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.
Similar content being viewed by others
References
Akaike, H.: A new look at the statistical model identification. IEEE Trans. Autom. Control 19, 716–723 (1974)
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)
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)
Bhatnagar, A., Ghose, S.: A latent class segmentation analysis of e-shoppers. J. Bus. Res. 57, 758–767 (2004)
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)
Bozdogan, H.: Model selection and Akaike’s information criterion(AIC): the general theory and its analytical extensions. Psychometrika 52, 345–370 (1987)
Butanay, G., Wortzel, L.H.: Distributor power versus manufacturer power: the customer role. J. Mark. 52, 52–63 (1988)
Collesei, U., Casarin, F., Vescovi, T.: Internet e i cambiamenti nei comportamenti d’acquisto del consumatore. Micro Macro Mark. 1, 33–50 (2001)
Dayton, C.M., Macready, G.B.: Concomitant-variable latent-class models. J. Am. Stat. Assoc. 83, 173–178 (1988)
Formann, A.K.: Mixture analysis of multivariate categorical data with covariates and missing entries. Comput. Stat. Data Anal. 51, 5236–5246 (2007)
Friedman, L.G., Furey, T.R.: The Channel Advantage. Butterworth-Heinemann, Burlington (2003)
Ganesh, J.: Converging trends within the European Union: insights from an analysis of diffusion patterns. J. Int. Mark. 6, 32–48 (1988)
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
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)
Goodman, L.A.: Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika 61, 215–231 (1974)
Keng, K., Tang, Y., Ghose, S.: Typology of online shoppers. J. Consum. Mark. 20, 139–156 (2003)
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)
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)
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)
Lazarsfeld, P.F., Henry, N.W.: Latent Structure Analysis. Houghton Mifflin, Boston (1968)
Lemmens, A., Croux, C., Dekimpe, M.G.: Consumer confidence in Europe: united in diversity? Int. J. Res. Mark. 24, 113–127 (2007)
Levitt, T.: The globalization of markets. Harv. Bus. Rev. 61, 92–102 (1983)
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)
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)
Magidson, J., Vermunt, J.K.: Latent class models for clustering: a comparison with K-means. Can. J. Mark. Res. 20, 36–43 (2002)
Muthén L.K., Muthén B.O.: Mplus Users Guide, Seventh Edition. Los Angeles, Muthén & Muthén (2012)
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)
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)
Payne, A.F.T., Frow, P.: A strategic framework for customer relationship management. J. Mark. 69, 167–176 (2005)
Saunders C.: Multiple channel buyers worth pursuing, Internet advertising report, January 28. http://www.internetnews.com (2002)
Schwarz, G.: Estimating the dimention of a model. Ann. Stat. 6, 461–464 (1978)
Sharma, A., Mehrotra, A.: Choosing an optimal channel mix in multichannel environments. Ind. Mark. Manag. 36, 21–28 (2006)
Skrondal, A., Rabe-Hesketh, S.: Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models. Chapman and Hall/CRC, Boca Raton (2004)
Solomon, M.R.: Consumer Behavior: Buying, Having and Being, 10th edn. Pearson Prentice Hall, Upper Saddle River (2012)
Steenkamp, J.B.E.M.: The role of national culture in international marketing research. Int. Mark. Rev. 18, 30–44 (2001)
Steenkamp, J.B.E.M., Ter Hofstede, F.: International market segmentation. Int. J. Res. Mark. 19, 185–213 (2002)
Vermunt, J.K.: Multilevel latent class models. Sociol. Methodol. 33, 213–239 (2003)
Vermunt, J.K.: Latent class and finite mixture models for multilevel data sets. Stat. Methods Med. Res. 17, 33–51 (2008)
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)
Vermunt, J.K., Magidson, J.: Latent class models for classification. Comput. Stat. Data Anal. 41, 531–537 (2003)
Vermunt, J.K., Magidson, J.: Technical Guide for Latent GOLD 4.0: Basic and Advanced, Statistical Innovations Inc. Statistical Innovations Inc., Belmont (2005)
Vermunt, J.K., Magidson, J.: LG-Syntax Users Guide: Manual for Latent GOLD 4.5 Syntax Module. Statistical Innovations Inc., Belmont (2008)
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)
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”.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
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
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40300-016-0100-0