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Navigating Transparency in AI-Powered Luxury Hospitality: A Dynamic Guest-Centric Approach

Tilen Pigac et al.

Cornell Hospitality Quarterly2026https://doi.org/10.1177/19389655261433944article
AJG 2ABDC A
Weight
0.50

Abstract

This study explores how artificial intelligence (AI) transparency can be designed to enhance trust and guest experience in luxury hospitality. Drawing on 50 semi-structured interviews with hotel guests across Europe, Asia, and North America, and segmented using the CEW Technology Comfort Scale, the research develops the Dynamic Transparency Protocol (DTP) framework. Findings reveal that transparency preferences vary across guest profiles and service stages, shaped by three adaptive mechanisms: user-centric adaptation, situational sensitivity, and emotional matching. Guests with lower digital comfort valued human-mediated, simplified disclosures, while digital elites demanded customizable dashboards and traceability. Across segments, emotional resonance emerged as critical for perceived fairness and trust, reframing transparency as both informational and affective. The study contributes by contextualizing transparency and trust frameworks in a luxury service setting and offers actionable guidance for managers on tiered transparency design, emotionally tuned interfaces, and hybrid human–AI mediation.

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https://doi.org/https://doi.org/10.1177/19389655261433944

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@article{tilen2026,
  title        = {{Navigating Transparency in AI-Powered Luxury Hospitality: A Dynamic Guest-Centric Approach}},
  author       = {Tilen Pigac et al.},
  journal      = {Cornell Hospitality Quarterly},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/19389655261433944},
}

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Navigating Transparency in AI-Powered Luxury Hospitality: A Dynamic Guest-Centric Approach

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.