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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.identifier.uri https://hdl.handle.net/10161/899
dc.description.abstract <p>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.</p><p>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.</p><p>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.</p>
dc.format.extent 792675 bytes
dc.format.mimetype application/pdf
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
dc.department Business Administration


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