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Journal of Gambling Studies

, Volume 28, Issue 1, pp 13–26 | Cite as

Identifying X-Consumers Using Causal Recipes: “Whales” and “Jumbo Shrimps” Casino Gamblers

  • Arch G. Woodside
  • Mann Zhang
Original Paper

Abstract

X-consumers are the extremely frequent (top 2–3%) users who typically consume 25% of a product category. This article shows how to use fuzzy-set qualitative comparative analysis (QCA) to provide “causal recipes” sufficient for profiling X-consumers accurately. The study extends Dik Twedt’s “heavy-half” product users for building theory and strategies to nurture or control X-behavior. The study here applies QCA to offer configurations that are sufficient in identifying “whales” and “jumbo shrimps” among X-casino gamblers. The findings support the principle that not all X-consumers are alike. The theory and method are applicable for identifying the degree of consistency and coverage of alternative X-consumers among users of all product-service category and brands.

Keywords

X-consumers QCA Casino gambler Whales Jumbo shrimps Causal recipe 

Notes

Acknowledgments

The authors thank Carol M. Megehee, Coastal Carolina University, USA for insightful comments on content and style to earlier versions of this article. The authors thank the reviewers and Jon E. Grant, Editor-in-Chief for their insightful comments on the first submission to the Journal of Gambling Studies.

References

  1. Assael, H., & Poltrack, D. F. (1994). Can demographic profiles of heavy users serve as surrogates for purchase behavior in selecting TV programs? Journal of Advertising Research, 34(1), 11–17.Google Scholar
  2. Bowling Alone: Data. (2011). http://www.bowlingalone.com/data.htm.
  3. Crumb, M. J. (2010). States go all in, expand gaming to plug the budget, March 16, p. 1. New York: The Associated Press. http://www.timesrepublican.com/page/content.detail/id/523860/States-go-all-in-expand-gaming-to-plug-the-budget.html.
  4. Fiss, P. C. (2009). Practical issues in QCA. Presentation at Academy of Management 2009. Available as of May 5, 2010. http://www-rcf.usc.edu/~fiss/QCA_PDW_2009_Fiss_Practical_Issues.pdf.
  5. Goldsmith, R. E., Flynn, L. R., & Bonn, M. (1994). An empirical study of heavy users of travel agencies. Journal of Travel Research, 33(1), 38–43.CrossRefGoogle Scholar
  6. Ladouceur, R. (2002). Understanding gambling and problem gambling: A step in the right direction. AGA Responsible Gaming Lecture Series, 1(1), 1–10.Google Scholar
  7. Lakey, C. E., Goodie, A. S., Lance, C. E., Stinchfield, R., & Winters, K. C. (2007). Examining DSM-IV criteria for pathological gambling: Psychometric properties and evidence from cognitive biases. Journal of Gambling Studies, 23, 479–498.PubMedCrossRefGoogle Scholar
  8. Olsen, W. K., & Nomura, H. (2008). Fuzzy set approach to poverty reduction compared with growth modeling. A full paper presented at the RC33 conference on social science methodology, Naples, Italy.Google Scholar
  9. Palmgreen, P., Lorch, E., Donohew, L., Harrington, N., D’Silva, M., & Helm, D. (1995). Reaching at-risk populations in a mass media drug abuse prevention campaign: Sensation seeking as a targeting variable. Drugs & Society, 8(34), 29–45.CrossRefGoogle Scholar
  10. Perfetto, R., & Woodside, A. G. (2009). Extremely frequent behavior in consumer research: Theory and empirical evidence for chronic casino gambling. Journal of Gambling Studies, 25, 297–316.PubMedCrossRefGoogle Scholar
  11. Ragin, C. C. (2000). Fuzzy-set social science. Chicago: University of Chicago Press.Google Scholar
  12. Ragin, C. C. (2007). Qualitative comparative analysis using fuzzy-sets (fsQCA). In B. Rihoux & C. C. Ragin (Eds.), Configurational comparative analysis. Thousand Oaks: Sage Publications.Google Scholar
  13. Ragin, C. C. (2008a). Redesigning social inquiry: Fuzzy sets and beyond. Chicago: University of Chicago Press.Google Scholar
  14. Ragin, C. (2008b). What is qualitative comparative analysis (QCA)? Presentation at 3th ESRC research methods festival. Available May 5, 2010, from http://eprints.ncrm.ac.uk/250/1/What_is_QCA.pdf.
  15. Ragin, C. C., & Rihoux, B. (2004). Qualitative comparative analysis (QCA): State of the art and prospects. Qualitative Methods, 2(2), 3–13.Google Scholar
  16. Rose, I. N. (2010). The third wave of legal gambling. Gambling and the Law. http://www.gamblingandthelaw.com/articles/253-the-third-wave-of-legal-gambling.html.
  17. Stinchfield, R. (2003). Reliability, validity, and classification accuracy of a measure of DSM-IV diagnostic criteria for pathological gambling. American Journal of Psychiatry, 160, 180–182.PubMedCrossRefGoogle Scholar
  18. Toce-Gerstein, M., Gerstein, D. R., & Volberg, R. A. (2003). A hierarchy of gambling disorders in the community. Addiction, 98, 1661–1672.PubMedCrossRefGoogle Scholar
  19. Twedt, D. W. (1964). How important is the “heavy-user”? Journal of Marketing, 28(1), 71–72.CrossRefGoogle Scholar
  20. Wansink, B., & Park, S. (2000). Comparison methods for identifying heavy users. Journal of Advertising Research, 40(4), 61–72.Google Scholar
  21. Winters, K. C., Specker, S., & Stinchfield, R. (2002). Measuring pathological gambling with the diagnostic interview for gambling severity (DIGS). In J. J. Marotta, J. A. Cornelius, & W. R. Eadington (Eds.), The downside: Problem and pathological gambling (pp. 143–148). Reno, NV: University of Nevada, Reno.Google Scholar
  22. Woodside, A. G. (2010). Bridging the chasm between survey and case study research: Research methods for achieving generalization, accuracy, and complexity. Industry Marketing Management, 39, 64–75.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Carroll School of Management, Department of MarketingBoston CollegeChestnut HillUSA
  2. 2.College of Business AdministrationUniversity of Rhode IslandKingstonUSA

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