Journal of Gambling Studies

, Volume 25, Issue 3, pp 297–316 | Cite as

Extremely Frequent Behavior in Consumer Research: Theory and Empirical Evidence for Chronic Casino Gambling

Original Paper

Abstract

The present study informs understanding of customer segmentation strategies by extending Twedt’s heavy-half propositions to include a segment of users that represent less than 2% of all households—consumers demonstrating extremely frequent behavior (EFB). Extremely frequent behavior (EFB) theory provides testable propositions relating to the observation that few (2%) consumers in many product and service categories constitute more than 25% of the frequency of product or service use. Using casino gambling as an example for testing EFB theory, an analysis of national survey data shows that extremely frequent casino gamblers do exist and that less than 2% of all casino gamblers are responsible for nearly 25% of all casino gambling usage. Approximately 14% of extremely frequent casino users have very low-household income, suggesting somewhat paradoxical consumption patterns (where do very low-income users find the money to gamble so frequently?). Understanding the differences light, heavy, and extreme users and non-users can help marketers and policymakers identify and exploit “blue ocean” opportunities (Kim and Mauborgne, Blue ocean strategy, Harvard Business School Press, Boston, 2005), for example, creating effective strategies to convert extreme users into non-users or non-users into new users.

Keywords

Casino Theory Chronic Segments Conjunctural conditions Media behavior 

References

  1. American Psychiatric Association. (1985). Diagnostic and statistical manual of mental disorders. Washington, DC: American Psychiatric Association.Google Scholar
  2. Armstrong, J. S., & Andress, J. G. (1970). Exploratory analysis of marketing data: Tree vs. regression. Journal of Marketing Research, 7(4), 487–492.CrossRefGoogle Scholar
  3. Barton, A. H. (1955). The concept of property-space in social research. In P. F. Lazarsfeld & M. Rosenberg (Eds.), Language of social research (pp. 40–53). Glencoe: Free.Google Scholar
  4. Bass, F., Tigert, D. T., & Lonsdale, R. (1968). Market segmentation: Group versus individual behavior. Journal of Marketing Research, 5(3), 264–270.CrossRefGoogle Scholar
  5. Bearden, W. O., & Etzel, M. J. (1982). Reference group influence on product and brand purchase decision. Journal of Consumer Research, 9(March), 183–194.CrossRefGoogle Scholar
  6. Belk, R. W. (1988). Possessions and the extended self. Journal of Consumer Research, 15, 139–168.CrossRefGoogle Scholar
  7. Belk, R. W., Mayer, R., & Bahn, K. (1982). The eye of the beholder: Individual differences in perceptions of consumption symbolism. In A. Mitchell (Ed.), Advances in consumer research (Vol. 9, pp. 523–530). Ann Arbor: Association for Consumer Research.Google Scholar
  8. Bennett, A., & Elman, C. (2006). Complex causal relations and case study methods: The example of path dependence. Political Analysis, 14(3), 250–267.CrossRefGoogle Scholar
  9. Bjelde, K., Chromy, B., & Pankow, D. (2008). Casino gambling among older adults in North Dakota. Journal of Gambling Studies, 24(4), 423–440.PubMedCrossRefGoogle Scholar
  10. Campbell, D. T. (1969). Reforms as experiments. American Psychologist, 24, 409–429.CrossRefGoogle Scholar
  11. Cerino, V. (1998). UNMC study reveals growing gambling addiction among Omaha’s older adults. University of Nebraska Medical Center, UNMC Public Affairs.Google Scholar
  12. Cocanougher, A. B., & Bruce, G. D. (1971). Socially distant reference groups and consumer aspirations. Journal of Marketing Research, 8(August), 79–81.Google Scholar
  13. Cook, T., & Campbell, D. T. (1979). Quasi-experimentation: Design & analysis issues for field settings. Dallas, TX: Houghton Mifflin Company.Google Scholar
  14. Cook, V, Jr., & Mindak, W. (1984). A search for constants: the “heavy user” revisited!. Journal of Consumer Marketing, 1(Spring), 79–81.Google Scholar
  15. Cooper, M. (2005). Sit and spin. Atlantic Monthly, December (http://www.theatlantic.com/doc/200512/slot-machines).
  16. Elman, C. (2005). Explanatory typologies in qualitative studies of international politics. International Organization, 59(2), 293–326.CrossRefGoogle Scholar
  17. Englis, B., & Solomon, M. (1995). To be and not to be: Lifestyle imagery, reference groups, and the clustering of America. Journal of Advertising, 24(Spring), 13–28.Google Scholar
  18. Frank, R. (1967). Correlates of buying behavior for grocery products. Journal of Marketing, 31(October), 48–53.CrossRefGoogle Scholar
  19. Frank, R., Massy, W., & Boyd, H. (1967). Correlates of grocery product consumption rates. Journal of Marketing Research, 4(2), 184–190.CrossRefGoogle Scholar
  20. George, A. L., & Bennett, A. (2005). Case studies and theory development in the social sciences. Cambridge, MA: MIT Press.Google Scholar
  21. Goldsmith, R. (2000). Characteristics of the heavy user of fashionable clothing. Journal of Marketing Theory and Practice, 8(4), 21.Google Scholar
  22. Goldsmith, R., & d’Hauteville, F. (1998). Heavy wine consumption: Empirical and theoretical perspectives. British Food Journal, 100(4), 184–190.CrossRefGoogle Scholar
  23. Goldsmith, R., & Litvin, S. (1999). Heavy users of travel agents: A segmentation analysis of vacation travelers. Journal of Travel Research, 38(2), 127–133.CrossRefGoogle Scholar
  24. Haley, R. I. (1968). Benefit segmentation: A decision oriented research tool. Journal of Marketing, 32(1), 30–35.Google Scholar
  25. Hollander, E., Buchalter, A., & DeCaria, C. (2000). Pathological gambling. The Psychiatric Clinics of North America, 23(3), 629–642.PubMedCrossRefGoogle Scholar
  26. Holman, R. M. (1980). Clothing as communications: An empirical investigation. In J. Olson (Ed.), Advances in consumer research (Vol. 7, pp. 372–377). Ann Arbor: Association for Consumer Research.Google Scholar
  27. Holman, R. M. (1981). Product use as communication: An appraisal of a venerable topic. In B. Enis & K. Roering (Eds.), Review of marketing (pp. 250–272). Chicago, IL: American Marketing Association.Google Scholar
  28. Hope, J., & Havir, L. (2002). You bet they’re having fun! Older Americans and casino gambling. Journal of Aging Studies, 16(2), 177–197.CrossRefGoogle Scholar
  29. Johansson, A., Grant, J. E., Kim, S. W., Odlaug, B. L., & Götestam, K. G. (2009). Risk factors for problematic gambling: A critical literature review. Journal of Gambling Studies, 25(1), 67–92.PubMedCrossRefGoogle Scholar
  30. Kim, W. C., & Mauborgne, R. (2005). Blue ocean strategy. Boston: Harvard Business School Press.Google Scholar
  31. Kusyszyn, I. (1984). The psychology of gambling. Annals of the American Academy of Political and Social Science, 474(July), 133–145.CrossRefGoogle Scholar
  32. Lazarsfeld, P. (1965). Qualitative measurement in social science: Classification, typologies, and indices. In D. Lerner & H. D. Lasswell (Eds.), The policy sciences (pp. 155–192). Stanford: Stanford University Press.Google Scholar
  33. Lesieur, H. R., & Blume, S. B. (1987). The South Oaks Gambling Screen (SOGS): A new instrument for the identification of pathological gamblers. American Journal of Psychiatry, 144, 1184–1188.PubMedGoogle Scholar
  34. Lehrer, J. (2007). The neuroscience of gambling. ScienceBlogs. http://scienceblogs.com/cortex/2007/07/the_neuroscience_of_gambling.php#more.
  35. Levy, S. (1959). Symbols for sale. Harvard Business Review, 37(July–August), 117–124.Google Scholar
  36. Levy, S. (1964). Symbolism and life style. In S. A. Greyser (Ed.), Toward scientific marketing (pp. 140–150). Chicago: American Marketing Association.Google Scholar
  37. Loudon, D., & Bitta, A. D. (1993). Consumer behavior: Concepts and applications. New York: McGraw Hill.Google Scholar
  38. Lowrey, T., Englis, B., Shavitt, S., & Solomon, M. (2001). Response latency verification of consumption constellations: Implications for advertising. Journal of Advertising, 30(1), 29–39.Google Scholar
  39. MacLaurin, D. J., & Wolstenholme, S. (2008). An analysis of the gaming industry in the Niagara region. International Journal of Contemporary Hospitality Management, 20(3), 320–331.CrossRefGoogle Scholar
  40. Marshall, K. (2007). Gambling. Perspectives on Labor and Income, Statistics Canada, Catalogue No. 75-001-XIE, May online edition.Google Scholar
  41. Moufakkir, O., Singh, A. J., Moufakkir-van der Woud, A., & Holecek, D. (2004). Impact of light, medium and heavy spenders on casino destinations: Segmenting gaming visitors based on amount of non-gaming expenditures. UNLV Gaming Research & Review Journal, 8(1), 59–71.Google Scholar
  42. O’Guinn, T., & Faber, R. (1989). Compulsive buying: A phenomenological exploration. Journal of Consumer Research, 16(2), 147–157.CrossRefGoogle Scholar
  43. 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.Google Scholar
  44. Petry, N. M., Stinson, F. S., & Grant, B. F. (2005). Comorbidity of DSM-IV pathological gambling and other psychiatric disorders: Results form the national epidemiologic survey on alcohol and related conditions. Journal of Clinical Psychiatry, 66(5), 564–574.PubMedCrossRefGoogle Scholar
  45. Ragin, C. C. (2000). Fuzzy-set social science. Chicago: The University of Chicago Press.Google Scholar
  46. Reichardt, C. (1979). The design and analysis of the non-equivalent group quasi-experiment. Unpublished doctoral dissertation, Northwestern University, Chicago.Google Scholar
  47. Shavitt, S., & Nelson, M. (2000). The social identity function in person perceptions: Communicated meanings of product preferences. In G. R. Maio & J. M. Olson (Eds.), Why we evaluate: Function of attitudes (pp. 37–57). Mahwaj, NJ: Lawrence Erlbaum Associates.Google Scholar
  48. Solomon, M. (1988). Mapping product constellations: A categorization approach to symbolic consumption. Psychology & Marketing, 5(3), 233–258.Google Scholar
  49. Solomon, M., & Assael, H. (1987). The forest or the trees? A gestalt approach to symbolic consumptions. In J. Umiker-Sebeok (Ed.), Marketing and semiotics: New directions in the study of sings for sale (pp. 189–218). Berlin: Mouton de Gruyter.Google Scholar
  50. Spotts, D., & Mahoney, E. (1991). Segmenting visitors to a destination region based on volume of their expenditures. Journal of Travel Research, 29(4), 24–31.CrossRefGoogle Scholar
  51. Stafford, J. E. (1966). Effects of group influences on consumer brand preferences. Journal of Marketing Research, 3(1), 68–75.CrossRefGoogle Scholar
  52. Sujan, M., & Bettman, J. (1989). The effects of brand positioning strategies on consumers’ brand and category perceptions: Some insights from schema research. Journal of Marketing Research, 26(4), 454–467.CrossRefGoogle Scholar
  53. Tigert, D., Lathrope, R., & Bleeg, M. (1971). The fast food franchises; Psychographic and demographic segmentation analysis. Journal of Retailing, 47(Spring), 81–90.Google Scholar
  54. Twedt, D. W. (1964). How important is the “heavy-user”? Journal of Marketing, 28(1), 71–72.CrossRefGoogle Scholar
  55. Ward, J., & Loken, B. (1986). The quintessential snack food: Measurement of product prototypes. In R. J. Lutz (Ed.), Advances in consumer research (Vol. 13, pp. 126–131). Provo, UT: Association for Consumer Research.Google Scholar
  56. Wolfgang, A. (1988). Gambling as a function of gender and sensation seeking. Journal of Gambling Behavior, 4(2), 71–77.CrossRefGoogle Scholar
  57. Woodside, A., Cook, V., & Mindak, W. (1987). Profiling the heavy travel segment. Journal of Travel Research, 25(4), 9–14.CrossRefGoogle Scholar
  58. Woodside, A., & Soni, P. (1991). Customer portfolio analysis for strategic development in direct marketing. Journal of Direct Marketing, 5(2), 6–19.CrossRefGoogle Scholar
  59. Woodside, A., & Trappey, R. (1996). Customer portfolio analysis among competing retail stores. Journal of Business Research, 35, 189–200.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.University of Rhode IslandKingstonUSA
  2. 2.Department of MarketingBoston College, Carroll School of ManagementChestnut HillUSA

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