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Journal of the Academy of Marketing Science

, Volume 38, Issue 6, pp 759–774 | Cite as

Transparent pricing: theory, tests, and implications for marketing practice

  • Robert E. CarterEmail author
  • David J. Curry
Original Empirical Research

Abstract

In today’s retail markets, products display opaque pricing, i.e., a single number that provides no information about the allocation of the retail proceeds among agents who bring the product to market. We study transparent pricing, which is an alternative strategy in which allocation information is revealed. We differentiate transparent pricing from related marketing practices such as social marketing, cause-related marketing, and pay-what-you-want. Using controlled experiments in multiple product categories with diverse sampling frames, we find that transparent prices systematically alter consumer utility functions and stated choice behavior. Our results support explanations drawn from both neoclassical and behavioral economic theory, including inequity aversion, procedural justice, and altruism. Classical theory predicts that price transparency should have little effect on consumer behavior. However, results from behavioral economics suggest that consumers may relax “self-interest” in the face of transparent prices, leading to counter-intuitive preferences. For example, in one set of studies we observe a significant proportion of consumers selecting the more expensive of two replicates of the same product. In another study, a subset of motorists willingly pays higher gasoline taxes for the same gallon of gas, increasing the overall price per gallon. We explain this behavior via parameterized utility functions that contain both self-interested and other-interested components moderated by characteristics of the decision-maker and characteristics of the choice context.

Keywords

Difference aversion Discrete-choice Fairness Price transparency Cost transparency Supply-chain 

Notes

Acknowledgement

Professor Curry thanks John Dinsmore, Grace Guo, Xiaoqi Han, Helene Deval, Doug Ewing, and Scott Wright – students in his PhD Seminar on Discrete-Choice Modeling – for insights drawn from hierarchical Bayesian analyses about the gasoline data from the Adult sample. This research was partially funded by a grant from the College of Business, University of Louisville.

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Copyright information

© Academy of Marketing Science 2010

Authors and Affiliations

  1. 1.Marketing Department, College of BusinessUniversity of LouisvilleLouisvilleUSA
  2. 2.College of BusinessUniversity of CincinnatiCincinnatiUSA

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