EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 170 (2023) SHS Web Conf., 170 (2023) 01014 References
Open Access
Issue
SHS Web Conf.
Volume 170, 2023
2023 International Conference on Digital Economy and Management Science (CDEMS 2023)
Article Number 01014
Number of page(s) 6
Section Artificial Intelligence and Digital Economy
DOI https://doi.org/10.1051/shsconf/202317001014
Published online 14 June 2023
  1. Guo Min, Fang Mengran. (2018) Artificial intelligence and productivity paradox: international experience [J]. Economic system reform, (05): 171–178. [Google Scholar]
  2. Deng Yue, Jiang Wanyi. (2022) How can intelligent transformation stimulate enterprise innovation ? -Explanation based on the diversity of manufacturing labor force [J]. Reform, (09): 108–122. [Google Scholar]
  3. Kusiak A., Dornfeld, D. (1991) Intelligent manufacturing systems [J]. Journal of Manufacturing Science and Engineering, 113 (2): 248. [Google Scholar]
  4. Davis J., Edgar T., Porter J., et al. (2012) Smart manufacturing, manufacturing intelligence and demand-dynamic performance [J]. Computers and Chemical Engineering. [Google Scholar]
  5. Zhou Jiajun, Yao Xifan. (2015) Advanced Manufacturing Technology and New Industrial Revolution [J]. Computer Integrated Manufacturing System, 21 (08): 1963–1978. [Google Scholar]
  6. Wang Xiwen. (2015) Intelligent manufacturing: the main direction of the new round of industrial revolution [J]. People’s Forum • Academic Frontier, (19): 68–79 + 95. [Google Scholar]
  7. Lyu Tie, Han Na. (2015) Intelligent Manufacturing: Global Trends and China’s Strategy [J]. People ’s Forum Academic Frontier, (11): 6–17. [Google Scholar]
  8. Han Jiangbo. (2017) Intelligent industrialization: a new perspective on the paradigm of industrialization development [J]. Economist, (10): 21–30. [Google Scholar]
  9. Jin Bei. (2018) Economic research on ’ high-quality development ’ [J]. China Industrial Economy, (04): 5–18. [Google Scholar]
  10. Ren Baoping, Li Yumo. (2018) Construction and transformation path of China ’s high-quality development evaluation system in the new era [J]. Journal of Shaanxi Normal University (Philosophy and Social Sciences Edition), 47 (03): 105–113. [Google Scholar]
  11. He Xiaoyu, Shen Kunrong. (2018) Modern economic system, total factor productivity and high-quality development [J]. Shanghai Economic Research, (06): 25–34. [Google Scholar]
  12. Wang Yiming. (2020) Centennial changes, high-quality development and building a new development pattern [J]. Managing the world, 36 (12): 1–13. [Google Scholar]
  13. Huang Sujian, Xiao Hongjun, Wang Xin. (2018) On the high-quality development of state-owned enterprises [J]. China ’s industrial economy, (10): 19–41. [Google Scholar]
  14. Qi Jia. (2019) Policy ideas to promote the high-quality development of private enterprises in China-Effect analysis of gazelle enterprise support policy [J]. Learning and practice, (02): 13–19. [Google Scholar]
  15. Yang Bo. (2019) Analysis of the evaluation index system of high-quality development of state-owned enterprises [J]. Friends of Accounting, (23): 45–49. [Google Scholar]
  16. Dai Guobao, (2019) Wang Yaqiu. High-quality development of private SMEs: connotation, dilemma and path [J]. Economic issues, (08): 54–61. [Google Scholar]
  17. Acemoglu D., Restrepo, P. (2018) The race between man and machine: implications of technology for growth, factor shares, and employment, 108 (6), 1488–1542. [Google Scholar]
  18. Zhou Wenhui, Wang Pengcheng, Yang Miao. (2018) Digital empowerment promotes mass customization technology innovation [J]. Scientific research, 36 (08): 1516–1523. [Google Scholar]
  19. Huang Qunhui, Yu Yongze, Zhang Songlin. (2019) Internet development and manufacturing productivity improvement: internal mechanism and Chinese experience [J]. China ’s industrial economy, (08): 5–23. [Google Scholar]
  20. Song Min, Zhou Peng, Si Haitao. (2021) Financial technology and enterprise total factor productivity- ’ empowerment ’ and credit rationing perspective [J]. China Industrial Economy, (04): 138–155. [Google Scholar]
  21. Wen Huwei, Zhong Qiming. (2021) Effect of Intelligent Development on Total Factor Productivity of Enterprises-Evidence from Manufacturing Listed Companies [J]. China Science and Technology Forum, (01): 84–94. [Google Scholar]
  22. Gordon R.J. (2016) The rise and fall of American growth: the US standard of living since the civil war [J]. Introductory Chapters. 70: 357–358. [Google Scholar]
  23. Li Bin. (2010) Does domestic R&D hinder the improvement of total factor productivity in China ? [J]. Science of Science, 28 (07): 1035–1042 + 1059. [Google Scholar]
  24. Sun Xiaohua, Wang Yun, Zheng Hui. (2014) Industry heterogeneity of R & D affecting total factor productivity - empirical evidence from China ’s manufacturing industry [J]. Journal of Management Engineering, 28 (03): 33–41. [Google Scholar]
  25. Wei Wei, Zhang Wanli, Xuan Yang. (2020) Labor structure, industrial intelligence and total factor productivity - Analysis based on China ’s 2004-2016 provincial panel data [J]. Journal of Shaanxi Normal University (Philosophy and Social Sciences Edition), 49 (04): 143–155. [Google Scholar]
  26. Cheng Hong, Yuan Luwen. (2020) Robot use, process innovation and quality improvement -empirical evidence from China Enterprise General Survey (CEGS) [J]. Southern Economy, (01): 46–59. [Google Scholar]
  27. Liu Jiaqi, Sun Puyang. (2021) How digital product imports can effectively promote enterprise innovation - an empirical analysis based on Chinese micro-enterprises [J]. International trade issues, (08): 38–53. [Google Scholar]
  28. Zhou Yu, Hui Ning, Chen Jinqiang. (2021) Research on Internet promoting innovation efficiency of high-tech industry [J]. Journal of Beijing University of Technology (Social Science Edition), 21 (06): 102–115. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.