Advertisement

Marketing Letters

, Volume 23, Issue 4, pp 943–957 | Cite as

Why consumers respond differently to absolute versus percentage descriptions of quantities

  • Danny Weathers
  • Scott D. Swain
  • Jay P. Carlson
Article

Abstract

Consumers often provide different evaluations of absolute and percentage descriptions of the same quantity. Prior research has attributed this to two factors: selection of distinct reference contexts and differential cognitive difficulty. However, in a preliminary study, we show that discrepancies in consumer evaluations of absolute and percentage quantities can arise even when these two factors are held constant. A series of studies provides evidence that (1) this effect is rooted in automatic, nonverbal associations between numerical stimuli and analogue magnitude coding and (2) the influence of analogue magnitude codes manifests across different kinds of quantities, different evaluations, and different processing modes.

Keywords

Analogue magnitude codes Face values Percentages Pricing Price evaluations 

Notes

Acknowledgement

The authors contributed equally to this research. They would like to thank the editor and two anonymous reviewers for the many helpful comments that substantially improved the quality and contribution of the manuscript.

References

  1. Cantlon, J., & Brannon, E. (2007). Basic math in monkeys and college students. PLoS Biology, 5, e328.CrossRefGoogle Scholar
  2. Chen, S.-F., Monroe, K., & Lou, Y.-C. (1998). The effects of framing price promotion messages on consumers’ perceptions and purchase intentions. Journal of Retailing, 74(3), 353–372.CrossRefGoogle Scholar
  3. Coulter, K., & Coulter, R. (2005). Size does matter: the effects of magnitude representation congruency on price perceptions and purchase likelihood. Journal of Consumer Psychology, 15(1), 64–76.CrossRefGoogle Scholar
  4. Dehaene, S. (1992). Varieties of numerical abilities. Cognition, 44(1–2), 1–42.Google Scholar
  5. Dehaene, S. (1997). The number sense. New York: Oxford University Press.Google Scholar
  6. DelVecchio, D., Krishnan, H., & Smith, D. (2007). Cents or percent? The effects of promotion framing on price expectations and choice. Journal of Marketing, 71, 158–170.CrossRefGoogle Scholar
  7. Denes-Raj, V., & Epstein, S. (1994). Conflict between intuitive and rational processing: when people behave against their better judgment. Journal of Personality and Social Psychology, 66(5), 819–829.CrossRefGoogle Scholar
  8. Estelami, H. (2003). Strategic implications of a multi-dimensional pricing environment. The Journal of Product and Brand Management, 12(5), 322–334.CrossRefGoogle Scholar
  9. Gallistel, C., & Gelman, R. (1992). Preverbal and verbal counting and computation. Cognition, 44(1–2), 43–74.CrossRefGoogle Scholar
  10. Gigerenzer, G. (2007). Gut feelings: the intelligence of the unconscious. New York: Viking.Google Scholar
  11. Gilovich, T., & Griffin, D. (2002). Introduction – heuristics and biases: then and now. In T. Gilovich, D. Griffin, & D. Kahneman (Eds.), Heuristics and biases: The psychology of intuitive judgment (pp. 1–17). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  12. Heath, T., Chatterjee, S., & France, K. (1995). Consumer evaluation of multiple versus single price change. Journal of Consumer Research, 22, 90–97.CrossRefGoogle Scholar
  13. Kahneman, D. (2011). Thinking fast and slow. New York, NY: Farrar, Straus, and Giroux.Google Scholar
  14. Keren, G. (2011). Perspectives on framing. New York, NY: Psychology Press/Taylor & Francis.Google Scholar
  15. Kim, H. M., & Kramer, T. (2006). The moderating effects of need for cognition and cognitive effort on responses to multi-dimensional prices. Marketing Letters, 17, 193–203.CrossRefGoogle Scholar
  16. Kirkpatrick, L. A., & Epstein, S. (1992). Cognitive–experiential self-theory and subjective probability: Further evidence for two conceptual systems. Journal of Personality and Social Psychology, 63(4), 534–544.CrossRefGoogle Scholar
  17. Krishna, A. (2009). Behavioral pricing. In V. Rao (Ed.), Handbook of pricing research in marketing (pp. 76–90). Northampton, MA: Edward Elgar Publishing.Google Scholar
  18. Monroe, K. (2003). Pricing: making profitable decisions (3rd ed.). New York, NY: McGraw-Hill.Google Scholar
  19. Monroe, K., & Lee, A. (1999). Remembering versus knowing: issues in buyers’ processing of price information. Journal of the Academy of Marketing Science, 27(2), 207–225.CrossRefGoogle Scholar
  20. Morwitz, V., Greenleaf, E., & Johnson, E. (1998). Divide and prosper: consumers’ reactions to partitioned prices. Journal of Marketing Research, 35, 453–463.CrossRefGoogle Scholar
  21. Nunes, J., & Boatwright, P. (2004). Incidental prices and their effect on willingness to pay. Journal of Marketing Research, 41, 457–466.CrossRefGoogle Scholar
  22. Price, P., & Matthews, T. (2009). From group diffusion to ratio bias: effects of denominator and numerator salience on intuitive risk and likelihood judgments. Judgment and Decision Making, 4(5), 436–446.Google Scholar
  23. Reder, L., & Ritter, F. (1992). What determines initial feeling of knowing? familiarity with question terms, not with the answer. Journal of Experimental Psychology, 18(3), 435–451.CrossRefGoogle Scholar
  24. Reyna, V., & Brainerd, C. (1993). Fuzzy memory and mathematics in the classroom. In G. Davies & R. Logie (Eds.), Memory in everyday life (pp. 91–119). Amsterdam, the Netherlands: North-Holland.CrossRefGoogle Scholar
  25. Reyna, V., Nelson, W., Han, P., & Dieckmann, N. (2009). How numeracy influences risk comprehension and medical decision making. Psychological Bulletin, 135(6), 943–973.CrossRefGoogle Scholar
  26. Slovic, P. (1972). From Shakespeare to Simon: speculations – and some evidence – about man’s ability to process information. Oregon Research Institute Monograph, 12(2).Google Scholar
  27. Stanovich, K., & West, R. (2008). On the relative independence of thinking biases and cognitive ability. Journal of Personality and Social Psychology, 94(4), 672–695.CrossRefGoogle Scholar
  28. Thomas, M., & Morwitz, V. (2005). Penny wise and pound foolish: the left-digit effect in price cognition. Journal of Consumer Research, 32, 54–64.CrossRefGoogle Scholar
  29. Thomas, M., & Morwitz, V. (2009a). Heuristics in numerical cognition: implications for pricing. In V. Rao (Ed.), Handbook of pricing research in marketing (pp. 132–149). Northampton, MA: Edward Elgar Publishing.Google Scholar
  30. Thomas, M., & Morwitz, V. (2009b). The ease of computation effect: the interplay of meta-cognitive experience and naïve theories in judgments of price difference. Journal of Marketing Research, 46, 81–91.CrossRefGoogle Scholar
  31. Thomas, M., Simon, D., & Kadiyali, V. (2010). The price precision effect: evidence from laboratory and market data. Marketing Science, 29(1), 175–190.CrossRefGoogle Scholar
  32. Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211, 453–458.CrossRefGoogle Scholar
  33. Vanhuele, M., & Dreze, X. (2002). Measuring the price knowledge shoppers bring to the store. Journal of Marketing, 66, 72–85.CrossRefGoogle Scholar
  34. Viswanathan, M., & Childers, T. (1996). Processing of numerical and verbal product information. Journal of Consumer Psychology, 5, 359–385.CrossRefGoogle Scholar
  35. Wolfe, C., & Reyna, V. (2010). Semantic coherence and fallacies in estimating joint probabilities. Journal of Behavioral Decision Making, 23, 203–223.CrossRefGoogle Scholar
  36. Yamaguchi, S. (1998). Biased risk perceptions among Japanese: illusion of interdependence among risk companions. Asian Journal of Social Psychology, 1(2), 117–131.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Danny Weathers
    • 1
  • Scott D. Swain
    • 2
  • Jay P. Carlson
    • 3
  1. 1.College of Business and Behavioral ScienceClemson UniversityClemsonUSA
  2. 2.College of Business AdministrationNortheastern UniversityBostonUSA
  3. 3.School of ManagementUnion Graduate College of Union UniversitySchenectadyUSA

Personalised recommendations