Technology forecasting in the National Research and Education Network technology domain using context sensitive Data Fusion

- A framework is developed for TF using SEM based context sensitive DF.
- An example model instantiation, with 3 DF levels, is provided for the NREN technology domain.
- Secondary data from the 2011 TERENA NREN compendium is used to analyse the example model instantiation.
- It is shown that government influence is positively related to an NRENs infrastructure and advanced services capabilities.
- It is also shown that an NREN's usage is positively related to its infrastructure and services capabilities.

Using inductive reasoning this paper develops a framework for the Structural Equation Modeling based context sensitive Data Fusion of technology indicators in order to produce Technology Forecasting output metrics. Data Fusion is a formal framework that defines tools, as well as the application of these tools, for the unification of data originating from diverse sources. Context sensitive Data Fusion techniques refine the generated knowledge using the characteristics of exogenous context related variables, which in the proposed framework entails non-technology related metrics. Structural Equation Modeling, which is a statistical technique capable of evaluating complex hierarchical dependencies between latent and observed constructs, has been shown to be effective in implementing context sensitive Data Fusion. For illustrative purposes an example model instantiation of the proposed framework is constructed for the case of the National Research and Education Network technology domain using knowledge gained through action research in the South African National Research Network, hypotheses from peer-reviewed literature and insights from the Trans-European Research and Education Network Association's annual compendiums for National Research and Education Network infrastructure and services trends. This example model instantiation hypothesizes that a National Research and Education Network's infrastructure and advanced services capabilities are positively related to one another, as well as to the contextual influence it experiences through government control. Also, positive relationships are hypothesized between a National Research and Education Network's infrastructure and advanced services capabilities and its usage, which is defined as the technology forecasting output metric of interest for this example. Data from the 2011 Trans-European Research and Education Network Association compendium is used in the Partial Least Square regression analysis of the example model instantiation, which confirms all hypothesized relationships, except the postulation that a National Research and Education Network's infrastructure and advanced services capabilities are positively related. This latter finding is explained by observing the prevalence of technology leapfrogging in the National Research and Education Network global community.