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Published in: Marketing Letters 2/2008

01-06-2008

Framing effects in mixed price bundling

Authors: Timothy J. Gilbride, Joseph P. Guiltinan, Joel E. Urbany

Published in: Marketing Letters | Issue 2/2008

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Abstract

In mixed price bundling, the consumer has the choice of buying the individual products separately, as part of a bundle with a discounted price, or not purchasing them at all. Framing effects refer to how the price of the bundle is presented to the consumer. Past studies have focused on perceptual measures and aggregate level results, and have only looked at a subset of different types of price framing in any one study. In this paper we use discrete choice data to investigate whether price framing affects choice in mixed price bundles. We find that the joint, integrated frame results in the highest proportion of respondents choosing the bundle and the fewest choosing “none.” When the prices of items in a bundle are itemized, some consumers are more likely to compare prices separately to their reference prices to evaluate the attractiveness of the deal, but this actually reduces the probability of purchasing the bundle. However, the majority of consumers do not use reference prices and instead follow a simple economic choice model.

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Footnotes
1
Additivity of reservation prices is a frequent assumption in economic analyses of price bundling. See Jedidi et al. (2003) for a discussion and empirical test of this assumption. Our empirical model follows the findings of Jedidi et al. and we do not assume additivity of reservation prices in our analysis.
 
2
The exception is Harlam et al. (1995), who use self-explicated reservation prices as a proxy for reference prices and measure purchase intentions.
 
3
The model of Jedidi et al. (2003) allows for correlated error terms, e.g. ε ~ N(0, Λ) while ours restricts the covariance matrix to equal the identity matrix. With the price coefficient restricted to equal −1, this should allow estimation of a full covariance matrix. However, using standard prior distributions, we were unable to obtain stable posterior distributions across all data sets and models with an unconstrained error covariance matrix. In order to facilitate comparisons, we adopted the more restrictive assumption.
 
4
Prior distributions on θ and Σ were chosen to be proper, but as uninformative as possible while still resulting in stable posterior distributions for θ and Σ. The chains appeared to converge quickly but were allowed to run for 50,000 iterations to ensure that the initial conditions were dissipated. A sample of every 10th from the next 250,000 iterations was used to calculate posterior means and standard deviations for elements of the hyper-parameters θ and Σ. Model fit statistics were calculated using a sample of every 180th observation from the last 180,000 iterations. Full model results are available from the authors.
 
5
The LMD represents a logarithmic penalty function for being “wrong” and the MSE is a quadratic penalty function. These two measures tend to penalize “low probability predictions” more than the “hit probability,” which is a linear penalty function.
 
6
In addition, see the study by Grewal et al. (1998) that calls into question the independence of “acquisition value” and “transaction value.”
 
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Metadata
Title
Framing effects in mixed price bundling
Authors
Timothy J. Gilbride
Joseph P. Guiltinan
Joel E. Urbany
Publication date
01-06-2008
Publisher
Springer US
Published in
Marketing Letters / Issue 2/2008
Print ISSN: 0923-0645
Electronic ISSN: 1573-059X
DOI
https://doi.org/10.1007/s11002-007-9030-1

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