Artificial Intelligence Governance for Advancing Manufacturing Upgrading： A Platform Data Asset Perspective

XIE Kang; ZHANG Cheng-hao

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Journal of Beijing Jiaotong University（Social Sciences Edition） ›› 2025, Vol. 24 ›› Issue (4) : 27-38.

Applied Economics Studies

Artificial Intelligence Governance for Advancing Manufacturing Upgrading： A Platform Data Asset Perspective

XIE Kang, ZHANG Cheng-hao

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Abstract

Against the research gap in the relationship between industrial upgrading and artificial intelligence （AI） governance， this paper explores AI governance for boosting manufacturing upgrading from the perspective of balancing liquidity and security of platform-based data assets， drawing on the multi-tier principal-agent model. The study reveals the following key findings： Enhancing the capabilities of platform-based AI improves the liquidity of data assets but simultaneously compromises their security. Therefore， a higher AI capability does not translate to a greater total utility for the platform； instead， there exists an optimal range of AI capability. This optimal range is primarily influenced by two endogenous variables—platform risk aversion and trust in AI—and two exogenous variables—data signal-to-noise ratio （SNR） and market stability. Therefore， it is suggested that during the intelligentization of the manufacturing industry， the industry can develop an AI governance system conducive to upgrading by focusing on four dimensions： the industry’s degree of risk aversion and level of trust in AI， data quality， and market stability.

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manufacturing upgrading / Artificial Intelligence governance / platform data assets / optimal AI capability

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XIE Kang, ZHANG Cheng-hao. Artificial Intelligence Governance for Advancing Manufacturing Upgrading： A Platform Data Asset Perspective[J]. Journal of Beijing Jiaotong University（Social Sciences Edition）. 2025, 24(4): 27-38

References

1 闫强明，张辉.建成现代化经济体系的挑战与发展对策［J］.北京交通大学学报（社会科学版），2025，24（1）：44-53.
2 张万里，刘婕.人工智能对产业结构升级的影响机制研究——基于资本-技能互补的理论分析［J］.经济经纬，2023，40（2）：99-110.
3 穆荣平，郭京京，李雨晨，等.人工智能赋能制造业创新驱动发展态势、问题和建议［J］.中国科学院院刊，2025，40（8）：1329-1339.
4 张树华，张夏添.生成式人工智能治理国际研究评述：焦点议题与立场分野［J］.社会科学研究，2025，（3）：25-36+227.
5 谢康，肖静华，刘帆.数字经济创新极点：平台创新、竞争与就业［M］.北京：经济管理出版社，2024.
6 SCHWIENBACHER A. Entrepreneurial risk-taking in crowd-funding campaigns［J］. Small Business Economics， 2018，51：843-859.
7 戴琼海.大模型技术：变革、挑战与机遇［J］.中国科学基金，2023，37（5）：713.
8 SHI S， GONG Y， GURSOY D. Antecedents of trust and adoption intention toward artificially intelligent recommendation systems in travel planning： A heuristicsystematic model［J］. Journal of Travel Research，2021，60（8）：1714-1734.
9 BIGMAN Y E， WILSON D， ARNESTAD M N， et al. Algorithmic Discrimination Causes Less Moral Outrage than Human Discrimination. ［J］. Journal of Experimental Psychology： General， 2023， 152（1）： 4-27.
10 JOHNSON D H. Signal-to-noise ratio［J］. Scholarpedia， 2006， 1（12）： 2088.
11 BOX G. Signal-To-Noise Ratios， Performance Criteria， and Transformations［J］. Technometrics， 1988， 30（1）： 1-17.
12 THANGJAI W， NIWITPONG S A. Confidence intervals for functions of signal-to-noise ratio with application to economics and finance［J］. Asian Journal of Economics and Banking， 2024， 8（2）： 199-218.
13 李振新，陈享光.资本驱动下数字平台扩张与金融化［J］.北京师范大学学报（社会科学版），2023，（4）：18-28.
14 COLLINS V， LANZ J. Managing data as an asset［J］. The CPA Journal， 2019， 89（6）： 22-27.
15 XU G， CHIANG J M H. The Core Value of Web3. 0： Recognition and Measurement of Data Assets［J］. Journal of Information Technology & Economic Development， 2023， 13（2）.