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Abstract

Due to rapid environmental change, policymakers no longer choose foresight issues based on their own experience. Instead, they need to consider all the possible factors that will influence new technological developments and formulate an appropriate future technological development strategy to the country through the technology foresight system. For the sake of gathering more objective evidence to convince stakeholders to support the foresight issues, researchers can employ bibliometric analysis to describe current scientific development and forecast possible future development trends. Through this process, a consensus is reached about the direction of future technology development. However, we believe that bibliometric analysis can do more for technology policy formulation, such as (1) offer quantitative data as evidence to support the results of qualitative analysis; (2) review the situations of literature publication in specific technological fields to seize the current stage of technology development; and (3) help us grasp the relative advantage of foresight issues development in Taiwan and the world and develop profound strategic planning in accordance with the concept of Revealed Comparative Advantage. For those reasons, our research will revisit the role that bibliometric analysis plays for nations while choosing the foresight issues. In addition, we will analyze the development of the technology policy in Taiwan based on bibliometric analysis, and complete the foresight issues selection by processing key issue integration, key word collection related to this field, the searching and confirmation of literature, development opportunities exploration, comparative development advantage analysis and the innovation-foresight matrix construction, etc.

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Abstract

The correlation between GDP and research publications is an important issue in scientometrics. This article provides further empirical evidence connecting revealed comparative advantage in national research with effects on economic productivity. Using quantitative time series analysis, this study attempts to determine the nature of causal relationships between research output and economic productivity. One empirical result is that there is mutual causality between research and economic growth in Asia, whereas in Western countries the causality is much less clear. The results may be of use to underdeveloped nations deciding how to direct their academic investment and industry policy.

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Scientometrics
Authors: Yuen-Hsien Tseng, Yu-I Lin, Yi-Yang Lee, Wen-Chi Hung and Chun-Hsiang Lee

Abstract  

In scientometrics for trend analysis, parameter choices for observing trends are often made ad hoc in past studies. For examples, different year spans might be used to create the time sequence and different indices were chosen for trend observation. However, the effectiveness of these choices was hardly known, quantitatively and comparatively. This work provides clues to better interpret the results when a certain choice was made. Specifically, by sorting research topics in decreasing order of interest predicted by a trend index and then by evaluating this ordering based on information retrieval measures, we compare a number of trend indices (percentage of increase vs. regression slope), trend formulations (simple trend vs. eigen-trend), and options (various year spans and durations for prediction) in different domains (safety agriculture and information retrieval) with different collection scales (72500 papers vs. 853 papers) to know which one leads to better trend observation. Our results show that the slope of linear regression on the time series performs constantly better than the others. More interestingly, this index is robust under different conditions and is hardly affected even when the collection was split into arbitrary (e.g., only two) periods. Implications of these results are discussed. Our work does not only provide a method to evaluate trend prediction performance for scientometrics, but also provides insights and reflections for past and future trend observation studies.

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