Early discovery of emerging multi-technology convergence for analyzing technology opportunities from patent data: the case of smart health.

Autor/inn/en	Wang, Juite; Hsu, Tzu-Yen
Titel	Early discovery of emerging multi-technology convergence for analyzing technology opportunities from patent data: the case of smart health.
Quelle	In: Scientometrics, (2023) 8, S.4167-4196 PDF als Volltext Verfügbarkeit
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0138-9130
DOI	10.1007/s11192-023-04760-z
Schlagwörter	Technology opportunity analysis; Technology convergence; Machine learning; Network analysis; Smart health
Abstract	Abstract Technology convergence (TC), in which technological innovation occurs by integrating at least two or more existing technologies into hybrid technologies, has been a growing innovation pattern in recent decades. Facing substantial and accelerated technological changes, it is important for technology-based firms to identify TC patterns in pursuing competitive strengths. However, most studies do not consider multi-TC with technical emergence. This research thus develops an anticipation methodology based on the network theory and association rule mining (ARM) for early identification of multi-TC patterns associated with emerging technology classes from patent data in the smart health industry. First, a technological knowledge flow network is built for subsequent analyses using smart health related patent data. Then, core-periphery analysis is used to identify peripheral technology classes which may become influential in the future. Several technical emergence indicators are developed to evaluate emergence scores of identified peripheral technology classes. Finally, ARM is used to discover intriguing multi-TC associated with emerging technology classes. We conclude that the research results are useful for R&D managers for early discovery of emerging multi-TC patterns to explore potential technological opportunities.
Erfasst von	OLC
Update	2024/1/01