Applied Mathematics and Nonlinear Sciences
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Journal

Applied Mathematics and Nonlinear Sciences


Volume
& Issue

Volume 9, Issue 1


Published
on

April 1, 2024


Pages


DOI

Article

Risk prediction and control of strategic operation of e-commerce enterprises based on economic management science

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Authors

Qingyu Hong Affiliation:
Yiwu Right Moon Trading Co., Yiwu, Zhejiang, 322000, China.
, Lei Luo Affiliation:
Finance Faculty, Jiangxi University of Finance and Economics, Nanchang, Jiangxi, 330013, China.
and Yanting Zhang Affiliation:
Tao Technology department, Taobao & Tmall Group, Hangzhou, Zhejiang, 310000, China.


Abstract

The burgeoning realm of Internet technology has ushered e-commerce into a pivotal economic role. However, navigating the myriad risks inherent in e-commerce operations is vital for the sustained growth of businesses in this sector. This study melds economic management principles with a deep dive into e-commerce risk management, focusing on predictive strategies and mitigation measures. We commence by dissecting the principal risk categories within e-commerce operations. Subsequently, we employ Structural Equation Modeling (SEM) and Particle Swarm Optimization-Generalized Regression Neural Network (PSO-GRNN) for quantitatively dissection of these risk factors. Our findings pinpoint internal, technological, and operational management risks as the critical triad influencing e-commerce strategic operations. Remarkably, the PSO-GRNN model’s risk prediction accuracy stands at 93.62%, outstripping conventional models significantly. Through this research, we offer a robust framework for e-commerce entities to enhance their strategic foresight and resilience, aiding in optimizing their strategic maneuvers.


Keywords

E-commerce enterprises, Strategic operations, Risk prediction, Economic management, 68M01


Citation

Hong, Q., Luo, L., & Zhang, Y. (2024). Risk prediction and control of strategic operation of e-commerce enterprises based on economic management science. Applied Mathematics and Nonlinear Sciences, 9(1). https://doi.org/10.2478/amns-2024-0763

Published by: Engineering Journals

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