Establishing the Relationship Between the Capital Structure, Intellectual Capital, and Financial Performance of SSA Insurance Companies
Bibliographic record
Abstract
This research examines the relationship between capital structure, intellectual capital, and financial performance among insurance companies in Sub-Saharan Africa (SSA). Anchored in a positivist paradigm, the study employed descriptive and quantitative methodologies, leveraging secondary panel data spanning from 2010 to 2022 across 122 insurance firms sampled from a population of 178 companies across 46 SSA countries. Utilizing a Panel Vector Error Correction Model (P-VECM), the analysis explored long-term equilibrium relationships and dynamic interactions among variables, including return on assets (ROAs), debt-to-equity ratio (DER), long-term debt (LTD), short-term debt (STD), Value-Added Intellectual Coefficient (VAIC™), and firm size (SIZE). Optimal lag lengths were determined through robust statistical criteria, ensuring model precision. The impulse response analysis revealed significant findings: variations in ROA negatively impacted intellectual capital (VAIC), leverage indicators (DER, LTD, and STD), and positively influenced firm size over a ten-period horizon. Specifically, decreases in ROA were consistently associated with reduced intellectual capital effectiveness and adverse financial liquidity conditions, while increased firm size correlated positively with improved financial performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".