Advertisement

Efficacy Expectations and Adherence: Evidence of Consumer Biases and Heuristics in Pharmaceutical Marketing

  • Veronika IlyukEmail author
  • Caglar Irmak
  • Thomas Kramer
  • Lauren Block
Chapter
Part of the International Series in Quantitative Marketing book series (ISQM, volume 20)

Abstract

Pharmaceutical non-adherence is a major issue in both the United States and worldwide. In fact, lack of medication adherence has been called “America’s other drug problem.” It is estimated that globally only about 50 % of patients take their medicines as prescribed, and in the United States the annual cost of poor adherence has been estimated to be approximately $177 billion. In this chapter, we cull from the vast body of work in consumer behavior those theories of consumer processing that are directly relevant to this behavioral problem. Although many factors influence (non)adherence to medicines, we focus our chapter on perceived efficacy since a consumer’s perception of poor product efficacy is one of the primary reasons for non-adherence with a particular medicine and a major cause of brand switching. We identify the biases, heuristics, and lay theories consumers use to infer and judge pharmaceutical product efficacy at two primary stages of the evaluation process: pre-consumption efficacy expectations that drive initial adherence and post-consumption efficacy judgments that drive continued adherence. For example, consumers employ a no-pain-no-gain rule of thumb when judging product efficacy such that products with stronger side effects or bad taste are judged more effective than those without. Given the detrimental consequences of non-adherence in terms of health risks to consumers and losses for the pharmaceutical industry in general, we suggest that efforts to enhance efficacy perceptions are key in creating value for all constituents in the pharmaceutical marketing chain—from manufacturers to end users.

Keywords

Pharmaceutical Marketer Duration Judgment Availability Heuristic Efficacy Expectation Product Efficacy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Adaval R, Monroe KB (2002) Automatic construction and use of contextual information for product and price evaluations. J Consum Res 28(4):572–588Google Scholar
  2. Agrawal N, Menon G, Aaker JL (2007) Getting emotional about health. J Mark Res 44(1):100–113Google Scholar
  3. Ajzen I, Fishbein M (1980) Understanding attitudes and predicting social behavior. Prentice-Hall, Englewood CliffsGoogle Scholar
  4. Aratani L (2009) Mainstream physicians try such alternatives as herbs, acupuncture and yoga. Washington Post, June 9Google Scholar
  5. Baker SM, Gentry JW, Rittenburg TL (2005) Building understanding of the domain of consumer vulnerability. J Macromark 25(2):128–139Google Scholar
  6. Barone MJ, Miniard PW (1999) How and when factual ad claims mislead consumers: examining the deceptive consequences of copy x copy interactions for partial comparative advertisements. J Mark Res 36(1):58–74Google Scholar
  7. Batra RK (2009) When good looks kill: an examination of consumer response to visually attractive product design. Adv Consum Res—Asia-Pac Conf Proc 8:252–253Google Scholar
  8. Becker MH (1974) The health belief model and personal health behavior. Health Educ Monogr 2(4):324–473Google Scholar
  9. Berg JS, Dischler J, Wagner DJ, Raia JJ, Palmer-Shevlin N (1993) Medication compliance: a healthcare problem. Ann Pharmacother 27(Suppl):5–24Google Scholar
  10. Bergus GR, Levin IP, Johnson C (1998) The influence of information order on patient decision making. Med Decis Making 18:460Google Scholar
  11. Bergus GR, Levin IP, Elstein AS (2002) Presenting risks and benefits to patients. J Gen Intern Med 17(8):612–617Google Scholar
  12. Berry DC (2006) Informing people about the risks and benefits of medicines: implications for the safe and effective use of medicinal products. Curr Drug Saf 1(1):121–126Google Scholar
  13. Berry DC, Knapp P, Raynor DK (2002) Provision of information about drug side-effects to patients. Lancet 359(9309):853–854Google Scholar
  14. Berry DC, Raynor DK, Knapp P (2003) Communicating risk of medication side-effects: an empirical evaluation of EU recommended terminology. Psychol Health Med 8(3):251–263Google Scholar
  15. Bettman JR, John DR, Scott CA (1986) Covariation assessment by consumers. J Consum Res 13(3):316–326Google Scholar
  16. Blackwell B, Bloomfield SS, Buncher CR (1972) Demonstration to medical students of placebo responses and non-drug factors. Lancet 299(7763):1279–1282Google Scholar
  17. Boulding W, Kirmani A (1993) A consumer-side experimental examination of signaling theory: do consumers perceive warranties as signals of quality? J Consum Res 20(1):111–123Google Scholar
  18. Bowman D, Heilman CM, Seetharaman PB (2004) Determinants of product-use compliance behavior. J Mark Res 41:324–338Google Scholar
  19. Briesacher BA, Andrade SE, Fouayzi H, Chan A (2008) Comparison of drug adherence rates among patients with seven different medical conditions. Pharmacotherapy 28(4):437–443Google Scholar
  20. Broniarczyk SM, Alba JW (1994) The role of consumers’ intuitions in inference-making. J Consum Res 21(3):393–407Google Scholar
  21. Buckalew LW, Coffield KE (1982) An investigation of drug expectancy as a function of capsule color and size and preparation form. J Clin Psychopharmacol 2(4):245–248Google Scholar
  22. Buckalew LW, Ross PJ (1991) Medication property effects on expectations of action. Drug Dev Res 23:101–108Google Scholar
  23. Burke RR, DeSarbo WS, Oliver RL, Robertson TS (1988) Deception by implication: an experimental investigation. J Consum Res 14(4):483–494Google Scholar
  24. Carlisle E, Shafir E (2005) Heuristics and biases in attitudes towards herbal medicines. In: Girotto V, Johnson-Laird PN (eds) The shape of reason: essays in honour of Paolo Legrenzi. Psychology Press, New York, pp 205–224Google Scholar
  25. Chandran S, Menon G (2004) When a day means more than a year: effects of temporal framing on judgments of health risk. J Consum Res 31(2):375–389Google Scholar
  26. Chernev A, Carpenter GS (2001) The role of market efficiency institutions in consumer choice: a case of compensatory inferences. J Mark Res 38(3):349–361Google Scholar
  27. Coffield KE, Buckalew LW (1988) A study of color preferences for drugs and implications for compliance and drug-taking. J Alcohol Drug Educ 34(1):28–36Google Scholar
  28. Corn D (2008) Obama and Clinton debate in Cleveland: no pain, no gain. Mother Jones, February 26. http://www.motherjones.com/mojo/2008/02/obama-and-clinton-debate-cleveland-nopain-no-gain. Accessed 29 April 2009
  29. Cosmides L, Tooby J (1996) Are humans good intuitive statisticians after all? Rethinking some conclusions of the literature on judgment under uncertainty. Cognition 58:1–73Google Scholar
  30. Cox AD, Cox D, Zimet G (2006) Understanding consumer responses to product risk information. J Mark 70:79–91Google Scholar
  31. Cox AD, Cox D, Mantel SP (2010) Consumer response to drug risk information: the role of positive affect. J Mark 74:31–44Google Scholar
  32. Creyer EH, Hrsistodoulakis I, Cole CA (2001) Changing a drug from Rx to OTC status: the consumer behavior and public policy implications of switch drugs. J Prod Brand Manage 10(1):52–64Google Scholar
  33. Deese J, Kaufman RA (1957) Serial effects in recall of unorganized and sequentially organized verbal material. J Exp Psychol 54(3):180–187Google Scholar
  34. Denes-Raj V, Epstein S (1994) Conflict between intuitive and rational processing: when people behave against their better judgment. J Pers Soc Psychol 66(5):819–829Google Scholar
  35. Dodds WB, Monroe KB, Grewal D (1991) Effects of price, brand, and store information on buyers’ product evaluations. J Mark Res 28(3):307–319Google Scholar
  36. Faro D (2010) Changing the future by reshaping the past: the influence of causal beliefs on estimates of time to onset. J Consum Res 37(2):279–291Google Scholar
  37. Finucane ML, Alhakami A, Slovic P, Johnson SM (2000) The affect heuristic in judgments of risks and benefits. J Behav Decis Making 13(1):1–17Google Scholar
  38. Gana K, Lourel M, Trouillet R, Fort I, Mezred D, Blaison C, Boudjemadi V, K’Delant P, Ledrich J (2010) Judgment of riskiness: impact of personality, naive theories and heuristic thinking among female students. Psychol Health 25(2):131–147Google Scholar
  39. Gigerenzer G, Hoffrage U (1995) How to improve Bayesian reasoning without instruction: frequency formats. Psychol Rev 102:684–704Google Scholar
  40. Gurm H, Litaker DG (2000) Framing procedural risks to patients: is 99% safe the same as a risk of 1 in 100? Acad Med 75(8):840–842Google Scholar
  41. Halpern DF, Blackman S, Salzman B (1989) Using statistical risk information to assess oral contraceptive safety. Appl Cogn Psychol 3(3):251–260Google Scholar
  42. Hammond D, Fong GT, McDonald PW, Brown SK, Cameron R (2004) Graphic Canadian cigarette warning labels and adverse outcomes: evidence from Canadian smokers. Am J Public Health 94(8):1442–1444Google Scholar
  43. Hawkins SA, Hoch SJ (1992) Low-involvement learning: memory without evaluation. J Consum Res 19(2):212–225Google Scholar
  44. Hawkins SA, Hoch SJ, Meyers-Levy J (2001) Low-involvement learning: repetition and coherence in familiarity and belief. J Consum Psychol 11(1):1–11Google Scholar
  45. Hendrickx L, Vlek C, Oppewal H (1989) Relative importance of scenario information and frequency information in the judgment of risk. Acta Psychol 72(1):41–63Google Scholar
  46. Hill RP (1995) Researching sensitive topics in marketing: the special case of vulnerable populations. J Public Policy Mark 14(1):143–148Google Scholar
  47. Hoffrage U, Lindsey S, Hertwig R, Gigerenzer G (2000) Communicating statistical information. Science 290(5500):2261–2262Google Scholar
  48. Hoy MG (1994) Switch drugs vis-a-vis Rx and OTC: policy, marketing, and research considerations. J Public Policy Mark 13:85–96Google Scholar
  49. Huber J, McCann J (1982) The impact of inferential beliefs on product evaluations. J Mark Res 19(3):324–333Google Scholar
  50. Ilyuk V, Block LG, Faro D (2012) The influence of task difficulty on perceived product efficacy. Working paperGoogle Scholar
  51. Institute of Medicine (2004) Health literacy: a prescription to end confusion. National Academies Press, Washington, DCGoogle Scholar
  52. Irmak C, Block LG, Fitzsimons G (2005) The placebo effect in marketing: sometimes you just have to want it to work. J Mark Res 42(4):406–409Google Scholar
  53. Jacobs KW, Nordan PM (1979) Classification of placebo drugs: effect of color. Percept Mot Skills 49(2):367–372Google Scholar
  54. Johnson RD (1987) Making judgments when information is missing: inferences, biases, and framing effects. Acta Psychol 66(1):69–82Google Scholar
  55. Johnson RD (1989) Making decisions with incomplete information: the first complete test of the inference model. Adv Consum Res 16(1):522–528Google Scholar
  56. Johnson RD, Levin IP (1985) More than meets the eye: the effect of missing information on purchase evaluations. J Consum Res 12(2):169–177Google Scholar
  57. Johnson EJ, Tversky A (1983) Affect, generalization, and the perception of risk. J Pers Soc Psychol 45(1):20–31Google Scholar
  58. Johnson EJ, Meyer RK, Ghose S (1989) When choice models fail: compensatory models in negatively correlated environments. J Mark Res 26(3):255–270Google Scholar
  59. Johnson EJ, Hershey J, Meszaros J, Kunreuther H (1993) Framing, probability distortions, and insurance decisions. J Risk Uncertainty 7(1):35–51Google Scholar
  60. Kahneman D, Tversky A (1979) Prospect theory: an analysis of decision under risk. Econometrica 47:263–291Google Scholar
  61. Kardes FR, Cronley ML, Kellaris JJ, Posavac SS (2004a) The role of selective information processing in price-quality inference. J Consum Res 31(2):368–374Google Scholar
  62. Kardes FR, Posavac SS, Cronley ML (2004b) Consumer inferences: a review of processes, bases, and judgment contexts. J Consum Psychol 14(3):230–256Google Scholar
  63. Katapodi MC, Facione NC, Humphreys JC, Dodd MJ (2005) Perceived breast cancer risk: heuristic reasoning and search for a dominance structure. Soc Sci Med 60(2):421–432Google Scholar
  64. Knapp P, Raynor DK, Berry DC (2004) Comparison of two methods of presenting risk information to patients about the side effects of medicines. Qual Saf Health Care 13(3):176–180Google Scholar
  65. Kramer T, Irmak C, Block L, Ilyuk V (2011) The effect of a no-pain, no-gain lay theory on product efficacy perceptions. Working paperGoogle Scholar
  66. Kraus N, Malmfors T, Slovic P (1992) Intuitive toxicology: expert and lay judgments of chemical risks. Risk Anal 12(2):215–232Google Scholar
  67. Kruglanski A, Webster DM (1996) Motivated closing of the mind: “seizing” and “freezing”. Psychol Rev 103(2):263–283Google Scholar
  68. Lipkus IM, Samsa G, Rimer BK (2001) General performance on a numeracy scale. Med Decis Making 21(1):37–44Google Scholar
  69. Loden J, Schooler C (2000) Patient compliance. Pharm Exec 20(7):88–94Google Scholar
  70. Loewenstein GF, Weber EU, Hsee CK, Welch N (2001) Risk as feelings. Psychol Bull 127(2):267–286Google Scholar
  71. Malenka DJ, Baron JA, Johansen S, Wahrenberger JW, Ross JM (1993) The framing effect of relative and absolute risk. J Gen Intern Med 8(1):543–548Google Scholar
  72. McMorrow B (2002) No pain, no gain. California Law Business, March 18. http://www.mcmorrowsavarese.com/nopainnogain.html. Accessed 29 April 2009
  73. McNeil BJ, Pauker SG, Sox HC, Tversky A (1982) On the elicitation of preferences for alternative therapies. N Engl J Med 306(21):1259–1262Google Scholar
  74. Menon G, Block LG, Ramanathan S (2002) We’re at as much risk as we are led to believe: effects of message cues on judgments of health risk. J Consum Res 28(4):533–549Google Scholar
  75. Menon A, Deshpande A, Perri M, Zinkhan G (2003) Consumers’ attention to the brief summary in print direct-to-consumer advertisements: perceived usefulness in patient-physician discussions. J Public Policy Mark 22:181–191Google Scholar
  76. National Council on Patient Information and Education (2007) Enhancing prescription medicine adherence: a national action plan. http://www.talkaboutrx.org/documents/enhancing_prescription_medicine_adherence.pdf. Accessed 1 Dec 2011
  77. Natter HM, Berry DC (2005) Effects of presenting the baseline risk when communicating absolute and relative risk reductions. Psychol Health Med 10(4):326–334Google Scholar
  78. Pain D (2009) No pain, no gain: more means less when it comes to profit and loss. The Independent, April 29. http://www.independent.co.uk/money/spend-save/no-pain-no-gain-more-means-lesswhen-it-comes-to-profit-and-loss-1670308.html. Accessed 29 April 2009Google Scholar
  79. Pechmann C, Ratneshwar S (1992) Consumer covariation judgments: theory or data driven? J Consum Res 19(3):373–386Google Scholar
  80. Pinto MB, Leonidas L (1994) The impact of office characteristics on satisfaction with medical care: a ‘before and after’ analysis. Health Mark Q 12(2):43–54Google Scholar
  81. Pontes MC, Pontes NM (1997) Variables that influence consumers’ inferences about physician ability and accountability. Health Care Manage Rev 22(2):7–20Google Scholar
  82. Purohit D, Srivastava J (2001) Effect of manufacturer reputation, retailer reputation, and product warranty on consumer judgments of product quality: a cue diagnosticity framework. J Consum Psychol 10(3):123–134Google Scholar
  83. Raghunathan R, Naylor RW, Hoyer WD (2006) The unhealthy = tasty intuition and its effects on taste inferences, enjoyment, and choice of food products. J Mark 70:170–184Google Scholar
  84. Rao AR, Monroe KB (1989) The effect of price, brand name, and store name on buyers’ perceptions of product quality: an integrative review. J Mark Res 26(3):351–357Google Scholar
  85. Rees D (2006) Feelings outweigh facts. Suppl Pharm Exec 5(6):28–33Google Scholar
  86. Rendón LI, Gans WL, Calleroz MD (1998) No pain, no gain: assessing the learning curve of collaboratives. New Dir Community Coll 103:71–83Google Scholar
  87. Rottenstreich Y, Hsee CK (2001) Money, kisses, and electric shocks: on the affective psychology of risk. Psychol Sci 12(3):185–190Google Scholar
  88. Rottenstreich Y, Kivetz R (2006) On decision making without likelihood judgment. Organ Behav Hum Decis Process 101(1):74–88Google Scholar
  89. Roullet B, Droulers O (2005) Pharmaceutical packaging color and drug expectancy. Adv Consum Res 32(1):164–171Google Scholar
  90. Rozin P, Fischler C, Shields C (2005) Conceptions of ‘natural’ in the domain of foods in France, Germany, Italy, U.K., and the USA. Unpublished manuscript, University of Pennsylvania, PhiladelphiaGoogle Scholar
  91. Rozin P, Spranca M, Krieger Z, Neuhaus R, Surillo D, Swerdlin A, Wood K (2004) Preference for natural: instrumental and ideational/moral motivations, and the contrast between foods and medicines. Appetite 43(2):147–154Google Scholar
  92. Sabate E (2003) Adherence to long-term therapies: evidence for action. World Health Organization, GenevaGoogle Scholar
  93. Sallis RE, Buckalew LW (1984) Relation of capsule color and perceived potency. Percept Mot Skills 58(3):897–898Google Scholar
  94. Schwartz LM, Woloshin S, Black WC, Welch HG (1997) The role of numeracy in understanding the benefit of screening mammography. Ann Intern Med 127(11):966–972Google Scholar
  95. Schwarz N (2002) Situated cognition and the wisdom of feelings: cognitive tuning. In: Feldman Barrett L, Salovey P (eds) The wisdom in feelings. Guilford, New York, pp 144–166Google Scholar
  96. Schwarz N, Song H, Xu J (2009) When thinking is difficult: metacognitive experiences as information. In: Wänke M (ed) Social psychology of consumer behavior. Psychology Press, New York, NY, pp 201–223Google Scholar
  97. Scitovszky T (1945) Some consequences of the habit of judging quality by price. Rev Econ Stud 12(2):100–105Google Scholar
  98. Sheridan SL, Pignone M (2002) Numeracy and the medical student’s ability to interpret data. Eff Clin Pract 5(1):35–40Google Scholar
  99. Shimp TA (1978) Do incomplete comparisons mislead? J Advertising Res 18:21–28Google Scholar
  100. Shiv B, Carmon Z, Ariely D (2005) Placebo effects of marketing actions: consumers may get what they pay for. J Mark Res 42(4):383–393Google Scholar
  101. Siegrist M (1997) Communicating low risk magnitudes: incidence rates expressed as frequency versus rates expressed as probability. Risk Anal 17(4):507–510Google Scholar
  102. Skurnik I, Yoon C, Park DC, Schwarz N (2005) How warnings about false claims become recommendations. J Consum Res 31(4):713–724Google Scholar
  103. Slovic P, Monahan J, MacGregor DG (2000) Violence risk assessment and risk communication: the effects of using actual cases, providing instruction, and employing probability versus frequency formats. Law Hum Behav 24:271–296Google Scholar
  104. Slovic P, Finucane ML, Peters E, MacGregor DG (2002) Rational actions or rational fools: implications of the affect heuristic for behavioral economics. J Socio-Econ 31(4):329–342Google Scholar
  105. Slovic P, Peters E, Finucane ML, MacGregor DG (2005) Affect, risk, and decision making. Health Psychol 24(4):S35–S40Google Scholar
  106. Song H, Schwarz N (2009) If it’s difficult to pronounce, it must be risky. Psychol Sci 20(2):135–138Google Scholar
  107. Stewart-Williams S, Podd J (2004) The placebo effect: dissolving the expectancy versus conditioning debate. Psychol Bull 130(2):324–340Google Scholar
  108. Stone ER, Frank Yates J, Parker AM (1994) Risk communication: absolute versus relative expressions of low-probability risks. Organ Behav Hum Decis Process 60(3):387–408Google Scholar
  109. Tilson HH (2004) Adherence or compliance? Changes in terminology. Ann Pharmacother 38:161–162Google Scholar
  110. Turett N (2005) Communications delivery chain. Pharm Exec 25(10):126–130Google Scholar
  111. Tversky A, Kahneman D (1974) Judgment under uncertainty: heuristics and biases. Science 185:1124–1131Google Scholar
  112. Wade G, Vrain KS (2005) Perfect package. Pharm Exec 24(10):114–115Google Scholar
  113. Wang W, Keh HT, Bolton LE (2010) Lay theories of medicine and a healthy lifestyle. J Consum Res 37(1):80–97Google Scholar
  114. Wanke M, Bohner G, Jurkowitsch A (1997) There are many reasons to drive a BMW: does imagined ease of argument generation influence attitudes? J Consum Res 24(2):170–177Google Scholar
  115. Wosinska M (2005) Direct-to-consumer advertising and drug therapy compliance. J Mark Res 42:323–332Google Scholar
  116. Wyer RS (2004) Social comparison and judgment: the role of situation models, narratives, and implicit theories. Erlbaum, MahwahGoogle Scholar
  117. Yamagishi K (1997) When a 12.86% mortality is more dangerous than 24.14%: implications for risk communication. Appl Cogn Psychol 11:495–506Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Veronika Ilyuk
    • 1
    Email author
  • Caglar Irmak
    • 2
  • Thomas Kramer
    • 3
  • Lauren Block
    • 1
  1. 1.Baruch College, City University of New YorkNew YorkUS
  2. 2.Terry College of BusinessUniversity of GeorgiaAthensUS
  3. 3.University of South CarolinaColumbiaUS

Personalised recommendations