Advertisement

Drivers of Medical Tourism at the Individual Level

  • Klaus Schmerler
Chapter
Part of the Developments in Health Economics and Public Policy book series (HEPP, volume 13)

Abstract

In Chap. 6, the author explores specific networks and network activities that underlie the more aggregated measures of cultural proximity in Chaps. 4 and 5. This chapter draws from stakeholder interviews and from an exploratory patient survey including a discrete choice experiment. The latter allows an investigation of the multilevel supply dimension outlined in Chap. 3 and a quantification of the country-of-origin effect associated with Germany. Additionally, this chapter inquires the role of recreation in medical tourism, into patients’ real consideration sets and into the role of numerous destination and individual characteristics for destination choice to answer secondary research questions that arose in Chaps. 2 and 3.

References

  1. Adamowicz, W., Boxall, P., Williams, M., & Louviere, J. (1998). Stated preference approaches for measuring passive use values: Choice experiments and contingent valuation. American Journal of Agricultural Economics, 80, 64–75.CrossRefGoogle Scholar
  2. Alsharif, M. J., Labonte, R., & Lu, Z. (2010). Patients beyond borders: A study of medical tourists in four countries. Global Social Policy, 10, 315–335.  https://doi.org/10.1177/1468018110380003.CrossRefGoogle Scholar
  3. Anderson, S. P., de Palma, A., & Thisse, J.-F. (1992). Discrete Choice Theory of Product Differentiation. Cambridge, MA: MIT Press.Google Scholar
  4. Bhat, C. R. (1995). A heteroscedastic extreme value model of intercity travel mode choice. Transportation Research Part B: Methodological, 29, 471–483.  https://doi.org/10.1016/0191-2615(95)00015-6.CrossRefGoogle Scholar
  5. Bliemer, M. C. J., & Rose, J. M. (2010). Construction of experimental designs for mixed logit models allowing for correlation across choice observations. Transportation Research Part B: Methodological, 44, 720–734.  https://doi.org/10.1016/j.trb.2009.12.004.CrossRefGoogle Scholar
  6. Bliemer, M. C. J., Rose, J. M., & Hensher, D. A. (2009). Efficient stated choice experiments for estimating nested logit models. Transportation Research Part B: Methodological, 43, 19–35.  https://doi.org/10.1016/j.trb.2008.05.008.CrossRefGoogle Scholar
  7. Boga, T. C., & Weiermair, K. (2011). Branding new services in health tourism. Tourism Review, 66, 90–106.CrossRefGoogle Scholar
  8. Boxall, P., Adamowicz, W. L., & Moon, A. (2009). Complexity in choice experiments: Choice of the status quo alternative and implications for welfare measurement. Australian Journal of Agricultural and Resource Economics, 53, 503–519.  https://doi.org/10.1111/j.1467-8489.2009.00469.x.CrossRefGoogle Scholar
  9. Braun, A. (2014). How to get it – Gesundheitstourismus am Beispiel des Maximalversorgers Klinikum Stuttgart. Ravensburger Tourismustag 2014Google Scholar
  10. Bunch, D. S., & Batsell, R. R. (1989). A Monte Carlo comparison of estimators for the multinomial logit model. Journal of Marketing Research, 26, 56–68.Google Scholar
  11. Cheng, S.-H. (2004). Physician performance information and consumer choice: A survey of subjects with the freedom to choose between doctors. Quality and Safety in Health Care, 13, 98–101.  https://doi.org/10.1136/qshc.2003.006981.CrossRefGoogle Scholar
  12. ChoiceMetrics. (2012). Ngene user manual & reference guide.Google Scholar
  13. Dawes, J. (2008). Do data characteristics change according to the number of scale points used? International Journal of Market Research, 50, 61–77.CrossRefGoogle Scholar
  14. de Bekker-Grob, E. W., Hol, L., Donkers, B., van Dam, L., Habbema, J. D. F., van Leerdam, M. E., Kuipers, E. J., Essink-Bot, M.-L., & Steyerberg, E. W. (2010). Labeled versus unlabeled discrete choice experiments in health economics: An application to colorectal cancer screening. Value Health, 13, 315–323.  https://doi.org/10.1111/j.1524-4733.2009.00670.x.CrossRefGoogle Scholar
  15. Fiebig, D. G., Keane, M. P., Louviere, J., & Wasi, N. (2010). The generalized multinomial logit model: Accounting for scale and coefficient heterogeneity. Marketing Science, 29, 393–421.  https://doi.org/10.1287/mksc.1090.0508.CrossRefGoogle Scholar
  16. Finstad, K. A. (2010). Response interpolation and scale sensitivity: Evidence against 5-point scales. Journal of Usability Studies, 5, 104–110.Google Scholar
  17. Gilmour, S. G., & Trinca, L. A. (2012). Optimum design of experiments for statistical inference. Applied Statistics, 61, 345–401.Google Scholar
  18. Greene, W. H., & Hensher, D. A. (2003). A latent class model for discrete choice analysis: Contrasts with mixed logit. Transportation Research Part B: Methodological, 37, 681–698.  https://doi.org/10.1016/S0191-2615(02)00046-2.CrossRefGoogle Scholar
  19. Hays, R. D., Bode, R., Rothrock, N., Riley, W., Cella, D., & Gershon, R. (2010). The impact of next and back buttons on time to complete and measurement reliability in computer-based surveys. Quality of Life Research, 19, 1181–1184.  https://doi.org/10.1007/s11136-010-9682-9.CrossRefGoogle Scholar
  20. Hensher, D. A., Rose, J. M., & Greene, W. H. (2007). Applied choice analysis: A primer (3rd ed.). Cambridge: Cambridge University Press.Google Scholar
  21. Hess, S., & Rose, J. M. (2009). Allowing for intra-respondent variations in coefficients estimated on repeated choice data. Transportation Research Part B: Methodological, 43, 708–719.  https://doi.org/10.1016/j.trb.2009.01.007.CrossRefGoogle Scholar
  22. Hess, S., & Rose, J. M. (2012). Can scale and coefficient heterogeneity be separated in random coefficients models? Transportation, 39, 1225–1239.  https://doi.org/10.1007/s11116-012-9394-9.CrossRefGoogle Scholar
  23. Hess, S., & Stathopoulos, A. (2013). A mixed random utility — Random regret model linking the choice of decision rule to latent character traits. Journal of Choice Modelling, 9, 27–38.  https://doi.org/10.1016/j.jocm.2013.12.005.CrossRefGoogle Scholar
  24. Hess, S., & Train, K. (2017). Correlation and scale in mixed logit models. Journal of Choice Modelling, 23, 1–8.  https://doi.org/10.1016/j.jocm.2017.03.001.CrossRefGoogle Scholar
  25. Hess, S., Rose, J. M., & Hensher, D. A. (2008). Asymmetric preference formation in willingness to pay estimates in discrete choice models. Transportation Research Part E: Logistics and Transportation Review, 44, 847–863.  https://doi.org/10.1016/j.tre.2007.06.002.CrossRefGoogle Scholar
  26. Huber, J., & Zwerina, K. (1996). The Importance of utility balance in efficient choice designs. Journal of Marketing Research, 33, 307–317.CrossRefGoogle Scholar
  27. International Medical Travel Journal. (2016). Medical tourism from Russia survey. Accessed July 20, 2016, from https://www.imtj.com/news/medical-tourism-russia-survey/
  28. Johnson, F. R., Lancsar, E., Marshall, D., Kilambi, V., Muhlbacher, A., Regier, D. A., Bresnahan, B. W., Kanninen, B., & Bridges, J. F. P. (2013). Constructing experimental designs for discrete-choice experiments: Report of the ISPOR conjoint analysis experimental design good research practices task force. Value Health, 16, 3–13.  https://doi.org/10.1016/j.jval.2012.08.2223.CrossRefGoogle Scholar
  29. Jones, B., & Nachtsheim, C. J. (2011). Efficient designs with minimal aliasing. Technometrics, 53, 62–71.  https://doi.org/10.1198/TECH.2010.09113.CrossRefGoogle Scholar
  30. Kahnemann, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47, 263–292.CrossRefGoogle Scholar
  31. Keane, M., & Wasi, N. (2012). Comparing alternative models of heterogeneity in consumer choice behavior. Journal of Applied Econometrics, 1018–1043.  https://doi.org/10.1002/jae.2304.
  32. Kessels, R., Groos, P., & Vandebroek, M. (2006). A comparison of criteria to design efficient choice experiments. Journal of Marketing Research, 43, 409–419.CrossRefGoogle Scholar
  33. Kuhfeld, W. F. (1997). Efficient experimental designs using computerized searches (Research Paper Series). Sawtooth Software.Google Scholar
  34. Kuhfeld, W. F., Tobias, R. D., & Garrat, M. (1994). Efficient experimental design with marketing research applications. Journal of Marketing Research, 31, 545–557.  https://doi.org/10.2307/3151882.CrossRefGoogle Scholar
  35. Lancsar, E., & Louviere, J. (2008). Conducting discrete choice experiments to inform healthcare decision making. PharmacoEconomics, 26, 661–677.  https://doi.org/10.2165/00019053-200826080-00004.CrossRefGoogle Scholar
  36. Leong, W., & Hensher, D. A. (2012). Embedding multiple heuristics into choice models: An exploratory analysis. Journal of Choice Modelling, 5, 131–144.  https://doi.org/10.1016/j.jocm.2013.03.001.CrossRefGoogle Scholar
  37. Leung, S.-O. (2011). A comparison of psychometric properties and normality in 4-, 5-, 6-, and 11-point Likert scales. Journal of Social Service Research, 37, 412–421.  https://doi.org/10.1080/01488376.2011.580697.CrossRefGoogle Scholar
  38. Louviere, J. J., Meyer, R. J., Bunch, D. S., Carson, R., Dellaert, B., Hanemann, W. M., Hensher, D., & Irwin, J. (1999). Combining sources of preference data for modeling complex decision processes. Marketing Letters, 10, 205–217.  https://doi.org/10.1023/A:1008050215270.CrossRefGoogle Scholar
  39. Louviere, J. J., Street, D., Carson, R., Ainslie, A., Deshazo, J. R., Cameron, T., Hensher, D., Kohn, R., & Marley, T. (2002). Dissecting the random component of utility. Marketing Letters, 13, 177–193.CrossRefGoogle Scholar
  40. Louviere, J. J., Hensher, D. A., Swait, J. D., & Adamowicz, W. (2007). Stated choice methods: Analysis and applications (5th ed.). Cambridge: Cambridge University Press.Google Scholar
  41. Louviere, J. J., Street, D., Burgess, L., Wasi, N., Islam, T., & Marley, A. A. J. (2008). Modeling the choices of individual decision-makers by combining efficient choice experiment designs with extra preference information. Journal of Choice Modelling, 1, 128–164.  https://doi.org/10.1016/S1755-5345(13)70025-3.CrossRefGoogle Scholar
  42. Lusk, J. L., & Norwood, F. B. (2005). Effect of experimental design on choice-based conjoint valuation estimates. American Journal of Agricultural Economics, 87, 771–785.CrossRefGoogle Scholar
  43. Maddala, G. S. (2008). Limited-dependent and qualitative variables in econometrics. Econometric society monographs (3rd ed.). Cambridge: Cambridge University Press.Google Scholar
  44. Mai, R. (2011). Der Herkunftslandeffekt: Eine kritische Würdigung des State of the Art. Journal Betriebswirtsch, 61, 91–121.  https://doi.org/10.1007/s11301-011-0075-0.CrossRefGoogle Scholar
  45. Mühlbacher, A. C., Zweifel, P., Kaczynski, A., & Johnson, F. R. (2016). Experimental measurement of preferences in health care using best-worst scaling (BWS): Theoretical and statistical issues. Health Economics Review, 6, 5.  https://doi.org/10.1186/s13561-015-0077-z.CrossRefGoogle Scholar
  46. Musa, G., Doshi, D., Wong, K. M., & Thirumoorthy, T. (2012). How satisfied are inbound medical tourists in Malaysia?: A study on private hospitals in Kuala Lumpur. Journal of Travel & Tourism Marketing, 29, 629–646.  https://doi.org/10.1080/10548408.2012.720150.CrossRefGoogle Scholar
  47. NaRanong, A., & NaRanong, V. (2011). The effects of medical tourism: Thailand’s experience. Bulletin of the World Health Organization, 89, 336–344.  https://doi.org/10.2471/BLT.09.072249.CrossRefGoogle Scholar
  48. Noree, T., Hanefeld, J., & Smith, R. (2014). UK medical tourists in Thailand: They are not who you think they are. Global Health, 10, 29.  https://doi.org/10.1186/1744-8603-10-29.CrossRefGoogle Scholar
  49. Pinnell, J. (2005). Comment on Huber: Practical suggestions for CBC studies (Research Paper Series). Sawtooth Software.Google Scholar
  50. Pollard, K. (2012). The medical tourism survey 2012. London: Intuition Communication Ltd.Google Scholar
  51. Pollard, K. (2013). Medical tourism climate survey 2013. London: Intuition Communication Ltd.Google Scholar
  52. Revelt, D., & Train, K. (2000). Customer-specific taste parameters and mixed logit: Households’ choice of electricity supplier (Economics Working Papers). Berkeley, CA: University of California.Google Scholar
  53. Rose, J. M., Hess, S., Bliemer, M. C. J., & Daly, A. (2009). The impact of varying the number of repeated choice observations on the mixed multinomial logit model. Noordwijkerhout: European Transport Conference.Google Scholar
  54. Rose, J. M., Hess, S., & Collins, A. T. (2013). What if my model assumptions are wrong? The impact of non-standard behaviour on choice model estimation. Journal of Transport Economics and Policy, 47, 245–263.Google Scholar
  55. Ryan, M., & Gerard, K. (2003). Using discrete choice experiments to value health care programmes: Current practice and future research reflections. Applied Health Economics and Health Policy, 2, 55–64.Google Scholar
  56. Shobokshi, ObAM. (2014). Vortrag im Rahmen der Tagung “Gesundheitstourismus”. Gesundheitstourismus, Klinikum Stuttgart, GermanyGoogle Scholar
  57. Siebertz, K., van Bebber, D., & Hochkirchen, T. (2010). Statistische Versuchsplanung: Design of experiments (DOE), VDI-Buch (1st ed.). Heidelberg: Springer.CrossRefGoogle Scholar
  58. Sonnier, G., Ainslie, A., & Otter, T. (2007). Heterogeneity distributions of willingness-to-pay in choice models. Quantitative Marketing and Economics, 5, 313–331.  https://doi.org/10.1007/s11129-007-9024-6.CrossRefGoogle Scholar
  59. Swait, J., & Adamowicz, W. (2001). The influence of task complexity on consumer choice: A latent class model of decision strategy switching. Journal of Consumer Research, 28, 135–148.  https://doi.org/10.1086/321952.CrossRefGoogle Scholar
  60. Train, K. (2009). Discrete choice methods with simulation. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  61. Train, K., & Weeks, M. (2005). Discrete choice models in preference space and willingness-to-pay space. In R. Scarpa & A. Alberini (Eds.), Applications of simulation methods in environmental and resource economics (Vol. 6, pp. 1–16). Berlin: Springer-Verlag.CrossRefGoogle Scholar
  62. Winkelmann, R., & Boes, S. (2009). Analysis of microdata (2nd ed.). Berlin: Springer.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Klaus Schmerler
    • 1
  1. 1.Martin Luther University Halle-WittenbergHalle (Saale)Germany

Personalised recommendations