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A multilevel latent class analysis of the purchasing channels among European consumers

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Abstract

This work aims at investigating similarities and differences in the ways of purchasing goods and services by European citizens—in particular the consumer behaviour on the preferred purchasing channels among web, phone, mail and sales representatives—by exploiting data collected through the Eurobarometer 69.1 survey in 2008. To this aim, we adopt a multilevel latent class solution, which allows to simultaneously cluster individuals and countries. The overall result is that most countries can be grouped in classes that follow a geographical division, while European citizens can be divided in classes with some specific profiles: a large proportion of consumers have not confidence with alternative purchasing channels yet, particularly among older respondents; most consumers still prefer to buy from sellers or providers located in their own country; more educated individuals show a widespread use of the web; a class of potential purchasers may be determined, particularly among younger people.

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References

  1. Akaike, H.: A new look at the statistical model identification. IEEE Trans. Autom. Control 19, 716–723 (1974)

    Article  MathSciNet  MATH  Google Scholar 

  2. Bartolucci, F., Bacci, S., Gnaldi, M.: MultiLCIRT: an R package for multidimensional latent class item response models. Comput. Stat. Data Anal. 71, 971–985 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  3. Bassi, F.: Latent class models for marketing strategies: an application to the Italian Pharmaceutical Market. Methodol. Eur. J. Res. Methods Behav. Soc. Sci. 5, 40–45 (2009)

    Google Scholar 

  4. Bhatnagar, A., Ghose, S.: A latent class segmentation analysis of e-shoppers. J. Bus. Res. 57, 758–767 (2004)

    Article  Google Scholar 

  5. Bijmolt, T.H.A., Paas, L.J., Vermunt, J.K.: Country and consumer segmentation: multi-level latent class analysis of financial product ownership. Int. J. Res. Mark. 21, 323–340 (2004)

    Article  Google Scholar 

  6. Bozdogan, H.: Model selection and Akaike’s information criterion(AIC): the general theory and its analytical extensions. Psychometrika 52, 345–370 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  7. Butanay, G., Wortzel, L.H.: Distributor power versus manufacturer power: the customer role. J. Mark. 52, 52–63 (1988)

    Article  Google Scholar 

  8. Collesei, U., Casarin, F., Vescovi, T.: Internet e i cambiamenti nei comportamenti d’acquisto del consumatore. Micro Macro Mark. 1, 33–50 (2001)

    Google Scholar 

  9. Dayton, C.M., Macready, G.B.: Concomitant-variable latent-class models. J. Am. Stat. Assoc. 83, 173–178 (1988)

    Article  MathSciNet  Google Scholar 

  10. Formann, A.K.: Mixture analysis of multivariate categorical data with covariates and missing entries. Comput. Stat. Data Anal. 51, 5236–5246 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  11. Friedman, L.G., Furey, T.R.: The Channel Advantage. Butterworth-Heinemann, Burlington (2003)

    Google Scholar 

  12. Ganesh, J.: Converging trends within the European Union: insights from an analysis of diffusion patterns. J. Int. Mark. 6, 32–48 (1988)

    Google Scholar 

  13. Gnaldi, M., Bacci, S., Bartolucci, F.: A multilevel finite mixture item response model to cluster examinees and schools. Adv. Data Anal. Classif. (2015). doi:10.1007/s11634-014-0196-0

  14. Henry, K.L., Muthén, B.: Multilevel latent class analysis: an application of adolescent smoking typologies with individual and contextual predictors. Struct. Equ. Model. 17, 193–215 (2010)

    Article  MathSciNet  Google Scholar 

  15. Goodman, L.A.: Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika 61, 215–231 (1974)

    Article  MathSciNet  MATH  Google Scholar 

  16. Keng, K., Tang, Y., Ghose, S.: Typology of online shoppers. J. Consum. Mark. 20, 139–156 (2003)

    Article  Google Scholar 

  17. Kumar, V., Ganesh, J., Echambadi, R.: Cross-national diffusion research: what do we know and how certain are we? J. Prod. Innov. Manag. 15, 255–268 (1998)

    Article  Google Scholar 

  18. Lanza S.T., Dziak J.J., Huang L., Wagner A.T., Collins L.M.: LCA Stata Plugin Users’ Guide (Version 1.2). University Park: The Methodology Center, Penn State (2015)

  19. Lazarsfeld, P.F.: The logical and mathematical foundation of latent structure analysis and the interpretation and mathematical foundation of latent structure analysis. In: Stouffer, S.A., et al. (eds.) Measurement and Prediction, pp. 362–472. Princeton University Press, Princeton (1950)

    Google Scholar 

  20. Lazarsfeld, P.F., Henry, N.W.: Latent Structure Analysis. Houghton Mifflin, Boston (1968)

    MATH  Google Scholar 

  21. Lemmens, A., Croux, C., Dekimpe, M.G.: Consumer confidence in Europe: united in diversity? Int. J. Res. Mark. 24, 113–127 (2007)

    Article  Google Scholar 

  22. Levitt, T.: The globalization of markets. Harv. Bus. Rev. 61, 92–102 (1983)

    Google Scholar 

  23. Lukočienė, O., Vermunt, J.K.: Determining the number of components in mixture models for hierarchical data. In: Fink, A., Berthold, L., Seidel, W., Ultsch, A. (eds.) Advances in Data Analysis, Data Handling and Business Intelligence, pp. 241–249. Springer, Berlin (2010)

    Google Scholar 

  24. Lukočienė, O., Varriale, R., Vermunt, J.K.: The simultaneous decision(s) about the number of lower- and higher-level classes in multilevel latent class analysis. Sociol. Methodol. 40, 247–283 (2010)

    Article  Google Scholar 

  25. Magidson, J., Vermunt, J.K.: Latent class models for clustering: a comparison with K-means. Can. J. Mark. Res. 20, 36–43 (2002)

    Google Scholar 

  26. Muthén L.K., Muthén B.O.: Mplus Users Guide, Seventh Edition. Los Angeles, Muthén & Muthén (2012)

  27. Mutz, R., Daniel, H.D.: University and student segmentation: multilevel latent-class analysis of students’ attitudes towards research methods and statistics. Brit. J. Educ. Psychol. 83, 280–304 (2013)

    Article  Google Scholar 

  28. Paccagnella, O., Varriale, R.: Asset ownership of the elderly across Europe: a multilevel latent class analysis to segment countries and households. In: Torelli, N., Pesarin, F., Bar-Hen, A. (eds.) Advances in Theoretical and Applied Statistics, pp. 383–393. Springer, Berlin (2013)

    Chapter  Google Scholar 

  29. Payne, A.F.T., Frow, P.: A strategic framework for customer relationship management. J. Mark. 69, 167–176 (2005)

    Article  Google Scholar 

  30. Saunders C.: Multiple channel buyers worth pursuing, Internet advertising report, January 28. http://www.internetnews.com (2002)

  31. Schwarz, G.: Estimating the dimention of a model. Ann. Stat. 6, 461–464 (1978)

    Article  MATH  Google Scholar 

  32. Sharma, A., Mehrotra, A.: Choosing an optimal channel mix in multichannel environments. Ind. Mark. Manag. 36, 21–28 (2006)

    Article  Google Scholar 

  33. Skrondal, A., Rabe-Hesketh, S.: Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models. Chapman and Hall/CRC, Boca Raton (2004)

    Book  MATH  Google Scholar 

  34. Solomon, M.R.: Consumer Behavior: Buying, Having and Being, 10th edn. Pearson Prentice Hall, Upper Saddle River (2012)

    Google Scholar 

  35. Steenkamp, J.B.E.M.: The role of national culture in international marketing research. Int. Mark. Rev. 18, 30–44 (2001)

    Article  Google Scholar 

  36. Steenkamp, J.B.E.M., Ter Hofstede, F.: International market segmentation. Int. J. Res. Mark. 19, 185–213 (2002)

    Article  Google Scholar 

  37. Vermunt, J.K.: Multilevel latent class models. Sociol. Methodol. 33, 213–239 (2003)

    Article  Google Scholar 

  38. Vermunt, J.K.: Latent class and finite mixture models for multilevel data sets. Stat. Methods Med. Res. 17, 33–51 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  39. Vermunt, J.K., Magidson, J.: Latent class cluster analysis. In: Hagenaars, J.A., McCutcheon, A.L. (eds.) Applied Latent Class Analysis, pp. 89–106. Cambridge University Press, Cambridge (2002)

    Chapter  Google Scholar 

  40. Vermunt, J.K., Magidson, J.: Latent class models for classification. Comput. Stat. Data Anal. 41, 531–537 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  41. Vermunt, J.K., Magidson, J.: Technical Guide for Latent GOLD 4.0: Basic and Advanced, Statistical Innovations Inc. Statistical Innovations Inc., Belmont (2005)

    Google Scholar 

  42. Vermunt, J.K., Magidson, J.: LG-Syntax Users Guide: Manual for Latent GOLD 4.5 Syntax Module. Statistical Innovations Inc., Belmont (2008)

    Google Scholar 

  43. Wollace, D.W., Giese, J.L., Johnson, J.L.: Customer retailer loyalty in the context of multiple channel strategies. J. Retail. 80, 249–263 (2004)

    Article  Google Scholar 

Download references

Acknowledgments

This research was partially supported by the University of Padua (Italy) with grant CPDA121180 “Statistical and econometric approach to marketing: applications and developments to customer satisfaction and market segmentation”.

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Correspondence to Omar Paccagnella.

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Dal Bianco, C., Paccagnella, O. & Varriale, R. A multilevel latent class analysis of the purchasing channels among European consumers. METRON 74, 293–309 (2016). https://doi.org/10.1007/s40300-016-0100-0

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