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The Impact of R&D Intensity on the Innovation Performance of Artificial Intelligence Enterprises-Based on the Moderating Effect of Patent Portfolio

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  • Yuanyuan Dong

    (School of Economics and Management, Harbin University of Science and Technology, Harbin 150001, China)

  • Zepeng Wei

    (School of Economics and Management, Harbin University of Science and Technology, Harbin 150001, China)

  • Tiansen Liu

    (School of Economics and Management, Harbin Engineering University, Harbin 150001, China
    School of Social and Behavioral Sciences, Nanjing University, Nanjing 210023, China
    State Key Laboratory of Pollution Control and Resource Reuse, Nanjing University, Nanjing 210023, China)

  • Xinpeng Xing

    (School of Business, Jiangnan University, Wuxi 214122, China)

Abstract

The patent portfolio affects the research and development (R&D) decisions of artificial intelligence enterprises, and provides rights protection for the enterprise’s product market, which is of great practical significance for the realization of innovation performance. The aim of this paper is to discover how the patent portfolio of artificial intelligence enterprises affects the relationship between R&D intensity and innovation performance. Based on the panel data of 164 listed enterprises in the A-share artificial intelligence concept sector of China, using the panel fixed effect regression method, the impact of R&D intensity on innovation performance was analyzed, and the moderating effect of the three dimensions of the patent portfolio on the two was examined. Studies have shown that the impact of R&D intensity on innovation performance is in an inverted U-shaped relationship. In addition, the diversity characteristics of the patent portfolio have a moderating effect on the relationship between R&D intensity and innovation performance, and when the enterprise is at a high level of diversity, the two have a U-shaped flip relationship. The size of the patent portfolio has a positive impact on innovation performance. The research results have theoretical and practical significance for the implementation of effective R&D management in artificial intelligence enterprise organizations.

Suggested Citation

  • Yuanyuan Dong & Zepeng Wei & Tiansen Liu & Xinpeng Xing, 2020. "The Impact of R&D Intensity on the Innovation Performance of Artificial Intelligence Enterprises-Based on the Moderating Effect of Patent Portfolio," Sustainability, MDPI, vol. 13(1), pages 1-17, December.
    • Handle: RePEc:gam:jsusta:v:13:y:2020:i:1:p:328-:d:473351
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    Cited by:

    1. Chunlian Chen & Babar Nawaz Abbasi & Ali Sohail, 2022. "Scientific Research of Innovation Ability of Universities in the United States of America and China," Sustainability, MDPI, vol. 14(21), pages 1-22, November.
    2. Xiaoli Li & Kun Li & Hao Zhou, 2022. "Impact of Inventor’s Cooperation Network on Ambidextrous Innovation in Chinese AI Enterprises," Sustainability, MDPI, vol. 14(16), pages 1-21, August.
    3. repec:osf:socarx:nftv3_v1 is not listed on IDEAS
    4. Leogrande, Angelo & Drago, Carlo & Mallardi, Giulio & Costantiello, Alberto & Magaletti, Nicola, 2024. "Patenting Propensity in Italy: A Machine Learning Approach to Regional Clustering," MPRA Paper 123081, University Library of Munich, Germany.
    5. Abderrahim Laachach, 2024. "Comparing Radical and Incremental Innovation in Venture Capital Syndication: Exploring the Influence of R&D Intensity and Sector-Specific Effects," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(4), pages 15550-15576, December.
    6. Panta Maria & Xygkogianni Maria, 2024. "Innovation in European SMEs: Exploring the Influence of Investments, Funding, Knowledge, and Intellectual Assets," Business & Entrepreneurship Journal, SCIENPRESS Ltd, vol. 13(2), pages 1-1.

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