Do Chief Executive Officer’s Attributes Impact on the Performance of Nigerian Firms?
Bibliographic record
Abstract
This study investigated the effect of Chief Executive Officer’s (CEO) attributes on the performance of manufacturing firms listed on the Nigeria Stock Exchange (NSE). In line with the ideals of upper echelon theory that firms are reflective of the cognitive behaviours of the CEO, we examined such attributes as CEO education, experience and gender on the performance and value of manufacturing firms. Secondary data were collected from the firms’ annual reports from 2013 to 2021, which was made suitable by the adoption of ex post facto research design. Thirty-six firms were purposely selected for the study wherein the data were analysed with the descriptive statistics, correlation and panel regression analysis. The results of the study indicate that CEO characteristics jointly have a significant effect on firm performance and firm value which were measured by Return on Equity (ROEQ) and Tobin’s Q (TOBNQ) respectively, of the manufacturing firms at 1% significant levels. The study therefore, recommends that CEO characteristics should not be independently sought for, but jointly as complementary components in individuals being considered for the CEO position. Additionally, appointing a female CEO should not be a fulfilling task, but a woman could be made the CEO if she possesses other complementary attributes, required for driving the firm towards greater performance and value, as would do by a male counterpart.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".