Despite significant government investment in Hyperloop transportation across various countries, research on its potential advantages and drawbacks from the tourist’s perspective remains scarce. Guided by prospect theory, this study examines the perceptions and behaviors of frequent (n = 386) and occasional (n = 214) travelers regarding Hyperloop technology, using multiple analytical methods such as partial least squares structural equation modeling (PLS-SEM), multi-group analysis (MGA), fuzzy-set Qualitative Comparative Analysis (fsQCA), and artificial neural networks (ANN). The study explores how four perceived benefits (economic, environmental, socio-cultural, and time-saving) and four perceived risks (functional, physical, psychological, and financial concerns) influence travelers’ intentions to use Hyperloop for tourism. The PLS-SEM and MGA analyses indicate that frequent travelers prioritize time-saving, while economic, environmental, socio-cultural benefits, and functional risks influence occasional travelers’ decisions. Additionally, fsQCA findings identify distinct benefit-risk combinations affecting travel decisions, and deep learning models highlight critical factors predicting Hyperloop adoption. This study provides theoretical insights and practical strategies for integrating Hyperloop into tourism planning and management.
AlizadehH.FarooqB.MorencyC.SaunierN. (2019). Frequent versus occasional drivers: A hybrid route choice model. Transportation Research Part F: Traffic Psychology and Behaviour, 64, 171–183. https://doi.org/10.1016/j.trf.2019.05.009
3.
BadgujarP.SharmaS.RahulD.SharmaS. K.UpadhyayR. K. (2024). Feasibility study of Mumbai-Pune Hyperloop corridor: Challenges and implementation. Research in Transportation Business & Management, 52, 101089. https://doi.org/10.1016/j.rtbm.2023.101089
4.
BaggioR. (2008). Symptoms of complexity in a tourism system. Tourism Analysis, 13(1), 1–20.
5.
BelarminoA.OzdemirO.DogruT. (2021). Always local?: Examining the relationship between peer-to-peer accommodations and restaurants. Journal of Hospitality and Tourism Management, 48, 289–300. https://doi.org/10.1016/j.jhtm.2021.07.003
6.
BrislinR. W. (1980). Translation and content analysis of oral and written materials. In TriandisH. C.BerryJ. W. (Eds.), Handbook of cross-cultural psychology: Methodology (pp. 389–444), Allyn and Bacon.
7.
CaiR.LeungX. Y.ChiC. G. Q. (2022). Ghost kitchens on the rise: Effects of knowledge and perceived benefit-risk on customers’ behavioral intentions. International Journal of Hospitality Management, 101, 103110. https://doi.org/10.1016/j.ijhm.2021.103110
8.
ChewE. Y. T.JahariS. A. (2014). Destination image as a mediator between perceived risks and revisit intention: A case of post-disaster Japan. Tourism Management, 40, 382–393. https://doi.org/10.1016/j.tourman.2013.07.008
9.
ChoS. H.AliF.ManhasP. S. (2018). Examining the impact of risk perceptions on intentions to travel by air: A comparison of full-service carriers and low-cost carriers. Journal of Air Transport Management, 71, 20–27. https://doi.org/10.1016/j.jairtraman.2018.05.005
10.
DayourF.ParkS.KimbuA. N. (2019). Backpackers’ perceived risks towards smartphone usage and risk reduction strategies: A mixed methods study. Tourism Management, 72, 52–68. https://doi.org/10.1016/j.tourman.2018.11.003
11.
FeathermanM. S.PavlouP. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451–474. https://doi.org/10.1016/S1071-5819(03)00111-3
12.
GaoD. (2019). Travel frequency and infectious diseases. SIAM Journal on Applied Mathematics, 79(4), 1581–1606. https://doi.org/10.1137/18M1211957
13.
GhaderS.DarziA.ZhangL. (2019). Modeling effects of travel time reliability on mode choice using cumulative prospect theory. Transportation Research Part C: Emerging Technologies, 108, 245–254. https://doi.org/10.1016/j.trc.2019.09.014
14.
GustafsonP. (2009). More cosmopolitan, no less local: The orientations of international travellers. European Societies, 11(1), 25–47. https://doi.org/10.1080/14616690802209689
15.
HairJ. F.Jr.MatthewsL. M.MatthewsR. L.SarstedtM. (2017). PLS-SEM or CB-SEM: Updated guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 107–123. https://doi.org/10.1504/IJMDA.2017.087624
16.
HairJ. F.RingleC. M.SarstedtM. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. https://doi.org/10.2753/MTP1069-6679190202
17.
HanH.KimS.HailuT. B.Al-AnsiA.LoureiroS. M. C.KimJ. J. (2024). Determinants of approach behavior for Chat GPT and their configurational influence in the hospitality and tourism sector: A cumulative prospect theory. International Journal of Contemporary Hospitality Management, 37(1), 113–139. https://doi.org/10.1108/IJCHM-07-2023-1072
18.
HanH.YuJ.KimW. (2019). An electric airplane: Assessing the effect of travelers’ perceived risk, attitude, and new product knowledge. Journal of Air Transport Management, 78, 33–42. https://doi.org/10.1016/j.jairtraman.2019.04.004
19.
HansenI. A. (2020). Hyperloop transport technology assessment and system analysis. Transportation Planning and Technology, 43(8), 803–820. https://doi.org/10.1080/03081060.2020.1828935
20.
HenselerJ.RingleC. M.SarstedtM. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115–135. https://doi.org/10.1007/s11747-014-0403-8
21.
HewJ. J.LeeV. H.LeongL. Y. (2023). Why do mobile consumers resist mobile commerce applications? A hybrid fsQCA-ANN analysis. Journal of Retailing and Consumer Services, 75, 103526. https://doi.org/10.1016/j.jretconser.2023.103526
22.
JanićM. (2021). Estimation of direct energy consumption and CO2 emission by high speed rail, transrapid maglev and Hyperloop passenger transport systems. International Journal of Sustainable Transportation, 15(9), 696–717. https://doi.org/10.1080/15568318.2020.1789780
23.
JohnsonJ. S. (2016). Improving online panel data usage in sales research. Journal of Personal Selling & Sales Management, 36(1), 74–85. https://doi.org/10.1080/08853134.2015.1111611
KangS. E. (2022). Travelers’ pro-environmental behaviors in the Hyperloop context: Integrating norm activation and AIDA models. International Journal of Tourism Research, 24(6), 813–826. https://doi.org/10.1002/jtr.2547
26.
KangS. E. (2024). How does the media influence Korean travelers’ intention to use Hyperloop trains?Transportation Letters. Advance online publication. https://doi.org/10.1080/19427867.2024.2366330
27.
KangS. E.ChungN.LeeK.ErulE.HanS. (2024). Residents’ support for Hyperloop development and their travel intention: A case study of Gyeongnam Province, South Korea. Asia Pacific Journal of Tourism Research, 29(6), 663–680. https://doi.org/10.1080/10941665.2024.2335266
28.
KangS. E.KimT. (2023). The influence of YouTube content on travelers’ intentions to use Hyperloop trains: Using trust transfer theory. Travel Behaviour and Society, 30, 281–290. https://doi.org/10.1016/j.tbs.2022.10.007
29.
KhanM. J.ChelliahS.KhanF.AminS. (2019). Perceived risks, travel constraints and visit intention of young women travelers: The moderating role of travel motivation. Tourism Review, 74(3), 721–738. https://doi.org/10.1108/TR-08-2018-0116
30.
KimM. J.HallC. M.ChungN.KimM.SohnK. (2023). What makes tourists use public transport? Value-belief-norm theory, environmental, social, and governance factors, and the sustainable development goals. Journal of Travel Research, 63(6), 1426–1441. https://doi.org/10.1177/00472875231191708
31.
KimM. J.KangS. E.HallC. M.KimJ. S.PromsivapallopP. (2024). Unveiling the impact of ChatGPT on travel consumer behaviour: Exploring trust, attribute, and sustainable-tourism action. Current Issues in Tourism. Advance online publication. https://doi.org/10.1080/13683500.2024.2355556
32.
KimS.ChoeJ. Y. (2019). Testing an attribute-benefit-value-intention (ABVI) model of local food consumption as perceived by foreign tourists. International Journal of Contemporary Hospitality Management, 31(1), 123–140. https://doi.org/10.1108/IJCHM-10-2017-0661
KorsgaardM. A.RobersonL. (1995). Procedural justice in performance evaluation: The role of instrumental and non-instrumental voice in performance appraisal discussions. Journal of Management, 21(4), 657–669. https://doi.org/10.1177/014920639502100404
35.
KumarS.SahooS.LimW. M.KrausS.BamelU. (2022). Fuzzy-set qualitative comparative analysis (fsQCA) in business and management research: A contemporary overview. Technological Forecasting and Social Change, 178, 121599. https://doi.org/10.1016/j.techfore.2022.121599
36.
LeeC. K.OlyaH.ParkY. N.KwonY. J.KimM. J. (2023). Sustainable intelligence and cultural worldview as triggers to preserve heritage tourism resources. Tourism Geographies, 25(2–3), 899–918. https://doi.org/10.1080/14616688.2021.2016934
37.
LeeK. H.HyunS. S. (2016). A model of value-creating practices, trusting beliefs, and online tourist community behaviors: Risk aversion as a moderating variable. International Journal of Contemporary Hospitality Management, 28(9), 1868–1894. https://doi.org/10.1108/IJCHM-01-2015-0002
38.
LeeT. H.JanF. H. (2019). Can community-based tourism contribute to sustainable development? Evidence from residents’ perceptions of the sustainability. Tourism Management, 70, 368–380. https://doi.org/10.1016/j.tourman.2018.09.003
39.
LeungX. Y.CaiR. (2021). How pandemic severity moderates digital food ordering risks during COVID-19: An application of prospect theory and risk perception framework. Journal of Hospitality and Tourism Management, 47, 497–505. https://doi.org/10.1016/j.jhtm.2021.05.002
40.
LiF.AwE. C. X.TanG. W. H.ChamT. H.OoiK. B. (2022). The Eureka moment in understanding luxury brand purchases! A non-linear fsQCA-ANN approach. Journal of Retailing and Consumer Services, 68, 103039. https://doi.org/10.1016/j.jretconser.2022.103039
41.
LiX.GongJ.GaoB.YuanP. (2021). Impacts of COVID-19 on tourists’ destination preferences: Evidence from China. Annals of Tourism Research, 90, 103258. https://doi.org/10.1016/j.annals.2021.103258
42.
LinG.LinM. S.SongH. (2024). An assessment of prospect theory in tourism decision-making research. Journal of Travel Research, 63(2), 275–297. https://doi.org/10.1177/00472875231171673
43.
LinV. S.JiangF.LiG.QinY. (2023). Impacts of risk aversion on tourism consumption: A hierarchical age-period-cohort analysis. Annals of Tourism Research, 101, 103607. https://doi.org/10.1016/j.annals.2023.103607
44.
LiuJ.YuM.ChenD.YangZ. (2022). Study on interior aerodynamic noise characteristics of the high-speed maglev train in the low vacuum tube. Applied Sciences, 12(22), 11444. https://doi.org/10.3390/app122211444
NicolauJ. L.ShinH.KimB.O’ConnellJ. F. (2023). The impact of loss aversion and diminishing sensitivity on airline revenue: Price sensitivity in cabin classes. Journal of Travel Research, 62(3), 685–698. https://doi.org/10.1177/00472875221093014
48.
OlyaH. G.Altinay GaziZ.Altinay AksalF.AltinayM. (2018), Behavioral intentions of disabled tourists for the use of peer-to-peer accommodations: An application of fsQCA. International Journal of Contemporary Hospitality Management, 30(1), 436–454. https://doi.org/10.1108/IJCHM-08-2016-0471
PappasI. O.PapavlasopoulouS.MikalefP.GiannakosM. N. (2020). Identifying the combinations of motivations and emotions for creating satisfied users in SNSs: An fsQCA approach. International Journal of Information Management, 53, 102128. https://doi.org/10.1016/j.ijinfomgt.2020.102128
PedersenM. J.NielsenC. V. (2016). Improving survey response rates in online panels: Effects of low-cost incentives and cost-free text appeal interventions. Social Science Computer Review, 34(2), 229–243.
53.
PodsakoffP. M.MacKenzieS. B.LeeJ. Y.PodsakoffN. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879. https://doi.org/10.1037/0021-9010.88.5.879
54.
PoonW. C.KoayK. Y. (2021). Hong Kong protests and tourism: Modelling tourist trust on revisit intention. Journal of Vacation Marketing, 27(2), 217–234. https://doi.org/10.1177/1356766720987881
RamY.HallC. M. (2022). The camp not taken: Analysis of preferences and barriers among frequent, occasional and noncampers. Leisure Sciences, 44(7), 915–938. https://doi.org/10.1080/01490400.2020.1731885
SharmaM.JoshiS.LuthraS.KumarA. (2024). Impact of digital assistant attributes on millennials’ purchasing intentions: A multi-group analysis using PLS-SEM, artificial neural network and fsQCA. Information Systems Frontiers, 26(3), 943–966. https://doi.org/10.1007/s10796-022-10339-5
60.
SmithK.GuoS.ZhuQ.DongX.LiuS. (2019). An evaluation of the environmental benefit and energy footprint of China’s stricter wastewater standards: Can benefit be increased?Journal of Cleaner Production, 219, 723–733. https://doi.org/10.1016/j.jclepro.2019.01.204
61.
van GoeverdenK.MilakisD.JanicM.KoningsR. (2018). Analysis and modelling of performances of the HL (Hyperloop) transport system. European Transport Research Review, 10(2), 1–17. https://doi.org/10.1186/s12544-018-0312-x
62.
WuP. L.YehS. S.WoodsideA. G. (2014). Applying complexity theory to deepen service dominant logic: Configural analysis of customer experience-and-outcome assessments of professional services for personal transformations. Journal of Business Research, 67(8), 1647–1670. https://doi.org/10.1016/j.jbusres.2014.03.012
63.
ZhouY.WangP.ZhengS.ZhaoM.LamW. H.ChenA.SzeN. N.ChenY. (2024). Modeling dynamic travel mode choices using cumulative prospect theory. Transportation Research Part A: Policy and Practice, 179, 103938. https://doi.org/10.1016/j.tra.2023.103938
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