Prioritizing Influential Factors on Tourist Decision-Making for Choosing Destinations Using Parsimonious Analytical Hierarchy Process (PAHP)

References

• Abastante, F., Corrente, S., Greco, S., Ishizaka, A., & Lami, I. M. (2018). Choice architecture for architecture choices: Evaluating social housing initiatives putting together a parsimonious AHP methodology and the Choquet integral. Land Use Policy, 78, 748–762. https://doi.org/10.1016/j.landusepol.2018.07.037
   Web of Science ®Google Scholar
• Abastante, F., Corrente, S., Greco, S., Ishizaka, A., & Lami, I. M. (2019). A new parsimonious AHP methodology: Assigning priorities to many objects by comparing pairwise few reference objects. Expert Systems with Applications, 127, 109–120. https://doi.org/10.1016/j.eswa.2019.02.036
   Web of Science ®Google Scholar
• Alptekin, G. I., & Büyüközkan, G. (2011). An integrated case-based reasoning and MCDM system for Web based tourism destination planning. Expert Systems with Applications, 38(3), 2125–2132. https://doi.org/10.1016/j.eswa.2010.07.153
   Web of Science ®Google Scholar
• Asadi, R., & Daryaei, M. (2011). Prioritization of pull factors of Turkey as a destination for Iranian tourists. Australian Journal of Basic and Applied Sciences, 5(10), 1510–1515.
   Google Scholar
• BKK. (2022). Centre for Budapest Transport (Budapesti Közlekedési Központ). 4 April, 2022. https://bkk.hu/en/tickets-and-passes/prices/single-tickets-valid-for-one-ride/.
   Google Scholar
• Borgers, A. W. J., Kemperman, A., Toll, L. M. M., & Timmermans, H. J. P. (2010). Measuring preferences for parking facilities in old residential areas. Proceedings of the 7th international symposium on city planning and environmental management in Asian countries, 1–14. AUBC.
   Google Scholar
• Budapest - Google Maps. (2022). Retrieved July 4, 2022, from https://www.google.com/maps/place/Budapest/@47.4812134,19.130303,11z/data = !3m1!4b1!4m5!3m4!1s0 ( 4741c334d1d4cfc9:0 ( 400c4290c1e1160!8m2!3d47.497912!4d19.040235.
   Google Scholar
• Celotto, E., Ellero, A., & Ferretti, P. (2015). Conveying tourist ratings into an overall destination evaluation. Procedia - Social and Behavioral Sciences, 188, 35–41. https://doi.org/10.1016/j.sbspro.2015.03.336
   Google Scholar
• Chapman, R., Keall, M., Howden-Chapman, P., Grams, M., Witten, K., Randal, E., & Woodward, A. (2018). A cost benefit analysis of an active travel intervention with health and carbon emission reduction benefits. International Journal of Environmental Research and Public Health, 15(5), 962. https://doi.org/10.3390/ijerph15050962
   PubMed Web of Science ®Google Scholar
• Ciptayani, P. I., Saptarini, N. G. A. P. H., Santiary, P. A. W., & Astawa, I. N. G. A. (2018). Decision support system for tourist destination using the combination of AHP and SAW. Proceedings - 2nd East Indonesia conference on computer and information technology: Internet of things for industry, EIConCIT 2018, 271–275. https://doi.org/10.1109/EIConCIT.2018.8878638.
   Google Scholar
• Corrente, S., Greco, S., & Ishizaka, A. (2016). Combining analytical hierarchy process and Choquet integral within non-additive robust ordinal regression. Omega, 61, 2–18. https://doi.org/10.1016/j.omega.2015.07.003
   Web of Science ®Google Scholar
• Do, Q. H., & Chen, J. F. (2013). Prioritizing the factor weights affecting tourism performance by FAHP. International Journal of Engineering Business Management, 5(1), 51. https://doi.org/10.5772/57141
   Google Scholar
• Dolnicar, S. (2005). Understanding barriers to leisure travel: Tourist fears as a marketing basis. Journal of Vacation Marketing, 11(3), 197–208. https://doi.org/10.1177/1356766705055706
   Google Scholar
• Duleba, S. (2022). Introduction and comparative analysis of the multi-level parsimonious AHP methodology in a public transport development decision problem. Journal of the Operational Research Society, 73(2), 230–243. https://doi.org/10.1080/01605682.2020.1824553
   Web of Science ®Google Scholar
• Duleba, S., & Moslem, S. (2021). User satisfaction survey on public transport by a new pahp based model. Applied Sciences, 11(21), 10256. https://doi.org/10.3390/app112110256
   Google Scholar
• Esztergár-Kiss, D. (2020). Trip chaining model with classification and optimization parameters. Sustainability, 12(16), 6422. https://doi.org/10.3390/su12166422
   Web of Science ®Google Scholar
• Esztergár-Kiss, D., & Tettamanti, T. (2019). Stakeholder engagement in mobility planning. In Autonomous vehicles and future mobility (pp. 113–123). Elsevier. https://doi.org/10.1016/B978-0-12-817696-2.00009-3.
   Google Scholar
• Ferguson, L. (2007). The United Nations world tourism organisation. New Political Economy, 12(4), 557–568. https://doi.org/10.1080/13563460701661587
   Web of Science ®Google Scholar
• Gasco, I., & Gattiglio, M. (2010). Geological map of the upper Gressoney Valley, Western Italian Alps. Journal of Maps, 6, 82–102. https://doi.org/10.4113/jom.2010.1121
   Web of Science ®Google Scholar
• Gidebo, H. B. (2021). Factors determining international tourist flow to tourism destinations: A systematic review. Journal of Hospitality Management and Tourism, 12(1), 9–17. https://doi.org/10.5897/JHMT2019.0276
   Google Scholar
• Givoni, M. (2007). Environmental benefits from mode substitution: Comparison of the environmental impact from aircraft and high-speed train operations. International Journal of Sustainable Transportation, 1(4), 209–230. https://doi.org/10.1080/15568310601060044
   Web of Science ®Google Scholar
• Heung, V. C. S., Qu, H., & Chu, R. (2001). The relationship between vacation factors and socio-demographic and travelling characteristics: The case of Japanese leisure travellers. Tourism Management, 22(3), 259–269. https://doi.org/10.1016/S0261-5177(00)00057-1
   Web of Science ®Google Scholar
• Hsu, T.-K., Tsai, Y.-F., & Wu, H.-H. (2009). The preference analysis for tourist choice of destination: A case study of Taiwan. Tourism Management, 30(2), 288–297. https://doi.org/10.1016/j.tourman.2008.07.011
   Web of Science ®Google Scholar
• Huang, Y., & Bian, L. (2009). A Bayesian network and analytic hierarchy process based personalized recommendations for tourist attractions over the Internet. Expert Systems with Applications, 36(1), 933–943. https://doi.org/10.1016/j.eswa.2007.10.019
   Web of Science ®Google Scholar
• Hwang, C.-L., & Masud, A. S. M. (2012). Multiple objective decision making—methods and applications: a state-of-the-art survey (Vol. 164). Springer Science & Business Media.
   Google Scholar
• Kellerman, A. (2016). Daily spatial mobilities: Physical and virtual. Routledge.
   Google Scholar
• Khuong, M. N., & Quyen, N. P. (2016). Factors affecting international tourists’ perceived service quality and return intention-a study in Ho chi minh city. Review of European Studies, 8(3), 133. https://doi.org/10.5539/res.v8n3p133
   Google Scholar
• Kumar, A., Sah, B., Singh, A. R., Deng, Y., He, X., Kumar, P., & Bansal, R. C. (2017). A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renewable and Sustainable Energy Reviews, 69, 596–609. https://doi.org/10.1016/j.rser.2016.11.191
   Web of Science ®Google Scholar
• Lew, A., & McKercher, B. (2006). Modeling tourist movements: A local destination analysis. Annals of Tourism Research, 33(2), 403–423. https://doi.org/10.1016/j.annals.2005.12.002
   Web of Science ®Google Scholar
• Liao, C. S., & Chuang, H. K. (2020). Tourist preferences for package tour attributes in tourism destination design and development. Journal of Vacation Marketing, 26(2), 230–246. https://doi.org/10.1177/1356766719880250
   Web of Science ®Google Scholar
• Mahdi, A., & Esztergár-Kiss, D. (2021). Modelling the accommodation preferences of tourists by combining Fuzzy-AHP and GIS methods. Journal of Advanced Transportation, 1–16. https://doi.org/10.1155/2021/9913513
   Web of Science ®Google Scholar
• Mahdi, A., & Esztergár-Kiss, D. (2022). Robust linear regression-based gis technique for modeling the processing time at tourism destinations. In Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics): Vol. 13335. LNCS. https://doi.org/10.1007/978-3-031-04987-3_38.
   Google Scholar
• Mahdi, A., Tettamanti, T., & Esztergár-Kiss, D. (2023). Modeling the time spent at points of interest based on google popular times. IEEE Access, 11, 88946–88959. https://doi.org/10.1109/ACCESS.2023.3305957
   Web of Science ®Google Scholar
• MoveCit. (2022). MoveCit project. 4 April, 2022. movecit.codecluster.io.
   Google Scholar
• Oh, J.-S., Mattingly, S. P., Al-Fuqaha, A. I., Hyun, K., Lee, S., Hasan, R. A., Famili, S., Irshaid, H., Hasan, M. M., & Rad, S. K. (2019). Monitoring daily activities and linking physical activity levels attributed to transportation mobility choices and built environment.
   Google Scholar
• Ortega, J., Tóth, J., & Péter, T. (2022). Applying geographic information system methodologies to estimate the catchment area accessibility of park-and-ride facilities. Periodica Polytechnica Transportation Engineering, 50(1), 69–78. https://doi.org/10.3311/PPtr.16384
   Google Scholar
• OSM. (2021). OpenStreetMap. 4 April, 2022. https://www.openstreetmap.org/#map=7/47.184/19.509.
   Google Scholar
• Pang, B., & Lee, L. (2009). Opinion mining and sentiment analysis. Computational Linguistics, 35(2), 311–312. https://doi.org/10.1162/coli.2009.35.2.311
   Web of Science ®Google Scholar
• Pinke-Sziva, I., Smith, M., Olt, G., & Berezvai, Z. (2019). Overtourism and the night-time economy: A case study of Budapest. International Journal of Tourism Cities, 5(1), 1–16. https://doi.org/10.1108/IJTC-04-2018-0028
   Web of Science ®Google Scholar
• Rudloff, C., Leodolter, M., Bauer, D., Auer, R., Brög, W., & Kehnscherper, K. (2015). Influence of weather on transport demand: Case study from the Vienna, Austria, Region. Transportation Research Record, 2482(1), 110–116. https://doi.org/10.3141/2482-14
   Google Scholar
• Saaty, T. L. (1994). Fundamentals of decision making and priority theory with the analytic hierarchy process. RWS publications.
   Google Scholar
• Saaty, T. L. (2004). Fundamentals of the analytic network process—multiple networks with benefits, costs, opportunities and risks. Journal of Systems Science and Systems Engineering, 13(3), 348–379. https://doi.org/10.1007/s11518-006-0171-1
   Google Scholar
• Saifullah, S. (2019). Fuzzy-AHP approach using normalized decision matrix on tourism trend ranking based-on social media. Jurnal Informatika, 13(2), 16. https://doi.org/10.26555/jifo.v13i2.a15268
   Google Scholar
• Saunders, L. E., Green, J. M., Petticrew, M. P., Steinbach, R., & Roberts, H. (2013). What are the health benefits of active travel? A systematic review of trials and cohort studies. PLoS One, 8(8), e69912. https://doi.org/10.1371/journal.pone.0069912
   PubMed Web of Science ®Google Scholar
• Smith, M. K., Sziva, I. P., & Olt, G. (2019). Overtourism and resident resistance in Budapest. Tourism Planning & Development, 16(4), 376–392. https://doi.org/10.1080/21568316.2019.1595705
   Web of Science ®Google Scholar
• Sylejmani, K., Dorn, J., & Musliu, N. (2017). Planning the trip itinerary for tourist groups. Information Technology & Tourism, 17(3), 275–314. https://doi.org/10.1007/s40558-017-0080-9
   Web of Science ®Google Scholar
• Trabert, Z., Duleba, M., Bíró, T., Dobosy, P., Földi, A., Hidas, A., Kiss, K. T., Óvári, M., Takács, A., & Várbíró, G. (2020). Effect of land use on the benthic diatom community of the Danube River in the Region of Budapest. Water, 12(2), 479. https://doi.org/10.3390/w12020479
   Web of Science ®Google Scholar
• Vermeir, I., Van Kenhove, P., & Hendrickx, H. (2002). The influence of need for closure on consumer’s choice behaviour. Journal of Economic Psychology, 23(6), 703–727. https://doi.org/10.1016/S0167-4870(02)00135-6
   Web of Science ®Google Scholar
• Wang, X., Li, X. R., Zhen, F., & Zhang, J. H. (2016). How smart is your tourist attraction?: Measuring tourist preferences of smart tourism attractions via a FCEM-AHP and IPA approach. Tourism Management, 54, 309–320. https://doi.org/10.1016/j.tourman.2015.12.003
   Web of Science ®Google Scholar
• Wu, L., Zhang, J., & Fujiwara, A. (2012). A tourist’s multi-destination choice model with future dependency. Asia Pacific Journal of Tourism Research, 17(2), 121–132. https://doi.org/10.1080/10941665.2011.616902
   Web of Science ®Google Scholar
• Yamagishi, K. D., Abellana, D. P. M., Tanaid, R. A. B., Tiu, A. M. C., Selerio E. F. Jr, Medalla, M. E. F., & Ocampo, L. A. (2021). Destination planning of small islands with integrated multi-attribute decision-making (MADM) method. Tourism Planning & Development, 1–20. doi:10.1080/21568316.2021.1953122
   Web of Science ®Google Scholar