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Response style corrected market segmentation for ordinal data

Author

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  • Bettina Grün

    (Johannes Kepler University Linz)

  • Sara Dolnicar

    (University of Queensland, St Lucia)

Abstract

Survey data collected for market segmentation studies is typically ordinal in nature. As such, it is susceptible to response styles. Ignoring response styles can lead to market segments which do not differ in beliefs, but merely in how segment members use survey answer options and which possibly occur in addition to the belief segments. We propose a finite mixture model which simultaneously segments and corrects for response styles, permits heterogeneity in both beliefs and response styles, accommodates a range of different response styles, does not impose a certain relationship between the response style and belief segments, and is suitable for ordinal data. The performance of the model is tested using both artificial and empirical survey data.

Suggested Citation

  • Bettina Grün & Sara Dolnicar, 2016. "Response style corrected market segmentation for ordinal data," Marketing Letters, Springer, vol. 27(4), pages 729-741, December.
    • Handle: RePEc:kap:mktlet:v:27:y:2016:i:4:d:10.1007_s11002-015-9375-9
    DOI: 10.1007/s11002-015-9375-9
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    References listed on IDEAS

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    Cited by:

    1. Raoofpanah, Iman & Zamudio, César & Groening, Christopher, 2023. "Review reader segmentation based on the heterogeneous impacts of review and reviewer attributes on review helpfulness: A study involving ZIP code data," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).

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