Six different approaches to defining and identifying promising technology through patent analysis

ABSTRACT

Under the rapidly changing technological environments, a significant effort has been undertaken to investigate promising technologies for the future and, accordingly, numerous studies have been conducted to develop an approach for identifying such technologies. One of the most frequently adopted approaches is patent analysis given that patent data are regarded as one of the most valuable and widely accepted sources to understand innovation activities. The term promising technology has been used in various contexts, but little effort has been made to provide a clear definition of it. As different perspectives may lead to different results, defining this concept is essential to obtain the best possible results. Thus, this study aims to develop a taxonomy of the promising technologies, and the following six perspectives along with their relevant patent analysis approaches are examined: key, outlier, vacant, emerging, new, and converging technologies. The research findings indicate that the concept of promising technology can be defined differently from diverse viewpoints, and this can be a basis for future studies.

KEYWORDS:

• Promising technology
• identification
• definition
• patent analysis

Disclosure statement

No potential conflict of interest was reported by the authors .

Additional information

Funding

This research was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Education [grant number NRF-2018R1D1A1B07045258] and the Ministry of Science and ICT [grant number NRF-2019R1F1A1063032].

Notes on contributors

Jeonghun Jee

Jeonghun Jee received his B.S. on Industrial Engineering from Ajou University, Suwon, Republic of Korea in 2019. He is currently a researcher at Technology Management & Data Intelligence Lab, Ajou University. His research interests include technology forecasting, patent analysis, and patent image data analysis.

Hyunjin Shin

Hyunjin Shin received her B.S. on information statistics from Korea National Open University, Seoul, Republic of Korea in 2020. She is currently a researcher at Technology Management & Data Intelligence Lab, Ajou University. Her research interests include technology forecasting and Bio-medical patent analysis.

Chulhyun Kim

Chulhyun Kim is an Associate Professor of Industrial and Management Engineering, Induk University. He received the degrees of BS, MS and PhD from Seoul National University. Before joining Induk University, he worked at IBM Korea and Korea Institute of R&D Human Resource Development. His research interest includes service science, technology management, and patent analysis.

Sungjoo Lee

Sungjoo Lee received her first Ph.D. on Industrial Engineering from Seoul National University, Seoul, Republic of Korea, in 2007, and her second Ph.D in Technology and Innovation Management from Science Policy Research Unit, Sussex University, UK, in 2019. She is currently a Professor of Industrial Engineering at Ajou University, Suwon, Republic of Korea. Before joining Ajou University, she spent one year at the Centre for Technology Management, Institute for Manufacturing, University of Cambridge, UK as a visiting scholar. Her research interests include technology roadmap, patent engineering, and R&D planning in small and medium-sized enterprises.