Network structure of innovation: can brokerage or closure predict patent quality?

A GAN-BO-XGBoost model for high-quality patents identification

The number of patents increases quickly, while more and more low-quality patents are emerging. It's important to identify high-quality patents from massive data quickly and accurately for organizational R&D decision-making and patent layout. However, due to low percentage of high-quality patents, it is challenging to identify them efficiently. In order to solve above problem, we reconstruct the existing index system for identifying high-quality patents by adding 4 features from technological strength of patentees. Furthermore, we propose an improved model by integrating resampling technique and ensemble learning algorithm. First, generative adversarial networks (GAN) are used to expand minority samples. Second, Extreme Gradient Boosting algorithm (XGBoost) with Bayesian optimization (BO) is used to identify high-quality patents. For clarity, this model is called a GAN-BO-XGBoost model. To test the effectiveness of above model, we use patent data in field of lithography technology. Tenfold cross-validation is carried out to evaluate the performance between our proposed model and other models. The results show that GAN-BO-XGBoost model performs better and it's more stable than other models.

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

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.

Global R&D Collaboration in the Development of Nanotechnology: The Impact of R&D Collaboration Patterns on Patent Quality

Nanotechnologies have been regarded as general-purpose, and interdisciplinary technology converged with different implications of technologies. This paper aims to explore the global Nano collaboration of research and development (R&D) by patent analysis to realize the sustainable development of nanotechnology and investigate the influence of global R&D collaboration on patent quality. The primary findings indicated that the number of issued patents grew sharply since 2002, and the major collaborative fields in terms of nanotechnology are nanoelectronic and nanomaterials. Finally, the collaboration among star assigners, global partnership, and the indicator of patent centrality had a positive impact on patent quality, except that university-industry collaboration did not show a significant effect on patent quality. One part of the reason is that industries have not yet fully recognised nanotechnology opportunities with a risk-averse attitude toward market uncertainties. Overall, the relationship of global R&D collaboration is an essential factor in promoting the sustainable development of the patent quality of nanotechnology. An effective patenting strategy and high-quality application of nanotechnology R&D could secure protection for innovations to reinforce core competitiveness in the business.

Network characteristics and patent value-Evidence from the Light-Emitting Diode industry

This study proposes a different angle to social network analysis that evaluates patent value and explores its influencing factors using the network centrality and network position. This study utilizes a logistic regression model to explore the relationships in the LED industry between patent value and network centrality as measured from out-degree centrality, in-degree centrality, in-closeness centrality, and network position, which is measured from effect size. The empirical result shows that out-degree centrality and in-degree centrality have significant positive effects on patent value and that effect size has a significant negative effect on patent value.

Measuring the performance of knowledge resources using a value perspective: integrating BSC and ANP

Purpose

– This paper aims to provide insight into how knowledge resources in R & D organizations can be effectively and separately measured for knowledge sharing and transfer. Knowledge is recognized as a durable strategic resource to obtain sustainable competitive advantage.

Design/methodology/approach

– The paper proposes a theoretical framework integrating an analytic network process (ANP) with a balanced scorecard (BSC) to measure the performance of knowledge resources under value perspective. Four indicators and three knowledge value (KV) components including labor value, technology value and utilization value are discussed. The model construction, problem structuring and calculation procedure for measuring the performance of knowledge resources based on ANP and BSC are demonstrated.

Findings

– Despite a number of models to assess the performance of knowledge resources being proposed, they highlighted a need for separately measuring under value perspective. With the aim of filling this gap, the main finding of the paper is to clarify relevant issues, providing a better framework for assessment of the performance of knowledge resources.

Research limitations/implications

– To handle the dynamic nature of knowledge, the research should take into account more advanced methods to measure the performance of knowledge resources. Both qualitative and quantitative methods should be utilized in future research.

Practical implications

– The consequences of measuring the performance of knowledge resources under value perspective may help managers to organize and arrange the separate knowledge resources, improving the knowledge resources exchange between different institutions in R & D organizations.

Originality/value

– The main contribution of this paper lies in the development of a comprehensive model, which incorporates diversified issues for conducting the performance of knowledge resources under value perspective.

Knowledge Flow Determinants of Patent Value: Evidence from Taiwan and South Korea Biotechnology Patents

Patents are important knowledge assets that enable firms to compete successfully and earn returns. To control spending on patent renewal fees, firms carefully analyze the value of their patents to determine whether patent rights need to be maintained, thereby rendering renewal decisions an opportunity to examine patent values. This study incorporates social network analysis and traditional patent indicators to empirically investigate the determinants affecting patent valuation by firms and research institutes in the Taiwanese and South Korean biotechnology industries. Results indicate that firms and research institutes value their patents differently. Certain indicators derived from knowledge flow and traditional patent indicators significantly affect patent value. Based on the findings, this study provides suggestions for policy-making.