Kenneth Griggs and Rosemary Wild
network, then betweenness centrality measures the fraction of that information expected to flow through vertex i on its way to its final destination.
The concept of degree of a node in a network is used extensively in the creation of mathematical models used to predict network behavior. Since“ virality” is an important concept in social networks, especially within the context of some government applications, it can be measured by models dependent on the degree of a node. One such measure is the basic reproductive number, R o, which predicts the average number of people a person passes an idea or information on to within a given network. If k i is the degree of a network node and r is the independent probability each person who has heard the information passes it on to each of his or her“ friends”, then the basic reproductive number R o is
= r
( 4)
In terms of virality within a social network, the degree of the node representing each person or group is critical since the number of people hearing the information will rise exponentially if R o is greater than 1, and the spread of the information will dissipate and die if R o is less than 1.
The viral exponential explosive nature of both membership and content transfer is a somewhat unique characteristic of SNA’ s. Indeed, this feature of social networking is one of the primary drivers behind social network adoption and a mathematical analysis would add rigor to any SNA classification scheme. Virality, betweeness, centrality, etc., can be planned for through thoughtful SNA design thereby potentially influencing the growth, pathing, formation of“ neighborhoods”, and control of the network.
3. Social networking adoption factors
An interesting aspect of the exponential growth of computer supported social networking is that its own growth has, in itself, been viral. The unique nature of the phenomenon has required the creation of new terminology to express the following general factors:
• Time – Participants in social networking can communicate synchronously or asynchronously and information transfer is instantaneous.
• Space – There is no inherent space limitation in the use of social networking. A“ neighborhood” is only a metaphor in the social networking virtual space.
• Medium – Connections to social networks are increasingly portable and transparent. Device interfaces allow for easy and direct access. The medium is disappearing in importance.
• Content – Social networking is increasingly characterized by“ high touch” rich media including high resolution imaging, audio, and video.
• Organizational Impact – The adoption of a social networking application by an organization is normally done in the service of organizational goals. The likely effect of the technology on the achievement of these goals is a measure of its impact and is an important metric.
During our research we found many references to the fact that there is a need for guidance to mitigate risks in the development of social networking applications( e. g., Turban et al., 2011). However, we generally found government policy guidance or suggested“ best practices” but no actual adoption models. In the development of our proposed adoption model we deduced that, from an organizational social networking application assessment standpoint, effective social networking technology should possess the following three relevant characteristics:
1. Expansion related – the depth, breadth, and speed of node interconnection. An application has varying degrees of expansion capability along spatial, temporal, and size scales. In general, a social media application in which nodes and edges develop rapidly at a global level would rank highly on an expansion‐related metric. However, expansion can be further sub‐divided into additional dimensions such as:
• Virality – the network growth factor of nodes and edges. Highly“ viral” applications are capable of forming nodes and node connections of great breadth and depth. This is a critical category that can be subjected to a statistical and graph theoretic analysis( see above).
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