About this Research Topic
Viewing innovation as a system is one of the essential concepts in quantitative science studies. System of innovation depends not only on the strength of the individual actors but also on the strength of the links between them; a common denominator of research in these areas is the systems approach. It is a complete description of the relationships among the actors involved in a system from a visualized, often dynamic, and complex perspective. Frameworks and conceptual understandings have been developed in National Innovation System (NIS), Technological Innovation System (TIS), Regional Innovation System (RIS) and Sectoral Innovation System (SIS) research. In addition, triple helix research and the interrelationships between Science-Technology-Business in innovation and the transition research from a multi-level perspective have been very influential.
The indicators based on bibliometrics, scientometrics or patentometrics were some of the significant factors influencing policy in the Innovation System. It has reinterpreted traditional theories and frameworks, provided new perspectives, and served as a significant source of evidence. However, science and technology indicators such as patent and article analysis alone are limited in their ability to deepen our knowledge of the multi-layered innovation system. When innovation is viewed as a system, it is necessary to propose data-oriented innovation system indicators. It is fascinating to understand the innovation ecosystem through the interactions and networks among multiple actors, their functions, the roles of various actors, and their evolution. Science-Technology-Business interconnects in a multi-layered manner. Developing specific metrics to measure and interpret the different systems from a more multi-layered perspective, rather than limiting them to a single layer (or level), would facilitate understanding the innovation system.
This Research Topic welcomes submissions in the following areas:
-Analysis of knowledge interactions among actors in innovation systems
-Development of data analysis methods to explain the multi-layered nature of the Science-Technology-Business innovation system
-Pioneering methods for quantitative analysis of the impact of industry and society on science and technology assimilation
- Socio-gram and social network analysis
-Bibliometric approach in Innovation management. (Patentometrics, Scientometrics, and Altmetrics for Innovation Systems)
-Knowledge spill over through collaborative research and development
-Development of metrics to quantitatively explain multilevel perspectives in Innovation.
- Long-term strategy of the innovation ecosystem, its dynamic development, and impact on other measures of collective action (sustainability, well-being, inequality, inclusiveness, etc.)
The indicators based on bibliometrics, scientometrics or patentometrics were some of the significant factors influencing policy in the Innovation System. It has reinterpreted traditional theories and frameworks, provided new perspectives, and served as a significant source of evidence. However, science and technology indicators such as patent and article analysis alone are limited in their ability to deepen our knowledge of the multi-layered innovation system. When innovation is viewed as a system, it is necessary to propose data-oriented innovation system indicators. It is fascinating to understand the innovation ecosystem through the interactions and networks among multiple actors, their functions, the roles of various actors, and their evolution. Science-Technology-Business interconnects in a multi-layered manner. Developing specific metrics to measure and interpret the different systems from a more multi-layered perspective, rather than limiting them to a single layer (or level), would facilitate understanding the innovation system.
This Research Topic welcomes submissions in the following areas:
-Analysis of knowledge interactions among actors in innovation systems
-Development of data analysis methods to explain the multi-layered nature of the Science-Technology-Business innovation system
-Pioneering methods for quantitative analysis of the impact of industry and society on science and technology assimilation
- Socio-gram and social network analysis
-Bibliometric approach in Innovation management. (Patentometrics, Scientometrics, and Altmetrics for Innovation Systems)
-Knowledge spill over through collaborative research and development
-Development of metrics to quantitatively explain multilevel perspectives in Innovation.
- Long-term strategy of the innovation ecosystem, its dynamic development, and impact on other measures of collective action (sustainability, well-being, inequality, inclusiveness, etc.)
Keywords: Innovation Ecosystem, knowledge spillovers
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