Author Identifier (ORCID)
Abstract
Generative Artificial Intelligence (GenAI) is becoming an integral part of travel planning. This fundamental transformation is changing the definition of travel decision delegation, calling for fresh research into responsible AI in tourism. Drawing on agency theory, trust theory and decision delegation framework, this study conceptualises five responsible AI characteristics (reliability, fairness, trustworthiness, accountability, transparency) as evaluative signals for travellers to assess GenAI across three decision delegation levels, i.e., attribute set, choice set, and final decision. Using a multi-method approach, we analyse data collected from 421 travellers. Findings suggest that reliability and accountability are consistent drivers of delegation across all levels, whereas trustworthiness is a threshold condition that becomes significant at the attribute level. We also found that excessive transparency can lead to cognitive overload and reduce willingness to GenAI decision delegation. Our findings encourage future research into responsible AI development in understanding system complexity, algorithmic explainability and traveller delegation confidence.
Keywords
accountability, automated travel planning, decision delegation, Generative AI, GenAI, reliability, responsible AI, transparency, trust
Document Type
Journal Article
Date of Publication
11-1-2026
Article Number
101512
ISSN
22119736
Volume
64
Publication Title
Tourism Management Perspectives
Publisher
Elsevier
School
School of Business and Law
RAS ID
100696
Funding Information
This research has been funded by Edith Cowan University under VC Professorial Research Fellow appointment.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Roy, S. K., Singh, G., Rasoolimanesh, S. M., Das, R., & Tehrani, A. N. (2026). Decision delegation to GenAI agents in travel planning: Responsible AI signals, delegation levels, and the transparency paradox. Tourism Management Perspectives, 64, Article 101512. https://doi.org/10.1016/j.tmp.2026.101512