Intellectual Capital Performance and Profitability of Banks: Evidence from Pakistan
Bibliographic record
Abstract
The study contributes to the existing literature on intellectual capital (IC) performance and profitability by extending evidence from Pakistan. The study examines the impact of IC performance on the profitability of Pakistani financial institutions. It further examines how corporate governance, bank specific, industry specific, and country specific indicators effect Pakistani banks’ profitability. The result reports both the linear and non-linear impact of IC performance on profitability, which affirms an inverted U–shaped relationship. Among the three value added intellectual coefficient (VAIC) components, capital employed efficiency (CEE), and human capital efficiency (HCE) are found to have a significantly positive and structural capital efficiency (SCE) is found to have a significantly negative impact on bank profitability. The study notes a positive impact on profitability of factors like board independence, directors’ compensation, and higher capitalization. It reports a negative impact on profitability of factors like board size, board meetings, credit risk, industry concentration and economic growth. The results also indicate low profitability of banks during the period of government transition. The study provides insights into the important profitability drives and suggests that the impact of investment in IC on profitability is limited to an extent. The findings of this study are likely to be useful for policy makers, management, and academics.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".