MétaCan
Menu
Back to cohort
Record W2969109938 · doi:10.1109/access.2019.2935005

The Business Model of Live Streaming Entertainment Services in China and Associated Challenges for Key Stakeholders

2019· article· en· W2969109938 on OpenAlexfundno aff
Yasheng Chen, Feng Xiong

Bibliographic record

VenueIEEE Access · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsnot available
FundersNational Natural Science Foundation of ChinaFundamental Research Funds for the Central UniversitiesXiamen UniversitySimon Fraser UniversityQueensland University of Technology
KeywordsBusiness modelLive streamingEntertainmentRevenueBusinessAdvertisingChinaService (business)Social mediaGeneral partnershipKey (lock)MarketingComputer scienceWorld Wide WebMultimediaPolitical scienceComputer security

Abstract

fetched live from OpenAlex

Live streaming entertainment services are a relatively recent phenomenon with an increasing market share. YY Live, which is a leading live streaming entertainment service provider in China, launched at $11.32 USD on Nasdaq in 2013 and is currently valued at $106.25 USD (as of 24th May 2018). In contrast to other popular social media platforms in the Western world, such as Facebook Live and Amazon Twitch, which rely on advertising revenue, the live streaming broadcast industry in China bases its business model on viewers buying virtual gifts. This business model has been accepted on Wall Street; thus, its success deserves further academic attention. This paper aims to reveal this industry's business model and explore the reasons for the widespread success of these streaming services. This study conducts interviews with officers from YY Live and performs a content analysis of news reports. The discussion demonstrates several challenges of current YY Live business models, especially the partnership between the host and host union and the psychological impact of the gift-giving model.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.436
Threshold uncertainty score0.888

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.060
GPT teacher head0.305
Teacher spread0.245 · 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 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

Citations34
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueIEEE AccessSame topicDigital Marketing and Social MediaFrench-language works237,207