Convergence Technology Opportunity Discovery for Firms Based on Technology Portfolio Using the Stacked Denoising AutoEncoder (SDAE)

Technology convergence, as a key driving force of innovation, has brought a burgeoning of research attention. Although numerous studies on technology convergence have been carried out, there were limitations in consideration of a firm's capability in technology convergence. This article propose...

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Vydané v:IEEE transactions on engineering management Ročník 71; s. 1 - 15
Hlavní autori: Kwon, Deuksin, Sohn, So Young
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.01.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Artificial neural networks
Collaborative filtering (CF)
Companies
Convergence
Industries
Noise reduction
Portfolios
Research and development
stacked denoising autoencoder (SDAE)
technical convergence competences
Technology Opportunity Discovery (TOD)
technology recommendation
ISSN:0018-9391, 1558-0040
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Popis
Shrnutí:Technology convergence, as a key driving force of innovation, has brought a burgeoning of research attention. Although numerous studies on technology convergence have been carried out, there were limitations in consideration of a firm's capability in technology convergence. This article proposes a framework for "Convergence Technology Opportunity Discovery" (CTOD) based on firms' technical convergence competence manifested in their patent portfolios, market competition, and technological growth potential. The present research, by employing a stacked denoising autoencoder, a deep neural network-based collaborative filtering method, provides reliable latent preference toward convergence technology for individual firms. Our CTOD framework is applied to three information technology and biotechnology firms to elaborately demonstrate its validity. Ultimately, the proposed framework is expected to provide practical assistance to organizations seeking technology convergence opportunities in various fields.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-9391
1558-0040
DOI:10.1109/TEM.2022.3208871