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Record W2883115254 · doi:10.5267/j.ijdns.2018.7.003

Moderating influence of corporate social responsibility on organizational performance of brewing transnational corporation

2018· article· en· W2883115254 on OpenAlexvenueno aff
Kowo Solomon, Omolola Sariat Akinbola, Ayodotun Stephen Ibidunni

Bibliographic record

VenueInternational Journal of Data and Network Science · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
Fundersnot available
KeywordsBrewingCorporate social responsibilityCorporationBusinessBusiness administrationAccountingPublic relationsPolitical scienceFood scienceFinanceChemistry

Abstract

fetched live from OpenAlex

In recent time, corporate social responsibility (CSR) has been a critical item on the agenda for many business firms.Organizations now go beyond their economic obligations and are particularly meticulous in considering and assessing the impact of their activities on the environment.The objectives of the study are to examine the effect of Environmental focused CSR activities on corporate image and also to investigate the effect of Ethical CSR activities on Customer Relation.240 copies of questionnaire were administered to Nigeria brewery staffs in Ogun State, Nigeria to get primary data that treated and tested appropriate research questions and hypotheses accordingly.The study adopted survey method and Cronbach Alpha for test retest reliability.The study found out that there was a significant relationship between ethical activity and customer relations and the relationship between environment activities and corporate image was significant.The study recommends the managements should ensure they plan strategically to enhance corporate social responsibility.

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)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.054
GPT teacher head0.306
Teacher spread0.252 · 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 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

Citations3
Published2018
Admission routes1
Has abstractyes

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