Multiattribute judgment: Acceptance of a new COVID-19 vaccine as a function of price, risk, and effectiveness

Abstract

This paper illustrates how to apply the RECIPE design to evaluate multiattribute judgment, reporting an experiment in which participants judged intentions to receive a new vaccine against COVID-19. The attributes varied were Price of the vaccine, Risks of side effects as reported in trials, and Effectiveness of the vaccine in preventing COVID. The RECIPE design is a union of factorial designs in which each of three attributes is presented alone, in pairs with each of the other attributes, and in a complete factorial with all other information. Consistent with previous research with analogous judgment tasks, the additive and relative weight averaging models with constant weights could be rejected in favor of a configural weight averaging model in which the lowest-valued attribute receives additional weight. That is, people are unlikely to accept vaccination if Price is too high, Risk is too high, or Effectiveness is too low. The attribute with the greatest weight was Effectiveness, followed by Risk of side-effects, and Price carried the least weight.

Keywords

• COVID
• vaccine
• averaging models
• conjoint measurement
• functional measurement
• importance of variables
• information integration
• multi-attribute utility
• recipe design
• weights of attributes
• configural weighting