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Record W2031885312 · doi:10.5539/ijms.v6n3p126

The Effect of Corporate Social Responsibility on Brand Building

2014· article· en· W2031885312 on OpenAlexvenueno aff
Ayogyam Alexander, Amo Francis, Lydia Asare Kyire, Hudu Mohammed

Bibliographic record

VenueInternational Journal of Marketing Studies · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
Fundersnot available
KeywordsBeneficiaryCorporate social responsibilityStakeholderBusinessMarketingSample (material)Variance (accounting)VariablesStakeholder theoryPsychologyPublic relationsAccountingStatisticsMathematicsPolitical science

Abstract

fetched live from OpenAlex

The objective of this research has been to find out the effect Corporate Social Responsibility (CSR) would have on the building of future brands in an organisation. Some beneficiary communities were segmented into focus groups for discussions on the ramifications of CSR. The discussions brought forth three main ramifications (namely; stakeholder expectations, collaborations and research & Development) which eventually led to the discovery of fifteen variables. A questionnaire was then prepared with these variables in mind. A sample of 200 employees from ten organizations was selected as respondents before these questionnaires were administered. The data collected on these variables were then analysed using factor analysis. The analysis revealed that, there exist strong correlations between some of the variables such as OLBS and PIC; CTP and PIC; COO and RCL and others as shown in the correlation matrix table. The analysis further indicated that the variables PIC, RCL, PSE, DVD, TT and OCD with Eigen values greater than 1 reflects a decreasing strength towards brand building as shown in the total variance table. Out of the 15 variables, component 1 with a loading of 10.840 best affects brand building instead of component 2.

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)0.027
metaresearch head score (Gemma)0.070
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.354
Threshold uncertainty score0.938

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0270.070
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 head0.030
GPT teacher head0.321
Teacher spread0.291 · 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.

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

Citations24
Published2014
Admission routes1
Has abstractyes

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