The Effect of Electronic Banking on the Performance of Supply Chain Management of Small and Medium Businesses
Bibliographic record
Abstract
In current age, e-commerce does not just imply online buying and selling, but implies an efficient business throughout business levels, in which supply chain management can be regarded as the major pillar. The aim of this survey is to study effect of use of electronic banking services and important instruments of e-banking on performance and dimensions of supply chain performance at the first level of the SCOR model in electronics businesses. The present study is an applied research type in terms of aim, which is categorized as descriptive correlation in terms of data collection. The statistical population consists of electric supplies stores in Bushehr, of which 107 stores were selected as sample group using simple random sampling method. The questionnaire has been used as data collection instrument, that its validity has been confirmed through face and content validity through Cronbach's alpha. To analyze data, structural equation modeling using software Lisrel was used. Findings of the present study indicate that the use of e-banking services has a significant effect on performance of supply chain management. Further, among all devices of e-banking services except for POS (Point of Sale) and mobile banking, rest of the devices have a significant effect on performance of supply chain management.
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 | 0.002 | 0.012 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".