Structural embeddedness and position: evidences from affiliation of patent with technological classifications

ABSTRACT

This paper contributes to the empirical identification of the structural positions in network through an exploration of the interactions between R&D activities (patents) and technological areas of patents (technological classifications). Affiliation network analysis was conducted to study the affiliation between patents and technological classifications. We identified some key patents and explored the differentiated positions of the core and periphery. The results of network analysis was further verified by statistical analysis. We found that the impacts of the centralities in position were significant. Technological implications and foresights are discussed in the conclusion.

KEYWORDS:

• Structural embeddedness
• network position
• core/periphery
• affiliation networks

Disclosure statement

No potential conflict of interest was reported by the author.

Notes on contributor

Calvin S. Weng is an associate professor, and his current position is the Dean of Research and Development Division in Takming University of Science and Technology, Taiwan. He received his Ph.D. degree in Management from National Yunlin University of Science and Technology in Taiwan, and MA degree in Actuarial Science from Roosevelt University in Chicago, IL, USA. His research interest focuses on social network analysis, technology management and strategy management. His works have appeared in Technological Forecasting and Social Change, IEEE Transactions on Engineering Management, Technology Analysis & Strategic Management, Asian Journal of Technology Innovation, International Journal of Innovation and Technology Management, International Journal of Services Technology and Management and, Foresight, Journal of the Knowledge Economy, Journal of Management and Business Research (in Chinese) … etc.

Notes

1 ‘In most cases, the scientific literature uses the concept of social networks metaphorically, ignoring the chances presented by SNA methods. At the same time, conventional empirical research in innovation and futures studies often disregards relational information. … .., on the other hand. SNA provides us with empirical tools that capture the social context and help to better understand how innovations are implemented and diffused and why social change takes place.’ (Kolleck 2013, 1).

2 ‘Small networks can focus on detailed elements of the graph structure while larger networks can mainly capture gross topology. Visualizing networks of tens of thousands of nodes requires further abstraction yet.’ (Moody, McFarland, and Bender-DeMoll 2005, 1213).

3 Moody, McFarland, and Bender-DeMoll (2005) and Moody (2001) suggest that abstracting the nodes into less than 100 or usually less than 50 nodes are appropriate for the purpose of analysis.