Journal of Marketing Analytics

, Volume 3, Issue 1, pp 5–13 | Cite as

Big data and the future of knowledge production in marketing research: Ethics, digital traces, and abductive reasoning

  • Mathieu Alemany OliverEmail author
  • Jean- Sébastien Vayre
Original Article


If Big Data has been widely discussed, only a few marketing researchers have actually paid attention to it. However the development of Big Data, even in the abstract, provides researchers with the opportunity to rethink our approach to gathering and applying knowledge. Many countries, companies and universities are investing millions of dollars in the development of Big Data. Some believe this era of data driven computational social science has the same potential as the emergence of cognitive science in the 1960s and should not be left to private companies or government agencies. Given the entrance of Big Data techniques to companies and the university setting, it is our role, as consumer researchers, to identify issues that are relevant to our field and to suggest a consumer research method adapted to Big Data. The first section of this article addresses ethical and epistemic issues to consider when conducting marketing research with Big Data. The second section suggests the use of abductive reasoning as a first step in the research process in order to bring context to consumers’ digital traces and make new theories emerge. Finally, we present the archetype-based analysis as an example of what researchers can do with Big Data when they adopt abductive, inductive and deductive approaches in the research process.


big data segmentation research ethics research methods abductive reasoning 


  1. Anderson, C. (2008) The end of theory: The data deluge makes the scientific method obsolete. Wired 16 July,, accessed 29 October 2014.
  2. Arsel, Z. and Thompson, C.J. (2011) Demythologizing consumption practices: How consumers protect their field-dependent identity investments from devaluing marketplace myths. Journal of Consumer Research 37 (5): 791–806.CrossRefGoogle Scholar
  3. Barton, D. and Court, D. (2012) Making advanced analytics work for you. Harvard Business Review 90 (October): 78–83.Google Scholar
  4. Bauman, Z. (2000) Liquid Modernity, Cambridge, UK: Polity.Google Scholar
  5. Belk, R.W. (1989) Effects of identification with comic book heroes and villains of consumption on materialism among former comic book readers. In: T.K. Srull (ed.) Advances in Consumer Research. Vol. 16. Provo, UT: Association for Consumer Research, pp. 414–419.Google Scholar
  6. Belk, R.W. (2013) Extended self in a digital world. Journal of Consumer Research 40 (3): 477–500.CrossRefGoogle Scholar
  7. Beyer, M.A. and Laney, D. (2012) The Importance of Big Data: A Definition. Stamford, CT: Gartner.Google Scholar
  8. Corneliussen, H.G. and Rettberg, J.W. (2011) Digital Culture, Play, and Identity: A World of Warcraft reader, Cambridge, MA: MIT Press.Google Scholar
  9. Cutler, A. and Breiman, L. (1994) Archetypal analysis. Technometrics 36 (4): 338–347.CrossRefGoogle Scholar
  10. Davis, K. and Patterson, D. (2012) Ethics of Big Data, Sebastopol, CA: O’Reilly.Google Scholar
  11. Desrosieres, A. (2002) The Politics of Large Numbers: A History of Statistical Reasoning, Cambridge, MA: Harvard University.Google Scholar
  12. Dunfee, T.W., Smith, N.C. and Ross, Jr. W.T. (1999) Social contracts and marketing ethics. Journal of Marketing 63 (3): 14–32.CrossRefGoogle Scholar
  13. Escalas, J.E. (2004) Narrative processing: Building consumer connections to brands. Journal of Consumer Psychology 14 (1–2): 168–180.CrossRefGoogle Scholar
  14. Ger, G. and Sandikci, O. (2006) Doing research on sensitive topics: Studying covered Turkish women. In: R.W. Belk (ed.) Handbook of Qualitative Research Methods in Marketing, Cheltenham, UK: Edward Elgar Publishing, pp. 509–520.Google Scholar
  15. Gitelman, L. (2013) ‘Raw Data’ is an Oxymoron, Cambridge, MA: The MIT Press.Google Scholar
  16. Goulding, C. (2005) Grounded theory, ethnography and phenomenology: A comparative analysis of three qualitative strategies for marketing research. European Journal of Marketing 39 (3/4): 294–308.CrossRefGoogle Scholar
  17. Greenwood, R. and Hinings, C.R. (1993) Understanding strategic change: The contribution of archetypes. Academy of Management Journal 36 (5): 1052–1081.CrossRefGoogle Scholar
  18. Hey, T., Tansley, S. and Tolle, K. (2009) The Fourth Paradigm, Redmond, WA: Microsoft Research.Google Scholar
  19. Hirschman, E.C. (2000) Consumers’ use of intertextuality and archetypes. In: S.J. Hoch and R.J. Meyer (eds.) Advances in Consumer Research. Vol. 27. Provo, UT: Association for Consumer Research, pp. 57–63.Google Scholar
  20. Hirschman, E.C. (2002) Metaphors, archetypes and the biological origins of semiotics. Semiotica 142 (1-4): 315–349.Google Scholar
  21. Holbrook, M.B. (1994) Ethics in consumer research: An overview and prospectus. In: C.T. Allen and D.R. John (eds.) Advances in Consumer Research. Vol. 21. Provo, UT: Association for Consumer Research, pp. 566–571.Google Scholar
  22. Holt, D.B. (2004) How Brands Become Icons: The Principles of Cultural Branding, Cambridge, MA: Harvard University.Google Scholar
  23. Johar, G.V., Holbrook, M.B. and Stern, B.B. (2001) The role of myth in creative advertising design: Theory, process and outcome. Journal of Advertising 30 (2): 1–25.CrossRefGoogle Scholar
  24. Jung, C.G. (1942/1958) The Collected Works of C. G. Jung. Vol. 11. London: Routledge.Google Scholar
  25. Jung, C.G. (1953/1968) The Collected Works of C. G. Jung. Vol. 12. Princeton, NJ: Princeton University.Google Scholar
  26. Knudsen, G.H. and Kjeldgaard, D. (2014) Big data in consumer culture research. Paper presented at the Consumer Culture Theory Conference; 29 June, Helsinki, Finland.Google Scholar
  27. Kozinets, R.V. (2002) The field behind the screen: Using netnography for marketing research in online communities. Journal of Marketing Research 39 (1): 61–72.CrossRefGoogle Scholar
  28. Latour, B. (1995) The ‘topofil’ of Boa Vista: A photo-philosophical montage. Common Knowledge 4 (1): 145–187.Google Scholar
  29. Levy, S.J. (1981) Interpreting consumer mythology: A structural approach to consumer behavior. Journal of Marketing 45 (3): 49–61.CrossRefGoogle Scholar
  30. Li, S., Wang, P., Louviere, J. and Carson, R. (2003) Archetypal analysis: A new way to segment markets based on extreme individuals. Paper presented at the ANZMAC Conference; 1–3 December, Adelaide, Australia.Google Scholar
  31. Lwin, M.O. and Williams, J.D. (2003) A model integrating the multidimensional developmental theory of privacy and theory of planned behavior to examine fabrication of information online. Marketing Letters 14 (4): 257–272.CrossRefGoogle Scholar
  32. MacInnis, D.J. (2011) A framework for conceptual contributions in marketing. Journal of Marketing 75 (4): 136–154.CrossRefGoogle Scholar
  33. Mark, M. and Pearson, C.S. (2001) The Hero and the Outlaw: Building Extraordinary Brands through the Power of Archetypes, New York: McGraw-Hill.Google Scholar
  34. Martin, D. and Schouten, J. (2012) Sustainable Marketing, Upper Saddle River, NJ: Prentice Hall.Google Scholar
  35. McCracken, G. (1995) Big Hair, Woodstock, NY: The Overlook.Google Scholar
  36. Merton, R.K. and Barber, E. (2004) The Travels and Adventures of Serendipity: A Study in Sociological Semantics and the Sociology of Science, Princeton, NJ: Princeton University.Google Scholar
  37. Peirce, C.S. (1994) Collected Papers of Charles Sanders Peirce, Cambridge, MA: Harvard University.Google Scholar
  38. Pettigrew, S.F. (2000) Ethnography and grounded theory: A happy marriage? In: S.J. Hoch and R.J. Meyer (eds.) Advances in Consumer Research. Vol. 27. Provo, UT: Association for Consumer Research, pp. 256–260.Google Scholar
  39. Reichertz, J. (2010) Abduction: The logic of discovery of grounded theory. In: A. Bryant and K. Charmaz (eds.) Handbook of Grounded Theory, London: Sage, pp. 214–228.Google Scholar
  40. Ruvio, A.A. and Belk, R.W. (2013) Identity and Consumption, New York: Routledge.Google Scholar
  41. Schau, H.J. and Gilly, M.C. (2003) We are what we post? Self‐presentation in personal web space. Journal of Consumer Research 30 (3): 385–404.CrossRefGoogle Scholar
  42. Searls, D. (2012) The Intention Economy: When Customers Take Charge, Cambridge, MA: Harvard School.Google Scholar
  43. Song, J.H., Kim, H.Y., Kim, S., Lee, S.W. and Lee, J.H. (2015) Effects of personalized e-mail messages on privacy risk: Moderating roles of control and intimacy. Marketing Letters, forthcoming.Google Scholar
  44. Strauss, A.L. (1987) Qualitative Analysis for Social Scientists, Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
  45. Strauss, A.L. and Corbin, J. (1990) Basics of Qualitative Research: Grounded Theory Procedures and Techniques, Newbury Park, CA: Sage.Google Scholar
  46. Thompson, C.J. (2004) Marketplace mythology and discourses of power. Journal of Consumer Research 31 (1): 162–180.CrossRefGoogle Scholar
  47. Tybout, A.M. and Zaltman, G. (1974) Ethics in marketing research: Their practical relevance. Journal of Marketing Research 11 (4): 357–368.CrossRefGoogle Scholar
  48. Veen, S.V. (1994) The consumption of heroes and the hero hierarchy of effects. In: C.T. Allen and D.R. John (eds.) Advances in Consumer Research. Vol. 21. Provo, UT: Association for Consumer Research, pp. 332–336.Google Scholar
  49. Venkatesh, A. (1998) Cybermarketscapes and consumer freedoms and identities. European Journal of Marketing 32 (7–8): 664–676.CrossRefGoogle Scholar
  50. Weinberg, B.D., Davis, L. and Berger, P.D. (2013) Perspectives on big data. Journal of Marketing Analytics 1 (4): 187–201.CrossRefGoogle Scholar
  51. Wertime, K. (2003) Building Brands and Believers: How to Connect with Consumers Using Archetypes, Singapore: John Wiley & Sons.Google Scholar

Copyright information

© Palgrave Macmillan, a division of Macmillan Publishers Ltd 2015

Authors and Affiliations

  1. 1.Aix Marseille Graduate School of Management (IAE Aix), CERGAM - 2ème étage, EA4225Aix en Provence cedex 2France

Personalised recommendations