International Journal of Service Excellence

Call for Papers

Special Issue: Service Quality in the Age of Artificial Intelligence

The rapid advancement of artificial intelligence (AI) technologies is reshaping how organizations design, deliver, and evaluate service quality. From AI-enabled personalization to predictive analytics and intelligent systems, these innovations provide unprecedented opportunities to enhance efficiency, responsiveness, and customer satisfaction. At the same time, they raise challenges concerning ethics, transparency, trust, and the delicate balance between automation and the human touch.

This special issue seeks to bring together diverse perspectives that deepen our understanding of how AI is transforming service excellence. We welcome contributions that are qualitative, quantitative, experimental, or review-based, as long as they advance theoretical insights, practical applications, or methodological innovations in this field.

Potential Topics and Scholarly Discussions

1. AI-enabled personalization and its impact on customer experience
   AI-driven personalization enhances customer engagement, satisfaction, and loyalty, yet raises questions about privacy and fairness. Recent studies highlight how tailored digital journeys affect consumer perceptions in social media and advertising contexts, offering both opportunities and ethical dilemmas (Teepapal, 2025; Hardcastle, Vorster, & Brown, 2025).
2. Intelligent systems for monitoring and improving service quality
   Intelligent analytics and AI-powered systems enable organizations to track and manage service delivery in real time, leading to more resilient and adaptive operations. Research shows customers increasingly assess service quality not just by outcomes but also by the processes AI enables (Mariani & Borghi, 2024; Yang, Blount, & Amrollahi, 2024).
3. Ethical, cultural, and trust-related challenges in AI-mediated services
   Despite efficiency gains, ethical issues such as algorithmic bias, transparency, and accountability remain central. Cultural perceptions of fairness and trust strongly influence consumer acceptance of AI-enabled services, particularly in contexts where identity diversity and morality are salient (Giroux, Kim, & Park, 2022; Lu & Zhang, 2025).
4. Human–AI collaboration in service delivery
   AI is increasingly deployed to augment rather than replace human service workers. Studies show that clarity in role division enhances service quality while mitigating over-reliance on automation. Human–AI partnerships are most effective when AI augments decision-making and humans provide empathy and contextual judgment (Lin, Wang, Shao, & Taylor, 2024; Schweitzer, Narayanan, McGuire, & De Cremer, 2025).
5. Service recovery and complaint handling in AI-enabled environments
   While AI chatbots and voice assistants can accelerate complaint resolution, their limited empathy may undermine customer satisfaction in high-stakes recovery situations. Empirical work suggests that emotional factors mediate how customers react to AI service failures, highlighting the continuing importance of human intervention in sensitive interactions (Li, Liu, Mao, Qu, & Chen, 2023; Hao, Dong, Zhang, & Demir, 2025).
6. Organizational change and adaptation during AI adoption
   Integrating AI into service delivery requires more than technological investment. Structural redesign, leadership commitment, and workforce reskilling are essential to prevent resistance and maximize benefits. Frameworks for AI adoption stress the importance of behavioral, cultural, and strategic alignment (Chalutz-Ben Gal & Ben-Yehuda, 2024; Schweitzer et al., 2025).
7. Predictive analytics and automation effects on customer satisfaction
   AI-enabled predictive tools and automation enhance responsiveness and personalization in service contexts. Yet excessive automation risks eroding the “human touch” valued by customers. Research shows that service robots and AI tools can boost satisfaction in sensitive situations, but careful design is needed to maintain authenticity (Guo, Gong, Xu, Wang, & Chen, 2024).
8. Future directions of service quality metrics in the AI era
   Traditional service quality measures may no longer suffice in environments where AI is central to customer interactions. Future research must explore new frameworks that capture both human and machine contributions to service value, combining metrics for efficiency, personalization, ethics, and trust.

Submission Deadline: 10 December 2025

Please follow the journal’s submission guidelines closely.

Submit articles here.

All submissions must contain original unpublished work not under review elsewhere. Accepted manuscripts will appear in a forthcoming issue of the International Journal of Service Excellence.

References

• Chalutz-Ben Gal, H., & Ben-Yehuda, B. (2024). Adoption of artificial intelligence: A TOP framework-based checklist for digital leaders. Business Horizons, 67(4), 357–368. https://doi.org/10.1016/j.bushor.2024.04.006
• Giroux, M., Kim, J., & Park, J. (2022). Artificial intelligence and declined guilt: Retailing morality comparison between human and AI. Journal of Business Ethics, 178(4), 1027–1041. https://doi.org/10.1007/s10551-021-04959-9
• Guo, L., Gong, L., Xu, Z., Wang, W., & Chen, M.-H. (2024). The role of service robots in enhancing customer satisfaction in embarrassing contexts. Journal of Hospitality and Tourism Management, 59, 116–126. https://doi.org/10.1016/j.jhtm.2024.04.008
• Hao, X., Dong, D., Zhang, Y., & Demir, E. (2025). When customers know it’s AI: Experimental comparison of human and LLM-based communication in service recovery. Journal of Marketing Communications. Advance online publication. https://doi.org/10.1080/13527266.2025.2540376
• Hardcastle, K., Vorster, L., & Brown, D. M. (2025). Understanding customer responses to AI-driven personalized journeys: Impacts on the customer experience. Journal of Advertising, 54(2), 176–195. https://doi.org/10.1080/00913367.2025.2460985
• Li, B., Liu, L., Mao, W., Qu, X., & Chen, Y. (2023). Voice AI service failure and customer complaint behavior: The mediation effect of customer emotion. Electronic Commerce Research and Applications, 59, 101261. https://doi.org/10.1016/j.elerap.2023.101261
• Lin, X., Wang, X., Shao, B., & Taylor, J. (2024). How chatbots augment human intelligence in customer services: A mixed-methods study. Journal of Management Information Systems, 41(4), 1016–1047. https://doi.org/10.1080/07421222.2024.2415773
• Lu, Y., & Zhang, J. (2025). Balancing identity diversity and product contexts: Understanding consumer trust in AI-enhanced chatbot services. Journal of Retailing and Consumer Services, 84, 104205. https://doi.org/10.1016/j.jretconser.2024.104205
• Mariani, M. M., & Borghi, M. (2024). Artificial intelligence in service industries: Customers’ assessment of service production and resilient service operations. International Journal of Production Research, 62(15), 5400–5416. https://doi.org/10.1080/00207543.2022.2160027
• Schweitzer, S., Narayanan, D., McGuire, J., & De Cremer, D. (2025). Leading AI adoption in organizations: Introducing a behavioral human-centered approach. International Journal of Human–Computer Interaction. Advance online publication. https://doi.org/10.1080/10447318.2025.2531287
• Teepapal, T. (2025). AI-driven personalization: Unraveling consumer perceptions in social media engagement. Computers in Human Behavior, 165, 108549. https://doi.org/10.1016/j.chb.2024.108549
• Yang, J., Blount, Y., & Amrollahi, A. (2024). Artificial intelligence adoption in a professional service industry: A multiple case study. Technological Forecasting and Social Change, 201, 123251. https://doi.org/10.1016/j.techfore.2024.123251