Social Network Analysis as a Research Method

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Table of Contents

Introduction
Attributes of relationship
Network analysis
Modern studies
Conclusion
References

Introduction

Social Network Analysis is considered a technique by which information and other resources are exchanged between ‘actors’. The network consists of the nodes and the relationships between ‘actors’ the ties. Like roads that connect cities and provide the flow of resources between them, the social network comprising relationships allows the flow of information between the ‘actors’. The relationship between actors, availability, and exchange of information among them is what is studied by an analysis. The exchange could be tangible like services, money, or goods or intangible like influence, social support, or information.

A researcher would derive a questionnaire that has questions framed to extract relevant information on any issue. It could even be a health-related topic.

The actor would be asked from where he got the messages on illnesses daily, whom he shares it with, and to whom he would go in a dire medical emergency. The analysis thus harps on relationships.

Attributes of relationship

The social structure produces the type of organization seen. It ranges from the well-knit family to a school or workplace to social relationships for spending the leisure together. To an analyst, the world is made up of networks. The nodes could belong to one or more networks. An author would be part of a fraternity that includes his office staff, his publishers, editors, proofreaders, well-wishers, and other authors forming one network. He would be having a family network in addition. If he is an accepted author, he would be rubbing shoulders with the top brass and high-flown officials in another network.

Relationships have 3 attributes; content, direction, and strength. Content deals with the exchange and delivery of information. When setting out to research for analysis, wondering about what to include in the questionnaire is a question in itself. We shall have to elicit the information based on three relationships: ties in intimate focus, informal focus, and leisure focus (Wegener 1991). In research on a computer science research project, six dimensions were assumed for the analysis; receiving work, giving work, collaborative writing, sociability, major emotional support, and computer programming.

Direction specifies which way the relationship works. It could be asymmetrical from employer to employee. It could be undirected where there is flow both ways but the amount exchanged may not be the same quantitatively. Undoubtedly relationships bring strength depending on their intensity. This tie strength could lead to social or instrumental support. Weak ties are as important for the dissemination of information.

Network analysis

Networks could be egocentric or whole. In the egocentric pattern, information would be sought from identified ties. On the whole, other similar information-seeking ties would be identified.

Network analysis uses 5 principles for examining a network. They include cohesion (strong relationships), structural equivalence( ties grouped by similarity in relationships), prominence( who is in charge), range ( extent of the network), and brokerage ( bridging connections to other networks)

Sociograms, graphs of measurement, are interpreted better when covering a small network of actors. Computer packages for social network analysis are available for dealing with large networks. They are gradual, structure, ancient, and negopy, Krackplot can produce sociograms from the data matrix. ( Carol Hawthornthwaite, Pg 326, Social Network Analysis,1996).

Financial markets have widespread networks, especially for their venture capital. The success of a business venture would probably rely on this network. (Whom You Know Matters: February 2007).

There are 3 reasons for analyzing the significance of syndication networks in the venture capital industry. VCs invite others to invest in their business in return for future reciprocity. Mutual willingness to invest in promising business would build their relationship. The information on good investments would be shared by both parties

A lead investor is the largest fund in a venture. A firm’s degree is the number of good venture capitals it has syndicated. Between is the shortest distance paths between the other VCs in the network. If the lead investor is better networked, the portfolio company’s survival probability increases. The positive coefficients degree, out-degree, and eigenvector indicate that a company that is better networked survives best. A measure of network centrality is assumed from the syndication data for the past 5 years. The California network appears to be the best.

Networks and Investment Performance” claims to be the first one elaborating the ‘performance consequences’ of the Venture capitals’ selection of networks. Secondly, it says that the documented returns of VCs suggest that improved network returns would strategically improve their performance while posing a barrier to potential new VCs. Thirdly, investors may select better networked VCs.

Fourthly, it elaborated on the possible ‘drivers’ or reasons for the enhanced performance. It also provided evidence regarding the evolution of the networked position.

Techniques that were used included the graph theory. Adjacency matrices of over 5 years were utilized. Then 5 centrality measures were constructed from 3 popular concepts: degree, closeness, and betweenness. Degree measures the number of relationships a person has. Closeness measures the number of good or quality relationships he has. Eigenvector centrality is the sum of his ties to other actors, each weighted by their respective centralities. In the VC network, the eigenvector centrality measures the extent of ties or connections of a VC to other well-connected VCs. A betweenness centrality implies that an actor has connections away from his immediate group. These relationships are beneficial to both his own and the other group.

Financial markets survive on strong relationships and networks rather than spot transactions. VC funds whose parent firms have more influential well-knit networks show the best performance. Portfolio companies of better networked VCs have a better chance of survival and growth in comparison with those others who die out before long. Investors should select VCs with thoroughly expansive quality networks for putting their money in.

A network analysis was done by students of the MIT Sloan School of Management and Boston University in a medium-sized executive recruiting firm. The information technology workers account for 70% of the labor force in the United States and are creating value at their workplace. It would be interesting to note how these mostly young people function in their network. The study revealed that information is quantifiable by direct observation of behavior, inflow through e-mails, and data on project quality controls. Data were used to determine the production power of individuals. The study revealed that efficient multi-tasking saves time and improves performance and that targeted ESS training could improve speed and firm performance. ( Aral S. “Information, Technology and Information worker productivity” ).

Assistant Professor Lei Chi studied online learning communities. She selected 30 students to take an online accounting course. They were selected based on the number of inter-relationships they had. It was assumed that offline social relationships are necessary to promote better online ones. More honesty is exhibited in online relationships. The honesty increases when there is a chance to meet once in a while. The online community is one where trust, cooperation, and reciprocity are cultivated. Earlier it was found that the relationships online are mostly loosely knit ones. When people move to other countries, social capital reduces. However online communication restores some of it. ( Lei Chi, ”Transplanting Social Capital to the Online World” ).

Comments Social network analysis is significant to a whole range of people and situations. High profile success, a person’s social commitment, his ability to make the most of opportunities, his turning to crime, his compassion for fellow humans reflects the kind of network he has been in.

Modern studies

Social programs may be established with success following a social network analysis.

Several modern studies sociology, anthropology, sociolinguistic, geography, social psychology, communication studies, information science, organizational studies, economics, and biology need to apply social network analysis in their approach for better assessment.

Analysis has become an easier feat with the advent of computers and appropriate software programs. Social network analysis has come in as a handy application to assess organizational behavior and the relationship between organizations.

The Health Care Sector can apply this kind of analysis when contagious diseases spread. Mental Health and Social support are also in need of it.

Animal social organizations refer to social network analysis for their functioning. Diffusing information and policy propaganda through the social network comes in useful to the Government. Business ventures utilize networks for their success.

Efficient multi-tasking and targeted ESS training would improve the performance of the Information Technology sector. A network gone awry could be the cause of many a terrorist action. Can we forget September 11? This is the kind that needs to be investigated and broken through serious effort.

Conclusion

The online community is a loose network of social relationships. This social network has made significant contributions to the honest sharing of information among billions of people across the world.

References

Carol Hawthornthwaite “Social Network Analysis: An Approach and Technique for the Study of Information Exchange”. LISR 18, 323-342 (1998).
Yael V. Hochberg et al, “ Whom you know matters: Venture Capital Networks and Investment Performance”. The Journal of Finance, Vol. LXII, No. 1.
Aral S. et al , “Information,Technology and Information worker productivity : Task Level Evidence.”. Web.
Asst. Prof. Lei Chi,”Transplanting Social Capital to the Online World” February 2008. Lally School of Technology and Management, Rensselaer, Troy.

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