A framework for evaluating e-Business models and Productivity Analysis forBanking Sector in India
Bibliographic record
Abstract
In the world of e-business the rapid growth of the market and fierce competition between the increasing numbers of participants add up to new innovations every day leading to short development cycles. New business models and a herd of start-up companies emerge every few months, to exploit the new opportunities. However, the business has had rough times trying to keep up with the rapid development of e-business. Despite the fact that more and more efforts are made to grasp the essentials of e-business and in particular e-business models, the existing literature on the subject is scattered. Moreover, the studies are quickly out-dated due to the fast phase of the 'new economy'. A clear need exists for an objective and up-to-date literature study of e-business models. This study is an effort to draw together some of the e-Business models and real-life experiments that has been circling around the e-business models. To study the sweeping changes brought about by e-initiative measures in the banking sector some banks were chosen, from public sector like SBI ,BOB etc and from private sector like ICICI, HDFC etc. The paper analyses a comparison of various models using metric method. The different elements of the metric include revenue generation, value proposition, infrastructure etc. A mathematical model taking into consideration various ranking and weightages to the elements of the metric has been developed to analyse whether investments in e-initiative increased productivity and profitability in the Indian banking system. The model suggests that the performance of the banking sector has improved considerably. Profitability, customer satisfaction, and many other parameters show a market improvement. It is believed that a mathematical approach proposed in this paper will find extensive application in other sectors of the economy also.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 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".