A study on relationship between information technology facilities and performance of banking industry
Bibliographic record
Abstract
The recent advances on information technology have made tremendous change on traditional banking.These days, people do not carry cash and prefer to use electronic devices such as point of sale system (POS) or PIN entry devices (PinPad) to do desirable transactions.These technologies could facilitate e-business and increase profitability in various industries including banking sector.The purpose of this paper is to investigate the effect of five new products namely ATM, POS Machines, PinPad machines, online and swift branches on banking performance indicators including return on assets (ROA), return on equities (ROE) and operating investment return (OIR).We use the information of 19 private and governmental banks, which were active in Iran over the period of 2005-2010.The study uses linear regression analysis as well as VAR technique to study the effects of the independent variables on bank performance indicators.The results indicate that while there are some weak and positive relationships between three technology indicators including POS, PinPad and online businesses and ROA as well as ROE, there is relatively strong and positive relationship between these three independent variables and OIR.In addition, while the results of VAR analysis have shown that any reduction on PinPad will reduce OIR but this reduction will disappear after approximately four periods.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".