Innovation Efficiency Research Articles

Cross-shareholding, financing constraints and firm innovation efficiency

Cross-shareholding by corporate subsidiaries and parent companies is a form of equity transaction within an enterprise group. This form of transaction can, to a certain extent, reduce the difficulty of corporate financing and improve the efficiency of corporate innovation. This paper analyzes the influence mechanism and effect of corporate sub-parent company cross-shareholding through a review of related theoretical and empirical studies, and conducts experimental research using instrumental variables, psm and other methods to eliminate endogeneity. The study's results indicate that cross-shareholding between corporate sub-parent companies positively affects corporate financing difficulties and innovation efficiency. The study also finds that there are some limitations and risks associated with its application, so enterprises should choose and use this form of transaction carefully in practice to maximize its potential advantages.

Research on the impact of China's reform to delegate power, streamline administration, and optimize government services on the technology innovation efficiency of the pharmaceutical manufacturing industry.

The government has recently implemented reforms aimed at delegating power, streamlining administration, and optimizing government services. This reform has eliminated barriers that impede the growth of various industries, thereby unleashing innovative potential. Additionally, there have been several medical policies, including changes to medical insurance and centralized volume-based procurement. China's pharmaceutical market has undergone significant changes, leading to increased demands for innovation technology efficiency in pharmaceutical manufacturing. The three-stage BCC theory was employed to assess the effectiveness of technology innovation in the industry under this reform. Calculate precise comprehensive technical efficiency values, pure technical efficiency values, and scale efficiency values for technological innovation in the pharmaceutical industry across 30 provinces from 2018 to 2020, after removing environmental factors. In 2020, Jiangsu and Shandong and nine other provinces reached the comprehensive technical efficiency frontier surface, joining Tianjin, Zhejiang, and Guangdong provinces. However, Gansu, Qinghai, Ningxia, and Xinjiang still need to catch up due to their smaller industrial scale and lack of technology. To ensure the effectiveness of reforms, it is crucial to fully consider provincial differences. Articulating national and provincial policies is necessary to allow efficient provinces to continue and allocate resources toward less efficient provinces to improve overall efficiency.

Does Artificial Intelligence Promote Firms’ Innovation Efficiency: Evidence from the Robot Application

Does Artificial Intelligence Promote Firms’ Innovation Efficiency: Evidence from the Robot Application

Retracted: Research on the Measurement, Evolution, and Driving Factors of Green Innovation Efficiency in Yangtze River Economic Belt: A Super-SBM and Spatial Durbin Model

Retracted: Research on the Measurement, Evolution, and Driving Factors of Green Innovation Efficiency in Yangtze River Economic Belt: A Super-SBM and Spatial Durbin Model

Retracted: Evaluation of Innovation Efficiency of High-Tech Enterprise Knowledge Supply Chain Based on AHP-DEA

Retracted: Evaluation of Innovation Efficiency of High-Tech Enterprise Knowledge Supply Chain Based on AHP-DEA

Research on the Influence and Spatial Effect of Carbon Trading Market on Green Innovation Efficiency of Industrial Enterprises

Based on the panel data of 30 provinces and cities in China from 2005 to 2020, a two-stage value chain model of green innovation of Chinese industrial enterprises is constructed and the green innovation efficiency of industrial enterprises is measured, in which coastal cities such as Guangdong, Beijing, and Shanghai have the highest innovation efficiency, and inland cities such as Ningxia, Inner Mongolia, and Gansu have the lowest efficiency. On this basis, a spatial double difference model is applied to reveal the effect of carbon trading market policies on urban green innovation efficiency and its spatio-temporal heterogeneity. The results show that the carbon trading market policy significantly affects the green innovation efficiency of industrial enterprises, and at the same time, the level of economic development, the level of foreign investment, and the industrial structure of each province and city also have a positive effect on the green innovation efficiency. There is also a spatial spillover effect of the carbon trading market policy, which not only has a significant incentive effect on the green innovation efficiency of the pilot cities, but also has a positive promotion effect on the green innovation efficiency of the neighboring cities. At the same time, the support of provincial and municipal governments significantly affects the spillover effect of carbon market policies.

Impact of financial development on innovation efficiency of high-tech industrial development zones in Chinese cities

Since 1988, China's high-tech industrial development zones (HIDZ) have achieved remarkable success in economic construction, playing a crucial role in both national and local economic growth. The development and innovation within HIDZ are intricately linked to external financial support. This study empirically examines the influence of financial development (FD) on the innovation efficiency of HIDZ (IEH) based on panel data from various Chinese cities spanning the years 2007–2020. The findings reveal the following: (1) Overall, enhancements in both financial scale (FS) and financial efficiency (FE) significantly contribute to the improvement of IEH. (2) The paper introduces industrial structure rationalization (ISR) as an intermediary variable and utilizes the Mediating Effect Model. The results show that the mediating effect of ISR on FS and FE is approximately 0.0164 and 0.0150, respectively. (3) Heterogeneity analysis of samples across different regions and periods indicates that the impact of FS on IEH follows a pattern of ‘western region > eastern region > central region’. Similarly, the impact of FE shows a trend of ‘central region > western region > eastern region’. Furthermore, the influence of FD during the period 2007–2012 is more pronounced than that of 2013–2020.

Will customer change affect enterprise innovation efficiency? A study from the perspective of social networks

An enterprise innovation strategy is the driving force for the healthy and sustainable development of enterprises, and high-quality, efficient innovation is important for improving enterprises' market value and promoting their high-quality development. Customer relationships are an important factor affecting enterprise product technology and enterprise innovation; however, few studies have evaluated the impact of customer change on innovation efficiency. Therefore, from the perspective of social capital, we use China's listed manufacturing companies' data from 2013 to 2020 to systematically examine how customer changes affect enterprise innovation performance, and test the impact of social networks on the relationship between customer change and enterprise innovation efficiency. The empirical research shows that customer change reduces enterprises' innovation efficiency, and that social network relationships have an intermediary effect on the relationship between customer change and innovation efficiency; that is, the social network relationships reduce the negative impact of customer change on enterprise innovation efficiency. Further analysis shows that this mediating effect is not obvious for enterprises experiencing large customer changes but is prominent for nonstate-owned or nontechnology-intensive enterprises. Our study enriches and expands the research on how customer relationships influence enterprise innovation efficiency, clarifies different mechanisms due to various “networks”, and provides new empirical evidence to enable enterprises to improve their competitiveness.

The effect of producer service agglomeration externalities on urban green innovation efficiency in China

For China's urban industrial layout and green innovation, determining which types of producer service agglomeration externalities (PSAE) are most conducive to the rise of green innovation efficiency (GIE) has theoretical and practical relevance. Based on the panel data of 274 prefecture-level cities in China from 2006 to 2019, this paper adopted the super-SBM model with unexpected output to evaluate the urban GIE. It used the Moran I index to judge the spatial auto-correlation of GIE, followed by the spatial Durbin model (SDM) to examine the spatial spillover effect of PSAE on the urban GIE. Then we used a comprehensive threshold effects model to investigate the non-linear relationship. The results demonstrate that Marshall-Arrow-Romer externality (MARE) and Jacobs externality (JACE) are curbing the urban GIE. Porter externality (PORTERE) inhibits the region and promotes the surrounding area. Furthermore, these effects also depend on the stage of economic development.

A Double-Layer Coupled Network Model of Network Density Effects on Multi-Stage Innovation Efficiency Dynamics: Agent-Based Modeling Methods

This paper proposes a double-layer coupled network model to analyze the multi-stage innovation activities of online, and the model consists of two layers: the online layer, which represents the virtual interactions among innovators, and the offline layer, which represents the physical interactions among innovators. The model assumes that the innovation activities are influenced by both the online and offline network structures, as well as the coupling effect between them. And it simulates the entire innovation process including knowledge diffusion and knowledge recombination. The model also incorporates the concept of network density, which measures the degree of network connectivity and cohesion (network structure). Observing the network density influence on innovation efficiency during the innovation process is realized through setting the selection mechanism and the knowledge recombination mechanism. The coupling relationship between the two layers of network density on the three stages of innovation is further discussed under the theoretical framework of the innovation value chain. Simulation and experimental results suggest that when the offline network density is constant, a higher online network density is not always better. When the online network density is low, the sparse structure of the online network reduces innovation efficiency. When the online network density is high, the structural redundancy caused by the tight network structure prevents innovation efficiency from improving. The results of the study help enterprises to adjust and optimize the internal cooperation network structure at different stages of innovation in order to maximize its effectiveness and improve the innovation efficiency of enterprises.

Research on spatial-temporal heterogeneity of driving factors of green innovation efficiency in Yangtze River Delta urban agglomeration—empirical test based on the Geographically Weighted Regression model

Firstly, based on the data of 40 cities in the Yangtze River Delta from 2010 to 2019, the paper uses the Unexpected Super-SBM model to estimate the green innovation efficiency of each city. On this basis, the paper establishes a Geographically Weighted Regression model to explore the spatial-temporal heterogeneity of the effects of influencing factors on green innovation efficiency. The findings indicate that: The direction and strength of each influencing element on the green innovation efficiency of the Yangtze River Delta cities are varied at the urban spatial scale. The degree of opening up significantly boosts the green innovation efficiency, but the impact intensity shows spatial differences. The green innovation efficiency is promoted by government technology support, but its intensity tends to weaken. In addition, financial support has a negative impact on most cities. Over time, the impact of economic development level on urban green innovation efficiency has changed from inhibition to promotion, and the impact intensity tends to weaken. Industrial structure and environmental regulation tend to show two effects of promotion and obstruction in different cities from weak inhibition. Industrial structure and environmental regulation tend to show two effects of promotion and obstruction in different cities from weak inhibition. The research conclusion of this paper has important theoretical significance and practical value for accelerating the construction of a green, low-carbon and circular economic system in the Yangtze River Delta region, and achieving the “dual carbon” goal.

Low-carbon innovation efficiency of high-tech industries and its influencing factors

Abstract China has a vast territory, with an abundance of natural resources. Industrial investment of provinces promulgating and implementing state policies and measures on the basis of the various regions and industries are slowly widening the gap, which leads to significant differences in the level of industrial development. The imbalance between development at both provincial and municipal levels has become more prominent. The state has formulated a policy of coordinated development of the regional economy. There are differences between regions and industries in the demand for technological innovation of high-tech industry. This article is from the regional perspective, industry perspective and the perspective of the two. This paper, through the research of technology innovation and output data of China’s 31 provinces, municipalities and autonomous regions, finds the delivery rate is an independent variable of the average number of employees, R&D funds, R&D internal expenditure, external spending, new product sales income, the absorption of funding expenditure, technical transformation of funds for fixed assets, and gross profit as a technological low-carbon innovation efficiency measure. Using factor analysis, multiple regression methods and SPSS software, from the perspective of two regions and industries, enables empirical analysis of the influencing factors of this variable.

The inverted U relationship between industrial intelligence and green innovation efficiency: evidence from China

ABSTRACT Whether industrial intelligence helps to enhance green innovation efficiency has important implications for environmental governance. Based on panel data from 30 Chinese provinces from 2006 to 2019, we employ the SBM model with undesirable outputs and a two-way fixed effects model to examine the impact of industrial intelligence on green innovation efficiency. The research mainly finds that: (1) An inverted U relationship exists between industrial intelligence and green innovation efficiency. (2) Energy efficiency is mediating in this inverted U relationship. (3) The relationship between industrial intelligence and green innovation efficiency is affected by several regional factors, such as geographic location, innovation capacity, and environmental regulation. Specifically, the inverted U relationship flattens out in the eastern region, as well as in regions with high environmental regulation and innovation capacity. This paper provides practical insights and suggests that policymakers seeking to enhance green innovation efficiency should apply industrial intelligence in moderation.

Research on the evolution and determinants of knowledge transfer network in patent-intensive industries

PurposeThis study aims to investigate the structural characteristics, spatial evolution paths and internal driving mechanisms of the knowledge transfer (KT) network in China’s patent-intensive industries (PIIs). The authors' goal is to provide valuable insights to inform policy-making that fosters the development of relevant industries. The authors also aim to offer a fresh perspective for future spatiotemporal studies on industrial KT and innovation networks.Design/methodology/approachIn this study, the authors analyze the patent transfer (PT) data of listed companies in China’s information and communication technology (ICT) industry, spanning from 2010 to 2021. The authors use social network analysis and the quadratic assignment procedure (QAP) method to explore the problem of China’s PIIs KT from the perspectives of technical characteristics evolution, network and spatial evolution and internal driving mechanisms.FindingsThe results indicate that the knowledge fields involved in the PT of China’s ICT industry primarily focus on digital information transmission technology. From 2010 to 2021, the scale of the ICT industry’s KT network expanded rapidly. However, the polarization of industrial knowledge distribution is becoming more serious. QAP regression analysis shows that economic proximity and geographical proximity do not affect KT activities. The similarity of knowledge application capacity, innovation capacity and technology demand categories in various regions has a certain degree of impact on KT in the ICT industry.Originality/valueThe current research on PIIs mainly focuses on measuring economic contributions and innovation efficiency, but less on KT in PIIs. This study explores KT in PIIs from the perspectives of technological characteristics, network and spatial evolution. The authors propose a theoretical framework to understand the internal driving mechanisms of industrial KT networks.

Achieving dynamic efficiency in pharmaceutical innovation: Identifying the optimal share of value and payments required.

It has been argued that cost-effectiveness analysis of branded pharmaceuticals only considers static efficiency, neglects dynamic effects and undermines incentives for socially valuable innovation. We present a framework for designing pharmaceutical pricing policy to achieve dynamic efficiency. We develop a coherent framework that identifies the long-term static and dynamic benefits and costs of offering manufacturers different levels of reward. The share of value that would maximise long-term population health depends on how the quantity and quality of innovation responds to payment. Using evidence of the response of innovation to payment, the optimal share of value of new pharmaceuticals to offer to manufacturers is roughly 20% (range: 6%-51%). Reanalysis of a sample of NICE technology appraisals suggests that, in most cases, the share of value offered to manufacturers and the price premium paid by the English NHS were too high. In the UK, application of optimal shares would offer considerable benefits under both a public health objective and a broader view of social welfare. We illustrate how an optimal share of value can be delivered through a range of payment mechanisms including indirect price regulation via the use of different approval norms by an HTA body.

Environmental regulation and green innovation efficiency: a revisit of the porter hypothesis based on Chinese cities

Environmental regulation and green innovation efficiency: a revisit of the porter hypothesis based on Chinese cities

Examining the Interplay Between Innovation Index, Innovation Efficiency and Sustainability Index: A Cross-Group Analysis of G7 and E7 Countries

Purpose: The main purpose of this study is to examine the innovation, sustainability and innovation efficiencies of G7 and E7 countries and to address the relationship between them. The investigation focuses on exploring the potential impact of innovation productivity on the sustainability index in both developed and emerging economies, along with examining potential underlying factors influencing this relationship.
 Methodology: The research employs Data Envelopment Analysis (DEA) to assess the innovation efficiency. This analysis considers two innovation outputs set against five inputs. Statistical methods such as t-test and correlation analysis are also used to investigate the interplay between innovation efficiency and the sustainability.
 Findings: The findings of this study reveal interesting results. First, no significant difference is observed regarding innovation efficiency between G7 and E7 groups. For the G7 countries, there is a positive correlation between the sustainability index and innovation efficiency, suggesting that more efficient innovation is associated with sustainability. Contrarily, there is a negative correlation between these indices in E7 countries, implying that effective innovation can lower the sustainability index.
 Originality: This study contributes novel insights into the relationship between innovation and sustainability by considering the G7 and E7 countries. Although various countries are evaluated in the literature, no comparison has been made for these two groups. Also, the identification of opposing correlations between two indices in developed and emerging economies constitutes a significant contribution to the literature.

Innovation for Economic Growth: G7 vs E7

Purpose: The main objective of this research is to examine the impact of R&D expenditures and Global Innovation Index ranking on per capita income in E7 and G7 country groups.
 Methodology: Logistic regression model was used as the research method in the study. Stata 18 Data Analysis and Statistical Software Program was used in the analysis of the data. At the hand of Stata 18 Data Analysis and Statistical Software Program, regression analysis was used to estimate the possible and unknown effects of independent variables on the dependent variable. During the collection of the data used in the study, the archive scanning method, which is one of the qualitative research methods, was used. Archival reports and official records were also used in the study. 
 Findings: According to the research results, 83.56% of the model is explained by explanatory variables. With all other explanatory variables constant, a 1% increase in R&D expenditure will result in an increase of 0.5243% on GDP per capita. At the same time, this coefficient gives the flexibility of GDP per capita relative to R&D expenditure (%GDP). It is also found out that there is a positive relationship between GDP per capita and R&D expenditure and also, there is a negative relationship between GDP per capita-GII ranking. In this study, which deals with the innovation efficiency of the G7 countries and the E7 countries, and the effect of this performance on the GDP per capita, it is seen that the G7 countries spend more on innovation.
 Originality: It contributes to the literature as there is no other study in the literature that deals with per capita income, Global Innovation Index ranking and R&D expenditures comparatively between the G7 and E7.

The impact of intellectual property protection on the performance of fossil fuel extraction and production companies in developing countries

This paper seeks to evaluate how intellectual property (IP) influences the sustainable performance of Chinese companies involved in fossil fuel exploration and production. The study meticulously delineates specific variables aimed at advancing sustainability through strategic IP measures. Utilizing data spanning from 2010 to 2021 and employing the Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) approach, we reveal a nuanced relationship between innovation efficiency and energy costs. A mere 1% improvement in innovation efficiency corresponds to a 0.6% short-term and 0.4% long-term reduction in energy costs. Larger workforces contribute to increased energy consumption, investments drive energy-intensive processes, and profitability accelerates production, amplifying energy demands. Additionally, we uncover the adverse impact of Information and Communication Technology (ICT) expenses on energy costs, emphasizing the transformative potential of digitalization. To enhance sustainability, we recommend strengthening IP strategies, providing incentives for the commercialization of green patents, leveraging green financing, and adopting Environmental, Social, and Governance (ESG) principles to attract ethical investors. Equipping a skilled workforce through training programs facilitates the integration of eco-friendly technologies with intellectual property strategies.

The impact of digital infrastructure on provincial green innovation efficiency-empirical evidence from China.

In the epoch of the digital economy, technological innovation and energy conservation are significantly facilitated by digital infrastructure, leading to substantial improvements in green innovation efficiency at the provincial level. This study employed the feasible generalized least square (FGLS) method to examine the effects of digital infrastructure on the green innovation efficiency across 30 provinces in the Chinese mainland, utilizing panel data from 2011 to 2020. Additionally, this investigation delves into the intervening role of industrial structure upgrading and the amplifying effects of environmental regulation and human capital on the process. Findings indicate that, to begin with, digital infrastructure contributes to the meaningful enhancement of green innovation efficiency within provinces. Subsequently, the industrial structure upgrading partially mediates the impact of digital infrastructure on the efficiency of provincial green innovation. Lastly, both human capital and environmental regulations amplify the beneficial influence of digital infrastructure on the effectiveness of green innovation at the provincial level. This study provides valuable insights into the mechanisms through which digital infrastructure boosts green innovation efficiency, aiding policymakers in formulating appropriate policies to augment digital infrastructure, thereby promoting provincial green innovation efficiency.