Journal of Applied Science and Engineering

Published by Tamkang University Press


Impact Factor



Lu WangThis email address is being protected from spambots. You need JavaScript enabled to view it.

Shunde Polytechnic, Foshan, Guangdong, China, 528333



Received: May 18, 2023
Accepted: August 27, 2023
Publication Date: November 16, 2023

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||  

Organizations are becoming more productive due to the digital economy because they can presently employ technologies that automate various tasks and procedures. The digital economy, which is driven by the network infrastructure, is an advanced field that is constantly evolving. As globalization continues to advance, so do customers’ perceptions of financial forecasting and analysis, even though the conventional economic evaluation and prediction model has relatively poor forecasting ability. Hence, the study offered improved time series strategies (ITSS) for productive advancements in the digital economy (DE)by adapting the time series framework. The research used the Technology-Organization-Environment (TOE) model on a systematic similar evaluation to investigate the digital economy in all 31 regions in China.The effectiveness of regional digital economy growth is presented in-depth in this study, with an emphasis on the interaction between technology, organization, and environment.Understanding and resolving socioeconomic, cultural, and regulatory variables in the environment dimension helps overcome difficulties and build trust. These TOE aspects could stimulate digital economy development, promoting sustainable growth and equitable prosperity in the age of technology. The study has also underlined the importance and necessity of developing the digital economy in an approach that is both environmentally sustainable and economically efficient. The findings reveal that the proposed approach has been well efficient in the application of time series models in forecasting the efficiency of digital economy development. Our method can provide 96% accuracy while being 92% prediction rate, 97% prediction time, 91% error rate, and 95 % electrode placement efficiency.

Keywords: digital economy, financial forecasting, improved time series strategies (ITSS), Technology–Organization–Environment (TOE).

  1. [1] Z. Li and Y. Liu, (2021) “Research on the Spatial Distribution Pattern and Influencing Factors of Digital Economy Development in China" IEEE Access 9: 63094– 63106. DOI: 10.1109/ACCESS.2021.3075249.
  2. [2] C. Ding, C. Liu, C. Zheng, and F. Li, (2021) “Digital economy, technological innovation and high-quality economic development: Based on spatial effect and mediation effect" Sustainability 14(1): 216. DOI: 10.3390/su14010216.
  3. [3] Q. Yang, H. Ma, Y. Wang, and L. Lin, (2022) “Research on the influence mechanism of the digital economy on regional sustainable development" Procedia Computer Science 202: 178–183. DOI: 10.1016/j.procs.2022.04.025.
  4. [4] X. Xiang, G. Yang, and H. Sun, (2022) “The impact of the digital economy on low-carbon, inclusive growth: Promoting or restraining" Sustainability 14(12): 7187. DOI: 10.3390/su14127187.
  5. [5] W. Shen, W. Xia, and S. Li, (2022) “Dynamic coupling trajectory and spatial-temporal characteristics of highquality economic development and the digital economy" Sustainability 14(8): 4543. DOI: 10.3390/su14084543.
  6. [6] L. Tang, B. Lu, and T. Tian, (2021) “Spatial correlation network and regional differences for the development of digital economy in China" Entropy 23(12): 1575. DOI: 10.3390/e23121575.
  7. [7] Y. Liu, Y. Yang, H. Li, and K. Zhong, (2022) “Digital economy development, industrial structure upgrading and green total factor productivity: Empirical evidence from China’s cities" International Journal of Environmental Research and Public Health 19(4): 2414. DOI: 10.3390/ijerph19042414.
  8. [8] S. Jiao and Q. Sun, (2021) “Digital economic development and its impact on econimic growth in China: Research based on the prespective of sustainability" Sustainability 13(18): 10245. DOI: 10.3390/su131810245.
  9. [9] V. Bilozubenko, O. Yatchuk, E. Wolanin, T. Serediuk, and M. Korneyev, (2022) “Comparison of the digital economy development parameters in the EU countries in the context of bridging the digital divide": DOI: 10.3390/su131810245.
  10. [10] A. U. Kobilov, D. P. Khashimova, S. G. Mannanova, and M. M. O. Abdulakhatov, (2022) “Modern content and concept of digital economy" International Journal of Multicultural and Multireligious Understanding 9(2): 375–378. DOI: 10.18415/ijmmu.v9i2.3524.
  11. [11] N. A. F. A. Aniqoh, (2020) “The role of digital economy to enhancing sustainable economic development" international journal of social science and business 4(4): 519–527. DOI: 10.23887/ijssb.v4i4.28881.
  12. [12] Z. Li, N. Li, and H. Wen, (2021) “Digital economy and environmental quality: Evidence from 217 cities in China" Sustainability 13(14): 8058. DOI: 10.3390/su13148058.
  13. [13] J. Zhou, H. Lan, C. Zhao, and J. Zhou, (2021) “Haze pollution levels, spatial spillover influence, and impacts of the digital economy: Empirical evidence from China" Sustainability 13(16): 9076. DOI: 10.3390/su13169076.
  14. [14] J. Zhang, Y. Lyu, Y. Li, and Y. Geng, (2022) “Digital economy: An innovation driving factor for low-carbon development" Environmental Impact Assessment Review 96: 106821. DOI: 10.1016/j.eiar.2022.106821.
  15. [15] G. Yu and X. Zhou, (2021) “The influence and countermeasures of digital economy on cultivating new driving force of high-quality economic development in Henan Province under the background of" double circulation"" Annals of Operations Research: 1–22. DOI: 10.1007/s10479-021-04325-4.
  16. [16] Y. Li, X. Yang, Q. Ran, H. Wu, M. Irfan, and M. Ahmad, (2021) “Energy structure, digital economy, and carbon emissions: evidence from China" Environmental Science and Pollution Research 28: 64606–64629. DOI: 10.1007/s11356-021-15304-4.
  17. [17] N. Kelchevskaya and E. Shirinkina, (2019) “Regional determinants of effective use of human capital in the digital economy":
  18. [18] J. Wang and G. Zhang, (2022) “Can environmental regulation improve high-quality economic development in China? The mediating effects of digital economy" Sustainability 14(19): 12143. DOI: 10.3390/su141912143.
  19. [19] J. Wang, B. Wang, K. Dong, and X. Dong, (2022) “How does the digital economy improve high-quality energy development? The case of China" Technological Forecasting and Social Change 184: 121960. DOI: 10.1016/j.techfore.2022.121960.
  20. [20] F. Dong, M. Hu, Y. Gao, Y. Liu, J. Zhu, and Y. Pan, (2022) “How does digital economy affect carbon emissions? Evidence from global 60 countries" Science of The Total Environment 852: 158401. DOI: 10.1016/j.scitotenv.2022.158401.
  21. [21] S. Chen, Q. Li, B. Lei, and N. Wang, (2021) “Configurational analysis of the driving paths of Chinese digital economy based on the technology–organization–environment framework" SAGE Open 11(4): 21582440211054500. DOI: 10.1177/21582440211054500.
  22. [22] E. Ozden and D. Guleryuz, (2022) “Optimized machine learning algorithms for investigating the relationship between economic development and human capital" Computational Economics 60(1): 347–373. DOI: 10.1007/s10614-021-10194-7.
  23. [23] Y. Dexiang, M. Shengdong, Y. Liu, G. Jijian, and L. Chaolung, (2023) “An Improved Deep-LearningBased Financial Market Forecasting Model in the Digital Economy" Mathematics 11(6): 1466. DOI: 10.3390/math11061466.



69th percentile
Powered by  Scopus

SCImago Journal & Country Rank

Enter your name and email below to receive latest published articles in Journal of Applied Science and Engineering.