Abstract ：

In the procedure of exploring science-technology linkages, non-patent literature (NPL) in patents, particularly scientific NPL, is considered to signal the relatedness between the developed technology and the cited science. However, many prior art search tools may not be powered with the cross-collection recommendation technique, or have limited cross-collection recommendation capabilities. In this paper, we present an approach to recommend scientific NPL for a focal patent on the basis of heterogeneous information network. This study views this cross-collection recommendation problem as a link prediction problem on the basis of meta-path counting approach. Extensive experiments on DrugBank dataset in the pharmaceutical field indicate that our approach is feasible and effective. This work provides a novel perspective on scientific NPL recommendation for a focal patent and opens up further possibilities for the linkages between science and technology. Nevertheless, more experiments in other fields are required to verify the recommended effects of the approach proposed in this study.

Keyword ：

Heterogeneous information network Link prediction Scientific NPL Meta-path counting Cross-collection recommendation

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Abstract ：

Given the significance of technology convergence for innovation and launching new products, accurately predicting technology convergence is critical to the pursuit of technological innovation. Although previous studies have been proposed to predict technology convergence, the framework using single-layer international patent classification (IPC) networks neglects the potential connections between components. This issue leads to the prediction that IPC nodes in separate components will not overlap. This study utilizes IPC, patentee, and topic information to construct a technology supernetwork model consisting of 'Topic-IPC-Patentee'three layers, aiming to overcome the issue of IPCs located in different components being unable to connect. Based on the supernetwork, we adopt a link prediction method based on superedge similarity to predict the technology convergence and a case analysis on the field of gene editing is conducted. According to experimental results, we observe that the technology supernetwork model can effectively predict the convergence of gene editing technologies, demonstrating robust predictive performance. The main contribution of this research is to provide a methodology that accurately predicts technology convergence, which can help firms deploy their research and development (R&D) strategies, policymakers develop insightful policies, and investors capitalize high-value projects. © 1988-2012 IEEE.

Keyword ：

Prediction models Patents and inventions

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Abstract ：

Given the contradictory empirical evidence on the relationship between green R&D expenditure and corporate Green Innovation performance (GIP), The present research study is a distinctive investigation into the moderating impacts of ESG reporting on this relationship. We utilized a data collection of 3,846, firm-year observations of A-share listed firms in China from 2016 to 2022 from CSMAR and Bloomberg databases. The firm's Corporate GIP is assessed and measured by looking at the total quantity of green patents. Lastly, models with multiple regression analyses and fixed effects were employed. The findings show that ESG reporting has a positive and significant impact on the association between corporate GIP and green R&D expenditure, implying its compensating and supportive function in the form of green signals in green outputs. This research could help executives and lawmakers, especially in developing countries to build innovative environmental strategies for business sustainability.

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Abstract ：

With the rapid developments in science and technology, technologies from various fields are increasingly converging. Technology convergence has become a primary source of disruptive technologies (DTs). Revealing the mechanisms of technology convergence in DTs and identifying their evolutionary pathways and trends are of great significance for the enterprise's R&D strategic decision-making and the government's innovation policy formulation. Previous studies on the evolutionary pathways of technology convergence have mainly relied on citation information and International Patent Classification (IPC) coclassification analysis. However, measuring technology convergence using patent citation information has a time lag, and patent IPC codes fail to capture microtechnological changes. Moreover, technology roadmapping (TRM) is widely recognized as a valuable method for studying evolutionary pathways and offering a systematic approach to mapping the evolution of functions or performance in DTs. However, existing TRM methods have mainly relied on expert opinion, which is a time-consuming and costly approach. Therefore, in this article, we integrated patent coclassification analysis, subject-action-object-prepositional phrase semantic analysis, and TRM to propose a framework for monitoring DTs' evolutionary pathways, revealing the technology convergence characteristics of DTs and identifying their trends. We employed smartphones as a case study to demonstrate the validity and flexibility of this framework. This article provides a novel approach for roadmapping DTs' evolutionary pathways and reveals the technology convergence characteristics in the emergence of DTs, thereby aiding the comprehension of DTs' emergence and development trends. This article also will be of interest to experts in smartphone technology R&D.

Keyword ：

Market research technology roadmapping (TRM) Patents Convergence Smart phones Transmission line measurements Disruptive technology (DT) evolutionary pathways smartphone technology Technological innovation Text mining technology convergence

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Abstract ：

Carbon neutrality achievement has had a major transformative effect on the economic and social development of countries and regions. Therefore, this study focuses on analyzing the effectiveness of environmental regulation policies in promoting institutional dividends and seeks to examine the relationship between environmental regulatory policies and green innovation development through the lens of green patents. Panel data from industrial enterprises listed in Shanghai and Shenzhen from 2010 to 2019 were used in this research, which employed fixed-effect and media-effect models to investigate the impacts and mechanisms of various types of environmental regulatory tools on corporate green innovation. The results showed that different types of environmental regulation tools have varying effects on corporate green innovation. Specifically, command-based and market-oriented environmental regulations have a positive "U-shaped"impact, whereas voluntary environmental regulations have an inverted "U-shaped"impact. Furthermore, it was found that the "innovation compensation effect"generated by the implementation of environmental regulation policies has a greater positive impact than the negative impact of the "compliance cost effect."Additionally, geographical location and enterprise ownership play a role in determining the impact of environmental regulations on corporate green innovation. These findings emphasize the importance of strengthening environmental regulatory policies to achieve regional carbon-neutral development. Our research provides recommendations and ideas for better achieving this goal. (c) 2023 Economic Society of Australia, Queensland. Published by Elsevier B.V. All rights reserved.

Keyword ：

Industrial enterprise Institutional dividends Green innovation Environmental regulation Carbon neutrality

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Abstract ：

Green technology innovation is a crucial factor in the global quest for sustainability. In 2017, China approved the establishment of Pilot Zones for Green Financial Reform and Innovations to build and improve the green financial system. Using a difference-in-difference (DID) model, this paper examines the impact of this pilot policy on corporate green technology innovation and its transmission mechanism based on the data of green patents of Chinese A-share listed manufacturing companies between 2014 and 2019. It is found that Pilot Zones for Green Financial Reform and Innovations have contributed to the growth of green technology innovations. This conclusion is confirmed after a series of robustness tests. The pilot policy's promotion of green technology innovations is mainly reflected in the application of corporate green invention patents. However, it is more significant for non-heavy polluting enterprises, high-tech certified enterprises, and large enterprises. The paper also finds that the pilot policy promotes corporate green technology innovation by promoting social financing, talent support, and government financial support.

Keyword ：

Difference-in-difference model Corporate green technology innovations Pilot zones for green finance reform and innovations

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Abstract ：

Nested structure is a structural feature that is conducive to system stability formed by co-evolution. In our opinion, it is just like what the biological species do in the mutualistic ecosystem that enterprises collaborate to apply for patents in the technical cooperation network, changing to form one dynamic equilibrium after another. In this paper, a nestedness-based analytical framework is built to reflect the topological stability of the technical cooperation network of Zhongguancun Science Park (Z-Park). We study why this technically mutualistic ecosystem can reach a stable equilibrium with time going by, as well as, we propose an index called Nestedness Disturbance Index (NDI) to study what the role park areas and technical fields play in the steady states.

Keyword ：

Technical cooperation Nested structure NODF Nestedness disturbance index HETCN model

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Abstract ：

The current patent value evaluations increasingly focus on serving realistic predictive scenarios, emphasizing the commercial potential of patents at the early stage from the ex-ante perspective. This requirement poses a serious challenge: those classical dynamic indicators that have been proved to be effective in the literature may not be valid for commercial patent potential prediction from the ex-ante perspective. Thereupon, this study groups the dynamic indicators into cross-sectional indicators and longitudinal indicators. Then, a patent commercial po-tential prediction framework is proposed from the ex-ante perspective, in which the impact of the chronological order on predictive models is investigated comprehensively. More specifically, this study collects the USPTO cancer-related dataset from 2003 to 2013 as the training set, and combines three dynamic indicators (cross-sectional, longitudinal, and mixed) with classical static indicators to test the prediction performance for the following five years (2014-2018). The biased results caused by the ex-post perspective are indeed observed, and the longitudinal indicators are more sensitive to commercial patent potential, especially in the early stage. The effect of the ex-post perspective will gradually weaken over time, and the cross-sectional indicators provide stable prediction performance three years later. These findings will be helpful for subsequent improvements of com-mercial patent potential prediction models.

Keyword ：

Ex -ante perspective Dynamic indicator Commercial potential Cross-sectional indicator Longitudinal Indicator

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Abstract ：

The commercialization process of energy storage patents affects the development of the energy storage industry. Clarifying the relationships between the characteristics of the applicants and patent transfer can facilitate technology transfer. In this study, China's energy storage patent data from 2009 to 2021 were divided by the rolling period. Based on this, the patent cooperation network, knowledge network, and transfer network were constructed via social network analysis. The analysis reveals the following. First, the number of industry -university-research institutions that jointly apply for patents is increasing, and the knowledge field is also expanding. Cooperation and transfer activities are more frequent in the eastern developed regions of China. Second, by using grey relational analysis theory, it was found that the degree centrality, closeness centrality, and clustering coefficient of the cooperation network are closely related to the transfer network. Compared with the cooperation network, the energy storage knowledge network has a more significant relationship with the transfer network. The knowledge elements stock of patent transferor and transferee is more relevant to patent transfer activity than the patent stock. This also proves that the knowledge and cooperation networks are decoupled. Additionally, patent applicants for energy storage patents should focus not only on their past average locations to maintain the competitive advantage, but also on future location trends to seize development opportunities. Finally, the proposed recommendations can help applicants facilitate patent transfer activities in the energy storage industry by adjusting the location of the network.

Keyword ：

Patent Social network analysis Energy storage Industry-university-research(IUR) cooperation Grey system theory

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Abstract ：

With the high integration of science and technology development, how to early identify technology opportunity is crucial for the governments' and enterprises' research and development (R&D) strategic planning and inno-vation policy to gain a first-mover advantage in the market competition environment. Most researchers have applied Subject-Action-Object (SAO) semantic mining approach or outlier detection method to mine scientific papers or patent information for identifying technology opportunity. However, few researchers have combined information from both scientific papers and patents to identify technology opportunity by integrating SAO se-mantic mining and outlier detection method. Therefore, this paper proposes a research framework that uses scientific papers and patents as data resources, and integrates SAO semantic mining and outlier detection method to identify technology opportunity. In this framework, we first use the SAO semantic mining method to mine technical problems and solutions contained in scientific papers and patents respectively. Then we conduct comparative analysis to identify potential technology opportunity in the gaps between scientific papers and patents. Secondly, we use a outlier detection method to identify outlier points in scientific papers, and we incorporate the outlier points into the analysis scope of technology opportunity identification. Finally, we combine the results of SAO semantic mining method with outlier detection method, and use expert knowledge to identify technology opportunity. The triboelectric nanogenerator technology is selected as a case study to verify the feasibility of this framework. The results show that the framework can effectively and comprehensively identify technology opportunity from the two levels of technical problems and technical solutions. This paper contributes to technology opportunity study, and will be of interest to triboelectric nanogenerator technology R&D experts.

Keyword ：

Outlier detection Technology forecasting Triboelectric nanogenerator Text mining Subject -action -object semantic analysis Technology opportunity

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