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Record W4399870347 · doi:10.54097/285mcd73

A Study of Advertising on People's Willingness to Buy

2024· article· en· W4399870347 on OpenAlexaff
Pengxu Zhu

Bibliographic record

VenueJournal of Education Humanities and Social Sciences · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsCanadian Celiac AssociationUniversity of Toronto
Fundersnot available
KeywordsAdvertisingCredibilityAdvertising account executiveAdvertising researchNative advertisingContext (archaeology)Appeal to emotionAdvertising campaignValue (mathematics)Variety (cybernetics)BusinessOnline advertisingInformative advertisingAppealMarketingThe InternetPolitical science

Abstract

fetched live from OpenAlex

In modern society, advertising plays an important role. This study examines the multifaceted effects of advertising on consumer buying behavior, focusing on how different advertising strategies influence people's willingness to buy. By combining a literature review, consumer surveys, and an analysis of advertising campaigns across a variety of media, the study examines key aspects of advertising, including emotional appeal, message content, and credibility. The study also delves into the role of digital media in enhancing the impact and effectiveness of advertising, particularly in the context of personalized and targeted advertising. The key findings suggest that emotional engagement, brand trust, and perceived value of advertisements play an important role in influencing consumers' willingness to buy. The study also suggests that while digital and targeted advertising is highly effective in influencing the younger demographic, traditional advertising media such as television and print continue to have a significant impact on the older demographic.

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)0.001
metaresearch head score (Gemma)0.010
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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.092
GPT teacher head0.346
Teacher spread0.253 · 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
Published2024
Admission routes1
Has abstractyes

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