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Record W2023779051 · doi:10.5539/ass.v10n8p103

Effect of Business Social Responsibility (BSR) on Performance of SMEs: Data Screening and Preliminary Analysis

2014· article· en· W2023779051 on OpenAlexvenueno aff
Abdullahi Hassan Gorondutse, Haim Hilman

Bibliographic record

VenueAsian Social Science · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsnot available
Fundersnot available
KeywordsExploratory factor analysisMultivariate analysisUnivariateNonprobability samplingMultivariate statisticsScale (ratio)KurtosisData collectionOrder (exchange)MarketingBusinessPsychologyStatisticsSociologyService (business)MathematicsFinance

Abstract

fetched live from OpenAlex

The increase on Business Social Responsibility (BSR) understanding in present-day businesses has led to postulations that the related behaviors would facilitate viable benefit to be attained as a firm different itself from its rivalry through such actions. Therefore, the objectives of this study was to carried out Data gathering in respect to Data screening and preliminary analysis in related to effect of business social responsibility on performance of SMEs in Nigeria. 514 respondents were derived from Small Scale Industries in Kano State Nigeria. The study employed purposive sampling techniques, equally the exercises was carried in order to suit the supposition of multivariate analysis. In view of that, an appraisal of Data missing, identify univariate and multivariate outlier and lastly, skewness and kurtosis were checked. In addition, factor analysis through Exploratory Factor Analysis (EFA) was also carried out. Similarly, all the exercises were prepared in Statistical Package for Social Science (SPSS) software version 18, and the preliminary analysis reveals convinced that the data fulfill condition of multivariate analysis. The findings will give an insight to further analysis its hope to provide understanding of how and why this may be diverse in a perspectives rising environment.

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)0.010
metaresearch head score (Gemma)0.005
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 score0.714
Threshold uncertainty score0.666

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.005
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.028
GPT teacher head0.300
Teacher spread0.272 · 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

Citations20
Published2014
Admission routes1
Has abstractyes

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