| Summary: | 碩士 === 國立臺灣大學 === 國家發展研究所 === 102 === A policy network empirical analysis of Taiwan''s emerging technology industry development programs using to research the policy network dialectical relationships and to evaluate performance of government policy subsidy for promoting industries development is a very important thesis issue.
The thesis uses policy networks dialectical approach to modify a model and researches in estimating, analyzing and graph-drawing for the policy networks of emerging technology industry development programs in Taiwan, it makes use of IBM SPSS AMOS (Structural Equation Models，SEM) and Gephi 0.8.2 beta for Mac to compute all real data of sampling survey, after analyzing to demonstrate the complex networks structure characteristics with several analysis processes such as average degree, average weighted degree, diameter/graph distance, average path length, number of shortest paths, number of shortest paths, modularity, number of communities, number of weakly/strongly connected components, clustering coefficient, average clustering coefficient, average weighted clustering coefficient, average node strength, eigenvector centrality sum change, average path length, link communities partition density, average neighborhood overlap, average embeddedness and ForceAtlas2 graph-drawing.
The conclusion reveals a special significance of the policy network empirical analysis research model and also draws out all complex networks structure motifs in Macro-level (network convergence of 7 emerging technology industries), Meso-level ( 7 emerging technology industries) and Micro-level ( 88 Research Projects). The thesis research successfully tests and verifies all complex networks structure characteristics of Taiwan''s emerging technology industry development programs and generates 9 major correlative measurement data compiled charts and 9 major motifs graphical complex network structure of these correlative policy networks. Therefore, these research results could provide a reference to our government for policy-making in the future.
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