Knowledge flows from business method software patents: influence of firms’ global social networks

Jiang, Jiaming; Goel, Rajeev K.; Zhang, Xingyuan

doi:10.1007/s10961-017-9645-1

Cited by 23 publications

(18 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A dominant theory suggests that highly cited patents contain an important technological advance [11], [12]. So we take the number of citations to measure patent impact.…”

Section: A Impact and Fixed Effect Factorsmentioning

confidence: 99%

“…Using citation linkages to indicate knowledge flow is an effective method in the existing literature [9]- [11]. Two kinds of patent citations are usually considered in the patent citation network: one kind is the linkages between patents and patents, the other is the linkages between patents and scientific literature.…”

Section: Introductionmentioning

confidence: 99%

“…The citations' number of a patent receiving from other patents accumulates over time and correlates to the patent's technological impact. A dominant theory suggested that highly cited patents contained important technological advances [11], [12]. The characteristics of knowledge flow could be identified through the patent citation network [13].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Revealing Structural Patterns of Patent Citation by a Two-Boundary Network Model Based on USPTO Data

Chi

Song

2020

IEEE Access

View full text Add to dashboard Cite

The patent is one of the carriers of scientific research and development, and an indicator of technical innovation. As a promising approach for modeling complex systems, complex networks could provide the sound theoretical framework for developing proper simulation models. Many researchers use the relations of patent citations and transfers to study knowledge propagate and output in the network. However, knowledge flow in patents should be fully considered by substantial and fruitful connections, both in the process of knowledge application and knowledge output. In this paper, we present a two-boundary network model with knowledge application boundary and knowledge output boundary to reveal the patent citation patterns in knowledge flow. The feasibility and effectiveness of the two-boundary network model are proved with theory and experiment. Utilizing 578,678 patents from the United States Patent and Trademark Office between 2015 and 2018 with the two-boundary network model, we put up a fixed effect ordinary least square equation to reveal the patent impacts of different structural patterns. Experimental results show that, in the perspective of structural patterns, the highly impacted patents without assigning are greatly influenced by their scientific literature references and provide knowledge for other assigned patents. However, considering all the fixed effect factors, patents that transfer knowledge from other patents to assigned patents are more likely to become highly impacted patents. Besides, we find the two-boundary network model fits the real patent knowledge flow well by comparing it with the other models. INDEX TERMS Two-boundary network model, knowledge flow, regression equation, fixed effect.

show abstract

“…A dominant theory suggests that highly cited patents contain an important technological advance [11], [12]. So we take the number of citations to measure patent impact.…”

Section: A Impact and Fixed Effect Factorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Revealing Structural Patterns of Patent Citation by a Two-Boundary Network Model Based on USPTO Data

Chi

Song

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Therefore, this study adopts the method of network analysis, using patents as nodes and connecting the flow of technical knowledge between patents to build a patent citation network, which measures the network position and network structure of patents. Bessen [ 4 ] finds that highly-cited patents are more valuable, besides, highly-cited patents also contained important technological advances [ 13 , 14 ]. Therefore, this study used four indicators of network characteristic: degree centrality, closeness centrality, eigenvector centrality, and network structure, to explore the relationship between network characteristic and highly-cited patents to fill this research gap.…”

Section: Introductionmentioning

confidence: 99%

Determinants of highly-cited green patents: The perspective of network characteristics

et al. 2020

View full text Add to dashboard Cite

With the rise of green technology, green patent value evaluation has become critical for enterprises. In order to explore the influencing factors of green patent value, this study takes the light-emitting diode (LED) industry as an example and then utilizes highly-cited green patents as the proxy of green patent value. The study aims to explore the influencing factors of green patent value with network position and structure from the perspective of network characteristics. The results indicate that out-degree centrality and network constraint have a significantly positive effect on green patent value.

show abstract

“…10 10 In a related aspect, Jiang et al (2019) use social network analysis to examine knowledge flows from business method software patents.…”

mentioning

confidence: 99%

Knowledge diffusion worldwide: Role of university–industry collaborations and beyond

Goel

2021

Manage Decis Econ

Self Cite

View full text Add to dashboard Cite

This paper tries to explain the drivers of knowledge diffusion across a large sample of nations, focusing on the influence of university–industry collaborations. However, we also consider knowledge‐diffusion contributions of creative outputs and joint ventures. Are the knowledge‐diffusion dividends from university–industry collaborations greater than the dividends from creative outputs or from joint ventures? Results show that greater university–industry collaborations increase knowledge diffusion, and these spillovers are reinforced by creative outputs but not by joint ventures. Another interesting and novel finding is that the influences of university collaborations and creative outputs are sensitive to the prevailing diffusion of knowledge.

show abstract

Knowledge flows from business method software patents: influence of firms’ global social networks

Cited by 23 publications

References 46 publications

Revealing Structural Patterns of Patent Citation by a Two-Boundary Network Model Based on USPTO Data

Revealing Structural Patterns of Patent Citation by a Two-Boundary Network Model Based on USPTO Data

Determinants of highly-cited green patents: The perspective of network characteristics

Knowledge diffusion worldwide: Role of university–industry collaborations and beyond

Contact Info

Product

Resources

About