Consumer Privacy Choice in Online Advertising: Who Opts Out and at What Cost to Industry?

Johnson, Garrett A.; Shriver, Scott K.; Du, Shaoyin

doi:10.1287/mksc.2019.1198

Cited by 155 publications

(81 citation statements)

References 33 publications

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“…Similarly, inadvertent regressive consequences appear in other papers in the special issue. Johnson et al (2020) show that opt-out rates appear to be higher for more technologically sophisticated, older, and wealthier people. More directly, Raval (2019) examines which consumers file complaints.…”

Section: Distributional Impactmentioning

confidence: 90%

Introduction to the Special Issue on Consumer Protection

2020

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Please scroll down for article-it is on subsequent pages With 12,500 members from nearly 90 countries, INFORMS is the largest international association of operations research (O.R.) and analytics professionals and students. INFORMS provides unique networking and learning opportunities for individual professionals, and organizations of all types and sizes, to better understand and use O.R. and analytics tools and methods to transform strategic visions and achieve better outcomes. For more information on INFORMS, its publications, membership, or meetings visit http://www.informs.org

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Section: Distributional Impactmentioning

confidence: 90%

Introduction to the Special Issue on Consumer Protection

2020

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“…Although various types of information may be used to generate user profiles, as shown in Figure 2, however, our focus is mainly on installed and used/unused apps 13 , the history resulted from web browsing or web searches, and the clicked ads i.e. collectively described as the 'Web & Apps' activity.…”

Section: Representing Apps In User Profilementioning

confidence: 99%

“…Research studies indicate, unless consumers have specifically consented to it, that the ad/tracking companies evaluate user behavior and tailor advertising to reach specific audiences. The American self-regulatory authority, AdChoices 3 program, presents a platform for consumers to opt-out of targeted ads and tracking done by the participating companies in WebChoices, however, this would result in less revenue, in addition to, presenting less relevant ads and lower click through rates, as evident in [13].…”

Section: Introductionmentioning

confidence: 99%

Joint Optimisation of Privacy and Cost of in-App Mobile User Profiling and Targeted Ads

Ullah¹

2022

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Online mobile advertising ecosystems provide advertising and analytics services that collect, aggregate, process and trade rich amount of consumer's personal data and carries out interests-based ads targeting, which raised serious privacy risks and growing trends of users feeling uncomfortable while using internet services. In this paper, we address user's privacy concerns by developing an optimal dynamic optimisation cost-effective framework for preserving user privacy for profiling, ads-based inferencing, temporal apps usage behavioral patterns and interest-based ads targeting. A major challenge in solving this dynamic model is the lack of knowledge of time-varying updates during profiling process. We formulate a mixed-integer optimisation problem and develop an equivalent problem to show that proposed algorithm does not require knowledge of time-varying updates in user behavior. Following, we develop an online control algorithm to solve equivalent problem using Lyapunov optimisation and to overcome difficulty of solving nonlinear programming by decomposing it into various cases and achieve trade-off between user privacy, cost and targeted ads. We carry out extensive experimentations and demonstrate proposed framework's applicability by implementing its critical components using POC `System App'. We compare proposed framework with other privacy protecting approaches and investigate that it achieves better privacy and functionality for various performance parameters.<br>

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“…Their estimates range from a loss of 4 percent to 65 percent in the respective value (Goldfarb and Tucker, 2011;Johnson et al, 2019;Marotta et al, 2019). The industry estimates are much closer to the upper end, where the latest study by Google is based on a random control trial that involves disabling cookies for randomly selected users and resulting in an average loss of revenue by 52 percent of publishers.…”

Section: Related Literaturementioning

confidence: 99%

Competition and Privacy in Online Markets: Evidence from the Mobile App Industry

2020

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Policy makers are increasingly concerned about the combination of market power and massive data collection in digital markets. This concern is fueled by the theoretical prediction that more market power causes firms to collect ever more data from their users. We investigate the relationship between market power and data collection empirically. We analyze data about more than 1.5 million mobile applications in several thousand submarkets of Google's Play Store. We observe these data for over two years and combine information on an app's data collection with information about its competitive environment. Our analysis highlights a robust positive relationship between market power and data collection. We find that more data are being collected in concentrated markets, and apps with higher market shares collect more data. This pattern robustly emerges across a series of cross-sectional and panel regressions as well as a series of specifications that exploit exogenous variation.

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Consumer Privacy Choice in Online Advertising: Who Opts Out and at What Cost to Industry?

Abstract: This paper studies consumers’ revealed privacy choice in online display advertising using real-world transaction data from an ad exchange.

Cited by 155 publications

References 33 publications

Introduction to the Special Issue on Consumer Protection

Introduction to the Special Issue on Consumer Protection

Joint Optimisation of Privacy and Cost of in-App Mobile User Profiling and Targeted Ads

Competition and Privacy in Online Markets: Evidence from the Mobile App Industry

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