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Our research aims to understand the mechanisms behind long-term sustainability transitions and how various elements co-evolve to shape the innovation activities we see today. By employing quantitative methods and network analysis, we seek to bridge methodological gaps in transition studies and provide efficient assessments of transition activities. Here are our interested technological themes and research projects. However, we also welcome other topics and ideas!
Our research aims to understand the mechanisms behind long-term sustainability transitions and how various elements co-evolve to shape the innovation activities we see today. By employing quantitative methods and network analysis, we seek to bridge methodological gaps in transition studies and provide efficient assessments of transition activities. Here are our interested technological themes and research projects. However, we also welcome other topics and ideas!
Projects
Advancements in Network Methodologies (undergoing):
We are exploring advanced network methodologies like Rsiena to address various datasets and issues.
Infrastructural Setting Studies (undergoing):
We are examining institutions and their qualities in relation to innovation activities, extending beyond physical systems to include regulations and norms.
Policy Frameworks and Strategies:
We are creating detailed policy frameworks and strategies to influence societal and economic structures through responsive and forward-looking policies.
Patent Network Analysis :
To examine technological development pathways in quantum technology and eHealth innovation fields, identifying focal actors and their interactive relationships within innovation ecosystems.
Machine learning :
We train models for patent classification and prediction by analyzing large volumes of patent data to identify and predict their technological domains, thereby improving classification accuracy and efficiency.
Corporate Technology Innovation Strategies and Development :
Explores the impact of Combined Innovation Ambidexterity (CIA) and Balanced Innovation Ambidexterity (BIA) strategies on firm performance in the context of Generative AI, and examines how internal innovation capability and external cluster embeddedness moderate the relationships between these innovation strategies and the firm's financial performance.
Research Interests
Patent Analysis
Network Analysis
Standard and Standardization Behavior
Entrepreneurship and Venturing Analysis
Transition and Evolution
Non-Practicing Entities (NPEs)
Innovation Management
Emerging Technologies
Innovation and Institutions
Open Data Applications
Research Tools
R | SQL | Python | Social Network Analysis | Topic modelling | Text Mining
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