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Science foresight using life-cycle analysis, text mining and clustering: A case study on natural ventilation
M Rezaeian1, H Montazeri2,3, R C G M Loonen2
1Faculty of Economics, Management & Accounting, Yazd University, Iran.
This study introduces a three-step science foresight framework combining life-cycle analysis, text mining, and clustering to identify research trends and knowledge gaps. This method effectively uncovers uncharted scientific areas, aiding future research planning.
Area of Science:
- Scientific foresight and research trend analysis.
- Technological forecasting and innovation management.
Background:
- Science foresight aids in identifying high-potential research directions for effective planning.
- A variety of science foresight approaches exist, but guidance on effective application is limited.
Purpose of the Study:
- To present a novel three-step methodological framework for science foresight.
- To demonstrate the framework's utility in identifying research gaps and future directions.
- To enhance the effective application of science foresight in research management.
Main Methods:
- A three-step framework integrating life-cycle analysis, text mining, and automated clustering for knowledge gap identification.
- Text mining is used to connect the framework's steps via research methodologies.
- The framework is applied to scientific literature on wind catchers to illustrate its potential.
Main Results:
- The framework successfully identified uncharted research areas within the wind catcher domain.
- A sensitivity analysis confirmed the importance of utilizing full-text data over abstracts and titles for text mining.
- The automated foresight analysis proved effective in uncovering novel research avenues.
Conclusions:
- The proposed methodological framework offers a robust approach to science foresight.
- Effective identification of knowledge gaps can guide future research endeavors.
- The framework provides valuable insights for strategic planning in science and technology development.
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