Artificial Intelligence and Innovation Management: A Research Review
DOI:
https://doi.org/10.54097/5d77fs48Keywords:
Artificial Intelligence, Innovation Management, Organizational StructureAbstract
In the era of digitalization, informatization, and intelligent transformation, artificial intelligence (AI) technology is developing rapidly, exerting a profound impact on various fields of the social economy. As a key factor for enterprises and organizations to maintain competitiveness in a competitive environment, the importance of innovation management has been increasingly emphasized. As the core driving force behind the new round of scientific and technological revolution and industrial transformation, AI not only improves production efficiency and optimizes resource allocation but also creates new industrial forms and business models, injecting new momentum into economic development. This paper reviews existing research in the field of AI and innovation management, examining relevant studies from three perspectives: the transformation of innovation management models driven by AI, the reform of organizational structures driven by AI, and the ethical challenges posed by AI applications in innovation management.
Downloads
References
[1] Füller, J., Hutter, K., Wahl, J., et al. (2022). How AI revolutionizes innovation management–perceptions and implementation preferences of AI-based innovators. Technological Forecasting and Social Change, 178, 121598.
[2] Yin, X. M., Wu, P. Q., Qian, Y. T., et al. (2024). Scenario-driven digital and intelligent technology innovation empowering new-quality productive forces: Theoretical logic and practical approaches. China Soft Science, (10), 18–31.
[3] An, T. L., Wu, Z. Y. (2025). A stratified measurement of corporate AI technology levels and their innovation effects: A global knowledge network perspective. China Industrial Economics, (06), 81–100.
[4] Ren, Z. G., Li, J., Zhao, H. C., et al. (2023). Big data-driven management decision-making: Research paradigms and development areas. Journal of Management Science, 26(08), 152–158.
[5] Agrawal, A., McHale, J., & Oettl, A. (2023). Superhuman science: How artificial intelligence may impact innovation. Journal of Evolutionary Economics, 33(5), 1473–1517.
[6] Jackson, I., Ivanov, D., Dolgui, A., et al. (2024). Generative artificial intelligence in supply chain and operations management: A capability-based framework for analysis and implementation. International Journal of Production Research, 62(17), 6120–6145.
[7] Li, X. T., Huang, L. Q., Guo, J. J., et al. (2025). A study on practical pathways for the development of new-quality productive forces driven by artificial intelligence. Industrial Technology and Economics, 44(04), 60–69.
[8] Lee, J., Suh, T., Roy, D., et al. (2019). Emerging technology and business model innovation: The case of artificial intelligence. Journal of Open Innovation: Technology, Market, and Complexity, 5(3), 44.
[9] Zhang, H. C., Yang, T. (2022). Technology-enabled evidence-based practice: Artificial intelligence driving paradigm shifts in government evidence-based decision-making. Journal of Tianjin Academy of Public Administration, 24(04), 44–56.
[10] Meng, A., Lin, J., & Luo, X. (2024). The impact of human AI skills on organizational innovation: The moderating role of digital organizational culture. Journal of Business Research, 182, 114786.
[11] Xue, L., Jiang, L. D. (2025). Generative AI-driven paradigm shifts in future industrial innovation. Bulletin of the Chinese Academy of Sciences, 40(05), 820–827.
[12] Chen, G. Q., Zeng, D. J., Wei, Q., et al. (2020). Paradigm shifts in decision-making and enabling innovation in a big data environment. Management World, 36(02), 95–105+220.
[13] Yu, L. A. (2022). Research on theories and methods of AI-based forecasting and decision optimization. Management Science, 35(01), 60–66.
[14] Vocke, C., Constantinescu, C., & Popescu, D. (2019). Application potentials of artificial intelligence for the design of innovation processes. Procedia CIRP, 84, 810–813.
[15] Haefner, N., Wincent, J., Parida, V., et al. (2021). Artificial intelligence and innovation management: A review, framework, and research agenda. Technological Forecasting and Social Change, 162, 120392.
[16] Bouschery, S. G., Blazevic, V., & Piller, F. T. (2023). Augmenting human innovation teams with artificial intelligence: Exploring transformer-based language models. Journal of Product Innovation Management, 40(2), 139–153.
[17] Mariani, M. M., Machado, I., & Nambisan, S. (2023). Types of innovation and artificial intelligence: A systematic quantitative literature review and research agenda. Journal of Business Research, 155, 113364.
[18] Bahoo, S., Cucculelli, M., & Qamar, D. (2023). Artificial intelligence and corporate innovation: A review and research agenda. Technological Forecasting and Social Change, 188, 122264.
[19] Yang, Y., Liu, S., Li, Y. W., et al. (2020). Big data marketing: A review and outlook. Theory and Practice of Systems Engineering, 40(08), 2150–2158.
[20] Liu, S., Huang, S. M. (2019). Artificial intelligence: The arrival of the “data computing power” era in marketing communication. Modern Communication (Journal of Communication University of China), 41(01), 7–15.
[21] Wang, Y. G., Zhang, S. Q., Zhang, E. W., et al. (2024). Research on digital marketing from an interactive perspective: An integrated framework and future outlook. Financial and Economic Forum, (05), 5–16.
[22] Zhou, Y. W., Lü, W. (2021). The underlying logic of artificial intelligence reshaping the retail industry: A review and outlook. Journal of Systems Management, 30(01), 180–190.
[23] Song, Z. M., Zhang, G. Y. (2023). How can latecomer enterprises in artificial intelligence achieve disruptive innovation? Technology and Economy, 42(12), 14–27.
[24] Wang, C. Y., Sun, J. (2024). A study on the development of “smart” language service enterprises in the era of artificial intelligence. China Translation, 45(02), 123–133.
[25] Jin, Y. X., Cao, X. (2023). A study on the mechanisms for cross-boundary innovation in emerging technology enterprises: A case study of the ZhiGui Express System. Scientific Decision-Making, (04), 152–172.
[26] Mariani, M., & Dwivedi, Y. K. (2024). Generative artificial intelligence in innovation management: A preview of future research developments. Journal of Business Research, 175, 114542.
[27] Sjödin, D., Parida, V., Palmié, M., et al. (2021). How AI capabilities enable business model innovation: Scaling AI through co-evolutionary processes and feedback loops. Journal of Business Research, 134, 574–587.
[28] Xiao, J. H., Hu, Y. S., & Wu, Y. (2020). Growth products: A case study of data-driven innovation in enterprise-user interaction. Management World, 36(03), 183–205.
[29] Ye, J. L., Xu, Q. (2019). Decentralization and centralization: The paradox of power in the age of artificial intelligence. Journal of Shanghai University (Social Sciences Edition), 36(06), 1–12.
[30] Zheng, J. H., Yang, J. R. (2024). Development pathways for industrial digitalization: Complementarity, dynamism, and strategic orientation. Review of Industrial Economics, (02), 60–71.
[31] Mao, W. (2020). Cultural drivers of corporate innovation in the big data era. Zhejiang Social Sciences, (06), 12–20+155.
[32] Jia, S. W., Yan, H. (2022). A systematic review of the concept, philosophical foundations, and consequences of algorithmic bias. Chinese Journal of Library Science, 48(06), 57–76.
[33] Liu, G. C., Yang, L. L. (2024). Research trends, operational mechanisms, and practical strategies for algorithm auditing in the era of artificial intelligence. Lanzhou Journal, 1–14.
[34] Ye, Q., Liu, Z. S. (2023). Causes and governance strategies for algorithmic gender bias in the context of artificial intelligence. Journal of Guizhou Normal University (Social Sciences Edition), (05), 54–63.
[35] Wen, F. M., Xie, X. F. (2023). A study on the ethical risks and regulatory pathways of algorithm applications in social governance in Europe and the United States. Journal of Southwest Minzu University (Humanities and Social Sciences Edition), 44(09), 130–139.
[36] Wu, X. K., Deng, K. Q. (2024). Data selection, information filtering, and collaborative governance behind algorithmic bias. China Publishing, (06), 10–15.
[37] Qiu, Y. Q., Chen, C. F. (2018). Choosing methods to open the “black box” of news transparency based on artificial intelligence and algorithms. Journal of Zhengzhou University (Philosophy and Social Sciences Edition), 51(05), 84–88+159.
[38] Buhmann, A., & Fieseler, C. (2021). Toward a deliberative framework for responsible innovation in artificial intelligence. Technology in Society, 64, 101475.
[39] Buhmann, A., & Fieseler, C. (2023). Deep learning meets deep democracy: Deliberative governance and responsible innovation in artificial intelligence. Business Ethics Quarterly, 33(1), 146–179.
[40] Kong, X. W., Tang, X. Z., Wang, Z. M. (2021). A review of research on the explainability of AI decision-making. Theory and Practice of Systems Engineering, 41(02), 524–536.
[41] Wang, H. Y. (2024). The value of algorithmic explainability and its path to legal regulation. Chongqing Social Sciences, (01), 120–135.
[42] Chen, J. B., Wang, M. (2024). Responsible artificial intelligence: The governance foundation for AIGC amid a crisis in technology ethics. Journal of Xi’an Jiaotong University (Social Sciences Edition), 44(01), 111–120.
[43] Gao, C. Y., Xie, Y. (2023). The impact and mechanisms of corporate privacy policies: A review and outlook from the user perspective. Journal of Capital University of Economics and Business, 25(05), 95–112.
[44] Feng, B. B., Meng, X. Y. (2024). Human-machine interaction: Users’ perceptions of privacy and boundary management in the era of artificial intelligence. Learning and Practice, (05), 31–42.
[45] Năstasă, A., Maer Matei, M. M., & Mocanu, C. (2023). Artificial intelligence: Friend or foe? Experts’ concerns on the European AI Act. Economic Computation & Economic Cybernetics Studies & Research, 57(3).
[46] Zhang, X., Wang, M. H. (2019). Trends in China’s artificial intelligence development and strategies for promotion. Reform, (09), 31–44.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
