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Evaluating Cruise Demand Forecasting Practices: A Delphi Approach

In: Cruise Sector Challenges

Author

Listed:
  • Hannah Kollwitz

    (Oxford Brooks University)

  • Alexis Papathanassis

    (Bremerhaven University of Applied Sciences)

Abstract

This work evaluates current cruise demand forecasting practices with the main focus drawn to the European branch. Various forecasts underline the immense growth potential of this particular cruise market tempting cruise operators to launch additional capacity in this market. Given under-capacities in the cruise sector, forecasts tend to focus on the expected availability of lower berths rather than the market’s demand for them. The implicit assumptions here are: Conditions of under-capacities and a near-100% capacity utilisation will persist in the foreseeable future. Those implicit assumptions potentially render forecasts into self-fulfilling prophecies thus warranting further examination and discussion. This study adopts a Delphi methodology in order to examine the influence and validity of such assumptions. Cruise industry experts were questioned on their expectations for the development of European cruise industry over the next ten years. The aim was to identify their perception of published forecasts and the influence they exert on the market’s development. Our research results confirm the tendency towards self-fulfilling prophecies. New cruise ships are financed and ordered on the basis of an increasing demand, which is nevertheless fuelled by lower prices set by cruise operators creating an ‘artificially-maintained’ under-capacity, reinforcing existing forecasting practices.

Suggested Citation

  • Hannah Kollwitz & Alexis Papathanassis, 2011. "Evaluating Cruise Demand Forecasting Practices: A Delphi Approach," Springer Books, in: Philip Gibson & Alexis Papathanassis & Petra Milde (ed.), Cruise Sector Challenges, chapter 3, pages 39-55, Springer.
    • Handle: RePEc:spr:sprchp:978-3-8349-6871-5_3
    DOI: 10.1007/978-3-8349-6871-5_3
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    Cited by:

    1. Zhang, Hanyuan & Song, Haiyan & Wen, Long & Liu, Chang, 2021. "Forecasting tourism recovery amid COVID-19," Annals of Tourism Research, Elsevier, vol. 87(C).
    2. Banerjee, Nilabhra & Morton, Alec & Akartunalı, Kerem, 2020. "Passenger demand forecasting in scheduled transportation," European Journal of Operational Research, Elsevier, vol. 286(3), pages 797-810.

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