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Record W4400987588 · doi:10.54097/3nj1sx98

A Study of the Influence of New Media on Luxury Marketing Strategy

2024· article· en· W4400987588 on OpenAlexaff
Xinyue Pang

Bibliographic record

VenueJournal of Education Humanities and Social Sciences · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Perception and Purchasing Behavior
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsAdvertisingBusinessMarketing

Abstract

fetched live from OpenAlex

The overall purchasing power of people in contemporary society has been steadily increasing in recent years because of the steadily increasing social wealth accumulation and the progressive improvement of the global economy's comprehensive capability. People are now consuming and sharing information in a new way because of the advent of social media and other mobile technologies. An increasing number of luxury businesses are updating their traditional marketing strategies and entering the online sales arena to adapt to the new media landscape. For the brand to achieve its goal of serving a wider range of consumer groups, it bids adieu to the upper echelons of the consumption chain, takes the initiative to approach customer direction, and allows consumers to have a deeper understanding and involvement. This paper elucidates the impact of new media on the diffusion of brand emotional value and the enhancement of customer connection in the luxury market through an analysis of the evolution and development of luxury marketing tactics within the context. This paper concludes that the development and expansion of the luxury market are significantly aided by new media, which is indispensable.

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)0.007
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 score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.127
GPT teacher head0.346
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

Citations0
Published2024
Admission routes1
Has abstractyes

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