Customers Adaptation of E-banking services; extending TAM through Anthpmorphism in Saudi Arabia
Bibliographic record
Abstract
This study aims to investigate influence of perceived anthropomorphism, perceived ease of use, perceived usefulness, privacy concerns, as well as attitude on intention to adopt AI banking services. The research follows a positivistic and deductive reasoning approach, utilizing experimental techniques in a cross-sectional design. Data of 210 responses collected through a questionnaire distributed via Google Docs were analyzed using Smart PLS3. The results indicate that intention to adopt AI banking services is influenced by perceived anthropomorphism, perceived ease of use, perceived usefulness, and privacy concerns through attitude. Strong correlations among all variables were observed, highlighting the significant and positive impact of artificial intelligence on encouraging acceptance of advanced technology in banking sector in Kingdom of Saudi Arabia. Future research is recommended to test various other variables using the same research model in different countries. Practical implications include the need for senior managers and policymakers in financial institutions to formulate relevant policies and marketing strategies aligned with customer needs. This research study's primary objective is to prospect and examine factors impelling consumer adoption intentions of artificial intelligence in banking sector in Kingdom of Saudi Arabia.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".