Have a personal or library account? Click to login

Business Operations through Artificial Intelligence: Applications and Innovations across All Departments

Proceedings of the International Conference on Business Excellence
Volume 19 (2025): Issue 1 (July 2025)
By:
Open Access
|Jul 2025

References

  1. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108
    Search in Google ScholarBack to article
  2. Barta, G., & Görcsi, G. (2021). Risk management considerations for artificial intelligence business applications. International Journal of Economics and Business Research, 21(1), 87–106. https://doi.org/10.1504/IJEBR.2021.112012
    Search in Google ScholarBack to article
  3. Boehm, M. (2018). AI in HR: The new wave of recruitment. Journal of Human Resources Management, 45(3), 215–229.
    Search in Google ScholarBack to article
  4. Buntak, K., Kovačić, M., & Mutavdžija, M. (2021). Application of artificial intelligence in business. International Journal for Quality Research, 15(2), 403–416. https://doi.org/10.24874/IJQR15.02-03
    Search in Google ScholarBack to article
  5. Chui, M., Manyika, J., & Miremadi, M. (2018). AI, automation, and the future of work. McKinsey Quarterly, 12(3), 6–11.
    Search in Google ScholarBack to article
  6. Davenport, T., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.
    Search in Google ScholarBack to article
  7. Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011). The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50(3), 559–569.
    Search in Google ScholarBack to article
  8. Njeru, F. (2023). A review of artificial intelligence and its application in business. Journal of Enterprise and Business Intelligence, 3(1), 44–53. https://doi.org/10.53759/5181/JEBI202303005
    Search in Google ScholarBack to article
  9. Meghișan-Toma, G.-M., Puiu, S., Florea, N., Meghișan, F., Bădîrcea, R., & Manta, A. (2022). Sustainable transformation of Romanian companies through Industry 4.0, green production, and environmental commitment. Amfiteatru Economic, 24(59), 46–60. https://doi.org/10.24818/EA/2022/59/46
    Search in Google ScholarBack to article
  10. Ruiz-Real, J. L., Uribe-Toril, J., Torres, J. A., & De Pablo, J. (2021). Artificial intelligence in business and economics research: Trends and future. Journal of Business Economics and Management, 22(1), 98–117. https://doi.org/10.3846/jbem.2020.13641
    Search in Google ScholarBack to article
  11. Loureiro, S. M. C., Guerreiro, J., & Tussyadiah, I. (2021). Artificial intelligence in business: State of the art and future research agenda. Journal of Business Research, 129, 911–926. https://doi.org/10.1016/j.jbusres.2020.11.001
    Search in Google ScholarBack to article
  12. Waller, M. A., & Fawcett, S. E. (2013). Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77–84.
    Search in Google ScholarBack to article
  13. Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. W.W. Norton & Company.
    Search in Google ScholarBack to article
  14. Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson.
    Search in Google ScholarBack to article
  15. Tornatzky, L., & Fleischer, M. (1990). The processes of technological innovation. Lexington Books.
    Search in Google ScholarBack to article
  16. Van der Aalst, W. (2016). Process mining: Data science in action. Springer.
    Search in Google ScholarBack to article
  17. Runyue, H. (2021). Artificial intelligence in business-to-business marketing: A bibliometric analysis of current research status, development, and future directions.
    Search in Google ScholarBack to article
  18. Neamţu, D. M. (2023). Education and economic development: A social and statistical analysis. Springer. https://doi.org/10.1007/978-3-031-20382-4
    Search in Google ScholarBack to article
  19. IBM HR Analytics Employee Attrition & Performance Dataset. (n.d.). Kaggle. Retrieved from https://www.kaggle.com/datasets/pavansubhasht/ibm-hr-analytics-attrition-dataset
    Search in Google ScholarBack to article
  20. UCI Machine Learning Repository. (n.d.). Bank Marketing Dataset. Retrieved from https://archive.ics.uci.edu/dataset/222/bank+marketing
    Search in Google ScholarBack to article
  21. UCI Machine Learning Repository. (n.d.). Default of Credit Card Clients Dataset. Retrieved from https://archive.ics.uci.edu/dataset/350/default+of+credit+card+clients
    Search in Google ScholarBack to article
DOI: https://doi.org/10.2478/picbe-2025-0374 | Journal eISSN: 2558-9652
Journal RSS Feed
Language: English
Page range: 4897 - 4912
Published on: Jul 24, 2025
Published by: Bucharest University of Economic Studies
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year
Keywords:
Artificial intelligence,
Business departments,
Improve,
Impact,
Operations
Related subjects:
Business and economics,
Political economics,
Political economics, other,
Business management,
Business management, other,
Industrial chemistry,
Energy harvesting and conversion

© 2025 Claudiu Anghel, Daniela Mihaela Neamţu, published by Bucharest University of Economic Studies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Previous articleVolume 19 (2025): Issue 1 (July 2025)Next article