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Record W4391259691 · doi:10.54097/jgrxmw41

Internet Celebrity Economy and College Students' Consumption Behavior

2024· article· en· W4391259691 on OpenAlexaff
Keliang Liu

Bibliographic record

VenueHighlights in Business Economics and Management · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsThe InternetDigital economyConsumption (sociology)FlourishingBusinessAdvertisingBusiness modelMultitudeMarketingInvestment (military)EconomyEconomicsPolitical scienceSociologySocial scienceComputer science

Abstract

fetched live from OpenAlex

The "Internet celebrity economy" is a flourishing business model within the digital economy that began to emerge around 2015. Stimulated by the ever-expanding Internet-driven economy, this influencer marketing-based strategy exhibits significant resilience across social media platforms. Engaging with numerous niche markets, the "Internet celebrity economy" persists, attracting various sizable investment opportunities and leaving a lasting impact on the present market for the next generation to inherit. Among the most notable emerging demographics who are sensitive to new things, university students have played a substantial role in propelling the "Internet celebrity economy" forward. Their consumption behavior, however, is influenced by a multitude of intangible factors and marketing strategies. This comprehensive paper provides an in-depth exploration of the concept of Internet celebrities, talk about the evolution of the Internet celebrity economy, analyze its unique business model, draw comparisons with traditional advertising strategies, delve into the intricate relationship between college students and Internet celebrities, discuss positive and negatives effects behind Internet celebrity economy, and finally, proposing potential solutions to address prevailing issues in the online business industry.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.983
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

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

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 head0.020
GPT teacher head0.273
Teacher spread0.252 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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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