Forecasting technology trends using text mining of the gaps between science and technology: The case of perovskite solar cell technology

Xin Li, Qianqian Xie, Tugrul Daim and Lucheng Huang

Technological Forecasting and Social Change, 2019, vol. 146, issue C, 432-449

Abstract: How to detect and identify the future trends of emerging technologies as early as possible is crucial for government R&D strategic planning and enterprises' practices. To avoid the weakness of using only scientific papers or patents to study the development trends of emerging technologies, this paper proposes a framework that uses scientific papers and patents as data resources and integrates the text mining and expert judgment approaches to identify technology evolution paths and forecast technology development trends within the short term. The perovskite solar cell technology is selected as a case study. In this case, the text mining and expert judgment methods are applied to analyze the technology evolution path, and gaps analysis between science and technology is used to forecast the technology development trend. This paper will contribute to the technology forecasting and foresight methodology, and will be of interest to solar photovoltaic technology R&D experts.

Keywords: Technology trend; Technology forecasting; Text mining; Perovskite solar cell technology (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (33)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:146:y:2019:i:c:p:432-449

DOI: 10.1016/j.techfore.2019.01.012

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