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Modelling Business Travel

Author

Listed:
  • Nada Kulendran
  • Kenneth Wilson

Abstract

Business travel is important and yet very few attempts have been made to model it. The aim of this study is to identify those economic variables that are most important in influencing business trips to Australia from four of Australia's most important travel and trade partners. Using a standard demand modelling approach suitably modified to deal with the motivations for business trips, Johansen's Full Information Maximum Likelihood technique is used to estimate the long-run relationship between business travel and its explanators. We find that the importance of the economic variables varies from country to country, although overall openness to trade and origin country real income are important variables explaining business travel.

Suggested Citation

  • Nada Kulendran & Kenneth Wilson, 2000. "Modelling Business Travel," Tourism Economics, , vol. 6(1), pages 47-59, March.
    • Handle: RePEc:sae:toueco:v:6:y:2000:i:1:p:47-59
    DOI: 10.5367/000000000101297460
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    Cited by:

    1. WAN, Shui-Ki & WANG, Shin-Huei & WOO, Chi-Keung, 2012. "Total tourist arrival forecast: aggregation vs. disaggregation," LIDAM Discussion Papers CORE 2012039, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Ioannis Chatziantoniou & Stavros Degiannakis & Bruno Eeckels & George Filis, 2016. "Forecasting tourist arrivals using origin country macroeconomics," Applied Economics, Taylor & Francis Journals, vol. 48(27), pages 2571-2585, June.
    3. Sudeshna Ghosh, 2021. "Business Confidence and Business Tourism in Japan," Journal of International Commerce, Economics and Policy (JICEP), World Scientific Publishing Co. Pte. Ltd., vol. 12(01), pages 1-23, February.
    4. Tsui, Wai Hong Kan & Fung, Michael Ka Yiu, 2016. "Causality between business travel and trade volumes: Empirical evidence from Hong Kong," Tourism Management, Elsevier, vol. 52(C), pages 395-404.
    5. Coshall, John T. & Charlesworth, Richard, 2011. "A management orientated approach to combination forecasting of tourism demand," Tourism Management, Elsevier, vol. 32(4), pages 759-769.
    6. Peng, Bo & Song, Haiyan & Crouch, Geoffrey I., 2014. "A meta-analysis of international tourism demand forecasting and implications for practice," Tourism Management, Elsevier, vol. 45(C), pages 181-193.
    7. Ketenci, Natalya, 2009. "The ARDL Approach to Cointegration Analysis of Tourism Demand in Turkey: with Greece as the substitution destination," MPRA Paper 86602, University Library of Munich, Germany.
    8. Uju Violet Alola & Darya Baeva & Andrew Adewale Alola, 2023. "Determining the (A)symmetric Role of Business–Consumer Confidence in Outward–Inward Tourism in Russia: A Competitiveness Perspective," International Journal of Global Business and Competitiveness, Springer, vol. 18(1), pages 22-34, June.
    9. Qingjie Zhou & Panpan Zhu & You Wu & Yinpeng Zhang, 2022. "Research on the Volatility of the Cotton Market under Different Term Structures: Perspective from Investor Attention," Sustainability, MDPI, vol. 14(21), pages 1-20, November.
    10. David Tan & Kan Tsui, 2017. "Investigating causality in international air freight and business travel: The case of Australia," Urban Studies, Urban Studies Journal Limited, vol. 54(5), pages 1178-1193, April.
    11. Long Wen & Chang Liu & Haiyan Song, 2019. "Forecasting tourism demand using search query data: A hybrid modelling approach," Tourism Economics, , vol. 25(3), pages 309-329, May.
    12. Hassan F. Gholipour & Behzad Foroughi, 2020. "Corruption and outbound business travels," Tourism Economics, , vol. 26(7), pages 1266-1281, November.
    13. Hassan F Gholipour & Reza Tajaddini & Usama Al-mulali, 2022. "Dutch Disease phenomenon and demand for international business travels: Panel ARDL/PMG estimation," Tourism Economics, , vol. 28(5), pages 1401-1415, August.
    14. Wai Hong Kan Tsui & Faruk Balli & David Tat Wei Tan & Oscar Lau & Mudassar Hasan, 2018. "New Zealand business tourism," Tourism Economics, , vol. 24(4), pages 386-417, June.
    15. Zhang, Yishuo & Li, Gang & Muskat, Birgit & Law, Rob & Yang, Yating, 2020. "Group pooling for deep tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 82(C).
    16. Egon Smeral, 2013. "Tourismus 2025: Entwicklungsperspektiven und Strategien für den ländlichen Raum," WIFO Studies, WIFO, number 47070, April.
    17. Kenichi Shimamoto, 2019. "Examining the Seasonality of Travel-Related Expenditure by Travel Purpose: The Case of Japan," Academica Turistica - Tourism and Innovation Journal, University of Primorska Press, vol. 12(1), pages 55-72.
    18. Mingming Hu & Haiyan Song, 2020. "Data source combination for tourism demand forecasting," Tourism Economics, , vol. 26(7), pages 1248-1265, November.
    19. Tihomir Stučka, 2002. "A Comparison of Two Econometric Models (OLS and SUR) for Forecasting Croatian Tourism Arrivals," Working Papers 8, The Croatian National Bank, Croatia.
    20. Serdar Ongan & Cem Işik & Dilek Özdemir, 2017. "The Effects of Real Exchange Rates and Income on International Tourism Demand for the USA from Some European Union Countries," Economies, MDPI, vol. 5(4), pages 1-11, December.
    21. Robaina, M. & Madaleno, M. & Silva, S. & Eusébio, C. & Carneiro, M.J. & Gama, C. & Oliveira, K. & Russo, M.A. & Monteiro, A., 2020. "The relationship between tourism and air quality in five European countries," Economic Analysis and Policy, Elsevier, vol. 67(C), pages 261-272.

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