Innovation Spillovers Research Articles

Research on interaction of innovation spillovers in the AI, Fin-Tech, and IoT industries: considering structural changes accelerated by COVID-19

This paper aims to probe the influence of innovation spillovers in the artificial intelligence (AI) and financial technology (Fin-tech) industries on the value of the internet of things (IoT) companies. Python was utilized to download public information from Yahoo Finance, and then the GARCH model was used to extract the fluctuations of cross-industry innovation spillovers. Next, the Fama–French three-factor model was used to explore the interactive changes between variables. The panel data regression analysis indicates that the more firms accept innovation spillovers from other industries, the better the excess return; however, this effect differs because of industrial attributes and the environmental changes induced by COVID-19. Additionally, this study finds that investing in large-cap growth stocks of IoT firms is more likely to yield excess returns. Finally, the study yields lessons for policy leverage to accelerate the upgrading and transformation of innovation-interactive industries by referring to the practices of Singapore and South Korea.

Innovation Spillovers from High-skilled Labor Shock

Innovation Spillovers from High-skilled Labor Shock

Local Vibrancy and Innovation Spillover

Local Vibrancy and Innovation Spillover

Local Innovation Spillovers of IPOs

Local Innovation Spillovers of IPOs

Impact of technological innovation on corporate leverage in China: The moderating role of policy incentives and market competition.

Due to its capital-related nature, corporate leverage is highly exposed to financial risk, and optimizing corporate leverage is an effective method of mitigating financial risk to maximize corporate value. We use a two-way fixed effects model to examine the impact of technological innovation on corporate leverage using panel data of A-share listed companies in the Chinese manufacturing sector from 2012 to 2020. The results show that technological innovation and corporate leverage exhibit significant heterogeneity in cross-sectional, spatial and temporal dimensions. By further distinguishing between the effects of policy incentives and market competition, we find that the former exerts an "investment crowding out" effect and the latter an "innovation spillover" effect. These factors mitigate the negative relationship between technological innovation and corporate leverage. In general, this study provides empirical evidence for the rational allocation of resources by the Chinese government, the development of innovation capabilities, and the adjustment of leverage by firms from various regions.

Environmental innovation, climate change and knowledge diffusion process: How can spillovers play a role in the goal of sustainable economic performance?

Technological innovation could affect climate change in multiple ways. On the one hand, technological innovation promotes economic growth and energy consumption, increasing carbon emissions. On the other hand, environmental innovations are devoted to reducing the impact of economic activity on climate change. This paper explores the impact of climate change in three economic areas (Europe, Japan and the USA) from 2002 to 2017 of two different innovation variables: the total firms' R&D investments and the knowledge innovation spillovers in the energy field. The findings indicate that the overall R&D process is detrimental to climate change variables, but the knowledge diffusion process of energy innovations positively impacts climate change mitigation. Policy implications are also related to institutional variables connected to environmental regulations.

Impact of technological innovation on carbon emissions in China's logistics industry: Based on the rebound effect

Technological innovation is an important way to reduce carbon emissions in the logistics industry. Using panel data of the logistics industry in China's 30 provinces from 2002 to 2019, a dynamic spatial Durbin model was constructed to explore the impact of logistics technological innovation on carbon emissions, and determine whether there is a rebound effect. Furthermore, the rebound effect of logistics technological innovation was calculated, and its spatial-temporal characteristics were analyzed. It was found that there is a “U”-shaped relationship between logistics technological innovation and carbon emissions at the national and regional levels, which means that the impact of logistics technological innovation on carbon emissions has a rebound effect. Moreover, the spatial spillover of technological innovation strengthens the rebound effect, while the optimization of the energy structure weakens it. Further calculation showed that the average spatial rebound effect is 60.61%, which is 22.66% higher than the average direct rebound effect. The spatial rebound effect has obvious regional heterogeneity, with the highest in the central region, followed by the eastern region, and the lowest in the western region. For temporal variation, at the national level, the spatial rebound effect was successively shown as a backfire effect, partial rebound effect, super energy-saving effect, and partial rebound effect. Similarly, three regions fluctuate circularly in these rebound effects with different forms. Thus, the rebound effect can be alleviated through energy structure optimization, energy price marketization, and a regional collaborative development mechanism.

Impact of urban innovation ability on industrial pollution governance in Chinese cities: taking the Yangtze River Economic Belt as an example.

This paper uses spatial measurement method, by selecting panel data from a total of 106 cities in 11 provinces and municipalities in the Yangtze River Economic Belt from 2006 to 2018, to conduct an empirical study on the impact mechanism of urban innovation ability on industrial pollution governance. The results show that there is a significant negative correlation between urban innovation ability and the degree of industrial pollution, and cities in the Yangtze River Economic Belt have a significant spatial connection. Specifically, this paper finds that urban innovation ability can have a positive impact on urban industrial pollution governance through industrial selection, production technology innovation, pollution discharge technology innovation, and environmental pollution treatment, which will help cities reduce industrial pollution; The industrial pollution governance level of individual cities is not only affected by the innovation ability of the city, but also by the innovation spillover of the surrounding cities. Area surrounded by cities with high urban innovation ability will benefit from the spillover of the surrounding cities. The industrial pollution governance level of individual cities is not only affected by the innovation ability of the city, but also by the innovation spillover of the surrounding cities. Areas surrounded by cities with high urban innovation ability will benefit from the spillover of the surrounding cities, thus improving the industrial pollution control and regional ecological environment. Based on this, this paper proposes relevant policy recommendations for urban industrial pollution governance from the perspective of improving urban innovation ability.

Spatial Correlation Network Structure and Influencing Factors of Two-Stage Green Innovation Efficiency: Evidence from China

With the continuous progress in global sustainable development, green innovation has become the primary driving force for the development of all countries and regions. China has implemented the strategy of constructing a cross-regional green innovation network. As the spatial correlation network structure of green innovation efficiency is complicated, it is necessary to study the change rules of the network structure to coordinate regional green and innovative development. In this paper, the Super-NSBM model is used to calculate the values of two-stage green innovation efficiency of China’s industrial enterprises from 2006 to 2019. Social network analysis is used to explore the rule of changes and causes of the spatial correlation network of two-stage green innovation efficiency. Our findings are as follows. Green innovation efficiency in the two stages presents the relationship of a non-adjacent complex network, and the network of green innovation and R&D efficiency is closely interconnected. Strong hierarchical correlation breaks down when searching for the best spatial configuration relationship. The transformation efficiency of the networked cooperation of green innovation achievements is stable. In the spatial correlation of green innovation and R&D efficiency, Guangdong, Shandong, Beijing, Jiangsu and Zhejiang are at the center of the network. In the spatial correlation of transformation efficiency of green innovation achievements, Shandong, Jiangsu, Guangdong, Henan and Hubei are in the center. The northern coastal areas fall within the scope of green innovation and R&D spillover has and have a close cooperation with the green innovation spillover plate in the southern coastal areas, making green innovation achievements spill over to the Chengdu-Chongqing region and northern region. The cooperation and connection of green innovation activities conform to the rule of geographical proximity. Environmental regulation and marketization are characterized by “hierarchy”, but the economic level is “non-hierarchical”. The government can implement relevant green innovation policies according to local characteristics. Our findings are of great significance to narrow regional green innovation gaps.

Green technology innovation spillover effect and urban eco-efficiency convergence: Evidence from Chinese cities

Urban eco-efficiency, as an indicator to evaluate urban sustainability, has attracted increased attention, and green technology innovation has been proved to be an effective method to promote eco-efficiency. However, due to regional inequality, the level of eco-efficiency and green technology innovation vary in different cities, which is not conducive to overall development and should be dealt with. Thus, this study explores the influencing mechanism of green technology innovation spillover effect on urban eco-efficiency convergence with mathematical analysis, proposes an SBM model with undesirable outputs to evaluate Chinese cities' eco-efficiency from 2004 to 2018, judges cities' spatial correlation based on Moran's I index, and applies spatial Durbin model to investigate green technology innovation's direct and spillover effects on urban eco-efficiency convergence with time and regional differences. The results show that although there are large spatial differences in Chinese urban eco-efficiency, the overall urban eco-efficiency has improved gradually, and green technology innovation spillover effect can promote urban eco-efficiency convergence, but the degrees differ in time and region. The results of this study can be useful for solving the problem of unequal regional and urban eco-efficiency development by managing the distribution and transfer of green technology innovation in cities.

The knowledge spillover of innovation

AbstractThis study advances our understanding of knowledge spillover of innovation, putting a firm’s own R&D investment and knowledge spillovers to a competitive test. We use three matched databases of 15,430 firms in the United Kingdom (UK) during the period 2002–2014 in order to demonstrate that knowledge spillovers emanating from R&D investment within and between industries have different effects on innovation compared to imitation and that the ability to access spillover is conditional on the firm’s own investment in R&D. This study furthers our understanding in two different ways. Firstly, it supports the two faces of the R&D story. Second, it demonstrates that the relationship between knowledge spillover and firm innovation depends on the firm’s own investment in R&D and reveals the positive effects of knowledge transfer as well as factors limiting the use of spillovers such as industry competition, transaction costs, and eventually innovation type.

Environmental Stringency and Green Innovation Spillovers to Emerging Market Industries

Industries in emerging markets often have exposure to countries with much more stringent environmental regulations. While partners in these countries may push their green innovation practices to emerging market industries through positive spillovers, institutional arbitrage and pollution haven arguments suggest that these industries will profit from their comparative advantage in non-green practices. Using a comprehensive sample of 18 manufacturing industries across 30 countries, we investigate these ideas by examining the institutional and competitive factors that influence the green innovation rate of emerging market industries. We find evidence that more exposure to countries with stringent environmental regulations reduces the rate of green innovation in emerging market industries. We find that this result is driven by industries from countries with low levels of environmental stringency and lower openness to global norms. This relationship is also driven by emerging market industries with higher technological sophistication, suggesting that these industries will focus on innovation in non-green technologies to serve the institutional arbitrage needs of their buyers and maintain their comparative advantage.

Does Technological Innovation Curb O3 Pollution? Evidence from Three Major Regions in China.

At the end of 2020, when China’s three-year Blue Sky Protection Campaign was successfully concluded, the main pollutants, led by O3, increased instead of decreasing, creating a new air pollution problem. In this paper, the impact of the technological innovation level on O3 pollution and its inter-regional differences across three major regions from 2014 to 2019 are studied using the dynamic spatial Durbin model. Generally, in terms of ozone pollution showing significant spatial correlation, technological innovations in China are still not effective in curbing ozone pollution. Furthermore, technological innovation is a key factor affecting ozone pollution, and it is heterogeneous, demonstrating that the impact of technological innovation on O3 pollution is different among regions. Technological innovation in Beijing–Tianjin–Hebei significantly reduces local O3 pollution with spillover, while technological innovation in the Yangtze River Delta instead significantly exacerbates local O3 pollution, and the impact of technological innovation on O3 pollution in the Fenwei Plain is not significant. Third, other factors in O3 pollution also differ between regions, with the number of cars and the amount of foreign capital actually utilized being the main factors. Therefore, we should pay attention to the spillover of O3 pollution and technological innovation and strengthen regional cooperation according to our own characteristics to effectively suppress O3 pollution. Finally, the findings of this paper are representative, which provides a possible reference for other similar national or regional studies.

Innovation efficiency and firm performance in a benchmarking context

This paper examines the effect of innovation efficiency on firm performance using the Greek version of the Community Innovation Survey (CIS) 2012–2014 and employs a dataset of 1,274 innovative manufacturing firms. Introducing a data envelopment analysis (DEA)‐type benchmarking strategy with non‐continuous knowledge inputs, the main findings suggest that innovation efficiency, although does not exert a direct impact on firm performance, moderates the relationship between the internally generated knowledge and firm performance. On the other hand, such a moderation relationship is not confirmed for the relationship between external knowledge and innovation spillovers on firm's performance. Although the impact of the embodied to innovation inputs spillovers, on firm performance, is always positive and significant, empirical results indicate that absorptive capacity exhibits diminishing returns with respect to performance.

Spillover and Re-Spillover in China’s Collaborative Innovation

The spillover effect serves as the basis of regional collaborative innovation. Existing research on innovation spillover focuses on the overall impact of a region's innovation factors on local and other regions' innovation activities. However, re-spillover may occur since the flow of innovation factors between any two regions may influence the innovation in third-party regions. This study quantifies labor flow, capital flow, and institutional learning between regions in China using a gravity model and a social network analysis model, and then applies a spatial econometric model to investigate innovation spillover and re-spillover. The results show that re-spillover can better explain levels of regional innovation. Capital, government support, labour flow, capital flow, and institutional learning have a positive spillover effect on local innovation, while labour flow also has positive spillover effects to other regions.

Analysis on the Evolution Mechanism of Open Environmental Protection Innovation among Equipment Manufacturing Oligopolistic Enterprises Based on Data Simulation

The global economic environment is becoming increasingly complex with the variable change in the competition regulation and the merging of technological innovation. A single equipment manufacturing enterprise cannot achieve the realization of an intelligent strategy. With the help of open innovation, the enterprise can build an intelligent manufacturing innovation ecosystem based on promoting internal re-engineering of enterprises and mutual opening of internal innovation resource processes among enterprises to strengthen cooperation. However, since open innovation is a complex collaborative innovation of multiorganizational cooperation, this paper analyzes and discusses the green open innovation cooperation strategy among oligopolistic enterprises in the equipment manufacturing industry under the influence of environmental regulation by using relevant models and theories of evolutionary game theory. In addition, the optimal combination under the existing situation is selected by using the model to simulate and analyze the selection trends and results of oligopolistic enterprises several times. The study shows that the probability of choosing an open green innovation strategy among oligopolistic equipment manufacturing firms is positively proportional to the excess benefit of open green innovation and government subsidies and inversely proportional to the excess benefit of knowledge spillover, government penalties, and the cost of open collaborative innovation and knowledge spillover between two firms.

Local Knowledge Management and Innovation Spillover

Tourism lifestyle entrepreneurs play an essential role in the innovation and competitiveness of tourist destinations. However, existing knowledge about the determinants of the innovation they generate is far from being fully exploited. There is still little knowledge about the innovation spillover effect, which is a determining factor for the destination's competitiveness. One of the key factors for innovation results from local knowledge management and the ability of tourism lifestyle entrepreneurs to turn this knowledge into innovation. To test the role of knowledge management in innovation and spillover, a survey was conducted on a sample of 115 tourism lifestyle entrepreneurs, with the hypotheses tested using structural equations. The results reveal that place attachment and knowledge acquisition have a direct effect on knowledge assimilation and community-centered strategy. Furthermore, it was also found that community-centered strategy and knowledge assimilation play a mediating role between place attachment and innovation capacity.

The impact mechanism of high-speed railway on regional green innovation spillover under multi-dimensional paths

As a major technological innovation, high-speed railway (HSR) has profoundly influenced the regional sustainable development (i.e., economy, society, and ecology). Meanwhile, green innovation is a critical catalyst for sustainable development. However, the impact of HSR on regional green innovation has received little attention. To fill the research gap, this paper reveals the driving effects of HSR on regional green innovation spillover by employing the three heat transfer methods in physics. Specifically, Stackelberg game models are constructed to imitate the production and sales distribution strategies of two competitive firms in the conduction path and the convection path. Furthermore, the regional green innovation spillover model under the radiation path is constructed. The main findings are as follows: (1) HSR has a significant positive impact on regional green innovation spillover. (2) After the HSR opening, apart from production cost and spillover elasticity, production distribution and carbon emission may affect the choice of regional green innovation in the conduction path. Sales distribution and price elasticity may have significant effects on the choice of regional green innovation in the convection path. (3) The size of regional green innovation spillover from HSR depends on changes in the accessibility of central and peripheral areas. (4) The regional green innovation spillover is negatively correlated with distance to HSR stations in the radiation path. This research provides a theoretical basis for improving the HSR network. Suggestions are put forward on how to carry out green innovation activities effectively. Innovation-driven strategies are suggested to be prioritized.

Decision-Making and the Contract of the Complementary Product Supply Chain Considering Consumers' Environmental Awareness and Government Green Subsidies.

The environmental awareness of consumers and enterprises has gradually increased, and green production and green consumption have become the main theme of social economy. On the other hand, the complementary product market has become an important source of competitive advantage for enterprises. Considering a complementary product supply chain, and taking account of the consumers’ environmental awareness and the green subsidies provided by the government, this paper examines members’ decisions in relation to four contract models based on game theory. By solving the model, it is shown that the government’s green subsidy plan improves the green degree of subsidized products and complementary products. Furthermore, compared to wholesale price contracts, revenue-sharing and cost-sharing contracts motivate manufacturers to improve the greenness of subsidized products, and they achieve a Pareto improvement for the whole supply chain and its members, when the contract parameters are appropriate. Numerical experiments also reveal that both the greenness of the complementary products and the profit for members increase with the green innovation spillover effect as a result of the complementary products and the scale of green consumers with environmental awareness in the market. This study provides good guidance for decision-making concerning the complementary product supply chain, and further contributes to environmental protection.

Innovation spillovers in production networks: Evidence from the establishment of national high-tech zones

Based on a novel dataset constructed with the supplier-consumer paired sample, this paper uses a difference-in-differences approach to evaluate the impact of national high-tech zones on firm performance. We find that the supplier's sales growth rate increased by about 61.35% after the approval of the national high-tech zone in the place where its largest customer was located. This spillover effect transmitted from the consumer to the supplier could be due to either knowledge spillover or demand expansion. Further study finds that the innovation spillover effect is stronger when the consumer company has a higher switching cost, when the supplier company has a stronger absorption capacity, and when the relationship between the supplier and the consumer is closer. Our paper suggests that the innovation policy effect would be amplified through the supply chain network.