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Scale heterogeneity in discrete choice experiment: An application of generalized mixed logit model in air travel choice

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  • Hossain, Ishrat
  • Saqib, Najam U.
  • Haq, Munshi Masudul

Abstract

This study explores to what extent scale heterogeneity (i.e., varying standard deviation of the errors across consumers) is important in air travel choice using data from a stated preference discrete choice experiment. We used generalized mixed logit model (GMIXL) that nests scale and taste heterogeneity in the context of air travel ticket choice. We found empirical evidence of the importance of often-neglected scale heterogeneity along with pre-dominant taste heterogeneity. The findings suggests that it is important to account for various forms of heterogeneity with GMIXL to model air travel demand to identify variety of consumer segments, which has implications for airline optimal menu of ticket offerings and pricing strategies.

Suggested Citation

  • Hossain, Ishrat & Saqib, Najam U. & Haq, Munshi Masudul, 2018. "Scale heterogeneity in discrete choice experiment: An application of generalized mixed logit model in air travel choice," Economics Letters, Elsevier, vol. 172(C), pages 85-88.
    • Handle: RePEc:eee:ecolet:v:172:y:2018:i:c:p:85-88
    DOI: 10.1016/j.econlet.2018.08.037
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    References listed on IDEAS

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    1. Denzil G. Fiebig & Michael P. Keane & Jordan Louviere & Nada Wasi, 2010. "The Generalized Multinomial Logit Model: Accounting for Scale and Coefficient Heterogeneity," Marketing Science, INFORMS, vol. 29(3), pages 393-421, 05-06.
    2. Andrew Collins & John Rose & Stephane Hess, 2012. "Interactive stated choice surveys: a study of air travel behaviour," Transportation, Springer, vol. 39(1), pages 55-79, January.
    3. Yuanyuan Gu & Arne Risa Hole & Stephanie Knox, 2013. "Fitting the generalized multinomial logit model in Stata," Stata Journal, StataCorp LP, vol. 13(2), pages 382-397, June.
    4. Hall, Jane & Fiebig, Denzil G. & King, Madeleine T. & Hossain, Ishrat & Louviere, Jordan J., 2006. "What influences participation in genetic carrier testing?: Results from a discrete choice experiment," Journal of Health Economics, Elsevier, vol. 25(3), pages 520-537, May.
    5. William Greene & David Hensher, 2010. "Does scale heterogeneity across individuals matter? An empirical assessment of alternative logit models," Transportation, Springer, vol. 37(3), pages 413-428, May.
    6. Michael Keane & Nada Wasi, 2013. "Comparing Alternative Models Of Heterogeneity In Consumer Choice Behavior," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(6), pages 1018-1045, September.
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    Citations

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    Cited by:

    1. Wen, Chieh-Hua & Huang, Chia-Jung & Fu, Chiang, 2020. "Incorporating continuous representation of preferences for flight departure times into stated itinerary choice modeling," Transport Policy, Elsevier, vol. 98(C), pages 10-20.
    2. Munoz, Claudia & Laniado, Henry, 2021. "Airline choice model for international round-trip flights: The role of travelers’ satisfaction and personality traits," Research in Transportation Economics, Elsevier, vol. 90(C).
    3. Min-Yen Chang & Yi-Sheng Hsu & Han-Shen Chen, 2021. "Choice Experiment Method for Sustainable Tourism in Theme Parks," Sustainability, MDPI, vol. 13(13), pages 1-16, June.
    4. Sharma, Abhijit & Woodward, Richard & Grillini, Stefano, 2020. "Unconditional quantile regression analysis of UK inbound tourist expenditures," Economics Letters, Elsevier, vol. 186(C).
    5. Gonçalves, Tânia & Pinto, Lígia M. Costa & Lourenço-Gomes, Lina, 2019. "Exploring distinct sources of heterogeneity in discrete choice experiment: An application to wine choice across European consumers," Economics Letters, Elsevier, vol. 178(C), pages 28-32.
    6. Mahdi Rezapour & Khaled Ksaibati, 2021. "Accommodating Taste and Scale Heterogeneity for Front-Seat Passenger’ Choice of Seat Belt Usage," Mathematics, MDPI, vol. 9(5), pages 1-11, February.

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    More about this item

    Keywords

    Choice experiment; Generalized logit; Scale heterogeneity; Air travel;
    All these keywords.

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • D11 - Microeconomics - - Household Behavior - - - Consumer Economics: Theory
    • L93 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Air Transportation

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