Unstable Consumer Learning Models: Structural Estimation and Experimental Examination

dc.contributor.advisor

Boulding, William

dc.contributor.advisor

Staelin, Richard

dc.contributor.author

Lovett, Mitchell James

dc.date.accessioned

2009-01-02T16:24:47Z

dc.date.available

2009-01-02T16:24:47Z

dc.date.issued

2008-10-21

dc.department

Business Administration

dc.description.abstract

This dissertation explores how consumers learn from repeated experiences with a product offering. It develops a new Bayesian consumer learning model, the unstable learning model. This model expands on existing models that explore learning when quality is stable, by considering when quality is changing. Further, the dissertation examines situations in which consumers may act as if quality is changing when it is stable or vice versa. This examination proceeds in two essays.

The first essay uses two experiments to examine how consumers learn when product quality is stable or changing. By collecting repeated measures of expectation data and experiences, more information enables estimation to discriminate between stable and unstable learning. The key conclusions are that (1) most consumers act as if quality is unstable, even when it is stable, and (2) consumers respond to the environment they face, adjusting their learning in the correct direction. These conclusions have important implications for the formation and value of brand equity.

Based on the conclusions of this first essay, the second essay develops a choice model of consumer learning when consumers believe quality is changing, even though it is not. A Monte Carlo experiment tests the efficacy of this model versus the standard model. The key conclusion is that both models perform similarly well when the model assumptions match the way consumers actually learn, but with a mismatch the existing model is biased, while the new model continues to perform well. These biases could lead to suboptimal branding decisions.

dc.format.extent

792675 bytes

dc.format.mimetype

application/pdf

dc.identifier.uri

https://hdl.handle.net/10161/899

dc.language.iso

en_US

dc.subject

Business Administration, Marketing

dc.subject

Business Administration, Marketing

dc.subject

Consumer learning

dc.subject

Bayesian statistics

dc.subject

Choice models

dc.subject

Dynamic linear model

dc.subject

Service quality

dc.title

Unstable Consumer Learning Models: Structural Estimation and Experimental Examination

dc.type

Dissertation

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
D_Lovett_Mitchell_a_200812.pdf
Size:
774.1 KB
Format:
Adobe Portable Document Format

Collections