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Fooled by Heteroscedastic Randomness: Local Consistency Breeds Extremity in Price-Based Quality Inferences

  • Bart De Langhe*
  • , Stijn M.J. Van Osselaer
  • , Stefano Puntoni
  • , Ann L. McGill
  • *Corresponding author for this work

Research output: Indexed journal article Articlepeer-review

15 Citations (Scopus)

Abstract

In some product categories, low-priced brands are consistently of low quality, but high-priced brands can be anything from terrible to excellent. In other product categories, high-priced brands are consistently of high quality, but quality of lowpriced brands varies widely. Three experiments demonstrate that such heteroscedasticity leads to more extreme price-based quality predictions. This finding suggests that quality inferences do not only stem from what consumers have learned about the average level of quality at different price points through exemplarmemory or rule abstraction. Instead, quality predictions are also based on learning about the covariation between price and quality. That is, consumers inappropriately conflate the conditional mean of quality with the predictability of quality. We discuss implications for theories of quantitative cue learning and selective information processing, for pricing strategies and luxury branding, and for our understanding of the emergence and persistence of erroneous beliefs and stereotypes beyond the consumer realm.

Original languageEnglish
Pages (from-to)978-994
Number of pages17
JournalJournal of Consumer Research
Volume41
Issue number4
DOIs
Publication statusPublished - 1 Dec 2014
Externally publishedYes

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