Maintaining tourism competitiveness is crucial for destinations’ long-term success. However, studies often lack longitudinal analyses that capture the evolving determinants of tourism competitiveness in both domestic and international markets. Taking China as a case study, this research introduces an innovative framework that integrates spatiotemporal machine learning with multimodal big data to examine dynamic factors influencing domestic and international tourism competitiveness. Several findings emerge: (1) overall, online popularity is essential for domestic tourism competitiveness, whereas the natural environment underpins international tourism competitiveness; (2) temporally, tourism infrastructure, government support, and online tourist perception are becoming more critical for tourism competitiveness over time, while tourism attractions’ role is waning; and (3) developed destinations are increasingly leveraging tourism infrastructure to enhance competitive advantage, and less-developed areas are benefiting more from online visibility. This research further contextualizes the dynamic nature of tourism competitiveness and offers strategies for sustainable tourism development. Also available in Chinese. See Supplemental Material for details.
Abreu-NovaisM.RuhanenL.ArcodiaC. (2016). Destination competitiveness: What we know, what we know but shouldn’t and what we don’t know but should. Current Issues in Tourism, 19(6), 492–512. https://doi.org/10.1080/13683500.2015.1091443
AlbayrakT.CaberM.Rosario González-RodríguezM.AksuA. (2018). Analysis of destination competitiveness by IPA and IPCA methods: The case of Costa Brava, Spain against Antalya, Turkey. Tourism Management Perspectives, 28, 53–61. https://doi.org/10.1016/j.tmp.2018.07.005
4.
AltinayL.KozakM. (2021). Revisiting destination competitiveness through chaos theory: The butterfly competitiveness model. Journal of Hospitality and Tourism Management, 49, 331–340. https://doi.org/10.1016/j.jhtm.2021.10.004
5.
AltuntasF.GokM. S. (2021). The effect of COVID-19 pandemic on domestic tourism: A DEMATEL method analysis on quarantine decisions. International Journal of Hospitality Management, 92, 102719. https://doi.org/10.1016/j.ijhm.2020.102719
6.
AndradesL.DimancheF. (2019). Destination competitiveness in Russia: Tourism professionals’ skills and competences. International Journal of Contemporary Hospitality Management, 31(2), 910–930. https://doi.org/10.1108/ijchm-11-2017-0769
7.
BaoR.LiuT. (2022). How does government attention matter in air pollution control? Evidence from government annual reports. Resources Conservation and Recycling, 185, 106435. https://doi.org/10.1016/j.resconrec.2022.106435
8.
BertocchiD.CamattiN.SalmasiL.van der BorgJ. (2023). Assessing the tourism sustainability of EU regions at the NUTS-2 level with a composite and regionalised indicator. Journal of Sustainable Tourism, 31(7), 1738–1755. https://doi.org/10.1080/09669582.2021.2000993
9.
ChanW. C.LoM. C.Wan IbrahimW. H.MohamadA. A.SuaidiM. K. B. (2022). The effect of hard infrastructure on perceived destination competitiveness: The moderating impact of mobile technology. Tourism Management Perspectives, 43, 100998. https://doi.org/10.1016/j.tmp.2022.100998
10.
ChenJ.WuJ.WangD.StanticB. (2025). Beyond static rankings: A tourist experience-driven approach to measure destination competitiveness. Tourism Management, 106, 105022. https://doi.org/10.1016/j.tourman.2024.105022
11.
ChiX.HanH. (2021). Emerging rural tourism in China’s current tourism industry and tourist behaviors: The case of Anji County. Journal of Travel & Tourism Marketing, 38(1), 58–74. https://doi.org/10.1080/10548408.2020.1862026
12.
CroesR. (2011). Measuring and explaining competitiveness in the context of small island destinations. Journal of Travel Research, 50(4), 431–442. https://doi.org/10.1177/0047287510368139
13.
CroesR.RidderstaatJ.ShapovalV. (2020). Extending tourism competitiveness to human development. Annals of Tourism Research, 80, 102825. https://doi.org/10.1016/j.annals.2019.102825
14.
CronjéD. F.du PlessisE. (2020). A review on tourism destination competitiveness. Journal of Hospitality and Tourism Management, 45, 256–265. https://doi.org/10.1016/j.jhtm.2020.06.012
15.
CrouchG. I. (2011). Destination competitiveness: An analysis of determinant attributes. Journal of Travel Research, 50(1), 27–45. https://doi.org/10.1177/0047287510362776
de la PeñaM. R.Núñez-SerranoJ. A.TurriónJ.VelázquezF. J. (2019). A new tool for the analysis of the international competitiveness of tourist destinations based on performance. Journal of Travel Research, 58(2), 207–223. https://doi.org/10.1177/0047287517746012
18.
DengN.LiX. (2018). Feeling a destination through the “right” photos: A machine learning model for dmos’ photo selection. Tourism Management, 65, 267–278. https://doi.org/10.1016/j.tourman.2017.09.010
19.
Domínguez VilaT.DarcyS.Alén GonzálezE. (2015). Competing for the disability tourism market – A comparative exploration of the factors of accessible tourism competitiveness in Spain and Australia. Tourism Management, 47, 261–272. https://doi.org/10.1016/j.tourman.2014.10.008
DwyerL.DragićevićV.ArmenskiT.MihaličT.Knežević CvelbarL. (2016). Achieving destination competitiveness: An importance–performance analysis of Serbia. Current Issues in Tourism, 19(13), 1309–1336. https://doi.org/10.1080/13683500.2014.944487
D’HauteserreA.-M. (2000). Lessons in managed destination competitiveness: The case of Foxwoods Casino Resort. Tourism Management, 21(1), 23–32. https://doi.org/10.1016/s0261-5177(99)00097-7
EvrenS.KozakN. (2018). Competitive positioning of winter tourism destinations: A comparative analysis of demand and supply sides perspectives–Cases from Turkey. Journal of Destination Marketing & Management, 9, 247–257. https://doi.org/10.1016/j.jdmm.2018.01.009
27.
FontX.Torres-DelgadoA.CraboluG.Palomo MartinezJ.KantenbacherJ.MillerG. (2023). The impact of sustainable tourism indicators on destination competitiveness: The European Tourism Indicator System. Journal of Sustainable Tourism, 31(7), 1608–1630. https://doi.org/10.1080/09669582.2021.1910281
28.
FuZ.HsuY. C.ChanC. S.LauC. M.LiuJ.YipP. S. F. (2024). Efficacy of ChatGPT in Cantonese sentiment analysis: Comparative study. Journal of Medical Internet Research, 26(1), e51069. https://doi.org/10.2196/51069
29.
González-RodríguezM. R.Díaz-FernándezM. C.Pulido-PavónN. (2023). Tourist destination competitiveness: An international approach through the travel and tourism competitiveness index. Tourism Management Perspectives, 47, 101127. https://doi.org/10.1016/j.tmp.2023.101127
30.
HamJ.KooC.ChungN. (2020). Configurational patterns of competitive advantage factors for smart tourism: An equifinality perspective. Current Issues in Tourism, 23(9), 1066–1072. https://doi.org/10.1080/13683500.2019.1566303
31.
HassanS. S. (2000). Determinants of market competitiveness in an environmentally sustainable tourism industry. Journal of Travel Research, 38(3), 239–245. https://doi.org/10.1177/004728750003800305
32.
HeathE. (2003). Towards a model to enhance destination competitiveness: A Southern African perspective. Journal of Hospitality and Tourism Management, 10(2), 124–141. http://hdl.handle.net/2263/6058
33.
HuangX.LuG.YinJ.TanW. (2021). Non-linear associations between the built environment and the physical activity of children. Transportation Research Part D: Transport and Environment, 98, 102968. https://doi.org/10.1016/j.trd.2021.102968
34.
KubickovaM.MartinD. (2020). Exploring the relationship between government and destination competitiveness: The TALC model perspective. Tourism Management, 78, 104040. https://doi.org/10.1016/j.tourman.2019.104040
35.
KumarS.DhirA. (2020). Associations between travel and tourism competitiveness and culture. Journal of Destination Marketing & Management, 18, 100501. https://doi.org/10.1016/j.jdmm.2020.100501
36.
KumarS.KumarD.Luis NicolauJ. (2024). How does culture influence a country’s travel and tourism competitiveness? A longitudinal frontier study on 39 countries. Tourism Management, 100, 104822. https://doi.org/10.1016/j.tourman.2023.104822
37.
LeeC.-K.SongH.-J.MjeldeJ. W. (2008). The forecasting of International Expo tourism using quantitative and qualitative techniques. Tourism Management, 29(6), 1084–1098. https://doi.org/10.1016/j.tourman.2008.02.007
38.
LeeJ.-S.ParkS. (2023). A cross-cultural anatomy of destination image: An application of mixed-methods of UGC and survey. Tourism Management, 98, 104746. https://doi.org/10.1016/j.tourman.2023.104746
39.
LeinonenJ.HamannU.SiderisI. V.GermannU. (2023). Thunderstorm nowcasting with deep learning: A multi-hazard data fusion model. Geophysical Research Letters, 50(8), e2022GL101626. https://doi.org/10.1029/2022gl101626
LiH.JiH.LiuH.CaiD.GaoH. (2022). Is a picture worth a thousand words? Understanding the role of review photo sentiment and text-photo sentiment disparity using deep learning algorithms. Tourism Management, 92, 104559. https://doi.org/10.1016/j.tourman.2022.104559
42.
LiH.LiuH.Hailey ShinH.JiH. (2024). Impacts of user-generated images in online reviews on customer engagement: A panel data analysis. Tourism Management, 101, 104855. https://doi.org/10.1016/j.tourman.2023.104855
43.
LiS.ChenT.WangL.MingC. (2018). Effective tourist volume forecasting supported by PCA and improved BPNN using Baidu index. Tourism Management, 68, 116–126. https://doi.org/10.1016/j.tourman.2018.03.006
44.
LiuZ.LuC.MaoJ.SunD.LiH.LuC. (2021). Spatial–temporal heterogeneity and the related influencing factors of tourism efficiency in China. Sustainability, 13(11), 5825. https://doi.org/10.3390/su13115825
45.
LiZ. (2022). Extracting spatial effects from machine learning model using local interpretation method: An example of SHAP and XGBoost. Computers, Environment and Urban Systems, 96, 101845. https://doi.org/10.1016/j.compenvurbsys.2022.101845
MoradiE.EhsaniM.SaffariM.Norouzi Seyed HosseiniR. (2022). Developing an integrated model for the competitiveness of sports tourism destinations. Journal of Destination Marketing & Management, 26, 100743. https://doi.org/10.1016/j.jdmm.2022.100743
49.
Navarro-NavarroJ.Juárez-TárragaF.Ramón-RodríguezA. B.Perles-RibesJ. F. (2024). The life cycle and competitive position of tourism destinations: A global empirical approach. Current Issues in Tourism, 27(23), 4203–4219. https://doi.org/10.1080/13683500.2023.2280161
50.
NetoA. Q.DimmockK.LohmannG.ScottN. (2020). Destination competitiveness: How does travel experience influence choice?Current Issues in Tourism, 23(13), 1673–1687. https://doi.org/10.1080/13683500.2019.1641070
51.
ÖzerM.KüçüksakaryaS.MaitiM. (2022). Nexus of tourism demand, economic growth, and external competitiveness in leading tourist destination countries. Tourism Management Perspectives, 42, 100965. https://doi.org/10.1016/j.tmp.2022.100965
52.
PejanovićR.DemirovićD.Glavaš-TrbićD.MaksimovićG.Tomaš-SiminM. (2017). Clusters as a factor of competitiveness of rural tourism destinations in the Danube region of the Republic of Serbia. Tourism Economics, 23(2), 475–482. https://doi.org/10.5367/te.2015.0509
53.
PorterM. E. (1990). The competitive advantage of nations (p. 21). Free Press.
54.
RenM.ParkS.XuY.HuangX.ZouL.WongM. S.KohS.-Y. (2022). Impact of the COVID-19 pandemic on travel behavior: A case study of domestic inbound travelers in Jeju, Korea. Tourism Management, 92, 104533. https://doi.org/10.1016/j.tourman.2022.104533
55.
RibeiroM. H. D. M.da SilvaR. G.MarianiV. C.CoelhoL. D. S. (2020). Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil. Chaos, Solitons, and Fractals, 135, 109853. https://doi.org/10.1016/j.chaos.2020.109853
56.
RitchieJ. R. B.CrouchG. I. (2003). The competitive destination: A sustainable tourism perspective. CABI.
57.
Rodríguez-DíazB.Pulido-FernándezJ. I. (2021). Analysis of the worth of the weights in a new travel and Tourism Competitiveness Index. Journal of Travel Research, 60(2), 267–280. https://doi.org/10.1177/0047287519899982
58.
Rodríguez-IbánezM.Casánez-VenturaA.Castejón-MateosF.Cuenca-JiménezP.-M. (2023). A review on sentiment analysis from social media platforms. Expert Systems with Applications, 223, 119862. https://doi.org/10.1016/j.eswa.2023.119862
59.
RomãoJ.NijkampP. (2019). Impacts of innovation, productivity and specialization on tourism competitiveness – A spatial econometric analysis on European regions. Current Issues in Tourism, 22(10), 1150–1169. https://doi.org/10.1080/13683500.2017.1366434
60.
Salinas FernándezJ. A.Guaita MartínezJ. M.Martín MartínJ. M. (2022). An analysis of the competitiveness of the tourism industry in a context of economic recovery following the COVID19 pandemic. Technological Forecasting and Social Change, 174, 121301. https://doi.org/10.1016/j.techfore.2021.121301
61.
Salinas FernándezJ. A.Serdeira AzevedoP.Martín MartínJ. M.Rodríguez MartínJ. A. (2020). Determinants of tourism destination competitiveness in the countries most visited by international tourists: Proposal of a synthetic index. Tourism Management Perspectives, 33, 100582. https://doi.org/10.1016/j.tmp.2019.100582
62.
ShiY.ZhongL.ChenT.YuH. (2016). Tourism competitiveness evaluation and spatio-temporal characteristics of Chinese border counties. Chinese Geographical Science, 26(6), 817–828. https://doi.org/10.1007/s11769-016-0822-1
SunS.WeiY.TsuiK.-L.WangS. (2019). Forecasting tourist arrivals with machine learning and internet search index. Tourism Management, 70, 1–10. https://doi.org/10.1016/j.tourman.2018.07.010
65.
TseS.TungV. W. S. (2022). Measuring the valence and intensity of residents’ behaviors in host–tourist interactions: Implications for destination image and destination competitiveness. Journal of Travel Research, 61(3), 565–580. https://doi.org/10.1177/0047287521997576
66.
Vinyals-MirabentS. (2019). European urban destinations’ attractors at the frontier between competitiveness and a unique destination image. A benchmark study of communication practices. Journal of Destination Marketing & Management, 12, 37–45. https://doi.org/10.1016/j.jdmm.2019.02.006
67.
WangD.ThunéllS.LindbergU.JiangL.TryggJ.TysklindM. (2022). Towards better process management in wastewater treatment plants: Process analytics based on SHAP values for tree-based machine learning methods. Journal of Environmental Management, 301, 113941. https://doi.org/10.1016/j.jenvman.2021.113941
WuD.LiH.WangY. (2023). Measuring sustainability and competitiveness of tourism destinations with data envelopment analysis. Journal of Sustainable Tourism, 31(6), 1315–1335. https://doi.org/10.1080/09669582.2022.2042699
70.
XuJ.AuT. (2023). Destination competitiveness since 2010: Research themes, approaches, and agenda. Tourism Review, 78(3), 665–696. https://doi.org/10.1108/tr-10-2022-0494
71.
YangW.LiY.LiuY.FanP.YueW. (2024). Environmental factors for outdoor jogging in Beijing: Insights from using explainable spatial machine learning and massive trajectory data. Landscape and Urban Planning, 243, 104969. https://doi.org/10.1016/j.landurbplan.2023.104969
72.
Zadeh BazarganiR. H.KiliçH. (2021). Tourism competitiveness and tourism sector performance: Empirical insights from new data. Journal of Hospitality and Tourism Management, 46, 73–82. https://doi.org/10.1016/j.jhtm.2020.11.011
73.
ZehrerA.SmeralE.HallmannK. (2017). Destination competitiveness—A comparison of subjective and objective indicators for winter sports areas. Journal of Travel Research, 56(1), 55–66. https://doi.org/10.1177/0047287515625129
74.
ZhangK.ChenY.LiC. (2019). Discovering the tourists’ behaviors and perceptions in a tourism destination by analyzing photos’ visual content with a computer deep learning model: The case of Beijing. Tourism Management, 75, 595–608. https://doi.org/10.1016/j.tourman.2019.07.002
75.
ZhangY.FuX.ZhangY.HuangT. (2025). From disruption to normalcy: Co-production public service and destination competitiveness. Tourism Management, 106, 104973. https://doi.org/10.1016/j.tourman.2024.104973
76.
ZhangZ.QiaoS.LiH.ZhangZ. (2022). How rainy-day blues affect customers’ evaluation behavior: Evidence from online reviews. International Journal of Hospitality Management, 100, 103090. https://doi.org/10.1016/j.ijhm.2021.103090
77.
ZhuK.DuL.FengY. (2023). Government attention on environmental protection and firms’ carbon reduction actions: Evidence from text analysis of manufacturing enterprises. Journal of Cleaner Production, 423, 138703. https://doi.org/10.1016/j.jclepro.2023.138703
78.
ZhuL.CuiT.RunaA.PanX.ZhaoW.XiangJ.CaoM. (2024). Robust remote sensing retrieval of key eutrophication indicators in coastal waters based on explainable machine learning. ISPRS Journal of Photogrammetry and Remote Sensing, 211, 262–280. https://doi.org/10.1016/j.isprsjprs.2024.04.007
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