Predictably Non-Bayesian: Quantifying Salience Effects in Physician Learning About Drug Quality

Nuno Camacho (), Bas Donkers and Stefan Stremersch ()
Additional contact information
Nuno Camacho: Erasmus School of Economics, Erasmus University Rotterdam, 3000 DR Rotterdam, The Netherlands
Stefan Stremersch: Erasmus School of Economics, Erasmus University Rotterdam, 3000 DR Rotterdam, The Netherlands; and IESE Business School, University of Navarra, 08034 Barcelona, Spain

Marketing Science, 2011, vol. 30, issue 2, 305-320

Abstract: 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: consumer learning; quasi-Bayesian learning models; behavioral modeling; medical decision making; physician learning; new drug adoption (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

Downloads: (external link)
http://dx.doi.org/10.1287/mksc.1100.0624 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ormksc:v:30:y:2011:i:2:p:305-320

Access Statistics for this article

More articles in Marketing Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().