientists have collected social network related data and have analyzed it using social network analysis. The study of social networks is beginning to be widely used in marketing. One of the reasons why it has taken so long to have an impact is because of the scarcity and difficulty in obtaining the requisite data.
Research in social network analysis is being performed by government agencies for use in defense programs. The Total Information Awareness program sponsored by the Defense Department is currently working on a project known as Scalable Social Network Analysis (SSNA). “SSNA aims to model networks of connections like social interactions, financial transactions, telephone calls, and organizational memberships” . They are attempting to model the social networks that terrorists belong to. The purpose of the SSNA algorithms program is to extend techniques of social network analysis to assist with distinguishing potential terrorist cells from legitimate groups of people, based on their patterns of interactions, and to identify when a terrorist group plans to execute an attack. This is an extremely ambitious project considering the scale of the social networks that these researchers are attempting to model. In order to be successful SSNA will require information on the social interactions of the majority of people around the globe. Since the Defense Department cannot easily distinguish between peaceful citizens and terrorists, it will be necessary for them to gather data on innocent civilians as well as on potential terrorists.