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On the Measurement of Perceived Consumer Risk
dc.contributor.authorHavlena, William J.en_US
dc.contributor.authorDeSarbo, Wayne S.en_US
dc.date.accessioned2010-06-01T22:28:24Z
dc.date.available2010-06-01T22:28:24Z
dc.date.issued1991-09en_US
dc.identifier.citationHavlena, William J.; DeSarbo, Wayne S. (1991). "On the Measurement of Perceived Consumer Risk." Decision Sciences 22(4): 927-939. <http://hdl.handle.net/2027.42/75462>en_US
dc.identifier.issn0011-7315en_US
dc.identifier.issn1540-5915en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/75462
dc.description.abstractThe role of perceived risk in consumer behavior has been studied extensively by academic researchers. This paper introduces a methodology for the measurement of the effects of product features, marketing mix components, and individual differences on perceived consumer risk based on theoretical foundations in the literature. A conjoint-type model based on paired comparison judgments is estimated to provide attribute weights. A modification of a stochastic multidimensional scaling-based vector model is then used to measure and summarize individual consumer differences with respect to the impact of brand attributes and marketing mix components on latent levels of perceived consumer risk. An illustration is provided using students’ risk perceptions of sports cars.en_US
dc.format.extent818081 bytes
dc.format.extent3109 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.publisherBlackwell Publishing Ltden_US
dc.rights1991 by the American Institute for Decision Sciencesen_US
dc.subject.otherConsumer Behavioren_US
dc.subject.otherMarketing Researchen_US
dc.subject.otherMultidimensional Scalingen_US
dc.subject.otherAnd Statistical Methodologyen_US
dc.titleOn the Measurement of Perceived Consumer Risken_US
dc.typeArticleen_US
dc.subject.hlbsecondlevelComputer Scienceen_US
dc.subject.hlbsecondlevelMathematicsen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumSchool of Business Administration, Marketing and Statistics Departments, University of Michigan, Ann Arbor, MI 48109-1234en_US
dc.contributor.affiliationotherGraduate School of Management, Marketing Department, Rutgers University, Newark, NJ 70102en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/75462/1/j.1540-5915.1991.tb00372.x.pdf
dc.identifier.doi10.1111/j.1540-5915.1991.tb00372.xen_US
dc.identifier.sourceDecision Sciencesen_US
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dc.owningcollnameInterdisciplinary and Peer-Reviewed


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