Measuring Repurchase Decisions by Accelerating Repurchase Behavior

Abstract

Repurchase intention questions are routinely used in marketing research and practices to measure consumers’ repurchase decisions. They are easy to use but suffer from a few biases and weak predictive power. This article identifies three biases, namely, projection bias, lack-of-context bias, and hypothetical bias. A new method, repurchase acceleration (RA), is proposed targeting these biases. In RA, researchers buy back respondents’ currently owned products to ensure they are in the market, provide them with a representative choice set to mimic the repurchase market context, and attach real-life consequences to mimic the incentives in their real-life repurchases. An empirical study demonstrates that RA predicts significantly better than repurchase intention models and captures more than 3 times as much information as the best repurchase intention model does. We recommend that RA is used for high-value, long purchase-cycle products for a precise measure of consumers’ repurchase decisions for high-stake marketing decisions.