IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i4p1379-d1334584.html
   My bibliography  Save this article

Research on the Evaluation of Regional Scientific and Technological Innovation Capabilities Driven by Big Data

Author

Listed:
  • Kun Liang

    (School of Business, Anhui University, Hefei 230601, China)

  • Peng Wu

    (School of Business, Anhui University, Hefei 230601, China)

  • Rui Zhang

    (School of Business, Anhui University, Hefei 230601, China)

Abstract

Scientific and technological innovation (STI) is an important internal driver of social and economic development. Reasonable evaluation of regional scientific and technological innovation (RSTI) capability helps discover shortcomings in the development of urban development and guides the allocation of scientific and technological resources and the formulation of policies to promote innovation. This paper analyzes new opportunities created by big data and artificial intelligence for the evaluation of RSTI capability, and based on this analysis, the collaborative evaluation schemes of multi-entity participation are investigated. In addition, considering the important value of unstructured data in evaluating STI, the Latent Dirichlet Allocation (LDA) topic model and sentiment analysis method are employed to analyze the construction of an evaluation indicator system that integrates scientific and technological news data. To fully utilize the respective advantages of human experts and machine learning in the field of complex issue evaluation, this paper proposes an RSTI capability evaluation model based on AHP-SMO human-machine fusion. This study promotes the integration of science and technology and economy and has theoretical and practical significance.

Suggested Citation

  • Kun Liang & Peng Wu & Rui Zhang, 2024. "Research on the Evaluation of Regional Scientific and Technological Innovation Capabilities Driven by Big Data," Sustainability, MDPI, vol. 16(4), pages 1-22, February.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:4:p:1379-:d:1334584
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/4/1379/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/4/1379/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chen Gong & Jian Liu & Jinping Chang, 2021. "Evolutionary Game Analysis of the Innovation Behavior of High-Tech Enterprises with Government Participation," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-15, September.
    2. Jianshi Wang & Yu Cheng & Chengxin Wang, 2022. "Environmental Regulation, Scientific and Technological Innovation, and Industrial Structure Upgrading in the Yellow River Basin, China," IJERPH, MDPI, vol. 19(24), pages 1-17, December.
    3. Zhang, Jun & Fu, Xiaoming & Morris, Harry, 2019. "Construction of indicator system of regional economic system impact factors based on fractional differential equations," Chaos, Solitons & Fractals, Elsevier, vol. 128(C), pages 25-33.
    4. Yang, Haochang & Li, Lianshui & Liu, Yaobin, 2022. "The effect of manufacturing intelligence on green innovation performance in China," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    5. Alonso Rodríguez-Navarro & Ricardo Brito, 2022. "The link between countries’ economic and scientific wealth has a complex dependence on technological activity and research policy," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2871-2896, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Genghua Tang & Hongxun Mai, 2022. "How Does Manufacturing Intelligentization Influence Innovation in China from a Nonlinear Perspective and Economic Servitization Background?," Sustainability, MDPI, vol. 14(21), pages 1-16, October.
    2. Wang, Ke-Liang & Sun, Ting-Ting & Xu, Ru-Yu & Miao, Zhuang & Cheng, Yun-He, 2022. "How does internet development promote urban green innovation efficiency? Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    3. Decai Tang & Jing Yan & Xin Sheng & Yuehao Hai & Valentina Boamah, 2023. "Research on Green Finance, Technological Innovation, and Industrial Structure Upgrading in the Yangtze River Economic Belt," Sustainability, MDPI, vol. 15(18), pages 1-17, September.
    4. Xiaojie Wang & Rongqing Han & Minghua Zhao, 2023. "Evaluation and Impact Mechanism of High-Quality Development in China’s Coastal Provinces," IJERPH, MDPI, vol. 20(2), pages 1-24, January.
    5. Pan, Xiongfeng & Wang, Mengyang & Li, Mengna, 2023. "Low-carbon policy and industrial structure upgrading: Based on the perspective of strategic interaction among local governments," Energy Policy, Elsevier, vol. 183(C).
    6. Hao Zhang & Xin Sun & Kailong Dong & Lianghui Sui & Min Wang & Qiong Hong, 2022. "Green Innovation in Regional Logistics: Level Evaluation and Spatial Analysis," IJERPH, MDPI, vol. 20(1), pages 1-20, December.
    7. Luo, Yusen & Lu, Zhengnan & Wu, Chao, 2023. "Can internet development accelerate the green innovation efficiency convergence: Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    8. Xiaozhong Li & Jun Ling, 2023. "The Impact of Manufacturing Intelligence on Green Development Efficiency: A Study Based on Chinese Data," Sustainability, MDPI, vol. 15(9), pages 1-19, May.
    9. Shuping Cheng & Lingjie Meng & Weizhong Wang, 2022. "The Impact of Environmental Regulation on Green Energy Technology Innovation—Evidence from China," Sustainability, MDPI, vol. 14(14), pages 1-23, July.
    10. Ying She & Yangu Deng & Meiling Chen, 2023. "From Takeoff to Touchdown: A Decade’s Review of Carbon Emissions from Civil Aviation in China’s Expanding Megacities," Sustainability, MDPI, vol. 15(24), pages 1-24, December.
    11. Lee, Chien-Chiang & Qin, Shuai & Li, Yaya, 2022. "Does industrial robot application promote green technology innovation in the manufacturing industry?," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    12. Wang, Qian & Ren, Shuming, 2022. "Evaluation of green technology innovation efficiency in a regional context: A dynamic network slacks-based measuring approach," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    13. Mehmet Pinar, 2023. "Do research performances of universities and disciplines in England converge or diverge? An assessment of the progress between research excellence frameworks in 2014 and 2021," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(10), pages 5731-5766, October.
    14. Lipeng Sun & Nur Ashikin Mohd Saat, 2023. "How Does Intelligent Manufacturing Affect the ESG Performance of Manufacturing Firms? Evidence from China," Sustainability, MDPI, vol. 15(4), pages 1-20, February.
    15. Yu Wang & Lin Zhang, 2023. "The Impact of Technology Innovation on Urban Land Intensive Use in China: Evidence from 284 Cities in China," Sustainability, MDPI, vol. 15(4), pages 1-23, February.
    16. Tingting Li & Dan Zhao & Guiyun Liu & Yuhong Wang, 2022. "How to Evaluate College Students’ Green Innovation Ability—A Method Combining BWM and Modified Fuzzy TOPSIS," Sustainability, MDPI, vol. 14(16), pages 1-17, August.
    17. Ding, Tao & Li, Jiangyuan & Shi, Xing & Li, Xuhui & Chen, Ya, 2023. "Is artificial intelligence associated with carbon emissions reduction? Case of China," Resources Policy, Elsevier, vol. 85(PB).
    18. Yiwei Wang & Ningze Yang, 2023. "Differences in High-Quality Development and Its Influencing Factors between Yellow River Basin and Yangtze River Economic Belt," Land, MDPI, vol. 12(7), pages 1-19, July.
    19. Liu, Jingling & Chen, Yanying & Liang, Feng Helen, 2023. "The effects of digital economy on breakthrough innovations: Evidence from Chinese listed companies," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    20. Haochang Yang & Xuan Zhu, 2022. "Research on Green Innovation Performance of Manufacturing Industry and Its Improvement Path in China," Sustainability, MDPI, vol. 14(13), pages 1-21, June.

    More about this item

    Keywords

    RSTI; big data; LDA; AHP-SMO;
    All these keywords.

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:4:p:1379-:d:1334584. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.