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Data, Trust, and Transparency in Personalized Advertising.

dc.contributor.authorStevenson, Darren M.
dc.date.accessioned2016-09-13T13:50:51Z
dc.date.availableNO_RESTRICTION
dc.date.available2016-09-13T13:50:51Z
dc.date.issued2016
dc.date.submitted2016
dc.identifier.urihttps://hdl.handle.net/2027.42/133246
dc.description.abstractAdvertising wields the power to change the way we see and experience the world and how we perceive those around us. Though marketing practices have long been characterized by information asymmetry, with individuals unable to see the extent to which data describing them is held by various organizations nor how it is used, recent developments have intensified this arrangement. For both firms and individuals, the personalization of online advertising content is justified by increased efficiencies. Marketers benefit from cost savings by reducing expenditure wasted on reaching individuals who fall outside the target audience, providing better return on advertising investment. Individuals benefit from advertising personalization by encountering marketing content they are measurably more likely to be interested in, filtering the cacophony of advertising marketers seek to distribute to different audiences. And yet the opaque processes by which advertising content is selectively presented online prevents individuals from making reasonable judgments about contemporary media systems and practices. In light of these challenges, in this dissertation I investigate how today’s evolving, digital marketing system organizes interaction between marketers and individuals. Three empirical studies are presented offering insights into how marketers envision their audiences, how audiences envision marketing practices, and, together, how each have come to understand and use the data, information, and communication technologies that now bind them together. In the first study, using participant observation I examine the nature and dynamics of third-party personal data available to marketers on digital ad-buying platforms. In the second study, drawing on a series of focus groups I uncover how individuals reason about advertising personalization, focusing on the mental models people rely on when interacting with advertising they perceive to be personalized. In the third study, across four experiments I examine the causal influences of transparency and trust on how individuals make judgments about personalized advertising. Viewed together, findings from this work may be of interest to marketing managers who rely on advertising personalization techniques, designers and developers of technologies leveraging consumer data collection, policymakers who oversee advertising and digital privacy matters, and academic researchers in the fields of communication, marketing, management, public policy, and human-computer interaction.
dc.language.isoen_US
dc.subjectCommunication
dc.subjectMarketing
dc.subjectPersonalized Advertising
dc.subjectPersonal Data
dc.subjectMental Model
dc.subjectHuman-Computer Interaction
dc.titleData, Trust, and Transparency in Personalized Advertising.
dc.typeThesisen_US
dc.description.thesisdegreenamePhD
dc.description.thesisdegreedisciplineCommunication Studies
dc.description.thesisdegreegrantorUniversity of Michigan, Horace H. Rackham School of Graduate Studies
dc.contributor.committeememberSandvig, Christian E
dc.contributor.committeememberEllison, Nicole
dc.contributor.committeememberDal Cin, Sonya
dc.contributor.committeememberGreenstein, Shane Mitchell
dc.subject.hlbsecondlevelBusiness (General)
dc.subject.hlbsecondlevelManagement
dc.subject.hlbsecondlevelMarketing
dc.subject.hlbsecondlevelScreen Arts and Cultures
dc.subject.hlbsecondlevelHumanities (General)
dc.subject.hlbsecondlevelCommunications
dc.subject.hlbsecondlevelInformation and Library Science
dc.subject.hlbsecondlevelSocial Sciences (General)
dc.subject.hlbtoplevelBusiness and Economics
dc.subject.hlbtoplevelHumanities
dc.subject.hlbtoplevelSocial Sciences
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/133246/1/dstev_1.pdf
dc.owningcollnameDissertations and Theses (Ph.D. and Master's)


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