Abstract:
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.