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International Journal of Business Management and Economics and Trade, 2022, 3(3); doi: 10.38007/IJBMET.2022.030302.

Legal Risk Problems and Countermeasures of Contract Management in State-owned Enterprises

Author(s)

Jun Liu, Ni Li, Shizhao Zhao and Kai Yu

Corresponding Author:
Jun Liu
Affiliation(s)

School of Economics and Management, Anshun University, Anshun, Guizhou, China

Abstract

Firstly, this paper combs and analyzes the relevant literature on the legal risk of contract management in state-owned enterprises, identifies and analyzes the legal risk of contract management from the two aspects of contract conclusion and contract performance, further studies and analyzes the difficulties faced by contract management in state-owned enterprises, and finally from the aspects of system improvement, system construction, personnel improvement From the aspects of legal risk transfer, this paper puts forward countermeasures and suggestions for the prevention and control of legal risk in contract management of state-owned enterprises, which provides a certain reference value for the research of contract management of state-owned enterprises.

Keywords

Contract Management, Legal Risk

Cite This Paper

Jun Liu, Ni Li, Shizhao Zhao and Kai Yu. Legal Risk Problems and Countermeasures of Contract Management in State-owned Enterprises. International Journal of Business Management and Economics and Trade (2022), Vol. 3, Issue 3: 10-18. https://doi.org/10.38007/IJBMET.2022.030302.

References

[1] Cold and Easy Wood Discussion on Legal Risk Prevention in Contract Management of State-Owned Enterprises.. Modern Economic Information, 2018 (7): 358

[2] Shi Yulei Legal Risks in Enterprise Contract Management and their Identification and Prevention. Enterprise Reform and Management, 2021 (1): 38, 47

[3] Wu Feng. Legal Risk Prevention Measures for Contract Management of State-Owned Construction Enterprises. Legal system and society, 2020 (22): 156-157

[4] Zeng Yizhou. On the Countermeasures Against Legal Risks In Contract Management of State-Owned Enterprises. Investment and Entrepreneurship, 2021 (8): 120126

[5] Liu Hengxin. On Contract Management and Legal Risk Prevention of State-Owned oil Enterprises. Regional Governance, 2021 (14): 77

[6] Lu Du. Analysis of Legal Risks and Preventive Countermeasures in Enterprise Contract Management. Research on Modern State-Owned Enterprises, 2021 (12): 54-55

[7] Li Junlin. Thoughts on Constructing the Working Mechanism of Enterprise Legal Counsel Participating in Enterprise Operation and Management. Urban Water Supply, 2021 (01): 81-88

[8] Zhang Li. On the Risks Existing in Modern Enterprise Contract Management and how to do well in Contract Management. Research on Modern State-Owned Enterprises, 2021 (04): 78-79

[9] Xu Renlong. Under the New Normal, State-Owned Enterprises Need To Improve the Legal Risk Prevention Mechanism. Zhejiang Economic, 2020 (07): 60-61

[10] LV Yanyong. Identification and Control of Legal Risk In Enterprise Contract Management. Research on Modern State-Owned Enterprises, 2019 (16): 73-74

11] Dong S, Wu Z G, Shi P, et al. Quantized control of Markov jump nonlinear systems based on fuzzy hidden Markov model. IEEE transactions on cybernetics, 2020, 49(7): 2420-2430. https://doi.org/10.1109/TCYB.2018.2813279

[12] Dalal M, Juneja M. Video Steganalysis to Obstruct Criminal Activities for Digital Forensics: A Survey. International Journal of Electronic Security and Digital Forensics, 2021, 10(4): 338-355. https://doi.org/ 10.1504/IJESDF.2018.095122 

[13] Behera S K, Dogra D P, Roy P P. Analysis of 3D Signatures Recorded using Leap Motion Sensor. Multimedia Tools and Applications, 2020, 77(11): 14029-14054. https://doi.org/10.1007/s11042-017-5011-4

[14]Rezaee M J, Sadatpour M, Ghanbari-Ghoushchi N, et al. Analysis and Decision Based on Specialist Self-Assessment for Prognosis Factors of Acute Leukemia Integrating Data-Driven Bayesian network and Fuzzy Cognitive Map. Medical & Biological Engineering & Computing, 2020, 58(11): 2845-2861. https://doi.org/10.1007/ s11517-020-02267-w

[15]Dargan S, Kumar M, Garg A, et al. Writer Identification System for Pre-Segmented Offline Handwritten Devanagari Characters using k-NN and SVM. Soft Computing, 2020, 24(13): 10111-10122. https://doi.org/10.1007/ s00500-019-04525-y

[16]Chutani S, Goyal A. A Review of Forensic Approaches to Digital Image Steganalysis. Multimedia Tools and Applications, 2021, 78(13): 18169-18204. https://doi.org/10.1007/s11042-019-7217-0

[17]Nalmpantis C, Vrakas D. Machine Learning Approaches for Non-Intrusive Load Monitoring: From Qualitative to Quantitative Comparation. Artificial Intelligence Review, 2022, 52(1): 217-243. https://doi.org/10.1007/ s10462-018-9613-7

[18]Pawar R V, Jalnekar R M, Chitode J S. Review of Various Stages in Speaker Recognition System, Performance Measures and Recognition Toolkits. Analog Integrated Circuits and Signal Processing, 2021, 94(2): 247-257. https://doi.org/10.1007/s10470-017-1069-1

[19]Panetta K, Wan Q, Agaian S, et al. A Comprehensive Database for Benchmarking Imaging Systems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 42(3): 509-520. https://doi.org/10.1109/TPAMI. 2018.2884458

[20]Lekshmi K R, Sherly E. An acoustic model and Linguistic Analysis for Malayalam Disyllabic Words: A Low Resource Language. International Journal of Speech Technology, 2021, 24(2): 483-495. https://doi.org/10.1007/ s10772- 021-09807-1

[21]Patil K A. Features and Methods of Human Age Estimation: Opportunities and Challenges in Medical Image Processing. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021, 12(1S): 294-318. https://doi.org/10.17762/turcomat.v12i1S.1770

[22]Rehman A, Naz S, Razzak M I. Writer Identification Using Machine Learning Approaches: A Comprehensive Review. Multimedia Tools and Applications, 2022, 78(8): 10889-10931. https://doi.org/10.1007/s11042-018-6577-1