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Academic Journal of Business & Management, 2021, 3(4); doi: 10.25236/AJBM.2021.030401.

The Transformation of Government Governance Model from the Perspective of Big Data

Author(s)

Dazhi Xu, Xiaoyong Xiao*

Corresponding Author:
Xiaoyong Xiao
Affiliation(s)

College of Economics and Management, Hunan University of Arts and Sciences, Changde 415000, China

*Corresponding Author

Abstract

The information age has become the main feature of the development of the times. Promoting the modernization of the national governance system and governance capabilities is the overall goal of comprehensively deepening reforms. It is also the requirements and challenges that social and economic development and technological progress pose to the country and government in the new environment. This article uses literature research methods and logical deduction research methods to find that traditional government governance models have important characteristics such as the economicization of governance goals, the hierarchical governance of governance structures, and the proceduralization of governance methods. Therefore, it is necessary to use new technologies such as big data to realize the transformation and application of science and technology, the transformation and innovation of thinking, and the transformation of government governance mode.

Keywords

big data, government governance, social governance

Cite This Paper

Dazhi Xu, Xiaoyong Xiao. The Transformation of Government Governance Model from the Perspective of Big Data. Academic Journal of Business & Management (2021) Vol. 3, Issue 4: 1-5. https://doi.org/10.25236/AJBM.2021.030401.

References

[1] Gang, L., Hanwen, Z. (2020) "An Ontology Constructing Technology Oriented on Massive Social Security Policy Documents", Cognitive Systems Research, 60, pp. 97-105.

[2] Lymer A. Second international meeting on artificial intelligence in accounting, finance and tax, Punta Umbria, Spain, 27–28 September 1996. Intelligent Systems in Accounting Finance & Management, 2015, 6(3):265-267.

[3] Lemley J, Bazrafkan S, Corcoran P. Deep learning for consumer devices and services: Pushing the limits for machine learning, artificial intelligence, and computer vision. IEEE Consumer Electronics Magazine, 2017, 6(2):48-56.

[4] Modongo C, Pasipanodya J G, Magazi B T, et al. Artificial Intelligence and Amikacin Exposures Predictive of Outcomes in Multidrug-Resistant Tuberculosis Patients. Antimicrobial Agents & Chemotherapy, 2016, 60(10):5928-5932.

[5] Chatila R, Firth-Butterflied K, Havens J C, et al. The IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems [Standards]. IEEE Robotics & Automation Magazine, 2017, 24(1):110-116.

[6] Polina M, Lucy O, Yury Y, et al. Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare. Oncotarget, 2018, 9(5):5665-5690.