Kenneth Griggs and Rosemary Wild
capture and create knowledge, engage citizens, and advance an understanding of relevant social connections, to name a few.
Typically, government organizations have employed social networking technology without much understanding of the daunting spectrum of factors inherent in a rapidly evolving technology. In addition to its obvious benefits, social networking technology carries with it some not‐so‐obvious risks and enables forms of unrecognized and unwanted behavior. Thus, social networking adoption is in need of a framework that offers government organizations guidance in the selection of social networking application types and that sheds light on the complex issues that arise from the use of the technology. At a macro level, social networks combine a number of time, space, and behavioral factors that can be described, analyzed and modeled. A relevant SNA framework must contain technological, behavioral, and organizational components as well.
In this paper we describe the many factors that define this new form of social dialogue and connectivity as it relates to use within government organizations. Our proposed model evolved from an exploration of these factions. In the following sections we present( 1) an overview of the mathematical foundations of network modeling,( 2) a description of the factors and their characteristics that form the basis of our proposed social networking adoption model,( 3) two illustrative examples of e‐Government applications of social media including a post hoc analysis of potential risk mitigation within the context of our proposed model, and lastly( 4) a visual schematic that illustrates how two different candidate social network applications can be compared for intended benefit / risk effectiveness.
2. Social network modelling
Research in social networking( both computer‐mediated and human‐mediated) is a well‐established subdiscipline of a range of social sciences including social psychology, sociology, anthropology, information sciences and others. In addition, graph theory, an area within computer science and mathematics, has been employed to model and explore social networks. Research on social networks began in the early 20th Century and focused on societal structures, whereas later work developed the use of social network graphs to describe the interplay between actors within a social network( White, 2008). Analytic software tools were developed as part of the effort( Freeman, 1996)( Hansen, et al., 2010) and the field grew rapidly. Social network graphing and analysis software has proliferated with a wide range of software tools now available( Ahn, et al., 2011). From an SNA adoption standpoint, these tools offer a means to measure some of the factors in the model described in this paper.
The mathematical study of networks in the form of graph theory has been around for centuries with a myriad of applications, including the study of social networks( Freeman, 1996)( Scott, 2000). With the advent of new social media as a means for both individuals and organizations to communicate and collaborate, there is a notable resurgence of interest in network research. The primary shift in network research represents a shift in scale. It is not uncommon now to study graphs with millions or even billions of nodes. A detailed review of the mathematical analysis of complex networks can be found in Newman( Newman, 2003). For our purposes, we will provide a simple overview of the network techniques and models that have been developed in an attempt to understand and predict the behavior of the communities they represent, a critical ability when designing a social network.
The study of networks addresses three primary goals:( 1) to create a visual representation of the connections among individuals or groups in the network;( 2) to analyze the network to answer questions about the organization or community the network represents using mathematical and statistical analyses; and( 3) to create models, such as mathematical or computer models, based on the analyses to make predictions about the behavior of an organization or community. Relative to the first goal, empirical studies are conducted that use a variety of techniques including interviews, direct observation, archives, questionnaires, etc., in an attempt to capture accurately the appropriate connections between people in the community of interest. The connections may represent personal as well as government agency relationships.
Our overview will focus primarily on the second goal: the analysis of complex networks. Empirical data collected can be used to perform network analysis using mathematical and statistical techniques. These analyses attempt to uncover the behavior of a network by helping to answer questions such as who are the most“ critical” members of a network( critical members can be defined as members who have the most
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