Questionnaire measures of consumers’ willingness to pay (WTP) and price sensitivity are biased, yet these declarative methods can aid managerial decision-making. Additional choices involve which question formats to use (open-ended or discrete choice) and how many questions (unique versus multiple). This paper aims to inform such choices for online data collection with an empirical evaluation of the size of the bias induced by four methods (price acceptability, price judgements, multiple discrete choices and single discrete choices) in a realistic choice context.
An experimental framework collects online data about a staple product whose price should be well known. Price sensitivity, WTP and their confidence intervals are derived from a logistic binary model of acceptability, then ranked to evaluate the size of the bias of each method, relative to an indirect benchmark.
Online data collections with self-administrated questionnaires lower respondents’ involvement and create substantial bias; hypothetical methods overestimate WTP and underestimate price sensitivity, especially with methods using unique questions (both discrete choice and price acceptability). Multiple questions (price judgements and repeated random discrete choices) increase attention to price information and reduce the bias. The round price effect also is notable in data collected by open-ended methods.
To measure declarative WTP and price sensitivity with online data collections, researchers should use a random discrete choices method. Price acceptability questions and split tests are not recommended. Price judgements provide reliable information about consumer reactions to prices, but the strong round price bias is problematic.
This study adds to marketing and economic literature by comparing actual measurement methods used by firms, rather than hypothetical versions, and offers strong external validity.
The author thanks Renaud Dédéyan for his suggestions on a previous version of this article, Toluna Quick Surveys® for online data collection and IRI for supplying retailer panel data.
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