Nexus between Intellectual Capital and Bank Productivity in India
Bibliographic record
Abstract
This paper empirically investigates the influence of intellectual capital on changes in total factor productivity of 36 BSE-listed banks in India from 2005 to 2019. This study employs a two-stage analysis that begins by investigating changes in total factor productivity using the Malmquist Productivity Index estimated through Data Envelopment Analysis, and then computes intellectual capital and its sub-components within the Value Added Intellectual Coefficients model framework. Then, using the System Generalised Method of Moments, we investigate the impact of intellectual capital on changes in total factor productivity. According to our findings, productivity growth is primarily driven by efficiency changes rather than technological changes. Furthermore, regression results show that the intellectual capital index and its two sub-components, human capital and capital employed, have a strong positive impact on bank productivity. This research could help bank senior executives measure their productivity and intellectual capital, identify relevant intellectual capital elements that contribute to productivity and develop future policies to encourage and improve their intellectual potential. Furthermore, this is one of the few studies in the Indian context that examines the nexus between intellectual capital and productivity using the Malmquist Productivity Index.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".