Does Intellectual Capital Influence Firm's Financial Performance in Bangladesh?
Bibliographic record
Abstract
This paper aimed to find the effects of intellectual capital and its components on firms’ financial performance in Bangladesh. A sample of 100 firms comprised of 48 manufacturing, 21 services, and 31 banking companies was studied for five years from 2017 to 2021, setting 500 firm-year observations. Data was collected purposively from secondary sources, such as annual reports of sampled companies. The robust fixed-effect regression model using STATA 14.2 software was applied to test the hypothesis due to autocorrelation and heteroscedasticity problems. The regression results documented that overall IC, human capital efficiency (HCE), and capital employed efficiency (CEE) positively and significantly enhanced the ROA and ROE of the firms. However, structural capital efficiency (SCE) and relational capital efficiency (RCE) negatively and insignificantly influenced the same. The study contributes to resource-based theory using econometric methods to extend the samples of both financial and non-financial companies. This study helps investors, managers, policy-makers, governments, and accounting regulatory bodies regarding the utilization pattern of invisible and tangible resources in Bangladeshi firms.
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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 | 0.000 |
| 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 | 0.002 |
| 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".