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Record W2967039096 · doi:10.1002/mar.21419

Social media influencers: A route to brand engagement for their followers

2020· article· en· W2967039096 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePsychology and Marketing · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsHog Administrative Marketing Services (Canada)
Fundersnot available
KeywordsInfluencer marketingCustomer engagementSocial mediaBrand engagementBrand communityAdvertisingContext (archaeology)AffectionBusinessEmpirical researchSocial media marketingProduct (mathematics)Brand awarenessPsychologyMarketingDigital marketingRelationship marketingMarketing managementComputer scienceSocial psychologyWorld Wide Web

Abstract

fetched live from OpenAlex

Abstract Social Media Influencers (SMIs) are micro‐celebrities with large followings on social media platforms who engage consumers and hold the potential to promote customer‐brand relationships across different product categories. SMIs have an existing relationship of trust with consumers, and consumers seek out the content created by SMIs for valuable information and advice. This study explores the process of brand engagement between consumers and brands in the digital content marketing environment, specifically examining the research question: Do SMIs act as a route to brand engagement for their followers? The context for this study is the beauty community on YouTube; over 60,000 user comments were analyzed through automated text analysis. This study is among the first to provide empirical evidence that SMIs do act as a route to brand engagement through the three dimensions of cognitive processing, affection and activation.

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.

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)0.003
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.880
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.075
GPT teacher head0.371
Teacher spread0.296 · 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