Innovation performance and influencing factors of low-carbon technological innovation under the global value chain: A case of Chinese manufacturing industry

Bi, Kexin; Huang, Ping; Wang, Xiangxiang

doi:10.1016/j.techfore.2016.07.024

Cited by 148 publications

(68 citation statements)

References 48 publications

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“…The NIS approach makes use of empirical material and analytical models developed in the innovation research to provide a foundation for government intervention for effective allocation of resources to foster firm innovativeness (Lundvall & Borras, 2005). A recent study by Bi et al (2016) show that government policies influence the innovation performance of low-carbon manufacturing industries, yet the effect is weak. Through innovation policies, the government plays an investor role to support firms financially in R&D activities, and encourages networking activities among firms involved in the development of innovation (Kang & Park, 2012).…”

Section: Stream 3: Contextual Determinantsmentioning

confidence: 99%

“…Finally, Jensen et al (2007) argue that innovation policies give priority to R&D activities in high-technology sectors and neglect the organisational learning and user-driven innovations and the strengthening of linkages in traditional manufacturing and service sectors. A recent study by Bi et al (2016) show that government policies influence the innovation performance of low-carbon manufacturing industries, yet the effect is weak. As a result, firms forgo the benefits of learning, by using, doing and interacting in these sectors.…”

Section: Stream 3: Contextual Determinantsmentioning

confidence: 99%

See 1 more Smart Citation

Empirical Studies on Innovation Performance in the Manufacturing and Service Sectors Since 1995: A Systematic Review

Kolluru

Mukhopadhaya

2017

J of App Econs and Policy

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This paper reviews the literature on firm innovation performance from 1995 until 2016. The empirical literature on this topic has been growing continuously over the last few decades. Previous research has investigated the explanatory variables and their impact on firm innovativeness separately. The present systematic review brings together all explanatory variables explored in the literature, classified according to their direction of causality and impact on innovation. These are represented in a comprehensive framework that includes three major research streams: internal, external and contextual indicators, and examines how they influence the innovative capabilities of firms. In addition to this re-examination, the study shows that the majority of these variables are inter-linked with other variables in explaining the relationship with innovation, which is represented in a comprehensive theoretical model. The aim of this review is to draw a general picture of the standing of the research on innovation performance, specifically in areas where unanimous results have already been achieved, and to highlight the opportunities for future research.

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Section: Stream 3: Contextual Determinantsmentioning

confidence: 99%

Section: Stream 3: Contextual Determinantsmentioning

confidence: 99%

Empirical Studies on Innovation Performance in the Manufacturing and Service Sectors Since 1995: A Systematic Review

Kolluru

Mukhopadhaya

2017

J of App Econs and Policy

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“…In order to further explore the mechanism of city centrality affecting the green innovation efficiency, this paper refers to the mediation effect test method proposed by Baron and Kenny [40], and the model is set as shown in Equations (7)- (9).…”

Section: Mediation Effect Modelmentioning

confidence: 99%

City Centrality, Migrants and Green Inovation Efficiency: Evidence from 106 Cities in the Yangtze River Economic Belt of China

Wang

Yang

Qin

2020

IJERPH

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Based on the panel data of 106 cities in the Yangtze River Economic Belt of China from 2007 to 2016, this paper explores the impact of city centrality on the green innovation efficiency and proves the mediation effect of migrants by using spatial econometric model. The results show that there are more and more innovation contacts between cities, and the innovation network is becoming more and more dense. The core cities of the downstream innovation network are mainly Yangzhou, Zhenjiang, Wuxi, Changzhou, Suzhou and Hangzhou; the core cities in the midstream are mainly Wuhan, Changsha and Yichun; the core cities in the upstream are Chengdu and Bazhong. There is an inverted U-shaped relationship between city centrality and green innovation efficiency. In addition, the influence curve of city centrality on the green innovation efficiency of surrounding cities is also inverted U-shaped. Cities with high city centrality attract a large number of migrants that come from cities with lower centrality to improve the green innovation efficiency, but the green innovation efficiency of cities with low city centrality will decline due to lack of talents. innovation increasingly implies long-term and far-reaching changes in technology, infrastructure, lifestyles and institutions. With the rapid growth of the world's population, living space has been dramatically reduced, and traditional production, manufacturing and business models largely cannot keep up with the pace of economic development. Natural resources are increasingly scarce, and environmental damage is becoming increasingly serious [3]. To reduce environmental pollution and energy consumption, countries are paying growing attention to green technology innovation to achieve the maximum economic and ecological benefits with the lowest levels of consumption and pollution [4]. Chen et al. define green innovation as "hardware or software innovation related to green products or processes, which include technological innovation related to energy conservation, pollution prevention, waste recycling and green product design [5]." Innovation is the fundamental driving force for promoting economic and social development, and green technology innovation is the key driving force for achieving low-carbon economic development and improving the efficiency of natural resources [6]. China has always placed innovation in an important position in the overall development of the country. Relevant data show that the number of R&D personnel in China increased from 304,000 in 2008 to 462,000 in 2017. Internal R&D expenditures over the same period increased from 68.79 billion yuan to 243.57 billion yuan, with an average annual growth rate of 15.08%. Bai [7], however, notes that increasing R&D is a necessary condition for building an innovative country; the focus should be not only on the amount of R&D investment but also on the efficiency of innovation, it is equally salient for developing countries with low levels of science and technology. Green innovation efficiency is a comprehensive effic...

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“…Mir, Casadesús, and Petnji (2016), whose study was an unprecedented contribution to innovation management literature, provided a novel analysis concerning the impact of a Standardized Innovation Management System (SIMS) on company innovation capability, innovation performance, and corporate results. Bi, Huang, Wang (2016) analyzed the innovation performance of low-carbon technological innovation activities under the global value chain and the influencing factors by combing the global value chain framework and linear innovation process model. Zeng, Xie, and Tam (2010) demonstrated the use of a system for the evaluation and measurement of innovation capabilities for a science park.…”

Section: Literature Reviewmentioning

confidence: 99%

Integration of Innovation Through Analytical Hierarchy Process (Ahp) in Project Management and Planning

Gunduz

Alfar

2019

Technological and Economic Development of Economy

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This paper examines the concept of innovation management in the construction industry by studying the major components of the innovation process such as the major drivers of innovation, barriers and obstacles that prevent innovation, the enablers that motivate innovation, the practices of innovation, and the benefits of innovation both at project and company level. This paper is based on the data collected by means of structured questionnaires and analyzed throughsurvey analysis. The survey, which consisted of 46 factors involved in the innovation process, was distributed to 121 different participants, and the collected data were analyzed by the relative importance index (RII), as well as the analytical hierarchy process (AHP). The RII rankings feed the AHP analysis as the first step to rank the factors. Later, the factors are ranked according to the AHP analysis. Based on these rankings, recommendations are made to contractors, consultants, project management professionals, owners and suppliers.

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Innovation performance and influencing factors of low-carbon technological innovation under the global value chain: A case of Chinese manufacturing industry

Abstract: This is a repository copy of Innovation performance and influencing factors of low-carbon technological innovation under the global value chain: A case of Chinese manufacturing industry.

Cited by 148 publications

References 48 publications

Empirical Studies on Innovation Performance in the Manufacturing and Service Sectors Since 1995: A Systematic Review

Empirical Studies on Innovation Performance in the Manufacturing and Service Sectors Since 1995: A Systematic Review

City Centrality, Migrants and Green Inovation Efficiency: Evidence from 106 Cities in the Yangtze River Economic Belt of China

Integration of Innovation Through Analytical Hierarchy Process (Ahp) in Project Management and Planning

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