Journal of Marketing Behavior > Vol 1 > Issue 3-4

Partitioning the Choice Task Makes Starbucks Coffee Taste Better

Michael Dorn, University of Bern, Switzerland, Michael.Dorn@imu.unibe.ch Claude Messner, University of Bern, Switzerland, Michaela Wänke, University of Mannheim, Germany, michaela.waenke@uni-mannheim.de
 
Suggested Citation
Michael Dorn, Claude Messner and Michaela Wänke (2016), "Partitioning the Choice Task Makes Starbucks Coffee Taste Better", Journal of Marketing Behavior: Vol. 1: No. 3-4, pp 363-384. http://dx.doi.org/10.1561/107.00000023

Published: 24 Feb 2016
© 2015 M. Dorn, C. Messner, and M. Wänke
 
Subjects
Behavioral Decision Making
 
Keywords
Choice overload effectOverchoiceToo-much-choiceSequential attribute-based processingCustomized decisions
 

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In this article:
1. Introduction
2. Study 1: The Starbucks Tasting
3. Study 2: The Tailor-made Suit Customization
4. General Discussion
5. Conclusions
References

Abstract

A corrected version of the article is available at http://dx.doi.org/10.1561/107.00000023-corr

Consumers are often less satisfied with a product chosen from a large assortment than a limited one. Experienced choice difficulty presumably causes this as consumers have to engage in a great number of individual comparisons. In two studies we tested whether partitioning the choice task so that consumers decided sequentially on each individual attribute may provide a solution. In a Starbucks coffee house, consumers who chose from the menu rated the coffee as less tasty when chosen from a large rather than a small assortment. However, when the consumers chose it by sequentially deciding about one attribute at a time, the effect reversed. In a tailored-suit customization, consumers who chose multiple attributes at a time were less satisfied with their suit, compared to those who chose one attribute at a time. Sequential attribute-based processing proves to be an effective strategy to reap the benefits of a large assortment.

DOI:10.1561/107.00000023

Online Appendix | 107.00000023_app.pdf

This is the article's accompanying appendix.

DOI: 10.1561/107.00000023_app