Performance Evaluation and Optimization Strategies for Digital Transformation in State-Owned Enterprises

Dasong Yang

doi:10.63313/EBM.9119

Authors

Dasong Yang Taishan Branch of Guangdong Tobacco Jiangmen Co., Ltd., Guangdong Jiangmen, 529000, China Author

DOI:

https://doi.org/10.63313/EBM.9119

Keywords:

State-owned enterprises, Digital transformation, Performance evaluation, Evaluation indicator system, Optimization strategy

Abstract

Against the backdrop of the digital economy era, digital transformation has become a critical measure for state-owned enterprises to enhance their core competitiveness. This study constructs an evaluation framework comprising “4 dimensions, 12 elements, and 36 indicators” based on the Digital Capability Maturity Model, proposing a composite evaluation method that integrates the Fuzzy Comprehensive Evaluation Method and the Ideal Solution Method. Addressing challenges in strategic planning, technology application, talent development, and organizational management during SOE digital transformation, the study proposes systematic optimization strategies across dimensions including top-level design, technical architecture, talent mechanisms, and organizational change. This provides theoretical guidance and practical reference for advancing SOE digital transformation.

References

[1] Yin X , Zhang X , Pei L ,et al.Optimization and benefit evaluation model of a cloud compu-ting-based platform for power enterprises[J].SCIENTIFIC REPORTS, 2025, 15(1).

[2] Zhang Q , Pang Y .The digital tax administration (DTA) and green transformation of heavy polluting Enterprises—Based on the policy effect evaluation of the 'Golden Tax Project Ⅲ'[J].Journal of cleaner production, 2025(Jan.1):486.

[3] Sirinanda M D , Balota J E .Practical Digital Transformation Framework for Small and Me-dium-Sized Enterprises in the UK[J].2024 IEEE International Conference on e-Business Engineering (ICEBE), 2024:168-174.

[4] Homaei M H , Bartolo A J D , Vila M ,et al.Digital Transformation in the Water Distribution System based on the Digital Twins Concept[J]. 2024.

[5] Guo Y .Data-driven optimisation of leadership models during enterprise digital transfor-mation[J].Applied Mathematics and Nonlinear Sciences, 2024, 9(1).

[6] Kryshtal H , Zgalat-Lozynska L , Denysiuk O ,et al.The impact of Industry 4.0 on the digital transformation of manufacturing enterprises in Ukraine[J].Naukovyi Visnyk Natsional-noho Hirnychoho Universytetu, 2023.

[7] Wang J , Liu Y , Wu W H .How does digital transformation drive green total factor produc-tivity? Evidence from Chinese listed enterprises[J].Journal of cleaner production, 2023, 406(Jun.20):136954.1-136954.15.

[8] Xu Z , Sukpasjaroen K .The model of financial performance evaluation of Guangdong real estate enterprises under digital transformation management[J].Journal of Infrastructure, Policy and Development, 2024, 8(9):6704.

[9] Azubuike C , Sule A K , Adepoju P A ,et al.Enhancing Small and Medium-Sized Enterprises (SMEs) Growth through Digital Transformation and Process Optimization: Strategies for Sustained Success[J].International Journal of Research and Scientific Innovation, 2025, XI(XII):890-900.

[10] Xiao Y .Digital transformation and green total energy efficiency in the context of the "dual carbon" goal ——Empirical evidence based on China's A-share energy listed compa-nies[J].Advances in Economics and Management Research, 2024, 12(1):797.