An empirical analysis on the adoption of electronic banking in the financial institutes using structural, behavioral and contextual factors
Bibliographic record
Abstract
This research examines contextual, structural and organizational factors, which can facilitate or slow down adoption of innovation in Electronic Banking in the financial Institutions.Threedimensional model co-structure, co-behavioral, contextual (3C) is used in this research.This schema is a logical model in the categories of models and many of concepts, events and organizational phenomena can be examined.Structural factors including type of the organization of institution, work distribution, preparing mobilization of resources and equipment and risk of decision-making sophistication influence on adoption of Electronic Banking.There are four contextual factors, which contribute in adoption of Electronic Banking including goals, strategies, culture and common norms.The five Behavioral Factors, which affect on electronic banking are connections and relations, skills and personal characters of employees, education, job satisfaction and banking work process.By studying the mentioned factors, we have realized that contextual factors plays important role on adoption of electronic Banking by employee and the behavioral and structural factors have minor impacts.The mentioned proposals are methods, which facilitate the adoption of electronic banking in the country.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".