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.
AamirS.AtsanN.KhanM. S. (2025). Going digital with multisided-platforms: Assessing the innovation adoption process from the perspectives of travel agents. Tourism and Hospitality Research, 25(1), 72–89. https://doi.org/10.1177/14673584231186535
2.
AffandiS.IshaqM. I.RazaA.AhmadR. (2025). AI assistant is my new best friend! Role of emotional disclosure, performance expectations and intention to reuse. Journal of Retailing and Consumer Services, 82, 104087. https://doi.org/10.1016/j.jretconser.2024.104087
3.
AnannyM.CrawfordK. (2016). Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media & Society, 20(3), 973–989. https://doi.org/10.1177/1461444816676645
4.
AndradaG.ClowesR. W.SmartP. R. (2023). Varieties of transparency: Exploring agency within AI systems. AI & Society, 38(4), 1321–1331. https://doi.org/10.1007/s00146-021-01326-6
5.
AssiourasI.LasererC.BuhalisD. (2025). The evolution of artificial empathy in the hospitality metaverse era. International Journal of Hospitality Management, 126, 104063. https://doi.org/10.1016/j.ijhm.2024.104063
6.
AyyildizA. Y.BaykalM.KocE. (2022). Attitudes of hotel customers towards the use of service robots in hospitality service encounters. Technology in Society, 70, 101995. https://doi.org/10.1016/j.techsoc.2022.101995
7.
BachT. A.KhanA.HallockH.BeltrãoG.SousaS. (2024). A systematic literature review of user trust in AI-enabled systems: An HCI perspective. International Journal of Human–computer Interaction, 40(5), 1251–1266. https://doi.org/10.1080/10447318.2022.2138826
BinnsR.VealeM.Van KleekM.ShadboltN. (2018, April21–26). “It’s reducing a human being to a percentage”: Perceptions of justice in algorithmic decisions [Paper presentation]. CHI Conference on Human Factors in Computing Systems, Montreal, QC, Canada. https://doi.org/10.1145/3173574.3173951
10.
BlazevicV.SidaouiK. (2022). The TRISEC framework for optimizing conversational agent design across search, experience and credence service contexts. Journal of Service Management, 33(4/5), 733–746. https://doi.org/10.1108/JOSM-10-2021-0402
BraunV.ClarkeV. (2021). Thematic analysis: A practical guide. SAGE.
13.
BraunV.ClarkeV. (2023). Toward good practice in thematic analysis: Avoiding common problems and be(com)ing a knowing researcher. International Journal of Transgender Health, 24(1), 1–6. https://doi.org/10.1080/26895269.2022.2129597
14.
BrochadoA.RitaP.MargaridoA. (2016). High tech meets high touch in upscale hotels. Journal of Hospitality and Tourism Technology, 7(4), 347–365. https://doi.org/10.1108/JHTT-07-2016-0038
15.
BuhalisD.SinartaY. (2019). Real-time co-creation and “nowness” service: Lessons from tourism and hospitality. Journal of Travel & Tourism Marketing, 36(5), 563–582. https://doi.org/10.1080/10548408.2019.1592059
BunzU. (2004). The computer-email-web (CEW) fluency scale-development and validation. International Journal of Human-Computer Interaction, 17(4), 479–506. https://doi.org/10.1207/s15327590ijhc1704_3
18.
CainL. N.MistryT. G.DouglasS.RahmanI.MoreoA. (2024). Luxury hotel technology trends: A multigenerational analysis. International Journal of Contemporary Hospitality Management, 36(12), 4031–4054. https://doi.org/10.1108/IJCHM-10-2023-1597
19.
CenizoC. (2025). Redefining consumer experience through artificial intelligence in the luxury retail sector. Journal of Retailing and Consumer Services, 87, 104416. https://doi.org/10.1016/j.jretconser.2025.104416
20.
ChakrabortyD.GunasekaranA.SubramanianG. (2025). Privacy vs. Personalization: GENAI’s role in technology adoption. Journal of Computer Information Systems, 1–18. https://doi.org/10.1080/08874417.2025.2534545
21.
ChenZ. (2025). Exploring the transformative potential of artificial intelligence in hospitality: A systematic review of applications and challenges. Cornell Hospitality Quarterly, 19389655251380983. https://doi.org/10.1177/19389655251380983
22.
ChoiK.WangY.SparksB. A.ChoiS. M. (2023). Privacy or security: Does it matter for continued use intention of travel applications?Cornell Hospitality Quarterly, 64(2), 267–282. https://doi.org/10.1177/19389655211066834
23.
DenzinN. K.LincolnY. S. (2011). The Sage handbook of qualitative research (5th ed.). SAGE.
24.
DinevT.HartP. (2006). An extended privacy calculus model for e-commerce transactions. Information Systems Research, 17(1), 61–80. https://doi.org/10.1287/isre.1060.0080
25.
DingA.LeeR. H.LegendreT. S.MaderaJ. (2022). Anthropomorphism in hospitality and tourism: A systematic review and agenda for future research. Journal of Hospitality and Tourism Management, 52, 404–415. https://doi.org/10.1016/j.jhtm.2022.07.018
26.
EhsanU.LiaoQ. V.MullerM.RiedlM. O.WeiszJ. D. (2021, May8–13). Expanding explainability: Towards social transparency in AI systems [Paper presentation]. Proceedings of the 2021 CHI conference on human factors in computing systems, Yokohama, Japan. https://doi.org/10.1145/3411764.3445188
27.
ElshaerI. A.AzazzA. M.FayyadS.AljoghaimanA.FathyE. A.FouadA. M. (2025). From asymmetry to satisfaction: The dynamic role of perceived value and trust to boost customer satisfaction in the tourism industry. Tourism and Hospitality, 6(2), 68. https://doi.org/10.3390/tourhosp6020068
28.
EnzC. A. (2012). Strategies for the implementation of service innovations. Cornell Hospitality Quarterly, 53(3), 187–195. https://doi.org/10.1177/1938965512448176
29.
FakfareP.ManosuthiN.LeeJ. S.JinM.HanH.KimJ. J. (2024). Data vulnerability and privacy risk among hotel guests who share personal data. Journal of Vacation Marketing, 13567667241276213. https://doi.org/10.1177/13567667241276213
30.
FloridiL.CowlsJ.BeltramettiM.ChatilaR.ChazerandP.DignumV.LuetgeC.MadelinR.PagalloU.RossiF.SchaferB.ValckeP.VayenaE. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5
31.
Florido-BenítezL. (2024). The cybersecurity applied by online travel agencies and hotels to protect users’ private data in smart cities. Smart Cities, 7(1), 475–495. https://doi.org/10.3390/smartcities7010019
32.
Fung SoKamK.LiX. (2023). Service innovation and emerging technologies in tourism and hospitality. Cornell Hospitality Quarterly, 64(2), 140–142. https://doi.org/10.1177/19389655231166819
33.
GhassemiM.Oakden-RaynerL.BeamA. L. (2021). The false hope of current approaches to explainable artificial intelligence in health care. The Lancet Digital Health, 3(11), e745–e750. https://doi.org/10.1016/S2589-7500(21)00208-9
34.
GonçalvesA. R.Costa PintoD.ShuqairS.MattilaA.ImanbayA. (2024). The paradox of immersive artificial intelligence (AI) in luxury hospitality: How immersive AI shapes consumer differentiation and luxury value. International Journal of Contemporary Hospitality Management, 36(11), 3865–3888. https://doi.org/10.1108/IJCHM-11-2023-1689
35.
GrimmelikhuijsenS. (2023). Explaining why the computer says no: Algorithmic transparency affects the perceived trustworthiness of automated decision-making. Public Administration Review, 83(2), 241–262. https://doi.org/10.1111/puar.13483
36.
GubaE. G.LincolnY. S. (1994). Competing paradigms in qualitative research. Handbook of Qualitative Research, 2, 163–194.
37.
GuestG.BunceA.JohnsonL. (2006). How many interviews are enough? An experiment with data saturation and variability. Field Methods, 18(1), 59–82. https://doi.org/10.1177/1525822X05279903
38.
GutierrizI.FerreiraJ. J.FernandesP. O. (2025). Digital transformation and the new combinations in tourism: A systematic literature review. Tourism and Hospitality Research, 25(2), 194–213. https://doi.org/10.1177/14673584231198414
39.
HanE.YinD.ZhangH. (2023). Bots with feelings: Should AI agents express positive emotion in customer service?Information Systems Research, 34(3), 1296–1311. https://doi.org/10.1287/isre.2022.1179
40.
HanH.HyunS. S. (2018). Role of motivations for luxury cruise traveling, satisfaction, and involvement in building traveler loyalty. International Journal of Hospitality Management, 70, 75–84. https://doi.org/10.1016/j.ijhm.2017.10.024
41.
HaynesN. C. (2020). Robots, artificial intelligence and service automation in travel, tourism and hospitality. Journal of Tourism Futures, 6(2), 191–192. https://doi.org/10.1108/JTF-06-2020-149
42.
HaynesN. C.EganD. (2024). Transient price setting in the era of automated systems: The ‘hands-on’hotel general manager lives on!. Journal of Revenue and Pricing Management, 23(1), 28–38. https://doi.org/10.1057/s41272-023-00447-5
43.
HeinI.DiefenbachS. (2025). Towards a comprehensive view on technology transparency: A cross-technology investigation of psychological and design factors around users’ transparency need and perception. International Journal of Human–Computer Interaction, 41, 15862–15879. https://doi.org/10.1080/10447318.2025.2502983
44.
HerderE.van MaarenO. (2020, July14–17). Privacy dashboards: The impact of the type of personal data and user control on trust and perceived risk [Paper presentation]. Adjunct Publication of the 28th ACM Conference on User Modeling, Adaptation and Personalization, Genoa, pp. 169–174. https://doi.org/10.1145/3386392.3399557
45.
HerrigerC.MerloO.EisingerichA. B.ArigayotaA. R. (2025). Context-contingent privacy concerns and exploration of the privacy paradox in the age of AI, augmented reality, Big Data, and the Internet of Things: Systematic review. Journal of Medical Internet Research, 27, e71951. https://doi.org/10.2196/71951
46.
Hospitality & Travel Working Group. (2024). HATPro implementation guide: Hospitality and travel profile standard (Version 1.0). Decentralized Identity Foundation. https://htwg.identity.foundation/introduction/
HuangY.JiaoJ.ZhuD. (2026). Empowering the traveler: The impact of algorithm transparency and customer control on customer experience. Asia Pacific Journal of Tourism Research, 31(2), 352–370. https://doi.org/10.1080/10941665.2025.2523339
49.
HussainW. (2025). The impact of AI-generated brand personalities on consumer-brand relationship quality: The mediating role of emotional resonance. In Impacts of AI-generated content on brand reputation (pp. 227–244). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3373-4327-3.ch009
50.
IoannouA.TussyadiahI.MillerG. (2021). That’s private! Understanding travelers’ privacy concerns and online data disclosure. Journal of Travel Research, 60(7), 1510–1526. https://doi.org/10.1177/0047287520951642
51.
IvanovS.WebsterC. (Eds.). (2019). Robots, artificial intelligence, and service automation in travel, tourism and hospitality. Emerald Publishing. https://doi.org/10.1108/9781787566873
KimJ. J.SohJ.KadkolS.SolomonI.YehH.SrivatsaA. V.NahassG. R.ChoiJ. Y.LeeS.NyugenT.AjiloreO. (2025). AI anxiety: A comprehensive analysis of psychological factors and interventions. AI and Ethics, 5, 3993–4009. https://doi.org/10.1007/s43681-025-00686-9
54.
KimT. W.JiangL.DuhachekA.LeeH.GarveyA. (2022). Do you mind if I ask you a personal question? How AI service agents alter consumer self-disclosure. Journal of Service Research, 25(4), 649–666. https://doi.org/10.1177/10946705221120232
LehtoX. Y.ParkS.MohamedM. E.LehtoM. R. (2021). Traveler attitudes toward biometric data-enabled hotel services: Can risk education play a role?Cornell Hospitality Quarterly, 64(1), 74–94. https://doi.org/10.1177/19389655211063204
57.
LiF. (Sam)SuQ.GuanJ.ZhangG. (2023). Communicate like humans? Anthropomorphism and hotel consumers’ willingness to pay a premium price. Journal of Hospitality and Tourism Management, 56, 482–492. https://doi.org/10.1016/j.jhtm.2023.08.008
58.
LiF. (Sam)ZhuD.LinM.-T. (Brian)KimP. B. (2024). The technology acceptance model and hospitality and tourism consumers’ intention to use mobile technologies: Meta-analysis and structural equation modeling. Cornell Hospitality Quarterly, 65(4), 461–477. https://doi.org/10.1177/19389655241226558
59.
LiebherrM.EnkelE.LawE. L. C.MousaviM. R.SammartinoM.SiebergP. (2026). Dynamic calibration of trust and trustworthiness in AI-enabled systems. International Journal on Software Tools for Technology Transfer, 1–17. https://doi.org/10.1007/s10009-026-00840-6
60.
LiuS. Q.VakeelK. A.SmithN. A.AlavipourR. S.WeiC. V.WirtzJ. (2024). AI concierge in the customer journey: What is it and how can it add value to the customer?Journal of Service Management, 35(6), 136–158. https://doi.org/10.1108/JOSM-12-2023-0523
61.
Liu-ThompkinsY.OkazakiS.LiH. (2022). Artificial empathy in marketing interactions: Bridging the human–AI gap in affective and social customer experience. Journal of the Academy of Marketing Science, 50(6), 1198–1218. https://doi.org/10.1007/s11747-022-00892-5
62.
LiyanaarachchiG.VigliaG.KurtaliqiF. (2024). Privacy in hospitality: Managing biometric and biographic data with immersive technology. International Journal of Contemporary Hospitality Management, 36(11), 3823–3840. https://doi.org/10.1108/IJCHM-06-2023-0861
63.
LoiM.FerrarioA.ViganòE. (2021). Transparency as design publicity: Explaining and justifying inscrutable algorithms. Ethics and Information Technology, 23(3), 253–263. https://doi.org/10.1007/s10676-020-09564-w
64.
LuL.CaiR.GursoyD. (2019). Developing and validating a service robot integration willingness scale. International Journal of Hospitality Management, 88, 102380. https://doi.org/10.1016/j.ijhm.2019.01.005
65.
MartinK.WaldmanA. (2023). Are algorithmic decisions legitimate? The effect of process and outcomes on perceptions of legitimacy of AI decisions. Journal of Business Ethics, 183(3), 653–670. https://doi.org/10.1007/s10551-021-05032-7
66.
McLeayF.OsburgV.YoganathanV.PattersonA. (2021). Replaced by a robot: Service implications in the age of the machine. Journal of Service Research, 24(1), 104–121. https://doi.org/10.1177/1094670520933354
67.
MemarianS.MalgondeO. S.KimD. J. (2025). Unraveling the privacy paradox: A comprehensive review of factors behind the discrepancy in online concerns and disclosure behavior. Information Systems Frontiers, 1–22. https://doi.org/10.1007/s10796-025-10617-y
68.
MoonH.YuJ.ChuaB. L.HanH. (2022). Hotel privacy management and guest trust building: A relational signaling perspective. International Journal of Hospitality Management, 102, 103171. https://doi.org/10.1016/j.ijhm.2022.103171
69.
NganH. F. B.EddyM. E.FongM. H. (2025). Trust, privacy and smart tourism technologies (STTs): Effects on satisfaction and destination loyalty. Journal of Vacation Marketing, 13567667251383176. https://doi.org/10.1177/13567667251383176
70.
PalinkasL. A.HorwitzS. M.GreenC. A.WisdomJ. P.DuanN.HoagwoodK. (2015). Purposeful sampling for qualitative data collection and analysis in mixed method implementation research. Administration and Policy in Mental Health and Mental Health Services Research, 42(5), 533–544. https://doi.org/10.1007/s10488-013-0528-y
71.
ParkK.YoonH. Y. (2024). Beyond the code: The impact of AI algorithm transparency signaling on user trust and relational satisfaction. Public Relations Review, 50(5), 102507. https://doi.org/10.1016/j.pubrev.2024.102507
72.
PentinaI.HancockT.XieT. (2023). Exploring relationship development with social chatbots: A mixed-method study of replika. Computers in Human Behavior, 140, 107600. https://doi.org/10.1016/j.chb.2022.107600
73.
SeyfiS.Vo-ThanhT.ZamanM. (2024). Hospitality in the age of Gen Z: A critical reflection on evolving customer and workforce expectations. International Journal of Contemporary Hospitality Management, 36(13), 118–134. https://doi.org/10.1108/IJCHM-01-2024-0035
74.
SiposD. (2025). The effects of AI-powered personalization on consumer trust, satisfaction, and purchase intent. European Journal of Applied Science, Engineering and Technology, 3(2), 14–24. https://doi.org/10.59324/ejaset.2025.3(2).02
75.
SongX.LiY.LeungX. Y.MeiD. (2024). Service robots and hotel guests’ perceptions: Anthropomorphism and stereotypes. Tourism Review, 79(2), 505–522. https://doi.org/10.1108/TR-04-2023-0265
76.
TracyS. J. (2010). Qualitative quality: Eight “big-tent” criteria for excellent qualitative research. Qualitative Inquiry, 16(10), 837–851. https://doi.org/10.1177/1077800410383121
77.
TrepteS.ScharkowM.DienlinT. (2020). The privacy calculus contextualized: The influence of affordances. Computers in Human Behavior, 104, 106115. https://doi.org/10.1016/j.chb.2019.08.022
78.
WallsA. R.OkumusF.WangY.KwunD. J. W. (2011). An epistemological view of consumer experiences. International Journal of Hospitality Management, 30(1), 10–21. https://doi.org/10.1016/j.ijhm.2010.03.008
79.
WinfieldA. F.JirotkaM. (2018). Ethical governance is essential to building trust in robotics and artificial intelligence systems. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 376(2133), 20180085. https://doi.org/10.1098/rsta.2018.0085
80.
WuQ.WangJ. (2025). Exploring the nexus between usage motivation and behavioral intention in metaverse hospitality and tourism: Moderation and mediation insights. International Journal of Contemporary Hospitality Management, 37(4), 1045–1064. https://doi.org/10.1108/IJCHM-05-2024-0685
81.
YangB.SunY.ShenX. L. (2025). Building harmonious human–AI relationship through empathy in frontline service encounters: Underlying mechanisms and journey stage differences. International Journal of Contemporary Hospitality Management, 37(3), 740–762. https://doi.org/10.1108/IJCHM-05-2024-0676
82.
YangY.LiuY.LvX.AiJ.LiY. (2022). Anthropomorphism and customers’ willingness to use artificial intelligence service agents. Journal of Hospitality Marketing & Management, 31(1), 1–23. https://doi.org/10.1080/19368623.2021.1926037
83.
YinR. K. (2018). Case study research and applications: Design and methods (6th ed.). SAGE.
