A Mobile Banking Adoption Model in the Jordanian Market: An Integration of TAM with Perceived Risks and Perceived Benefits
Bibliographic record
Abstract
Although consumer perceptions of the risks of adopting e-banking have been studied by many researchers, the perceived risk variable has only been examined as a single construct, which fails to reveal the actual attributes of perceived risk and clarify why consumers refuse to use such banking services. In order to provide a more comprehensive clarification of the perceived risks of adopting m-banking in Jordan, a more in-depth study of the characteristics of the perceived risks was conducted. The current research is designed to integrate the five dimensions of the perceived risk with the TAM in order to present a more comprehensive model of m-banking acceptance and adoption in Jordan. As such, a conceptual model and 8 hypotheses are tested with a sample of 404 mobile phone users, and analysed quantitatively. The findings of the current study provided support for the research model and for most of the hypotheses regarding the relationship among the model’s variables. In particular, the research model presented in this paper is unique in that it synergistically combines the TAM variables along with perceived benefits, the various dimensions of perceived risks, attitude and behavioral intention in evaluating the decision to adopt m-Banking.
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 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 | 0.005 | 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 | 0.001 |
| 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".