Doctoral Supervisor- BSL-aSSIST Dual Doctoral Degree Program
Nationality: South Korea
Professional Career & Mini Bio
Dr. Sehoon Kim holds a Doctorate in Business Administration (DBA) from Business School Lausanne (BSL), Switzerland, and a PhD in Business Administration from aSSIST Business School (Seoul School of Integrated Sciences & Technologies), South Korea.
Dr. Kim joined BSL as a Doctoral Supervisor in 2024. His research focuses on innovation and business transformation across diverse industries, with particular emphasis on organizational behavior, customer experience, entrepreneurship, and technology adoption. He employs a blend of quantitative methods, such as machine learning and data analysis, and qualitative approaches to deliver comprehensive insights.
Dr. Kim’s research has garnered significant recognition, with multiple publications in prestigious SSCI and Scopus-indexed journals. Notably, one of his articles in Frontiers in Psychology ranks among the top 1% in views, outperforming 99% of all articles in the journal. He also serves as a reviewer for several leading academic publications in business strategy, innovation, and communication, further underscoring his expertise in empirical and theoretical research.
Beyond academia, Dr. Kim has served as a Research Professor at the Institute for Industrial Policy Studies (IPS) in Seoul. Additionally, he has held senior leadership roles at Toyota Motor Korea, where he has spearheaded initiatives in Mobility-as-a-Service, AI-driven business systems, customer experience, and HR development.
For further details on Dr. Sehoon Kim’s academic profile, please see:
https://scholar.google.com/citations?hl=ko&user=GWvBRuMAAAAJ
https://sites.google.com/view/sehoon-kim/home
Favorite research topics
- Business Model Innovation
- Business Sustainability, ESG, SDGs
- Digital Transformation and Technological Disruptions
- Automotive, Mobility, and Retail Industries
- Entrepreneurship and Innovation
- Organizational Behavior and Customer Experience
- HR Development topics
- Machine Learning and Data Envelopment Techniques
- Qualitative methods (i.e., Causal Layered Analysis)
