Predictably Non-Bayesian: Quantifying salience effects in physician learning about drug quality
Experimental and survey-based research suggests that consumers often rely on their intuition and cognitive shortcuts to make decisions. Intuition and cognitive shortcuts can lead to suboptimal decisions and, especially in high-stakes decisions, to legitimate welfare concerns. In this paper, we propose an extension of a Bayesian learning model that allows us to quantify the impact of salience-the fact that some pieces of information are easier to retrieve from memory than others-on physician learning. We show, using data on actual prescriptions for real patients, that physicians' belief formation is strongly influenced by salience effects. Feedback from switching patients-the ones the physician decided to switch to a clinically equivalent treatment-receives considerably more weight than feedback from other patients. In the category we study, salience effects slowed down physicians' speed of learning and the adoption of a new treatment, which raises welfare concerns. For managers, our findings suggest that firms that are able to eliminate, or at least reduce, salience effects to a greater extent than their competitors can speed up the adoption of new treatments. We explore the implications of these results and suggest alternative applications of our model that are relevant for policy makers and managers.
|Keywords||Behavioral modeling, Consumer learning, Medical decision making, New drug adoption, Physician learning, Quasi-Bayesian learning models|
|JEL||Consumer Economics: Theory (jel D11)|
|Persistent URL||dx.doi.org/10.1287/mksc.1100.0624, hdl.handle.net/1765/25989|
|Series||ERIM Top-Core Articles|
|Journal||Marketing Science: the marketing journal of INFORMS|
Camacho, N.M.A, Donkers, A.C.D, & Stremersch, S. (2011). Predictably Non-Bayesian: Quantifying salience effects in physician learning about drug quality. Marketing Science: the marketing journal of INFORMS, 30(2), 305–320. doi:10.1287/mksc.1100.0624