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Data, Trust, and Transparency in Personalized Advertising.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Data, Trust, and Transparency in Personalized Advertising./
Author:	Stevenson, Darren M.
Description:	1 online resource (320 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 78-07(E), Section: A.
Subject:	Mass communication. -
Online resource:	click for full text (PQDT)
ISBN:	9781369590098

Data, Trust, and Transparency in Personalized Advertising.
Stevenson, Darren M.

Data, Trust, and Transparency in Personalized Advertising. - 1 online resource (320 pages)

Source: Dissertation Abstracts International, Volume: 78-07(E), Section: A.

Thesis (Ph.D.)--University of Michigan, 2016.

Includes bibliographical references

Advertising 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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369590098Subjects--Topical Terms:

1179310
Mass communication.
Index Terms--Genre/Form:

554714
Electronic books.

Data, Trust, and Transparency in Personalized Advertising.
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504 $a Includes bibliographical references
520 $a Advertising 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.
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