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Record W1514219670 · doi:10.4102/sajbm.v42i2.494

How sociable? An exploratory study of university brand visibility in social media

2011· article· en· W1514219670 on OpenAlexaff
Elsamari Botha, Mana Farshid, Leyland Pitt

Bibliographic record

VenueSouth African Journal of Business Management · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Marketing and Social Media
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsSocial mediaVisibilityReputationCredibilityAdvertisingCompetitor analysisBusinessMarketingExploratory researchPublic relationsSociologyPolitical scienceGeography

Abstract

fetched live from OpenAlex

Social media has changed both the way in which organizations and their brands interact with their customers and the way in which business gets done. Brands are attempting to utilize social media to reach existing customers, gain new ones and build or maintain credibility and reputation. More importantly, brands need to measure their visibility in the most popular social media relative to that of competitors. This study describes a tool for collecting brand visibility information by looking at the visibility of various South African university brands and their relative positioning from a social media perspective. Correspondence analysis is then used to portray the various university brands in a multi-dimensional space so that they can be contrasted with each other in terms of their visibility in social media. The findings indicate that South African university brands are not distinctly positioned in social media and that none of them seems to currently have a concerted strategy for engaging its stakeholders in a particular social media. This means that there are both opportunities for those who manage these brands, and also threats to these institutions for taking a laissez fair attitude to social media in these times when social media are coming to dominate the Internet in particular and media in general.

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)0.003
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.053
GPT teacher head0.243
Teacher spread0.190 · 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 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

Citations41
Published2011
Admission routes1
Has abstractyes

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