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Record W1989998381 · doi:10.1016/j.intmar.2015.01.002

The Impact of Market Competition on Search Advertising

2015· article· en· W1989998381 on OpenAlexaff
Yupin Yang, Qiang Lu, Guanting Tang, Jian Pei

Bibliographic record

VenueJournal of Interactive Marketing · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Market Behavior and Pricing
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCompetition (biology)BusinessSearch advertisingAdvertisingMarket competitionIndustrial organizationOnline advertisingEconomicsComputer scienceThe InternetWorld Wide WebMarket economy

Abstract

fetched live from OpenAlex

Although search advertising has gained popularity in recent years, research on the content of search advertising is scarce. This study develops a conceptual framework to understand how market competition affects what a firm advertises in its search ads. Search advertisements from two industries (i.e., hotel and car industries) are used to test hypotheses developed from the conceptual framework. The findings indicate that in a highly competitive market (1) firms engage in more price advertising in their search ads and (2) intermediaries are more likely to increase price advertising in their search ads than brand suppliers. More interestingly, competition from intermediaries and brand suppliers has different effects on the content of search advertising by intermediaries and brand suppliers. These findings enhance the understanding of firms’ behaviors in determining their search advertising content based on the intensity of market competition.

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)0.004
metaresearch head score (Gemma)0.022
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0040.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.030
GPT teacher head0.313
Teacher spread0.283 · 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

Citations12
Published2015
Admission routes1
Has abstractyes

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