Oct 2026· Tourism Management Perspectives· 74 references
AI in Service Interactions
Abstract
Artificial intelligence (AI) empowers tourists by improving their decision-making efficiency, while simultaneously undermining their sense of autonomy and control, thereby producing disempowering effects. The pathways and mechanisms underlying these effects require in-depth exploration. Based on conservation of resources (COR) theory, this study investigates the underlying mechanism and boundary conditions of how AI autonomy influences tourists' continuance usage intention in travel contexts. A total of 318 valid samples were collected via the Credamo platform, and a moderated mediation model was employed to test the proposed hypotheses. The results indicate that AI autonomy positively influences tourists' continuance usage intention through cognitive offloading, while simultaneously exerting a negative influence through loss of control. Perceived AI explainability serves as a significant moderator: it positively moderates the relationship between AI autonomy and cognitive offloading, while attenuating the relationship between AI autonomy and loss of control. This study not only enriches theoretical research on AI empowerment in tourism contexts but also provides practical guidance for tourism enterprises to optimize AI tool design and achieve effective AI empowerment.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.
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