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The Influencing Factors, Regional Difference and Temporal Variation of Industrial Technology Innovation: Evidence with the FOA-GRNN Model

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  • Yongli Zhang

    (School of Business Administration, Wonkwang University, 460 Iksandae-ro, Iksan 54538, Jeonbuk, Korea
    School of Management Science and Engineering, Hebei GEO University, Shijiazhuang 050031, China)

  • Sanggyun Na

    (School of Business Administration, Wonkwang University, 460 Iksandae-ro, Iksan 54538, Jeonbuk, Korea)

  • Jianguang Niu

    (School of Business Administration, Wonkwang University, 460 Iksandae-ro, Iksan 54538, Jeonbuk, Korea
    School of Management Science and Engineering, Hebei GEO University, Shijiazhuang 050031, China)

  • Beichen Jiang

    (School of Business Administration, Wonkwang University, 460 Iksandae-ro, Iksan 54538, Jeonbuk, Korea
    School of Management Science and Engineering, Hebei GEO University, Shijiazhuang 050031, China)

Abstract

Technology innovation is a motivating force for sustainable development. The recognition and measurement of influencing factors are a basic prerequisite of technology innovation research. In response to the gaps and shortages of existing theories and methods, this paper builds the impact indicators of technology innovation, the proposed FOA-GRNN model, and analyzes the influencing factors, regional differences and temporal variations of technology innovation based on industrial above-scale enterprises of 31 provinces in China from 2008 to 2015. The empirical results show that innovation investment is a determinant of technology innovation in China, and is more and more significant; meanwhile a wide gap of innovation resource between Eastern China and Western China exists. In general, the enterprise scale has a negative effect: with enlargement of enterprise in China, the innovation efficiency of enterprise will decline, while the effect has regional disparity, with positive influence in Central and Western China, and negative influence in Eastern China. Government support has negative effects on technology innovation: indirect equity investment contributes more to technology innovation than direct fund support. Innovation environment has positive and weak effects on technology innovation, but it is the biggest obstacle in Western China, and the innovation environment in China has improved continuously. This paper provides new evidence that can shine some light on determining the factors affecting technology innovation, and also presents a novel approach, which comprises characteristics of nonlinear function approximation, high accuracy and a small sample.

Suggested Citation

  • Yongli Zhang & Sanggyun Na & Jianguang Niu & Beichen Jiang, 2018. "The Influencing Factors, Regional Difference and Temporal Variation of Industrial Technology Innovation: Evidence with the FOA-GRNN Model," Sustainability, MDPI, vol. 10(1), pages 1-19, January.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:1:p:187-:d:126983
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    References listed on IDEAS

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    2. Tsui-Yii Shih, 2018. "Determinants of Enterprises Radical Innovation and Performance: Insights into Strategic Orientation of Cultural and Creative Enterprises," Sustainability, MDPI, vol. 10(6), pages 1-22, June.
    3. Bahoo, Salman & Cucculelli, Marco & Qamar, Dawood, 2023. "Artificial intelligence and corporate innovation: A review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
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    5. Hongchen Li & Huijun Qi & Hongjian Cao & Li Yuan, 2022. "Industrial Policy and Technological Innovation of New Energy Vehicle Industry in China," Energies, MDPI, vol. 15(24), pages 1-17, December.
    6. Xiangdong Chen & Ruixi Li & Xin Niu & Ulrich Hilpert & Valerie Hunstock, 2018. "Metropolitan Innovation and Sustainability in China—A Double Lens Perspective on Regional Development," Sustainability, MDPI, vol. 10(2), pages 1-26, February.
    7. Yue-Gang Song & Yu-Long Zhou & Ren-Jie Han, 2018. "Neural networks for stock price prediction," Papers 1805.11317, arXiv.org.
    8. Chengliang Liu & Qingbin Guo, 2019. "Technology Spillover Effect in China: The Spatiotemporal Evolution and Its Drivers," Sustainability, MDPI, vol. 11(6), pages 1-14, March.
    9. Jiangfeng Hu & Zhao Wang & Qinghua Huang & Xiaoqin Zhang, 2019. "Environmental Regulation Intensity, Foreign Direct Investment, and Green Technology Spillover—An Empirical Study," Sustainability, MDPI, vol. 11(10), pages 1-15, May.
    10. Liang, Yi & Niu, Dongxiao & Hong, Wei-Chiang, 2019. "Short term load forecasting based on feature extraction and improved general regression neural network model," Energy, Elsevier, vol. 166(C), pages 653-663.

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