The Adoption of "E-Banking" by Lebanese Banks
Bibliographic record
Abstract
This article examines the organizational, structural, and strategic factors that can speed up or slow down the adoption of E-banking innovations by financial institutions in the Lebanese market. A conceptual model is developed based on a review of the major innovation adoption theories and other research findings, and tested with a survey administered through a census of the population of Lebanese banks. Findings revealed that the organizational variables of bank size, presence of functional divisions, technical staff and infrastructure, as well as the degree of international experience and tolerance of risk of the decision makers exert a significant impact on the adoption of E-banking. Two structural characteristics, the internal technological environment, and relative advantage of the innovations were also found to impact adoption. The two strategic factors related to the banks’ degree of international operations were also found to positively influence the adoption of E-banking. Recommendations were provided on ways of accelerating the rate of adoption of E-banking in an important developing Middle Eastern country.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".