Have a personal or library account? Click to login
The Impact of AI on Service Industries: A Bibliometric Study of Current Research Trends Cover

The Impact of AI on Service Industries: A Bibliometric Study of Current Research Trends

Open Access
|Jul 2025

References

  1. Adam, M., Wessel, M., & Benlian, A. (2021). AI-based chatbots in customer service and their effects on user compliance. Electronic Markets, 31(2), 427–445.
  2. Araujo, T. (2018). Living up to the chatbot hype: The influence of anthropomorphic design cues and communicative agency framing on conversational agent and company perceptions. Computers in Human Behavior, 85, 183–189.
  3. Bhowmik, A., Sannigrahi, M., Chowdhury, D., Dwivedi, A. D., & Rao Mukkamala, R. (2022). DBNex: Deep Belief Network and Explainable AI based Financial Fraud Detection. In 2022 IEEE International Conference on Big Data (pp. 3033–3042). IEEE.
  4. Bigman, Y. E., Yam, K. C., Marciano, D., Reynolds, S. J., & Gray, K. (2021). The threat of racial and economic inequality increases preference for algorithm decision-making. Computers in Human Behavior, 122, 106859.
  5. Biswas, B., Sengupta, P., Kumar, A., Delen, D., & Gupta, S. (2022). A critical assessment of consumer reviews: A hybrid NLP-based methodology. Decision Support Systems, 159, 113799.
  6. Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., … & Amodei, D. (2020). Language models are few-shot learners. In Proceedings of the 34th Conference on Neural Information Processing Systems (NeurIPS).
  7. Chang, T., & Morales, V. (2025). Responsible AI in healthcare: A bibliometric analysis of ethical and operational issues. Journal of Service Management, 37(1), 49–66.
  8. Chung, M., Ko, E., Joung, H., & Kim, S. J. (2020). Chatbot e-service and customer satisfaction regarding luxury brands. Journal of Business Research, 120, 74–85.
  9. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT) (pp. 4171–4186).
  10. Doshi-Velez, F. (2021). The present and future of AI. Harvard John A. Paulson School of Engineering and Applied Sciences.
  11. Espina-Romero, L., Noroño Sánchez, J. G., Gutiérrez Hurtado, H., Dworaczek Conde, H., Solier Castro, Y., Cervera Cajo, L. E., & Rio Corredoira, J. (2023). Which industrial sectors are affected by artificial intelligence? A bibliometric analysis of trends and perspectives. Sustainability, 15(16), 12176.
  12. Feigenbaum, E. A. (1984). Knowledge engineering: The applied side of artificial intelligence. Annals of the New York Academy of Sciences, 426(1), 91–107.
  13. Fitzsimmons, J. A., Fitzsimmons, M. J., & Bordoloi, S. K. (2014). Service Management: Operations, Strategy, Information Technology (9th ed.). McGraw-Hill Education.
  14. García-Díaz, V., Pascual-Espada, J., Pelayo G-Bustelo, C., & Cueva-Lovelle, J. M. (2015). Towards a standard-based domain-specific platform to solve machine learning-based problems. International Journal of Interactive Multimedia and Artificial Intelligence, 3(5), 6.
  15. Huang, M.-H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155–172.
  16. Jeremy Cook (n.d.). Introduction to Machine Learning on AWS. QA
  17. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
  18. Lu, V. N., Wirtz, J., Kunz, W. H., Paluch, S., Gruber, T., Martins, A., & Patterson, P. G. (2020). Service robots, customers, and service employees: What can we learn from academic literature and where are the gaps? Journal of Service Theory and Practice, 30(3), 361–391.
  19. Luo, X., Tong, S., Fang, Z., & Qu, Z. (2019). Machines vs. humans: The impact of AI chatbot disclosure on customer purchases. Marketing Science, 38(6), 937–947.
  20. McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. (1955). A proposal for the Dartmouth summer research project on artificial intelligence. Retrieved from Stanford University website.
  21. Minsky, M., & Papert, S. (1969). Perceptrons: An introduction to computational geometry. MIT Press.
  22. Mishra, R. K., Raj, H., Urolagin, S., Jothi, J. A. A., & Nawaz, N. (2022). Cluster-based knowledge graph and entity-relation representation on tourism economical sentiments. Applied Sciences, 12(8105), 8105.
  23. Mitchell, M. (1998). An Introduction to Genetic Algorithms. MIT Press.
  24. Nadella, S. (2016, June 28). Microsoft CEO Satya Nadella: Humans and A.I. can work together to solve society’s challenges. Slate.
  25. Ok, Ş. (2024). The impact of artificial intelligence on the service industry and consumer behavior: A bibliometric analysis. The Eurasia Proceedings of Science, Technology, Engineering & Mathematics (EPSTEM), 27, 203–213.
  26. Rosenblatt, F. (1957). The Perceptron—a perceiving and recognizing automaton (Project Para). Cornell Aeronautical Laboratory.
  27. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533–536.
  28. Sarker, I. H. (2021). Deep learning: A comprehensive overview on techniques, taxonomy, applications and research directions. SN Computer Science, 2(1), 420.
  29. Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2(1), 160.
  30. Sheehan, B., Jin, H. S., & Gottlieb, U. (2020). Customer service chatbots: Anthropomorphism and adoption. Journal of Business Research, 115, 14–24.
  31. Smith, P., & Garcia, A. (2025). Bridging AI analytics and human-centric service designs: A roadmap for retail experiences. Service Science, 17(2), 58–70.
  32. Takayanagi, H. (2019). Expectation for AI (Artificial Intelligence) on elderly medical care. Japanese Journal of Geriatrics, 56, 254–259.
  33. Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433–460.
  34. Van Eck, N. J., & Waltman, L. (2014). Visualizing bibliometric networks. In Y. Ding, R. Rousseau, & D. Wolfram (Eds.), Measuring scholarly impact (pp. 285–320). Springer.
  35. Zou, K. H., & Li, J. Z. (2022). Enhanced Patient-Centricity: How the Biopharmaceutical Industry Is Optimizing Patient Care through AI/ML/DL. Healthcare, 10(10), 1997.
Language: English
Page range: 3594 - 3608
Published on: Jul 24, 2025
Published by: The Bucharest University of Economic Studies
In partnership with: Paradigm Publishing Services
Publication frequency: 1 times per year

© 2025 Cristina Vasilescu, Militaru Gheorghe, published by The Bucharest University of Economic Studies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.