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Record W2547657864 · doi:10.5539/ibr.v9n12p55

Corporate Social Responsibility and Facebook: A Splashy Combination?

2016· article· en· W2547657864 on OpenAlexvenueno aff
Hubert Korzilius, Maria Margarita Arias

Bibliographic record

VenueInternational Business Research · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate social responsibilityBusinessAdvertisingAffect (linguistics)Brand imageMarketingProduct (mathematics)SustainabilitySocial mediaPsychologyPublic relationsComputer science

Abstract

fetched live from OpenAlex

Literature widely explores Corporate Social Responsibility (CSR), Online Social Networks and consumer behavior individually. However, research linking them has been scarce. Therefore, this study aims to assess the effect of CSR information provided through Facebook on consumers’ brand image and purchase intention, considering the role of consumer´s product involvement. A fictitious brand profile “Splash Citrus” was designed for an online experiment conducted with participants from two countries, Colombia and the Netherlands, studying the effect of the stimulus, communication channel, Facebook versus Video commercial, on purchase intention and brand image. There was evidence that participants receiving CSR information through Facebook have a higher Brand image than participants receiving information through a video commercial. This effect on brand image appears particularly in higher product involved participants. Cross-cultural values did not affect these relationships. CSR and Facebook thus seem a splashy combination allowing managers to implement innovative strategies to achieve financial, social, and, environmental sustainability.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.139
GPT teacher head0.426
Teacher spread0.287 · 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 designQualitative
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
Published2016
Admission routes1
Has abstractyes

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