Distance Elasticity of Tourism Demand
Abstract
Understanding the behaviours of potential customers is crucial for effective management. Distance to destination and travel time are among the most important variables which influence customers' behaviours. The authors focused on the relation between the potential level of tourist demand and the distance to be travelled by tourists. The subject of the analysis presented in the paper shows the measurement of distance demand elasticity with selected variables that influence the tendency to stay far from an everyday environment for leisure time travels. The authors created a method called Distance Elasticity Meter (DEM), which allows to analyse a range of optimal travel times for different target groups. The results can be used to estimate the potential demand of a specific destination and for segmentation analysis. DEM enables the determination of a potential target market in conjunction with the communication access of certain destinations, but it can also have other applications.
References
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[29] Masiero, L. and Nicolau, J.L. 2012. Tourism Market Segmentation Based on Price Sensitivity. Journal of Travel Research 51(4): 426–435. DOI: 10.1177/0047287511426339
[30] McCarthy, L., et al. 2017. Factors influencing travel mode choice among families with young children (aged 0–4): a review of the literature. Transport Reviews, 37(6): 767–781. DOI: 10.1080/01441647.2017.1354942
[31] McKercher, B. 2018. The impact of distance on tourism: a tourism geography law. Tourism Geographies 20(5): 905–909. DOI: 10.1080/14616688.2018.1434813
[32] McKercher, B., Chan, A. and Lam, C. 2008. The Impact of Distance on International Tourist Movements. Journal of Travel Research, 47(2): 208–224. DOI: 10.1177/0047287508321191
[33] Montello, D.R. 1991. The measurement of cognitive distance: Methods and construct validity. Journal of Environmental Psychology, 11(2): 101–122. DOI: 10.1016/S0272-4944(05)80071-4
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[39] Patterson, I. and Pegg, S. 2009. Marketing the Leisure Experience to Baby Boomers and Older Tourists. Journal of Hospitality Marketing & Management, 18(2–3): 254–272. DOI: 10.1080/19368620802594136
[40] Pérez-Ortega, J., et al. 2020. The K -Means Algorithm Evolution. In: Introduction to Data Science and Machine Learning. IntechOpen. DOI: 10.5772/intechopen.85447
[41] Price, L. and Matthews, B. 2013. Travel time as quality time: Parental attitudes to long distance travel with young children. Journal of Transport Geography 32: 49–55. DOI: 10.1016/j.jtrangeo.2013.08.001
[42] Roll, O., Achterberg, L.-H. and Herbert, K.-G. 2010. Innovative Approaches to Analyzing the Price Sensitivity Meter. Results of an international comparative study. In: COMBI2010 Conference Proceedings (eds. T Riihelä and M Mattila), Helsinki, 2010, pp. 181–193.
[43] Salamandic, E., Alijosiene, S. and Gudonaviciene, R. 2014. Price Sensitivity Measurement Depending on Brand Awareness: A Case of Ziede Brand. Procedia - Social and Behavioral Sciences, 156: 473–478. DOI:10.1016/j.sbspro.2014.11.224
[44] Scheiner, J. 2010. Interrelations between travel mode choice and trip distance: trends in Germany 1976-2002. Journal of Transport Geography, 18(1): 75–84. DOI: 10.1016/j.jtrangeo.2009.01.001
[45] Sirakaya, E. and Woodside, A.G. 2005. Building and testing theories of decision making by travellers. Tourism Management, 26(6): 815–832. DOI: 10.1016/j.tourman.2004.05.004
[46] Süli, D. and Martyin-Csamangó, Z. 2020. The Impact of Social Media in Travel Decision-making Process among the Y and Z Generations of Music Festivals in Serbia and Hungary. Turizam, 24(2): 79–90. DOI:10.5937/turizam24-24678.
[47] Thrane, C. 2015. Examining tourists’ long-distance transportation mode choices using a Multinomial Logit regression model. Tourism Management Perspectives, 15: 115–121. DOI: 10.1016/j.tmp.2014.10.004
[48] Walmsley, D.J. and Jenkins, J.M. 1992. Cognitive Distance: A Neglected lssue in Travel Behavior. Journal of Travel Research, 31(1): 24–29. DOI: 10.1177/004728759203100106
[49] Xue, L. and Zhang, Y. 2020. The effect of distance on tourist behavior: A study based on social media data. Annals of Tourism Research, 82: 102916. DOI: 10.1016/j.annals.2020.102916
[50] Yuan, C. and Yang, H. 2019. Research on K-Value Selection Method of K-Means Clustering Algorithm. J 2(2): 226–235. DOI: 10.3390/j2020016
[51] Zhang, J., et al. 1999. The travel patterns and travel distance of tourists to national parks in China. Asia Pacific Journal of Tourism Research, 4(2): 27–34. DOI: 10.1080/10941669908722041
[2] Ankomah, P.K., Crompton, J.L. and Baker, D. 1996. Influence of cognitive distance in vacation choice. Annals of Tourism Research 23(1): 138–150. DOI: 10.1016/0160-7383(95)00054-2
[3] Basoglu, K.A. and Yoo, J.J.E. 2015. Soon or Later? The Effect of Temporal Distance on Travel Decisions. Journal of Travel and Tourism Marketing, 32: 62–S75. DOI: 10.1080/10548408.2014.997957
[4] Chhabra, S. 2015. Determining the Optimal Price Point: Using Van Westendorp’s Price Sensitivity Meter. In: Chatterjee S, Singh NP, Goyal D., et al. (eds) Managing in Recovering Markets. Springer Proceedings in Business and Economics. New Delhi: Springer, pp. 257–270. DOI: 10.1007/978-81-322-1979-8_20
[5] Cozzio, C., Tokarchuk, O. and Maurer, O. 2020. The effect of price bundling on tourists’ extra expenditure: a mental budget approach. Current Issues in Tourism. Routledge. DOI: 10.1080/13683500.2020.1849045
[6] Currie, RR, Wesley, F. and Sutherland, P. 2008. Going where the Joneses go: Understanding how others influence travel decision-making. International Journal of Culture, Tourism and Hospitality Research, 2(1): 12–24. DOI: 10.1108/17506180810856112
[7] Decrop, A. and Snelders, D. 2005. A grounded typology of vacation decision-making. Tourism Management, 26(2): 121–132. DOI: 10.1016/j.tourman.2003.11.011
[8] Dellaert, B.G.C., Ettema, D.F. and Lindh, C. 1998. Multi-faceted tourist travel decisions: A constraint-based conceptual framework to describe tourists’ sequential choices of travel components. Tourism Management, 19(4): 313–320. DOI: 10.1016/S0261-5177(98)00037-5
[9] Dibb, S. et al. 1991. Marketing: Concepts and Strategies, European Edition. Boston: Houghton Mifflin Company.
[10] Dominique-Ferreira, S. and Antunes, C. 2019. Estimating the price range and the effect of price bundling strategies: An application to the hotel sector. European Journal of Management and Business Economics 29(2): 166–181. DOI: 10.1108/EJMBE-04-2019-0066
[11] Dunne, G., Flanagan, S. and Buckley, J. 2010. Towards an understanding of international city break travel. International Journal of Tourism Research, 12(5): 409–417. DOI: 10.1002/jtr.760
[12] Dunne, G., Flanagan, S. and Buckley, J. 2011. Towards a decision-making model for city break travel. International Journal of Culture, Tourism and Hospitality Research, 5(2): 158–172. DOI:10.1108/17506181111139573
[13] Dwityas, N.A. and Briandana, R. 2017. Social Media in Travel Decision Making Process. International Journal of Humanities and Social Science, 7(7). Available at: https://www.researchgate.net/publication/322749479
[14] Gitelson, R. and Kerstetter, D. 1995. The influence of friends and relatives in travel decision-making. Journal of Travel and Tourism Marketing, 3(3): 59–68. DOI: 10.1300/J073v03n03_04
[15] Harmon, R.R., Unni, R. and Anderson, T.R. 2007. Price sensitivity measurement and new product pricing: A cognitive response approach. In: Portland International Conference on Management of Engineering and Technology, 2007, pp. 1961–1967. DOI: 10.1109/PICMET.2007.4349523
[16] Harrison-Hill, T. 2001. How far is a long way? Contrasting two cultures’ perspectives of travel distance. Asia Pacific Journal of Marketing and Logistics, 13(3): 3–17. DOI: 10.1108/13555850110764801
[17] Hernández-Méndez, J, Muñoz-Leiva, F. and Sánchez-Fernández, J. 2015. The influence of e-word-of-mouth on travel decision-making: consumer profiles. Current Issues in Tourism, 18(11): 1001–1021. DOI:10.1080/13683500.2013.802764
[18] Hornik, J. and Zakay, D. 1996. Psychological Time: The Case of Time and Consumer Behaviour. Time & Society, 5(3): 385–397. DOI: 10.1177/0961463X96005003007
[19] Jain, J. and Lyons, G. 2008. The gift of travel time. Journal of Transport Geography, 16(2): 81–89. DOI:10.1016/j.jtrangeo.2007.05.001
[20] Jeng, J. and Fesenmaier, D.R. 2002. Conceptualizing the travel decision-making hierarchy: A review of recent developments. Tourism Analysis, 7(1): 15–32. DOI: 10.3727/108354202108749925
[21] Khoo-Lattimore, C., Prayag, G. and Cheah, B.L. 2015. Kids on Board: Exploring the Choice Process and Vacation Needs of Asian Parents With Young Children in Resort Hotels. Journal of Hospitality Marketing and Management, 24(5): 511–531. DOI: 10.1080/19368623.2014.914862
[22] Kirilenko, A.P., Stepchenkova, S.O. and Hernandez, J.M. 2019. Comparative clustering of destination attractions for different origin markets with network and spatial analyses of online reviews. Tourism Management, 72: 400–410. DOI: 10.1016/j.tourman.2019.01.001
[23] Lamb, C.W., Hair, J.F. and McDaniel, C. 2011. Essentials of Marketing 7th Edition. Cengage Learning.
[24] Larsen, G.R. and Guiver, J.W. 2013. Understanding tourists’ perceptions of distance: A key to reducing the environmental impacts of tourism mobility. Journal of Sustainable Tourism, 21(7): 968–981. DOI:10.1080/09669582.2013.819878
[25] Lebrun, A.M. 2014. Representations of a Destination City Break. Analysis Based on Free Associations. Journal of Travel and Tourism Marketing, 31(2): 195–210. DOI: 10.1080/10548408.2014.873312
[26] Lee, S. and Kwak, M. 2020. Consumer Valuation of Remanufactured Products: A Comparative Study of Product Categories and Business Models. Sustainability 12(18): 1–29. Available at: https://ideas.repec.org/a/gam/jsusta/v12y2020i18p7581-d413503.html
[27] Li, M., Xu, W. and Chen, Y. 2020. Young children’s vacation experience: Through the eyes of parents. Tourism Management Perspectives, 33: 100586. DOI: 10.1016/j.tmp.2019.100586
[28] MacEachren, A.M. 1980. Travel time as the basis of cognitive distance. Professional Geographer, 32(1): 30–36. DOI: 10.1111/j.0033-0124.1980.00030.x
[29] Masiero, L. and Nicolau, J.L. 2012. Tourism Market Segmentation Based on Price Sensitivity. Journal of Travel Research 51(4): 426–435. DOI: 10.1177/0047287511426339
[30] McCarthy, L., et al. 2017. Factors influencing travel mode choice among families with young children (aged 0–4): a review of the literature. Transport Reviews, 37(6): 767–781. DOI: 10.1080/01441647.2017.1354942
[31] McKercher, B. 2018. The impact of distance on tourism: a tourism geography law. Tourism Geographies 20(5): 905–909. DOI: 10.1080/14616688.2018.1434813
[32] McKercher, B., Chan, A. and Lam, C. 2008. The Impact of Distance on International Tourist Movements. Journal of Travel Research, 47(2): 208–224. DOI: 10.1177/0047287508321191
[33] Montello, D.R. 1991. The measurement of cognitive distance: Methods and construct validity. Journal of Environmental Psychology, 11(2): 101–122. DOI: 10.1016/S0272-4944(05)80071-4
[34] Nazeer, K.A.A. and Sebastian, M.P. 2009. Improving the Accuracy and Efficiency of the k-means Clustering Algorithm. Proceedings of the World Congress on Engineering I (July 2009): 6.
[35] Nejati, M. and Mohamed, B. 2014. Investigating the key factors influencing the travel decisions of international tourists. International Journal of Leisure and Tourism Marketing, 4(2): 106. DOI:10.1504/ijltm.2014.065877
[36] Nickerson, N.P. and Jurowski, C. 2001. The influence of children on vacation travel patterns. Journal of Vacation Marketing 7(1): 19–30. DOI: 10.1177/135676670100700102
[37] Nicolau, J.L. and Más, F.J. 2006. The influence of distance and prices on the choice of tourist destinations: The moderating role of motivations. Tourism Management 27(5): 982–996. DOI:10.1016/j.tourman.2005.09.009
[38] Nilashi, M. et al. 2022. What is the impact of eWOM in social network sites on travel decision-making during the COVID-19 outbreak? A two-stage methodology. Telematics and Informatics, (69) 101795. DOI:https://doi.org/10.1016/j.tele.2022.101795
[39] Patterson, I. and Pegg, S. 2009. Marketing the Leisure Experience to Baby Boomers and Older Tourists. Journal of Hospitality Marketing & Management, 18(2–3): 254–272. DOI: 10.1080/19368620802594136
[40] Pérez-Ortega, J., et al. 2020. The K -Means Algorithm Evolution. In: Introduction to Data Science and Machine Learning. IntechOpen. DOI: 10.5772/intechopen.85447
[41] Price, L. and Matthews, B. 2013. Travel time as quality time: Parental attitudes to long distance travel with young children. Journal of Transport Geography 32: 49–55. DOI: 10.1016/j.jtrangeo.2013.08.001
[42] Roll, O., Achterberg, L.-H. and Herbert, K.-G. 2010. Innovative Approaches to Analyzing the Price Sensitivity Meter. Results of an international comparative study. In: COMBI2010 Conference Proceedings (eds. T Riihelä and M Mattila), Helsinki, 2010, pp. 181–193.
[43] Salamandic, E., Alijosiene, S. and Gudonaviciene, R. 2014. Price Sensitivity Measurement Depending on Brand Awareness: A Case of Ziede Brand. Procedia - Social and Behavioral Sciences, 156: 473–478. DOI:10.1016/j.sbspro.2014.11.224
[44] Scheiner, J. 2010. Interrelations between travel mode choice and trip distance: trends in Germany 1976-2002. Journal of Transport Geography, 18(1): 75–84. DOI: 10.1016/j.jtrangeo.2009.01.001
[45] Sirakaya, E. and Woodside, A.G. 2005. Building and testing theories of decision making by travellers. Tourism Management, 26(6): 815–832. DOI: 10.1016/j.tourman.2004.05.004
[46] Süli, D. and Martyin-Csamangó, Z. 2020. The Impact of Social Media in Travel Decision-making Process among the Y and Z Generations of Music Festivals in Serbia and Hungary. Turizam, 24(2): 79–90. DOI:10.5937/turizam24-24678.
[47] Thrane, C. 2015. Examining tourists’ long-distance transportation mode choices using a Multinomial Logit regression model. Tourism Management Perspectives, 15: 115–121. DOI: 10.1016/j.tmp.2014.10.004
[48] Walmsley, D.J. and Jenkins, J.M. 1992. Cognitive Distance: A Neglected lssue in Travel Behavior. Journal of Travel Research, 31(1): 24–29. DOI: 10.1177/004728759203100106
[49] Xue, L. and Zhang, Y. 2020. The effect of distance on tourist behavior: A study based on social media data. Annals of Tourism Research, 82: 102916. DOI: 10.1016/j.annals.2020.102916
[50] Yuan, C. and Yang, H. 2019. Research on K-Value Selection Method of K-Means Clustering Algorithm. J 2(2): 226–235. DOI: 10.3390/j2020016
[51] Zhang, J., et al. 1999. The travel patterns and travel distance of tourists to national parks in China. Asia Pacific Journal of Tourism Research, 4(2): 27–34. DOI: 10.1080/10941669908722041
Published
2022-09-30
How to Cite
BĘBEN, Robert; KRAUS, Zuzanna; PÓŁBRAT, Izabela.
Distance Elasticity of Tourism Demand.
Journal of Environmental Management and Tourism, [S.l.], v. 13, n. 6, p. 1798-1810, sep. 2022.
ISSN 2068-7729.
Available at: <https://journals.aserspublishing.eu/jemt/article/view/7290>. Date accessed: 26 apr. 2024.
Section
Article
Copyright© 2023 The Author(s). Published by ASERS Publishing 2023. This is an open access article distributed under the terms of CC-BY 4.0 license.