So Young Sohn

Professor

Yonsei University

South Korea

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So Young Sohn is a Professor at Yonsei University, South Korea. Her research interests include AI adoption patterns, sustainable technology, and the intersection of intellectual property with innovation. Recent articles have addressed areas such as green efficiency strategies, assistive technology for visual impairments, and fintech adoption in commercial banks.

So Young Sohn

Research Interests

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Articles (18)

Discovering AI adoption patterns from big academic graph data

Cosmetic patent and female invention

Green efficiency strategy considering cyclical relationships among CO2 emissions, green patents, and green bonds

Comparison of patenting trends and alliance patterns among countries with a majority religion: structural topic model and network analysis

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

Identification of Sustainable Convergence Technology for Batteries via Multiplex Link Prediction (August 2024)

Processing-in-Memory Development Strategy for AI Computing Using Main-Path and Doc2Vec Analyses

A firm’s creation of proprietary knowledge linked to the knowledge spilled over from its research publications: the case of artificial intelligence

Digital Therapeutics Product Repositioning via Multiplex Link Prediction Based on Genomic, Chemical, and Technological Information

Mission Efficiency Analysis of For-Profit Microfinance Institutions with Categorical Output Variables

Assistive Technology: By or for Mainstream Technology? A Case of Visual Impairments

Cost-Benefit Analysis to Assess the Effectiveness of an External Airbag and Autonomous Emergency Braking System

Perceived importance of intellectual property protection methods by Korean SMEs involved in product innovation and their value appropriation

CTM and QFD analysis: Framework for fintech adoption priority in commercial banks

Publisher Correction: A credit scoring model based on the Myers–Briggs type indicator in online peer-to-peer lending

Novel method for detection of mixed-type defect patterns in wafer maps based on a single shot detector algorithm

Recommendation of investment portfolio for peer-to-peer lending with additional consideration of bidding period

A credit scoring model based on the Myers–Briggs type indicator in online peer-to-peer lending

Collaborators (2)

Su Jung Jee

Yonghan Ju

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