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Record W7133019339

Targeting, Advertising and Privacy

2023· dissertation· W7133019339 on OpenAlexafffund
Ruizhi Zhu

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldBusiness, Management and Accounting
TopicConsumer Market Behavior and Pricing
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsConsumer privacyProduct (mathematics)Competition (biology)Personally identifiable informationInformation privacyTargeted advertisingConsumption (sociology)PreferenceMarket power
DOInot available

Abstract

fetched live from OpenAlex

The recent advancements in information technologies have revolutionized the way firms interact with consumers and collect consumer information on a large scale.Traditional marketing methods have given way to more personalized and targeted approaches, enabled by the wealth of big data available from social media and other platforms. This surge in data collection allows businesses to gain deep insights into consumer behavior, enabling them to tailor their offerings and communication strategies more effectively. However, this data-driven revolution has raised concerns surrounding privacy and the protection of personal information. As a result, this thesis analyzes various perspectives – targeting, advertising and pricing – of firms in such markets and how different entities are affected by information acquisition and privacy regulations. Chapter 1 develops a general equilibrium model of informative advertising to examine the implications of privacy regulations on consumer welfare. Firms reach consumers by placing ads on an advertising platform. Privacy regulations affect ad targetability by either facilitating or hindering the identification of consumers’ preferences. We show that it is possible for some consumers to exhibit a preference for privacy purely for instrumental reasons simply because the presence of consumers with flexible preferences introduces the possibility of greater competition in the product market leading to lower prices and greater consumption for some or all consumers. The platform’s market power in the ad market and the possibility of such cross-selling in the product market—products intended for pickier consumers selling to consumers with flexible preferences under privacy—are critical factors. Chapter 2 studies the dynamic pricing strategies of firms while gradually collecting information about consumers with changing tastes. How should the firm personalize its offers and change them dynamically to learn as well as to adapt to changing tastes when it cannot commit to future behavior? I build a continuous-time bargaining model with one-sided incomplete information where a buyer’s binary type is publicly revealed through Brownian motion and the binary type changes via a Poisson process. Changing tastes benefits both types of consumers at a cost to the firm. If the firm is restricted to constant prices and can use the acquired information to select consumers, it is better off than under dynamic prices. The continuation bargaining process gets resolved slower under constant prices than under flexible prices, which makes consumers more willing to accept a given offer. Chapter 3 investigates the effect of a tailored news report and its targeted release by a politically biased firm on the equilibrium level of media bias with heterogeneous voters. Targeted media strategies include selective information disclosure and audience targeting. When the media firm cannot commit to either strategy, targeted media provides less media bias than traditional media. With full commitment, however, targeted media does not necessarily generate more media bias because selective audience targeting may be more effective in channeling the bias.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.008
Scholarly communication0.0070.006
Open science0.0010.002
Research integrity0.0060.003
Insufficient payload (model declined to judge)0.0100.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.308
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

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