EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 723 (2026) E3S Web Conf., 723 (2026) 04013 References
Open Access
Issue
E3S Web Conf.
Volume 723, 2026
2026 International Conference on Artificial Intelligence in Energy and Infrastructure (AIEI 2026)
Article Number 04013
Number of page(s) 6
Section Intelligent Infrastructure, Iot, Robotics & Sustainable Engineering
DOI https://doi.org/10.1051/e3sconf/202672304013
Published online 08 July 2026
  1. L. Chen, Y. Gao, and L. Yu, How does artificial intelligence change the demand for labor? Analysis based on big data from recruitment platforms. Management World 6, 74–91 (2024). [Google Scholar]
  2. L. Alekseeva, J. Azar, M. Giné, S. Samila, and B. Taska, The demand for AI skills in the labor market. Labour Economics 71, 102002 (2021). [Google Scholar]
  3. A. Zirar, S. I. Ali, and N. Islam, Worker and workplace artificial intelligence coexistence. Technovation 124, 102747 (2023). [CrossRef] [Google Scholar]
  4. A. Kljucnikov, E. G. Popkova, and B. S. Sergi, Global labor markets and workplaces in the age of intelligent machines. Journal of Innovation & Knowledge 8(4), 100407 (2023). [Google Scholar]
  5. B. Y. Kassa and E. K. Worku, The impact of artificial intelligence on organizational performance: The mediating role of employee productivity. Journal of Open Innovation: Technology, Market, and Complexity 11(1), 100474 (2025). [Google Scholar]
  6. M. Rožman, P. Tominc, and B. Milfelner, Maximizing employee engagement through artificial intelligent organizational culture in the context of leadership and training of employees: Testing linear and non-linear relationships. Cogent Business & Management 10(2), 2248732 (2023). [Google Scholar]
  7. N. Tusquellas, R. Palau, and R. Santiago, Analysis of the potential of artificial intelligence for professional development and talent management: A systematic literature review. International Journal of Information Management Data Insights 4(2), 100288 (2024). [Google Scholar]
  8. A. M. Ağça, S. Faye, and D. Khadraoui, A survey on trusted distributed artificial intelligence. IEEE Access 10, 55308–55337 (2022). [Google Scholar]
  9. J. Chen, Artificial intelligence-driven supply chain transformation: Platform reconstruction, ecosystem reshaping, and advantage rebuilding. Contemporary Economic Management 45(5), (2023). [Google Scholar]
  10. S. Dalal, U. K. Lilhore, S. Simaiya, M. Radulescu, and L. Belascu, Improving efficiency and sustainability via supply chain optimization through CNNs and BiLSTM. Technological Forecasting and Social Change 209, 123841 (2024). [Google Scholar]
  11. F. Olan, K. Spanaki, W. Ahmed, and G. Zhao, Enabling explainable artificial intelligence capabilities in supply chain decision support making. Production Planning & Control 36(6), 808–819 (2024). https://doi.org/10.1080/09537287.2024.2313514 [Google Scholar]
  12. H. Nozari, A. Szmelter-Jarosz, and J. Ghahremani-Nahr, Analysis of the challenges of artificial intelligence of things (AIoT) for the smart supply chain (Case study: FMCG industries). Sensors 22(8), 2931 (2022). [Google Scholar]
  13. N. Saibani, J. A. Ghania, M. H. S. Akmara, W. K. Boon, M. R. Ravia, M. A. M. Nawawia, and N. A. I. M. Asri, Latest advancement of technologies in supply chain management: An overview. Jurnal Kejuruteraan 33(4), 785–791 (2021). [Google Scholar]
  14. T. Wang, H. Chen, R. Dai, and D. Zhu, Intelligent logistics system design and supply chain management under edge computing and Internet of Things. Computational Intelligence and Neuroscience (1), 823762 (2022). [Google Scholar]
  15. A. Yadav, R. K. Garg, and A. Sachdeva, Artificial intelligence applications for information management in sustainable supply chain management: A systematic review and future research agenda. International Journal of Information Management Data Insights 4(2), 100292 (2024). [Google Scholar]
  16. L. Xu, S. Mak, and A. Brintrup, Will bots take over the supply chain? Revisiting agent-based supply chain automation. International Journal of Production Economics 241, 108279 (2021). [Google Scholar]
  17. H. Guo and P. Polak, Finance centralization - Research on enterprise intelligence. Humanities and Social Sciences Communications 11, 1536 (2024). [Google Scholar]
  18. X. Zhang and G. Tian, Intelligent financial theory and new opportunities for development based on the digital economy. Finance and Accounting Communications 22, 22–28 (2022). [Google Scholar]
  19. X. Hou, Design and application of intelligent financial accounting model based on knowledge graph. Mobile Information Systems 1, 8353937 (2022). [Google Scholar]
  20. L. Huang and H. Lu, Design of intelligent financial data management system based on higher-order hybrid clustering algorithm. PeerJ Computer Science 10, 1799 (2024). [Google Scholar]
  21. R. Ding, Enterprise intelligent audit model by using deep learning approach. Computational Economics 59(4), 1335–1354 (2022). [Google Scholar]
  22. R. Qin, Research on the construction of intelligent financial system using generative artificial intelligence. Intelligent Finance 24, 26–30 (2024). [Google Scholar]
  23. D. Zeng, Y. Li, L. Tang, J. Chen, Z. Liu, G. Kou, and J. Li, Research on decision intelligence theory and methods. Journal of Management Sciences in China 24(8), 18–25 (2021). [Google Scholar]
  24. M. Javaid, A. Haleem, R. P. Singh, and R. Suman, Artificial intelligence applications for Industry 4.0: A literature-based study. Journal of Industrial Integration and Management 7(1), 83–111 (2022). [CrossRef] [Google Scholar]
  25. O. Pilipczuk, Transformation of the business process manager profession in Poland: The impact of digital technologies. Sustainability 13(24), 13690 (2021). [Google Scholar]
  26. M. Garouani, A. Ahmad, M. Bouneffa, M. Hamlich, G. Bourguin, and A. Lewandowski, Towards big industrial data mining through explainable automated machine learning. International Journal of Advanced Manufacturing Technology 120(1–2), 1169–1188 (2022). [Google Scholar]
  27. P. Jiménez-Schlegl, Learning in autonomous and intelligent systems: Overview and biases from data sources. ARBOR Ciencia, Pensamiento y Cultura 197(802), 627 (2021). [Google Scholar]
  28. M. M. M. Peeters, J. van Diggelen, K. van den Bosch, A. Bronkhorst, M. A. Neerincx, J. M. Schraagen and S. Raaijmakers, Hybrid collective intelligence in a human-AI society. AI & Society 36, 217–238 (2021). [Google Scholar]
  29. V. Kolbjørnsrud, Designing the intelligent organization: Six principles for human-AI collaboration. California Management Review 66(2), 44–64 (2024). [Google Scholar]
  30. M. Mariani and Y. K. Dwivedi, Generative artificial intelligence in innovation management: A preview of future research developments. Journal of Business Research 175, 114542 (2024). [Google Scholar]
  31. N. Anumbe, C. Saidy, and R. Harik, A primer on the factories of the future. Sensors 22(15), 5834 (2022). [Google Scholar]
  32. C. J. Turner, R. Ma, J. Chen, and J. Oyekan, Human in the loop: Industry 4.0 technologies and scenarios for worker mediation. IEEE Access 9, 103950– 103966 (2021). [Google Scholar]
  33. P. Ye, X. Wang, W. Zheng, Q. Wei, and F.-Y. Wang, Parallel cognition: Hybrid intelligence for human-machine interaction. Frontiers of Information Technology & Electronic Engineering 23(12), 1765–1779 (2022). [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.