Consumers' ability to identify a surplus when returns to attributes are nonlinear

Abstract

Previous research in multiple judgment domains has found that nonlinear functions are typically processed less accurately than linear ones. This empirical regularity has potential implications for consumer choice, given that nonlinear functions (e.g., diminishing returns) are commonplace. In two experimental studies we measured precision and bias in consumers' ability to identify surpluses when returns to product attributes were nonlinear. We hypothesized that nonlinear functions would reduce precision and induce bias toward linearization of nonlinear relationships. Neither hypothesis was supported for monotonic nonlinearities. However, precision was greatly reduced for products with nonmonotonic attributes. Moreover, assessments of surplus were systematically and strongly biased, regardless of the shape of returns and despite feedback and incentives. The findings imply that consumers use a flexible but coarse mechanism to compare attributes against prices, with implications for the prevalence of costly mistakes.

Keywords

• consumer choice
• function learning
• multiattribute decision making
• nonlinear returnsnakeywords