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Record W2095678300

Persuasion or Information? Advertising and Pricing of Image Goods

2008· article· en· W2095678300 on OpenAlexaff
Sebnem Ucar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Market Behavior and Pricing
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDuopolyMonopolyAdvertisingProduct (mathematics)Quality (philosophy)BusinessMicroeconomicsPersuasionStrategic complementsEconomicsMarketing
DOInot available

Abstract

fetched live from OpenAlex

This paper examines a monopoly firm’s decision on price and advertising in a market where exclusivity matters. Two forms of advertising are analyzed: (a) informative advertising, in which the firm provides real information about the product’s existence, features and quality, and (b) image advertising, in which the firm communicates an image for the product that allows buyers to associate themselves with. Consumers are both image-conscious and “snobbish. ” In other words, both image and exclusivity give them utility. Therefore, image advertis-ing increases consumers ’ willingness to pay even though they are assumed to be fully rational. In equilibrium only a fraction of consumers will buy the image good. We analyze the effects of income dispersion, the inherent nature of the product, and the existence of a strategic competitor on the equilibrium outcome and welfare. The monopolist makes lower profits selling to more snobbish con-sumers if the product is of high quality. An increase in consumers ’ snobbishness causes the share of image advertising to decrease if the product is of low quality. Also, it is shown that the monopolist advertises more than the two duopolists combined, and more consumers are served under monopoly than under duopoly, leading to an increase in total welfare.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0040.004
Open science0.0000.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.019
GPT teacher head0.238
Teacher spread0.219 · 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 designObservational
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

Citations2
Published2008
Admission routes1
Has abstractyes

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