Testing Control, Innovation and Enjoy as External Variables to the Technology Acceptance Model in a North American French Banking Environment
Bibliographic record
Abstract
Nowadays banks are enhancing major objectives to challenge competition, competitiveness and growth. To comply with these new objectives they have developed new innovative channels of contacts and distribution of financial services to customers relying on the net: ‘the Internet channel’. Based on the ‘Technology Acceptance Model’ this research will evaluate the impact of external latent variables ‘Control’, ‘Innovation’ and ‘Enjoy’ on the internal TAM model latent variables ‘Ease of Use’, ‘Perceived Usefulness’, ‘Attitude towards Using’ and ‘Intention to Use’ in a North American French Banking Environment. Results show a well structured model for on-line banking financial services that complies pretty well with all major criteria of structural equation modeling norms. The ‘Control’ latent variable has a significant effect on the TAM model latent variables ‘Ease of Use and ‘Attitude towards Using’ while ‘Innovation’ has a sole impact on ‘Intention to Use’. The ‘Enjoy’ latent variable has substantial impacts on ‘Ease of Use’, ‘Attitude towards Using’ and ‘Intention to Use’.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".