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Record W4310870435 · doi:10.18280/ijsdp.170720

Influence of Corporate Social Responsibility on Business Evaluation of Mobile Communication Network MTN in Nigeria

2022· article· en· W4310870435 on OpenAlexvenueno aff
Imhade P. Okokpujie, Ishoma M. Odigilia, Kennedy Okokpujie, Roselyn E. Subair, Adebayo Ogundipe, Lagouge K. Tartibu, Omolayo M. Ikumapayi

Bibliographic record

VenueInternational Journal of Sustainable Development and Planning · 2022
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsCorporate social responsibilityWorkforceBusinessDescriptive statisticsSustainabilityPopulationDescriptive researchMarketingPublic relationsEconomic growthSociologySocial scienceEnvironmental healthStatisticsEconomicsPolitical scienceMedicineMathematics

Abstract

fetched live from OpenAlex

The sustainability of any telecommunication industry lies in the environment, workforce, human development, and community development. This research assessed the effect of corporate social responsibility on the business evaluation of MTN Nigeria, Abuja. The study employed a descriptive survey research design—the population of this study comprised entire staff members of MTN Nigeria Abuja. A sample of 100 members of staff was used in this study using the convenience sampling technique. The primary data was employed to gather information from MTN, Abuja staff. The data collected were subjected to statistical analysis, using frequency and percentage. The hypothesis formulated for the study was analyzed using chi-square statistics at a 95% confidence level. The study found that the Corporate Social Responsibility (CSR) activities of MTN improved and significantly affected the business performance of MTN Nigeria. CSR tasks were recommended to be very organized and executed to have the most extreme effect. Additionally, corporate associations ought to strengthen deeds to educate the general culture on their essential responsibilities, different accountabilities to different associates, and functional/monetary limits.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.935
Threshold uncertainty score0.257

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.027
GPT teacher head0.293
Teacher spread0.266 · 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 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

Citations2
Published2022
Admission routes1
Has abstractyes

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