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Designed Uncertainty in Mystery Products

Author

Listed:
  • Alaa Elgayar

    (HU Berlin)

  • Daniel Guhl

    (HU Berlin)

  • Lucas Stich

    (University of Würzburg)

  • Martin Spann

    (LMU Munich)

Abstract

Mystery products deliberately hide key attributes until after purchase and have become a common strategy in retail and services, yet systematic evidence on how to design them effectively remains limited. This research studies two managerial levers---outcome-set composition and uncertainty framing (risk vs. ambiguity)---in two incentive-aligned choice experiments: an induced-value lab study with vertically differentiated outcomes and a large-scale choice-based conjoint on apparel with horizontally differentiated brands. Willingness-to-pay is shaped primarily by the structure of the outcome set: when a dominant outcome is included, consumers discount the mystery product; when outcomes are similar in value, a premium can emerge. Ambiguity reduces valuation primarily when outcome differentiation is high, and it shifts attention away from brand and ``mystery'' cues toward tangible attributes such as fit and color. In market simulations, mystery products are more price-elastic than fully specified alternatives and shift profits toward participating brands, especially weaker ones, while non-participants lose. Overall, the results inform when and how designed uncertainty can be used as a marketing instrument.

Suggested Citation

  • Alaa Elgayar & Daniel Guhl & Lucas Stich & Martin Spann, 2026. "Designed Uncertainty in Mystery Products," Rationality and Competition Discussion Paper Series 565, CRC TRR 190 Rationality and Competition.
    • Handle: RePEc:rco:dpaper:565
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    References listed on IDEAS

    as
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    Keywords

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    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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