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Consumer Learning of New Binary Attribute Importance Accounting for Priors, Bias, and Order Effects

Chylinski, Mathew; Roberts, John; Hardie, Bruce G.S.

Description

This paper develops and calibrates a simple yet comprehensive set of models for the evolution of binary attribute importance weights, based on a cue-goal association framework. We argue that the utility a consumer ascribes to an attribute comes from its association with the achievement of a goal. We investigate how associations may be represented and then track back the relationship of these associations to the utility function. We explain why we believe this to be an important problem before...[Show more]

dc.contributor.authorChylinski, Mathew
dc.contributor.authorRoberts, John
dc.contributor.authorHardie, Bruce G.S.
dc.date.accessioned2015-12-07T22:39:10Z
dc.identifier.issn0732-2399
dc.identifier.urihttp://hdl.handle.net/1885/23747
dc.description.abstractThis paper develops and calibrates a simple yet comprehensive set of models for the evolution of binary attribute importance weights, based on a cue-goal association framework. We argue that the utility a consumer ascribes to an attribute comes from its association with the achievement of a goal. We investigate how associations may be represented and then track back the relationship of these associations to the utility function. We explain why we believe this to be an important problem before providing an overview of the extensive literature on learning models. This literature identifies key phenomena and provides a foundation for our modeling of binary attribute importance learning, which can test for three departures from "rational" learning-bias, existence of priors, and the unequal weighting of sample observations (order effects). We apply our models in a laboratory setting under a number of different relationship strengths, and we find that, in our application, consumers' learning about attribute-goal associations exhibits bias and the effects of prior beliefs when the sample realizations occur with and without noise, and order effects when the sample realizations occur with noise. We provide an example of how our models can be extended to learning about more than one attribute.
dc.publisherInstitute for Operations Research and the Management Sciences (INFORMS)
dc.sourceMarketing Science: the marketing journal of INFORMS
dc.subjectKeywords: Associative learning; Consumer utility; Preference dynamics
dc.titleConsumer Learning of New Binary Attribute Importance Accounting for Priors, Bias, and Order Effects
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolume31
dc.date.issued2012
local.identifier.absfor150503 - Marketing Management (incl. Strategy and Customer Relations)
local.identifier.ariespublicationu5034689xPUB28
local.type.statusPublished Version
local.contributor.affiliationChylinski, Mathew, University of New South Wales
local.contributor.affiliationRoberts, John, College of Business and Economics, ANU
local.contributor.affiliationHardie, Bruce G.S., London Business School
local.description.embargo2037-12-31
local.bibliographicCitation.issue4
local.bibliographicCitation.startpage549
local.bibliographicCitation.lastpage566
local.identifier.doi10.1287/mksc.1120.0719
local.identifier.absseo910403 - Marketing
dc.date.updated2016-02-24T11:33:07Z
local.identifier.scopusID2-s2.0-84865001217
local.identifier.thomsonID000307492000001
CollectionsANU Research Publications

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