The Impact of ICT on the Profitability of Indian Banks: The Moderating Role of NPA
Bibliographic record
Abstract
The role of Information and Communications Technology (ICT) cannot be ignored in today’s era of working. Its effects are studied in several sectors by various researchers. This study covers the impact of ICT on the profitability of banks. Thirty-three banks are operating in India. A sample period of 10 years (2010 to 2019) was studied. The study also provides insight into how ICT helps the banks’ profitability during and post-COVID-19. A panel data analysis is performed to estimate the results. This study found that ICT adversely impacts banks’ profitability (NIM) in India in a linear association. However, the quadratic association indicates a positive U-curved relationship between ICT and profitability. In addition, the Net of Non-Performing Assets significantly but negatively impacts the connectivity of ICT and profitability. The findings imply that banks should invest in ICT to maximize the long run. The findings have no significant implication on all stakeholders, including policymakers, shareholders, and managers, to consider implementing ICT tools as an essential factor in enhancing a bank’s profitability in the long run. In addition, the level of otherwise lowered investments in ICT cannot be a fruitful step. The current study augments the existing literature on banking by providing novel evidence on the association of ICT with profitability under the influence of NPA. This study argues for the application of ICT in banks in order to increase their profitability. ICT helps the bank maintain transparency, accountability, and even the reach of financial services increases. This situation again leads to the enhancement of the country’s economy.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".