Current Research Projects

Publications and Invited Revisions

  1. Cho, K. Y., & Critcher, C. R. (2025). Doubling-back aversion: A reluctance to make progress by undoing it. Psychological Science, 36(5), 332-349. [link]
    People prefer to continue on a route that they know is longer than retrace their steps to take a shorter route
  2. Cho, K. Y., Baum, S. M., & Evers, E. R. K. Liking scales fail to accurately capture preferences for probabilistic outcomes. Conditionally accepted at Psychological Science. [link]
    Liking scales make participants predominantly factor in probabilities and neglect payoffs when used on probabilistic outcomes.
  3. Cho, K. Y., & Evers, E. R. K. When stating the obvious backfires: Consumers dislike products that advertise positive but obvious attributes. Revising for 2nd round of revision at Journal of Consumer Research. [link]
    People dislike products that advertise an attribute that is positive but that goes without saying (e.g., water being gluten-free)
  4. Cho, K. Y., Geiser, A. E., & Nelson, L. D. Too basic to be expert: Consumers believe popular preferences indicate a lack of expertise. Revising for 2nd round of revision at Journal of Consumer Research. [link]
    People believe that someone with popular but well-attested tastes cannot be an expert (e.g., if they say they like Picasso).

Selected Works in Progress

  1. Ryan, W. H., Cho, K. Y., Hong, C., & Evers, E. R. K. Add-on vs. all-in pricing: Price presentation in menus skews consumer preferences.
    People are more likely to purchase premium options when prices are presented in all-in format (the total price) than in add-on format (base price plus additional prices).
  2. Cho, K. Y., & Critcher, C. R. Choose now or wait? When and why choice set fluctuations prompt choice.
    Consumers are more likely to commit to the best remaining option after directly experiencing changes in available choices rather than merely learning about them.
  3. Cho, K. Y., Gershon, R., & Jiang, Z. Targeting referrers by value: Why who refers matters more than how many.
    Analyses on a dataset of over 40 million customers show that higher-value customer make higher-value referrals; field experiments show that it is profitable to give higher referral incentives to such customers.
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