34.10 Consumer well-being & Food Consumption Decisions

(M. I. Norton, Mochon, and Ariely 2012)

  • The IKEA effect: increase in volaution of self-made products

  • People evaluate their own creation similar in value to that of experts, and expect other to have similar opinions

  • Labor leads to love only when one has successful task completion

    • When the task is destroyed or incomplete, there is no IKEA effects

Raghunathan, Naylor, and Hoyer (2006)

  • The portrayal of unhealthy product increases food’s inferred, and actual taste, and more preferable when the is more hedonically salient.

  • This result is robust among those who believe the negative correlation between healthiness and tastiness as well as those who do believe in such correlation.

  • To change this negative correlation beliefs, authors suggest that we can educate consumers with better information about the definition of “healthy”

  • The unhealthy = tasty intuition can come from

    • Internal source (e.g., personal; experience and self-observation). There is an inverse relationship (compensatory) between wholeness and hedonic potential

    • External source (e.g., environmental cues)

Shah et al. (2014)

  • (Price) Surcharge or Unhealthy label along cannot change the demand for unhealthy food

  • The combination of both can reduce demand for unhealthy food.

  • Among women, unhealthy label can be as effective as unhealthy label + surcharge

  • Among men, unhealthy label increases the demand for unhealthy foods as compared to unhealthy label + surcharge.

C. Berry, Burton, and Howlett (2017)

  • Since there is no formal definition of “natural”, companies exploited this loopholes.

  • based on activation theory and inferential processing, the mediation path from natural claims to product evaluation is via consumers’ attribute inferences.

P. J. Liu et al. (2019)

  • Consumers use type as the primary dimension and quantity as the secondary dimension in judging food’s healthiness

Woolley and Liu (2020)

  • Magnitude estimates refers to when “consumers judge whether something has”very few” to “many” calories” (p. 147).

    • Under which, people think that a smaller portion of healthy food has more calories than a larger portion of healthier food

    • Sensitive to type (healthy vs. unhealthy)

  • Numeric estimates refers when “consumers estimate a number of calories” (p. 147)

    • Under which , people think that a larger portion of healthier food has more calories than a small proportion of healthy food.

    • Sensitive to type (healthy vs. unhealthy) and quantity (large vs. small)

  • Food healthiness is processed before quantity.

  • The two modes will converge if quantity information is made first (primary) or in an intuitive way.

Moorman (1990)

  • Consumers characteristics (familiarity and motivation) and stimulus characteristics (info format and content) influence information processing and decision quality of how they use nutrition information

Haws et al. (2019)

  • People choose any-size-same-price beverage because they think they can get more value (in financial terms). This effect is so strong that with calorie postings, this demand is still intact.

    • Finding is robust under diet vs. non-diet beverage (rule out that customers don’t see value in getting more calories, but only from saving money).
  • Graphic health intervention can still have an effect on the appeal of larger sizes

Other resources:

  • Robitaille et al. (2021)

  • Longoni, Bonezzi, and Morewedge (2019)

  • VanEpps et al. (2021)

  • Woolley and Liu (2020)

References

Berry, Christopher, Scot Burton, and Elizabeth Howlett. 2017. “Its Only Natural: The Mediating Impact of Consumers Attribute Inferences on the Relationships Between Product Claims, Perceived Product Healthfulness, and Purchase Intentions.” Journal of the Academy of Marketing Science 45 (5): 698–719. https://doi.org/10.1007/s11747-016-0511-8.
Haws, Kelly L., Peggy J. Liu, Steven K. Dallas, John Cawley, and Christina A. Roberto. 2019. “Any Size for a Dollar: The Effect of Any-Size-Same-Price Versus Standard Pricing on Beverage Size Choices.” Journal of Consumer Psychology 30 (2): 392–401. https://doi.org/10.1002/jcpy.1129.
Liu, Peggy J., Kelly L. Haws, Karen Scherr, Joseph P. Redden, James R. Bettman, and Gavan J. Fitzsimons. 2019. “The Primacy of What over How Much: How Type and Quantity Shape Healthiness Perceptions of Food Portions.” Management Science 65 (7): 3353–81. https://doi.org/10.1287/mnsc.2018.3098.
Longoni, Chiara, Andrea Bonezzi, and Carey K Morewedge. 2019. “Resistance to Medical Artificial Intelligence.” Journal of Consumer Research 46 (4): 629–50. https://doi.org/10.1093/jcr/ucz013.
Moorman, Christine. 1990. “The Effects of Stimulus and Consumer Characteristics on the Utilization of Nutrition Information.” Journal of Consumer Research 17 (3): 362. https://doi.org/10.1086/208563.
Norton, Michael I, Daniel Mochon, and Dan Ariely. 2012. “The IKEA Effect: When Labor Leads to Love.” Journal of Consumer Psychology 22 (3): 453–60.
Raghunathan, Rajagopal, Rebecca Walker Naylor, and Wayne D. Hoyer. 2006. “The Unhealthy = Tasty Intuition and Its Effects on Taste Inferences, Enjoyment, and Choice of Food Products.” Journal of Marketing 70 (4): 170–84. https://doi.org/10.1509/jmkg.70.4.170.
Robitaille, Nicole, Nina Mazar, Claire I. Tsai, Avery M. Haviv, and Elizabeth Hardy. 2021. “Increasing Organ Donor Registrations with Behavioral Interventions: A Field Experiment.” Journal of Marketing 85 (3): 168–83. https://doi.org/10.1177/0022242921990070.
Shah, Avni M., James R. Bettman, Peter A. Ubel, Punam Anand Keller, and Julie A. Edell. 2014. “Surcharges Plus Unhealthy Labels Reduce Demand for Unhealthy Menu Items.” Journal of Marketing Research 51 (6): 773–89. https://doi.org/10.1509/jmr.13.0434.
VanEpps, Eric M., Andras Molnar, Julie S. Downs, and George Loewenstein. 2021. “Choosing the Light Meal: Real-Time Aggregation of Calorie Information Reduces Meal Calories.” Journal of Marketing Research 58 (5): 948–67. https://doi.org/10.1177/00222437211022367.
Woolley, Kaitlin, and Peggy J. Liu. 2020. “How You Estimate Calories Matters: Calorie Estimation Reversals.” Edited by Amna Kirmani and Lauren Block. Journal of Consumer Research 48 (1): 147–68. https://doi.org/10.1093/jcr/ucaa059.