Title

Forecasting Technology Trends Using Text Mining of the Gaps Between Science and Technology: The Case of Perovskite Solar Cell Technology

Published In

Technological Forecasting and Social Change

Document Type

Citation

Publication Date

9-1-2019

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.

Description

© 2019 Elsevier Inc. All rights reserved.

DOI

10.1016/j.techfore.2019.01.012

Persistent Identifier

https://archives.pdx.edu/ds/psu/30910

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