This study examines hotel guests’ perceptions of AI-generated versus human-generated pricing, focusing on the roles of trustworthiness and fairness in booking preferences. While revenue management traditionally emphasizes price outcomes, growing interest in transparency raises questions about whether consumers should be informed about how prices are determined, particularly when AI is involved. We conducted two experiments using a discrete choice framework and used logistic regression to investigate how guests respond to different pricing sources. Study 1 held price constant, and Study 2 introduced variable price levels to estimate willingness to pay. Results consistently showed a preference for human-generated pricing. Trust in human pricing reduced the likelihood of selecting AI-driven options, while fairness perceptions increased the likelihood of choosing AI pricing. These findings contribute to an emerging discussion on the relevance of pricing process disclosure. Focusing on hotel guests, this study highlights the consumer perspective in evaluating AI adoption in revenue management.
AkerlofGA (1970) The market for “lemons”: quality uncertainty and the market mechanism. Quarterly Journal of Economics84(3): 488–500.
2.
AlrawadiehZAlrawadiehZÇetinG (2021) Digital transformation and revenue management: evidence from the hotel industry. Tourism Economics27(2): 328–345.
3.
AntonioNDe AlmeidaANunesL (2017) Predicting hotel booking cancellations to decrease uncertainty and increase revenue. Tourism & Management Studies13(2): 25–39.
4.
AydinNBirbilSI (2018) Decomposition methods for dynamic room allocation in hotel revenue management. European Journal of Operational Research271(1): 179–192.
5.
BarneyJ (1991) Firm resources and sustained competitive advantage. Journal of Management17(1): 99–120.
6.
Ben-AkivaMELermanSR (1985) Discrete Choice Analysis: Theory and Application to Travel Demand. MIT Press, Vol. 9.
BoltonLEWarlopLAlbaJW (2003) Consumer perceptions of price (un)fairness. Journal of Consumer Research29(4): 474–491.
9.
BoltonLEKehHTAlbaJW (2010) How do price fairness perceptions differ across culture?Journal of Marketing Research47(3): 564–576.
10.
Boto-GarcíaDMarielPPinoJB, et al. (2022) Tourists’ willingness to pay for holiday trip characteristics: a discrete choice experiment. Tourism Economics28(2): 349–370.
CrossRGHigbieJACrossZN (2011) Milestones in the application of analytical pricing and revenue management. Journal of Revenue and Pricing Management10: 8–18.
Denizci GuilletBShiX (2019) Can revenue management be integrated with customer relationship management?International Journal of Contemporary Hospitality Management Management31(2): 978–997.
19.
DoborjehZHemmingtonNDoborjehM, et al. (2022) Artificial intelligence: a systematic review of methods and applications in hospitality and tourism. International Journal of Contemporary Hospitality Management34(3): 1154–1176.
EganDHaynesNC (2019) Manager perceptions of big data reliability in hotel revenue management decision making. International Journal of Quality & Reliability Management36(1): 25–39.
22.
ErdemMJiangL (2016) An overview of hotel revenue management research and emerging key patterns in the third millennium. Journal of Hospitality and Tourism Technology Technology7(3): 300–312.
23.
ErdemTSwaitJ (1998) Brand equity as a signaling phenomenon. Journal of Consumer Psychology Psychology7(2): 131–157.
24.
ErdmannAYazdaniMMas IglesiasJM, et al. (2024) Pricing powered by artificial intelligence: an assessment model for the sustainable implementation of AI supported price functions. Informatica35(3): 529–556.
25.
EvansJSBT (2008) Dual-processing accounts of reasoning, judgment, and social cognition. Annual Review of Psychology59: 255–278.
26.
FassnachtMUnterhuberS (2016) Consumer response to online/offline price differentiation. Journal of Retailing and Consumer Services28: 137–148.
27.
FilieriRLinZPinoG, et al. (2021) The role of visual cues in eWOM on consumers' behavioral intention and decisions. Journal of Business Research135: 663–675.
28.
GarbarinoELeeOF (2003) Dynamic pricing in internet retail: effects on consumer trust. Psychology and Marketing20(6): 495–513.
29.
GarciaDTolvanenJWagnerAK (2022) Demand estimation using managerial responses to automated price recommendations. Management Science68(11): 7918–7939, Click or tap here to enter text.
30.
GazzoliGGon KimWPalakurthiR (2008) Online distribution strategies and competition: are the global hotel companies getting it right?International Journal of Contemporary Hospitality Management20(4): 375–387.
31.
GefenD (2000) E-commerce: the role of familiarity and trust. Omega28(6): 725–737.
32.
Gonzalez-JimenezHCosta PintoD (2024) Can AI robots foster social inclusion? Exploring the role of immersive augmentation in hospitality. International Journal of Contemporary Hospitality Management36(11): 3889–3905.
GursoyDLiYSongH (2023) ChatGPT and the hospitality and tourism industry: an overview of current trends and future research directions. Journal of Hospitality Marketing & Management32(5): 579–592.
36.
HannaRCLemonKNSmithGE (2019) Is transparency a good thing? How online price transparency and variability can benefit firms and influence consumer decision making. Business Horizons62(2): 227–236.
37.
HausmanJAWiseDA (1978) A conditional probit model for qualitative choice: discrete decisions recognizing interdependence and heterogeneous preferences. Econometrica46(2): 403–426.
38.
HawsKLBeardenWO (2006) Dynamic pricing and consumer fairness perceptions. Journal of Consumer Research33(3): 304–311.
39.
HoyosD (2010) The state of the art of environmental valuation with discrete choice experiments. Ecological Economics69(8): 1595–1603.
40.
IvanovSWebsterC (2020) Robots in tourism: a research agenda for tourism economics. Tourism Economics26(7): 1065–1085.
41.
KahnemanD (2011) Thinking, Fast and Slow. Farrar, Straus and Giroux.
42.
KahnemanDKnetschJLThalerRH (1986) Fairness as a constraint on profit seeking: entitlements in the market. The American Economic Review76(4): 728–741.
43.
KempermanA (2021) A review of research into discrete choice experiments in tourism: launching the annals of tourism research curated collection on discrete choice experiments in tourism. Annals of Tourism Research87: 103137.
44.
KimHWXuYGuptaS (2012) Which is more important in internet shopping, perceived price or trust?Electronic Commerce Research and Applications11(3): 241–252.
KyriakouKOtterbacherJ (2023) In humans, we trust: multidisciplinary perspectives on the requirements for human oversight in algorithmic processes. Discover Artificial Intelligence Intelligence3(1): 44.
47.
LancsarELouviereJ (2008) Conducting discrete choice experiments to inform healthcare decision making: a user’s guide. PharmacoEconomics26(8): 661–677.
48.
LeeSWSharmaA (2025) Beyond rate parity: examining offer uniqueness and channel credibility in hotel pricing. Tourism Economics31(2): 309–331.
49.
LeeMJeongMSheaLJ (2021) Length of stay control: is it a fair inventory management strategy in hotel market?Tourism Economics27(2): 307–327.
50.
LouviereJJFlynnTNCarsonRT (2010) Discrete choice experiments are not conjoint analysis. Journal of Choice Modelling3(3): 57–72.
51.
MalcDMumelDPisnikA (2016) Exploring price fairness perceptions and their influence on consumer behavior. Journal of Business Research69(9): 3693–3697.
52.
MarianiMMPerez-VegaRWirtzJ (2022) AI in marketing, consumer research and psychology: a systematic literature review and research agenda. Psychology and Marketing39(4): 755–776.
MasieroLHeoCYPanB (2015) Determining guests’ willingness to pay for hotel room attributes with a discrete choice model. International Journal of Hospitality Management49: 117–124.
55.
MasieroLYangYQiuRT (2019) Understanding hotel location preference of customers: comparing random utility and random regret decision rules. Tourism Management Management73: 83–93.
56.
MayerRCDavisJHSchoormanFD (1995) An integrative model of organizational trust. Academy of Management Review20(3): 709–734.
57.
McFaddenD (1974). Conditional logit analysis of qualitative choice behavior. In ZarembkaP (Ed.), Frontiers in Econometrics (pp. 105–142). Academic Press.
58.
MerkertRBeckM (2017) Value of travel time savings and willingness to pay for regional aviation. Transportation Research Part A: Policy and Practice96: 29–42.
59.
MilmanATasciAD (2023) The influence of dynamic pricing on consumer trust, value, and loyalty relationships in theme parks. Journal of Vacation Marketing29(3): 386–408.
60.
MohammedIDenizci GuilletB (2025) Heuristics and biases in human–algorithm interaction and hotel revenue management override decision-making. International Journal of Contemporary Hospitality Management Journal of Contemporary Hospitality Management37(2): 358–379.
61.
NooneBMMattilaAS (2009) Hotel revenue management and the internet: the effect of price presentation strategies on customers' willingness to book. International Journal of Hospitality Management28(2): 272–279.
62.
OhanianR (1990) Construction and validation of a scale to measure celebrity endorsers' perceived expertise, trustworthiness, and attractiveness. Journal of Advertising19(3): 39–52.
ParkSLehtoX (2021) Understanding the opaque priority of safety measures and hotel customer choices after the COVID-19 pandemic: an application of discrete choice analysis. Journal of Travel & Tourism Marketing38(7): 653–665.
65.
PearmainDSwansonJKroesE, et al. (1991) Stated Preference Techniques: A Guide to Practice. 2nd edition. Steer Davies Gleave and Hague Consulting Group.
66.
PereiraLNCerqueiraV (2022) Forecasting hotel demand for revenue management using machine learning regression methods. Current Issues in Tourism25(17): 2733–2750.
67.
PiercyNFCravensDWLaneN (2010) Thinking strategically about pricing decisions. Journal of Business Strategy31(5): 38–48.
68.
PriesterARobbertTRothS (2020) A special price just for you: effects of personalized dynamic pricing on consumer fairness perceptions. Journal of Revenue and Pricing Management Management19: 99–112.
SallJStephensMLLehmanA, et al. (2017) JMP Start Statistics: A Guide to Statistics and Data Analysis Using JMP. 6th edition. SAS Institute.
71.
SetiawanRAchyarA (2012) Effects of perceived trust and perceived price on customers’ intention to buy in online Store in Indonesia. ASEAN Marketing Journal4(1): 26–36.
SparksBABrowningV (2011) The impact of online reviews on hotel booking intentions and perception of trust. Tourism Management32(6): 1310–1323.
74.
TalluriKTVan RyzinGJ (2006) The Theory and Practice of Revenue Management. Springer Science & Business Media, Vol. 68.
75.
ThurstoneLL (1927) A law of comparative judgment. Psychological Review34(5): 273–286.
76.
TrainKE (2009) Discrete Choice Methods with Simulation. 2nd edition. Cambridge University Press.
77.
TrainKWeeksM (2005) Discrete choice models in preference space and willingness-to- pay space. In: ScarpaRAlberiniA (eds) Applications of Simulation Methods in Environmental and Resource Economics. Springer, 1–16.
78.
VermaRPlaschkaGLouviereJ (2002) Configuring service operations in accordance with customer needs and preferences. Journal of Service Research1(3): 262–274.
79.
WangXLYoonjoung HeoCSchwartzZ, et al. (2015) Revenue management: progress, challenges, and research prospects. Journal of Travel & Tourism Marketing Marketing32(7): 797–811.
80.
WirtzJKimesSE (2007) The moderating role of familiarity in fairness perceptions of revenue management pricing. Journal of Service Research9(3): 229–240.
81.
XiaLMonroeKBCoxJL (2004) The price is unfair! A conceptual framework of price fairness perceptions. Journal of Marketing68(4): 1–15.
82.
YeCLiuJRenF, et al. (2000) Design of experiment and data analysis by JMP® (SAS institute) in analytical method validation. Journal of Pharmaceutical and Biomedical Analysis23(2-3): 581–589.
83.
ZhangBHuangXLiN, et al. (2017) A novel hybrid model for tourist volume forecasting incorporating search engine data. Asia Pacific Journal of Tourism Research22(3): 245–254.
