Multidimensional Perspectives and Pathways of AI-Empowered Modern Management Research

作者

  • Jing Ning Faculty of Finance and Economics, Guangxi Science& Technology Normal University, Liu Zhou, China 作者
  • Jun Tan Faculty of Finance and Economics, Guangxi Science& Technology Normal University, Liu Zhou, China 作者

DOI:

https://doi.org/10.63944/4mj.JFEMR

关键词:

AI; management research; algorithmic governance; human-machine collaboration; knowledge networks

摘要

Artificial intelligence (AI) technology is profoundly reshaping the global management ecosystem, transforming its role from a tool for efficiency to a structural force driving organizational change. This study, grounded in the context of China's modernization, systematically explores the multidimensional applications of AI technology in management research and the challenges it faces. The study finds core challenges in the current management field, including a crisis of adaptability between the industrial-era paradigm and the intelligent ecosystem, the dissipation of governance effectiveness caused by algorithmic black boxes, and cognitive barriers to human-machine collaboration. These issues stem from the conflict between mechanistic cognition and complex systems, the imbalance between instrumental and value rationality, and the paradigmatic differences between biological and machine intelligence. To address these challenges, the study proposes three solutions: building an AI-enabled distributed dynamic knowledge network, establishing a hierarchical and transparent governance system, and developing cognitive coupling interfaces. This research not only provides new perspectives for innovation in management theory but also offers practical paths for AI management practice in the Chinese context.

作者简介

  • Jing Ning, Faculty of Finance and Economics, Guangxi Science& Technology Normal University, Liu Zhou, China

    Faculty of Finance and Economics, Guangxi Science& Technology NormalUniversity, LiuZhou, China

    Faculty ofManagement Science, Dhonburi Rajabhat University, Bangkok.Thailand

参考文献

[1] Wu, H., Li, G., & Zheng, H. (2024). How does digital intelligence technology enhance supply chain resilience? Sustainable framework and agenda. Annals of Operations Research, 1-23. DOI: https://doi.org/10.1007/s10479-024-06104-3

[2] Brunner, D., Legat, C., & Seebacher, U. (2024). Towards Next Generation Data-Driven Management: Leveraging Predictive Swarm Intelligence to Reason and Predict Market Dynamics. In Collective Intelligence (pp. 152-203). CRC Press. DOI: https://doi.org/10.1201/9781032690711-8

[3] Xu, S., & Zhang, X. (2023, April). Augmenting human cognition with an ai-mediated intelligent visual feedback. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1-16). DOI: https://doi.org/10.1145/3544548.3580905

[4] Oluwagbade, E., Vincent, A., Oluwole, O., & Animasahun, B. (2023). Lifecycle governance for explainable AI in pharmaceutical supply chains: a framework for continuous validation, bias auditing, and equitable healthcare delivery. Int J Eng Technol Res Manag, 7(11), 54.

[5] Figueiredo, R., Santos, M., & Pereira, L. (2025). Knowledge sharing and intrinsic motivation in innovation-driven firms. European Management Review, 22(3), 288–305. https://doi.org/10.1111/emre.12589. DOI: https://doi.org/10.1111/emre.12589

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已出版

2025-09-15

期次

栏目

文章

如何引用

Jing Ning, & Jun Tan. (2025). Multidimensional Perspectives and Pathways of AI-Empowered Modern Management Research. 经济管理前沿研究, 1(1), 477-486. https://doi.org/10.63944/4mj.JFEMR