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Record W2145591607 · doi:10.5539/ibr.v7n11p73

Corporate Ownership, Corporate Control and Corporate Performance in Sub-Saharan African: Evidence from Nigeria

2014· article· en· W2145591607 on OpenAlexvenueno aff
Ioraver N. Tsegba, Wilson E. Herbert, Emeka E. Ene

Bibliographic record

VenueInternational Business Research · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsInsiderPanel dataBusinessStock exchangeEquity (law)AccountingControl (management)Foreign ownershipState ownershipOrdinary least squaresMonetary economicsEmerging marketsEconomicsFinanceEconometricsForeign direct investment

Abstract

fetched live from OpenAlex

This paper investigates the relation between corporate ownership and corporate performance of listed companies in Nigeria, a foremost Sub-Saharan African country during the period 2002-2007. The data is obtained from the firms’ annual reports and accounts and the Nigerian Stock Exchange daily performance reports. The combination of 70 firms and six-year period studied provides a balanced panel with 420 observations for panel data analysis. The results from the ordinary least square (OLS) regression analyses show that there is a strong connection between foreign ownership structure and firm performance. Foreign ownership structure is found to exhibit significant improvements in firm performance; it adumbrates eclectic competitive advantages in ownership, control and internalization respects over other types of ownership structure. We find no statistically significant relation between concentrated ownership and firm performance. Insider or managerial ownership, however, exhibits significant decline in firm performance. These findings are consistent with the view that firm performance is a negative predictor of insider ownership. We also find support for the notion that management is apathetic to holding equity stakes in their underperforming firms.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.097
GPT teacher head0.285
Teacher spread0.188 · 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

Citations12
Published2014
Admission routes1
Has abstractyes

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