Strong ties and weak ties of the knowledge spillover network in the semiconductor industry

Wang, Chun‐chieh; Sung, Hui-Yun; Chen, Dar‐Zen; Huang, Mu‐Hsuan

doi:10.1016/j.techfore.2017.02.011

Cited by 64 publications

(41 citation statements)

References 48 publications

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“…Knowledge flows can also be observed at more localized levels, within an industry or within a company, for instance when skills are transferred from experts to a novice (Bjursell and Florin Sädbom, 2018 ). In the context of innovation, collaborative patterns in patent activities have been faster in information diffusion compared to weak ties (Wang et al, 2017 ). Furthermore, companies with large in-house capabilities benefit more by internal and self-promoted collaboration, than by collaborations due to spillovers from the outside or motivated by external factors (Grillitsch and Nilsson, 2015 ).…”

Section: Previous Literaturementioning

confidence: 99%

Collaborative Patterns, Productivity, and Research Impact in the Careers of Star Researchers in a Japanese Semiconductor Company

Maeki

Mejía

Kajikawa

2020

Front. Res. Metr. Anal.

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This study analyzes how characteristics in the careers of star researchers affect the outcomes of research and development (R&D), based on a case study in a Japanese semiconductor company. By analyzing the collaboration network of patent coinventors in the company, we observe that long-term exposition and collaboration with other high-achieving researchers play a significant role in determining a successful career, that is, in terms of productivity and impact. Also, a deeper exploration of the characteristics of a selected group of star researchers in a company's R&D division helped to identify that it takes 10-15 years to generate remarkable achievements in the form of filing patents that are widely cited at a later stage. This period is followed by low productivity, thereby revealing productivity peaks such as those observed in the artistic and scientific careers but at different times. Industry researchers tend to follow a more fixed pattern. Additionally, we analyzed the influence of having star researchers in coinventor teams. Our results suggest that staying aligned in one research direction, long-term exposure to a diverse group of researchers, and early mentorship helped the researchers in our study to attain their achievements.

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Section: Previous Literaturementioning

confidence: 99%

Collaborative Patterns, Productivity, and Research Impact in the Careers of Star Researchers in a Japanese Semiconductor Company

Maeki

Mejía

Kajikawa

2020

Front. Res. Metr. Anal.

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“…Zhang and Ashuri (2018) discovered social networks in BIM-based collaborative design practices and examine the relationship between the characteristics of the design social network by SNA. Adopting patent bibliometrics and SNA, Wang, Sung, Chen, and Huang (2017) examined structures of semiconductor companies' R&D cooperative networks and channels of knowledge spillovers. Ye, Yu, and Li (2013) analyzed 36,731 enterprise-enterprise cooperative patents and found that the most prominent subgroup has small-world features and supports patent data as a useful indicator for measuring enterprise performance.…”

Section: Social Network Analysis In Cooperation Researchmentioning

confidence: 99%

Patent Cooperative Patterns and Development Trends of Chinese Construction Enterprises: A Network Analysis

Zhong

Hei

et al. 2019

Journal of Civil Engineering and Management

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Despite the rapid development of Chinese construction industry, there has been little research effort directed towards exploring patent cooperative patterns and evolution trends of construction enterprises, especially from the perspective of the patent development network. This paper extracts implicit collaborative information and introduces Social Network Analysis (SNA) method to conduct the patentometric analysis based on patent data from the “Top 500 Chinese Construction Enterprises” sourced from PatSnap database. The enterprise-enterprise networks and enterprise-university networks are analyzed quantitatively. The results reveal that: 1) there is a rising trend in the number of patents and patentees; 2) state-owned enterprises play a dominant role in patent development; 3) most of patents are classified as International Patent Classification E04G21; 4) the cooperative relationships are mainly within enterprises and their subsidiaries; 5) when enterprises choose to cooperate with universities, in addition to professional qualification, geographical factors should also be considered. Finally, the development and patent evolution trends are discussed. Some useful suggestions are proposed. The contribution lies in: (a) providing a visualization of the implicit collaboration information of patents in Chinese construction enterprises; (b) revealing cooperative patterns of construction enterprises on patents; and (c) providing enterprises some useful suggestions for patent cooperation.

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“…The methodology is very relevant for knowledge production investments, but it is heavily dependent on data that might not be readily available everywhere. Two examples of recent studies that address spillover effects in knowledge production are Wang et al (2017) and Neves and Sequeira (2018). Wang et al (2017) estimated the spillover effects in the semiconductor industry to find that the strength of the networking ties between companies explain the level of spillover effect in the knowledge production process.…”

Section: Review Of Previous Workmentioning

confidence: 99%

An empirical knowledge production function of agricultural research and extension: The case of the University of California Cooperative Extension

Chatterjee

Dinar

González‐Rivera

2018

Technological Forecasting and Social Change

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Our study examines empirically the impact of agricultural research inputs on the creation and dissemination of knowledge by the University of California Cooperative Extension (UCCE). We formulate a conceptual framework to understand the relationship between the agricultural research inputs employed by UCCE and the knowledge shared. We develop an index of knowledge based on a weighted average of the various modes through which knowledge is produced by UCCE's agricultural research for all counties in the state of California during 2007-2013. Empirical results indicate significant positive impacts of research inputs on the production of knowledge. We find research input, such as number of research positions measured as full-time equivalent (FTE), level of salary per researcher (including seniority and status), and investment in research infrastructure per FTE, positive and significant. Our models suggest diminishing marginal knowledge returns to research infrastructure, and a linear knowledge production function with respect to the number of FTE and the salary per FTE in the UCCE system.

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Strong ties and weak ties of the knowledge spillover network in the semiconductor industry

Cited by 64 publications

References 48 publications

Collaborative Patterns, Productivity, and Research Impact in the Careers of Star Researchers in a Japanese Semiconductor Company

Collaborative Patterns, Productivity, and Research Impact in the Careers of Star Researchers in a Japanese Semiconductor Company

Patent Cooperative Patterns and Development Trends of Chinese Construction Enterprises: A Network Analysis

An empirical knowledge production function of agricultural research and extension: The case of the University of California Cooperative Extension

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