The moderating effect of absorptive capacity on transnational knowledge spillover and the innovation quality of high-tech industries in host countries: Evidence from the Chinese manufacturing industry

Duan, Yunlong; Liu, Shilin; Cheng, Hao; Chin, Tachia; Luo, Xuan

doi:10.1016/j.ijpe.2020.108019

Cited by 55 publications

(47 citation statements)

References 114 publications

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“…The novelty, market value, and technological value of green invention patents are higher than that of green utility patents. The examination and approval of green invention patents have more stringent standards for novelty and technological content (Duan et al, 2021). Therefore, compared with green utility patents, the quality of green invention patents is higher (Hu et al, 2020).…”

Section: According To the International Patent Classification (Ipc) G...mentioning

confidence: 99%

Economic policy, regulatory policy, or soft policy: Which category of policy can effectively improve the green innovation of Chinese wind power industry?

2022

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Although wind power is clean in general, its potential adverse impacts on ecology and environment still cannot be ignored. To improve green innovation of wind power industry, the Chinese government has issued a series of policies. With the microdata from listed Chinese wind power enterprises from 2006 to 2019, this study examines the impacts of policies on green innovation. The results indicate that economic policy, regulatory policy, and soft policy significantly improve the green innovation. Unfortunately, the incentive effect only scratches the surface. The further analysis suggests that all categories of policies contribute to the low‐quality green innovation only.

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Section: According To the International Patent Classification (Ipc) G...mentioning

confidence: 99%

Economic policy, regulatory policy, or soft policy: Which category of policy can effectively improve the green innovation of Chinese wind power industry?

2022

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“…To understand variation in the performance of innovating subjects in terms of the reciprocal behaviors of subgroups within their networks, scholars have focused on knowledge flow ( Duan et al, 2021a , 2022 ), resource sharing ( Lacerda and van den Bergh, 2020 ) and value creation ( Chin et al, 2021 ) among other variables. However, such research has tended to overlook the importance of heterogeneous reciprocity behaviors that are rooted in psychological factors.…”

Section: Introductionmentioning

confidence: 99%

Psychological Distance Impacts Subgroup Reciprocity in Technological Innovation Networks: The Mediating Role of Divisive Faultlines

Deng

Gao

et al. 2022

Front. Psychol.

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As information flows at ever-increasing speeds across technological innovation networks, it is crucial to optimize reciprocity among partnering enterprises. However, the impact of psychological distance on subgroup reciprocity in such networks has not yet been investigated. To address this gap, the current study drew on theories of faultlines and cohesive subgroups to model the relationship between psychological distance and subgroup reciprocity within technological innovation networks. Our hypotheses were tested using data from 174 respondents working in Yunnan Province, China. The results were as follows: first, psychological distance had negative effects on subgroup reciprocity in technological innovation networks; second, relationship-divisive and innovation-divisive faultlines negatively impacted reciprocity within and between subgroups; third, the faultlines partially mediated the negative relationship between psychological distance and intra-subgroup reciprocity; and fourth, the negative relationship between psychological distance and subgroup reciprocity was not mediated by the faultlines. The findings uncover the psychological mechanism of subgroup reciprocity within technological innovation networks. They will inform the decision-making process of enterprises when selecting partners within their technological innovation networks and support the development of effective reciprocal relationships with other innovators.

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“…After the financial crisis, developed countries implemented the reindustrialization policy to attract the return of high-end manufacturing industries, leading to profound changes in global manufacturing (Dong et al, 2020). They have formulated and implemented relevant strategic policies to vigorously promote the development of high-tech industries and seize the manufacturing high point of future industrial competition (Duan et al, 2021). This poses new challenges to the development of China's manufacturing industry.…”

Section: Introductionmentioning

confidence: 99%

The driving effect of technological innovation on green development: dynamic efficiency spatial variation

Cheng

Wen

Yang

2022

Environ Sci Pollut Res

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High-tech industry has become an important position for international competition due to its strong technological innovation capabilities. Green development of the manufacturing industry is an important measure to respond to the profound changes in the global manufacturing industry, and technological innovation is the core force to drive the green development of the manufacturing industry. However, DEA model usually employed to measure the efficiencies of the innovation and green development, the internal transmission mechanism has not been thoroughly explored. In order to open the black box, this paper utilize the high-tech industry data to measure the efficiency of technological innovation process by using network Slack Based Measure (NSBM), Global Malmquist Luenberger index model (GML) is employed to evaluate and decompose the green development efficiency of high-tech industry, and the influencing mechanism between the efficiency of technological innovation and the efficiency of green development is explored by the spatial Dubin model. The results indicate that green transformation of the manufacturing industry has an obvious spatial linkage effect. In addition, technological innovation efficiency is conducive to the green development efficiency, while economic transformation efficiency and urbanization are not conducive to the green development efficiency.

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The moderating effect of absorptive capacity on transnational knowledge spillover and the innovation quality of high-tech industries in host countries: Evidence from the Chinese manufacturing industry

Cited by 55 publications

References 114 publications

Economic policy, regulatory policy, or soft policy: Which category of policy can effectively improve the green innovation of Chinese wind power industry?

Economic policy, regulatory policy, or soft policy: Which category of policy can effectively improve the green innovation of Chinese wind power industry?

Psychological Distance Impacts Subgroup Reciprocity in Technological Innovation Networks: The Mediating Role of Divisive Faultlines

The driving effect of technological innovation on green development: dynamic efficiency spatial variation

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