Multidimensional indicators to identify emerging technologies: Perspective of technological knowledge flow

Man Jiang, Siluo Yang and Qiang Gao

Journal of Informetrics, 2024, vol. 18, issue 1

Abstract: The identification of emerging technologies (ETs) is pivotal for advancing technological innovation. However, current methods fail to sufficiently clarify ETs' innovation mechanisms and lack a consistent perspective to integrate the five attributes proposed by Rotolo. This paper presents an innovative term-level framework to identify and comprehend ETs through the perspective of technological knowledge flow (TKF). By dissecting TKF comprehensively, encompassing aspects of knowledge absorption, growth, and diffusion, we construct and explicate multidimensional indicators reflective of ETs' attributes, including relatively rapid growth, radical novelty, coherence, prominent impact, as well as uncertainty and ambiguity. Through the analysis of digital medical patent dataset, our framework proves effective in assessing emergent scores and pinpointing ETs with specificity at the term level, clarifying their technological components and efficacy. This is beneficial for technology developers to overcome technical difficulties and strategic decision makers to manage IP for competitive advantage.

Keywords: Emerging technologies; Technological knowledge flow; Technology innovation; Scientometric indicators; Digital medical technology (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1751157723001086
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:infome:v:18:y:2024:i:1:s1751157723001086

DOI: 10.1016/j.joi.2023.101483

Access Statistics for this article

Journal of Informetrics is currently edited by Leo Egghe

More articles in Journal of Informetrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().