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Using the data mining method to assess the innovation gap: A case of industrial robotics in a catching-up country

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  • Kong, Dejing
  • Zhou, Yuan
  • Liu, Yufei
  • Xue, Lan

Abstract

It is critical for “catching-up” countries to narrow innovation gaps with developed countries by developing emerging industries. This research introduces a data-mining based method to systematically assess the national innovation gap that is specifically for emerging industries. The method examines the five key attributes of emerging industries, including the ownership of platform technologies, globalization intention, international knowledge position, university-industry linkage, and cross-disciplinary technology development. In particular, this method combines data-mining with experts' knowledge to build patent-training examples, and then uses a support vector machine-based classifier to single out all high-quality patents for each innovation attribute. Based on the selected high-quality patents, the authors utilize a factorial design analysis to systematically evaluate the innovation gap between countries. This method can significantly reduce measurement bias of traditional single patent indicators. In addition, it also can robustly adjust measuring weights in response to the specifics of each innovation attribute, while traditional multi-attribute evaluation methods cannot. As a result, this research empirically shows that China' industrial robot sector has apparent innovation gaps compared to developed economies, specifically in university-industry linkage, cross-disciplinary competence, and globalization intention, and this calls for the attention of policy makers and industrial experts.

Suggested Citation

  • Kong, Dejing & Zhou, Yuan & Liu, Yufei & Xue, Lan, 2017. "Using the data mining method to assess the innovation gap: A case of industrial robotics in a catching-up country," Technological Forecasting and Social Change, Elsevier, vol. 119(C), pages 80-97.
    • Handle: RePEc:eee:tefoso:v:119:y:2017:i:c:p:80-97
    DOI: 10.1016/j.techfore.2017.02.035
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    Cited by:

    1. Hu, Feng & Xi, Xun & Zhang, Yueyue, 2021. "Influencing mechanism of reverse knowledge spillover on investment enterprises’ technological progress: An empirical examination of Chinese firms," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    2. Yuan Zhou & Fang Dong & Yufei Liu & Liang Ran, 2021. "A deep learning framework to early identify emerging technologies in large-scale outlier patents: an empirical study of CNC machine tool," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 969-994, February.
    3. Huanyong Ji & Guannan Xu & Yuan Zhou & Zhongzhen Miao, 2019. "The Impact of Corporate Social Responsibility on Firms’ Innovation in China: The Role of Institutional Support," Sustainability, MDPI, vol. 11(22), pages 1-20, November.
    4. Huailan Liu & Zhiwang Chen & Jie Tang & Yuan Zhou & Sheng Liu, 2020. "Mapping the technology evolution path: a novel model for dynamic topic detection and tracking," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2043-2090, December.
    5. Dejing Kong & Jianzhong Yang & Lingfeng Li, 2020. "Early identification of technological convergence in numerical control machine tool: a deep learning approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 1983-2009, December.
    6. 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).
    7. Ruxu Sheng & Rong Zhou & Ying Zhang & Zidi Wang, 2021. "Green Investment Changes in China: A Shift-Share Analysis," IJERPH, MDPI, vol. 18(12), pages 1-15, June.
    8. Hongyu Li & Zhiqiang Lu & Qili Yin, 2023. "The Development of Fintech and SME Innovation: Empirical Evidence from China," Sustainability, MDPI, vol. 15(3), pages 1-14, January.
    9. Zhou, Yuan & Dong, Fang & Kong, Dejing & Liu, Yufei, 2019. "Unfolding the convergence process of scientific knowledge for the early identification of emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 205-220.
    10. Abdol Majid Saadat Nezhad & Tahmoures Sohrabi & Nasrollah Shadnoosh & Abbas Toloie Eshlaghy, 2017. "A New Approach to Challenges of Venture Capital in Financing the Industrial Clusters through Cooperative Models and Venture Funds in Iran," International Journal of Economics and Financial Issues, Econjournals, vol. 7(6), pages 111-119.
    11. Caselli, Mauro & Fracasso, Andrea & Traverso, Silvio, 2021. "Robots and risk of COVID-19 workplace contagion: Evidence from Italy," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    12. Richarz, Jan & Wegewitz, Stephan & Henn, Sarah & Müller, Dirk, 2023. "Graph-based research field analysis by the use of natural language processing: An overview of German energy research," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    13. Yogesh K. Dwivedi & A. Sharma & Nripendra P. Rana & M. Giannakis & P. Goel & Vincent Dutot, 2023. "Evolution of Artificial Intelligence Research in Technological Forecasting and Social Change: Research Topics, Trends, and Future Directions," Post-Print hal-04292607, HAL.
    14. Xu, Guannan & Wu, Yuchen & Minshall, Tim & Zhou, Yuan, 2018. "Exploring innovation ecosystems across science, technology, and business: A case of 3D printing in China," Technological Forecasting and Social Change, Elsevier, vol. 136(C), pages 208-221.
    15. Jihong Chen & Kai Zhang & Yuan Zhou & Yufei Liu & Lingfeng Li & Zheng Chen & Li Yin, 2019. "Exploring the Development of Research, Technology and Business of Machine Tool Domain in New-Generation Information Technology Environment Based on Machine Learning," Sustainability, MDPI, vol. 11(12), pages 1-38, June.
    16. Piotr Tomasz Makowski & Yuya Kajikawa, 2021. "Automation-driven innovation management? Toward Innovation-Automation-Strategy cycle," Papers 2103.02395, arXiv.org.
    17. Ruxu Sheng & Juntian Du & Songqi Liu & Changan Wang & Zidi Wang & Xiaoqian Liu, 2021. "Solar Photovoltaic Investment Changes across China Regions Using a Spatial Shift-Share Analysis," Energies, MDPI, vol. 14(19), pages 1-14, October.
    18. Yuan Zhou & Fang Dong & Yufei Liu & Zhaofu Li & JunFei Du & Li Zhang, 2020. "Forecasting emerging technologies using data augmentation and deep learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 1-29, April.
    19. Guannan Xu & Weijie Hu & Yuanyuan Qiao & Yuan Zhou, 2020. "Mapping an innovation ecosystem using network clustering and community identification: a multi-layered framework," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 2057-2081, September.
    20. Yawei Wang & Frauke Urban & Yuan Zhou & Luyi Chen, 2018. "Comparing the Technology Trajectories of Solar PV and Solar Water Heaters in China: Using a Patent Lens," Sustainability, MDPI, vol. 10(11), pages 1-29, November.
    21. Zi Ye & Chen Zou & Yongchun Huang, 2022. "Impact of Heterogeneous Spatial Structure on Regional Innovation—From the Perspectives of Efficiency and Gap," Sustainability, MDPI, vol. 14(19), pages 1-22, September.
    22. Xu, Shuo & Hao, Liyuan & Yang, Guancan & Lu, Kun & An, Xin, 2021. "A topic models based framework for detecting and forecasting emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    23. Makowski, Piotr Tomasz & Kajikawa, Yuya, 2021. "Automation-driven innovation management? Toward Innovation-Automation-Strategy cycle," Technological Forecasting and Social Change, Elsevier, vol. 168(C).

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