Abstract
This exploratory study deals with the characterization of non-buyers groups in the context of business-to-consumer electronic commerce (B2C-EC), based on their motivations for not purchasing on the Internet and explores which factors would incline them to make a first purchase on a website. In order to do so, a household panel survey was taken to 1075 Spanish respondents and analyzed with a Latent Class Analysis (LCA) approach for grouping both consumers’ motivations to reject online shopping and possible motivations to start buying online. After the definition of both sets of groups, a k-means clustering was performed in order to relate both groups in disjoint sets. The results from our study show that there are mainly three types of causes for not shopping through the electronic channel –namely, absence of physical presence of the goods or channel preference, security concerns and privacy risks, and lack of internet access and/or skills– and six different attitudes towards future use of Internet as a shopping channel, revealing a total of ten different sets of non-buyers. Implications for theory and practice are discussed in the final section.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Akaike, H.: Information Theory and an Extension of the Maximum Likelihood Principle. In: Petrov, B.N., Csake, F. (eds.) Second International Symposium on Information Theory, pp. 267–281. Akademiai Kiado, Budapest (1973)
Aldred, C.R., Smith, S.M., Swinyard, W.R.: E-shopping lovers and fearful conservatives: a market segmentation analysis. International Journal of Retail & Distribution Management 34(4/5), 308 (2006)
Barnes, S.J., Bauer, H.H., Neumann, M.N., Huber, F.: Segmenting cyberspace: a customer typology for the internet. European Journal of Marketing 41(1/2), 71–93 (2007)
Bhatnagar, A., Ghose, S.: A latent class segmentation analysis of e-shoppers. Journal of Business Research 57, 758–767 (2004)
Brengman, M., Geuens, M., Weitjers, B., Smith, S.M., Swinyard, W.R.: Segmenting Internet shoppers based on their Web-usage-related lifestyle: a cross-cultural validation. Journal of Business Research 58, 79–88 (2005)
Brown, M., Pope, N., Voges, K.: Buying or browsing? An exploration of shopping orientations and online purchase intention. European Journal of Marketing 37(11), 1666–1684 (2003)
Chen, S., Li, J.: Factors Influencing the Consumers’ Willingness to Buy in E-Commerce. In: International Conference on E-Business and Information System Security, EBISS 2009, May 23-24, pp. 1–8 (2009)
Garson, G.D.: Latent class analysis, from Statnotes: Topics in Multivariate Analysis (2009), http://faculty.chass.ncsu.edu/garson/pa765/statnote.html (Date of retrieval: October 30, 2009)
Ganesh, J., Reynolds, K.E., Luckett, M., Pomirleanu, N.: Online Shopper Motivations, and e-Store Attributes: An Examination of Online Patronage Behavior and Shopper Typologies. Journal of Retailing 86(1), 106–115 (2010)
Hagenaars, J.A., McCutcheon, A.L.: Applied Latent Class Analysis. Cambridge University Press, Cambridge (2002)
Jayawardhena, C., Wright, L.T., Dennis, C.: Consumers online: intentions, orientations and segmentation. International Journal of Retail & Distribution Management 35(6), 515–526 (2007)
Kau, A.K., Tang, Y.E., Ghose, S.: Typology of online shoppers. The Journal of Consumer Marketing 20(2), 139 (2003)
Linzer, D.A., Lewis, J.: poLCA: Polytomous Variable Latent Class Analysis. Version 1.1 (2009), http://userwww.service.emory.edu/~dlinzer/poLCA (Date of retrieval: November 05, 2009)
McLachlan, G.J., Krishnan, T.: The EM Algorithm and Extensions. John Wiley & Sons, New York (1997)
Ng, C.F.: Satisfying shoppers’psychological needs: From public market to cyber-mall. Journal of Environmental Psychology 23, 439–455 (2003)
Plummer, J.T.: The Concept and Application of Life Style Segmentation. The Journal of Marketing 38(1), 33–37 (1974)
Rohm, A.J., Swaminathan, V.: A typology of online shoppers based on shopping motivations. Journal of Business Research 57, 748–757 (2004)
Schwartz, G.: Estimating the Dimension of a Model. The Annals of Statistics 6, 461–464 (1978)
Smith, W.R.: Product Differentiation and Market Segmentation as Alternative Marketing Strategies. Journal of Marketing 21(1/4), 3 (1956)
Swinyard, W.R., Smith, S.M.: Why people (don’t) shop online: A lifestyle study of the internet consumer. Psychology & Marketing 20(7), 567 (2003)
Udo, G.J.: Privacy and security concerns as major barriers for e-commerce: a survey study. Information Management & Computer Security 9(4), 165–174 (2001)
Urueña, A.: e-commerce B2C 2009. National Spanish Observatory of Telecommunications and Information Society (Ministry of Industry, Trade and Commerce) (2009), http://www.ontsi.red.es/articles/detail.action?id=4001&request_locale=en (Date of retrieval: September 20, 2010)
Vermunt, J.K., Magidson, J.: Latent class cluster analysis. In: Hagenaars, McCutcheon (eds.) Advances in Latent Class Models, ch. B1. Cambridge University Press, Cambridge (2000)
Ye, Q., Li, G., Gu, B.: A cross-cultural validation of the web usage-related lifestyle scale: An empirical investigation in China. Electronic Commerce Research and Applications 10(3), 304–312 (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hernández-García, Á., Iglesias-Pradas, S., Urueña-López, A. (2013). A Characterization of Non-buyers in B2C E-Commerce and the Drivers to Turn Them into E-Shoppers. In: Lytras, M.D., Ruan, D., Tennyson, R.D., Ordonez De Pablos, P., García Peñalvo, F.J., Rusu, L. (eds) Information Systems, E-learning, and Knowledge Management Research. WSKS 2011. Communications in Computer and Information Science, vol 278. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35879-1_10
Download citation
DOI: https://doi.org/10.1007/978-3-642-35879-1_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35878-4
Online ISBN: 978-3-642-35879-1
eBook Packages: Computer ScienceComputer Science (R0)