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Zheng Liu
Centre of Innovation and Development, Nanjing University of Science and Technology, Nanjing 210094, China

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Journal article
Published: 02 July 2021 in Journal of Open Innovation: Technology, Market, and Complexity
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With digitalization and the support of policies, the creative industries have shown rapid growth in the last 20 years. Open forms of collective learning, user engagement and social networks have become popular to generate IPs and values. Meanwhile, government policy can support the sectors through subsidies, regulations, standardization, and protections at regional and national levels. This paper aims to explore the role of government policy in the innovation of creative industries from a macro dynamic perspective. The research method combines a structured literature review, a secondary document review of industry reports and government policy, and thematic content analysis. Through in-depth studies of the UK’s and China’s animation sectors, the paper identifies key elements of closed innovation, social innovation, and open innovation systems in the market. Comparisons of national government policies since 2000 reveal different approaches for countries where creative sectors are well-established, and for those starting with limited knowledge resources. A dynamic model is developed to address the evolution of macro dynamic innovation systems and the role of policies as interactive mechanisms. Practical implementation and future research areas are also suggested.

ACS Style

Zheng Liu. The Impact of Government Policy on Macro Dynamic Innovation of the Creative Industries: Studies of the UK’s and China’s Animation Sectors. Journal of Open Innovation: Technology, Market, and Complexity 2021, 7, 168 .

AMA Style

Zheng Liu. The Impact of Government Policy on Macro Dynamic Innovation of the Creative Industries: Studies of the UK’s and China’s Animation Sectors. Journal of Open Innovation: Technology, Market, and Complexity. 2021; 7 (3):168.

Chicago/Turabian Style

Zheng Liu. 2021. "The Impact of Government Policy on Macro Dynamic Innovation of the Creative Industries: Studies of the UK’s and China’s Animation Sectors." Journal of Open Innovation: Technology, Market, and Complexity 7, no. 3: 168.

Journal article
Published: 16 November 2020 in Journal of Open Innovation: Technology, Market, and Complexity
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In the context of the application of artificial intelligence in an intellectual property trading platform, the number of demanders and suppliers that exchange scarce resources is growing continuously. Improvement of computational power promotes matching efficiency significantly. It is necessary to greatly reduce energy consumption in order to realize the machine learning process in terminals and microprocessors in edge computing (smart phones, wearable devices, automobiles, IoT devices, etc.) and reduce the resource burden of data centers. Machine learning algorithms generated in an open community lack standardization in practice, and hence require open innovation participation to reduce computing cost, shorten algorithm running time, and improve human-machine collaborative competitiveness. The purpose of this study was to find an economic range of the granularity in a decision tree, a popular machine learning algorithm. This work addresses the research questions of what the economic tree depth interval is and what the corresponding time cost is with increasing granularity given the number of matches. This study also aimed to balance the efficiency and cost via simulation. Results show that the benefit of decreasing the tree search depth brought by the increased evaluation granularity is not linear, which means that, in a given number of candidate matches, the granularity has a definite and relatively economical range. The selection of specific evaluation granularity in this range can obtain a smaller tree depth and avoid the occurrence of low efficiency, which is the excessive increase in the time cost. Hence, the standardization of an AI algorithm is applicable to edge computing scenarios, such as an intellectual property trading platform. The economic granularity interval can not only save computing resource costs but also save AI decision-making time and avoid human decision-maker time cost.

ACS Style

Tao Li; Lei Ma; Zheng Liu; Kaitong Liang. Economic Granularity Interval in Decision Tree Algorithm Standardization from an Open Innovation Perspective: Towards a Platform for Sustainable Matching. Journal of Open Innovation: Technology, Market, and Complexity 2020, 6, 149 .

AMA Style

Tao Li, Lei Ma, Zheng Liu, Kaitong Liang. Economic Granularity Interval in Decision Tree Algorithm Standardization from an Open Innovation Perspective: Towards a Platform for Sustainable Matching. Journal of Open Innovation: Technology, Market, and Complexity. 2020; 6 (4):149.

Chicago/Turabian Style

Tao Li; Lei Ma; Zheng Liu; Kaitong Liang. 2020. "Economic Granularity Interval in Decision Tree Algorithm Standardization from an Open Innovation Perspective: Towards a Platform for Sustainable Matching." Journal of Open Innovation: Technology, Market, and Complexity 6, no. 4: 149.

Journal article
Published: 06 November 2020 in Journal of Open Innovation: Technology, Market, and Complexity
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High-tech zones are an important platform for local governments in China to carry out regional collaborative innovation and an important carrier for the construction of a regional innovation ecosystem. The evolution path of innovation ecosystem in a high-tech zone is divided into three stages: enterprise collection, industrial cluster, and system integration. The innovation subjects form a complex network system that transcends the physical boundary. This paper studies the relationship between innovation input, innovation output, and innovation environment from the perspective of cluster innovation ecosystem structure. Using data mining technology, this paper establishes an index variable system of the innovation ecosystem in a high-tech zone, which includes innovation input, innovation output, and innovation environment. Based on the data of the Nanning National High-tech Zone in China, empirical tests were carried out, using factor analysis and regression analysis to analyze the quantitative relationship between the input, output, and innovation environment of the Nanning High-tech Zone’s innovation ecosystem, and to explain the relationship between each other and the overall innovation of the high-tech zone. This research has certain practical significance for enriching and perfecting the theory of industrial clusters and studying the evolution of the innovation ecosystem of high-tech zones from a micro level. It has important, enlightening significance as a reference for the construction of innovative high-tech zones and the enhancement of high-tech zones’ independent innovation capabilities.

ACS Style

Xiaojing Huang; Lei Ma; Rao Li; Zheng Liu. Determinants of Innovation Ecosystem in Underdeveloped Areas—Take Nanning High-Tech Zone in Western China as an Example. Journal of Open Innovation: Technology, Market, and Complexity 2020, 6, 135 .

AMA Style

Xiaojing Huang, Lei Ma, Rao Li, Zheng Liu. Determinants of Innovation Ecosystem in Underdeveloped Areas—Take Nanning High-Tech Zone in Western China as an Example. Journal of Open Innovation: Technology, Market, and Complexity. 2020; 6 (4):135.

Chicago/Turabian Style

Xiaojing Huang; Lei Ma; Rao Li; Zheng Liu. 2020. "Determinants of Innovation Ecosystem in Underdeveloped Areas—Take Nanning High-Tech Zone in Western China as an Example." Journal of Open Innovation: Technology, Market, and Complexity 6, no. 4: 135.

Journal article
Published: 12 September 2019 in Sustainability
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Patent protection is a critical aspect of sustainable technology innovation, which is currently facing the challenge of patent risk. This study aimed to help enterprises prevent and avoid patent risk in a global view of technology innovation, and to propose a systematic evaluation model for patent risk. By combining the entropy method with the analytic hierarchy process (AHP), this study constructed an analytic hierarchy model of patent risk. Some indexes in the model were selected based on the summary of prior literature, and other indexes were selected according to experts’ communication, which helped us to generalize the patent risk as comprehensively as possible. The AHP evaluation results determined the weight and relative materiality for each risk factor, which were contained in a criteria layer and a sub-criteria layer. The entropy method integrated the evaluation weights of different experts’ opinions. By dividing the risk factors into three categories, namely “high”, “medium”, or “low”, according to the priority degree, the risk priority ranking was obtained. Suggestions are discussed regarding support for enterprises in dealing with patent risk that may occur during international trade or other commercial activities.

ACS Style

Ben Zhang; Lei Ma; Zheng Liu; Ping Wang. Sustainable Technology Innovation Path Recognition: An Evaluation of Patent Risk of International Trade. Sustainability 2019, 11, 5002 .

AMA Style

Ben Zhang, Lei Ma, Zheng Liu, Ping Wang. Sustainable Technology Innovation Path Recognition: An Evaluation of Patent Risk of International Trade. Sustainability. 2019; 11 (18):5002.

Chicago/Turabian Style

Ben Zhang; Lei Ma; Zheng Liu; Ping Wang. 2019. "Sustainable Technology Innovation Path Recognition: An Evaluation of Patent Risk of International Trade." Sustainability 11, no. 18: 5002.