Effects of Ownership Structure on Intellectual Capital: Evidence from Publicly Listed Banks in Bangladesh
Bibliographic record
Abstract
This study explored the impacts of ownership structure (OS) on intellectual capital (IC) and its components. Data were gathered from 31 Dhaka Stock Exchange-listed banks for five years, from 2017 to 2021, consisting of 155 observations as balanced panel data. The study used the modified value-added intellectual coefficient (MVAIC) model to track the IC efficiency. The robust fixed effects model was employed for regression analysis to test the hypotheses. The research found that sponsor director ownership is negatively associated with the MVAIC, human capital efficiency (HCE), and structural capital efficiency (SCE) but positively with relational capital efficiency (RCE). High institutional and public ownership are positively linked with SCE but negatively with RCE. Foreign ownership is only positively associated with banks’ MVAIC and HCE. The regression results showed that high institutional ownership (IO) significantly enhanced the MVAIC and HCE. Foreign and public ownership positively influenced banks’ MVAIC, HCE, and capital employed efficiency (CEE) but negatively impacted RCE. The findings of this study will help banks’ policymakers with ownership mixes for the optimum utilization of banks’ resources. Management may assess IC’s efficiency level for proper supervision and use of knowledge resources to boost bank profitability. Also, the findings will help investors make prudent investment decisions. This is the first study to focus on OS and IC with diverse elements in Southeast Asia, especially Bangladesh, an emerging market.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".