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Record W3041229007 · doi:10.5430/ijfr.v11n4p370

Social Media Cost and the Levels of Cash Flow Among Listed Banks in Emerging Economies in Africa

2020· article· en· W3041229007 on OpenAlexvenueno aff
Olaoluwa Umukoro, Olubukunola Ranti Uwuigbe, Imoleayo F Obigbemi, Uwalomwa Uwuigbe

Bibliographic record

VenueInternational Journal of Financial Research · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsnot available
Fundersnot available
KeywordsCash flowLeverage (statistics)Operating cash flowFinanceBusinessEmerging marketsCash flow forecastingMonetary economicsEconomics

Abstract

fetched live from OpenAlex

We examine the effect of social media advertising on the operating, financing and investing level of cash flow based on listed banks in emerging economies in Africa. We employ control variables such as board size and financial leverage to control other factors not captured in our model. Results obtained reveal that social media costs aid all levels of cash flow in Ghana and South-Africa. Social media costs in Botswana have a significant impact with operating and financing cash flow and an insignificant effect with investing cash flow. The results obtained from Kenya revealed a significant relationship between the independent variable and the financing and investing cash flow while an insignificant relationship was statistically obtained when social media cost was regressed against the operating level of cash flow. The Tanzanian results reveal a significant impact with the financing and investing level of cash flow but an insignificant value was obtained from the operating level of cash flow. The Nigerian results yielded an insignificant value, from the operating and financing level of cash flow, while investing cash flow generated a significant relationship.

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)0.006
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 score0.757
Threshold uncertainty score0.759

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.117
GPT teacher head0.337
Teacher spread0.220 · 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
Published2020
Admission routes1
Has abstractyes

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