Understanding mobile payments through the lens of innovation resistance and planned behavior theories
Bibliographic record
Abstract
Despite the numerous advantages that different mobile payments can provide, their acceptance, and adoption rates are still relatively low. This study aims at investigating mobile payments and demonstrates how drivers and barriers that influence behavioral intentions to use mobile payments interact and support one another by combining the theory of planned behavior (TPB) and the innovation resistance theory (IRT). A self-administered online survey was employed to gather data from 341 users of mobile payments in the State of Kuwait. To test the proposed model and its hypotheses, responses were analyzed using a partial least square structural equation modeling approach (PLS-SEM). The results show that usage, value, risk, and tradition resistance-related factors are significant barriers towards behavioral intentions to use mobile payments, while the image barrier is insignificant. The findings also affirmed that perceived behavioral control and attitudes motivate and influence consumers’ behavioral intentions; however, the subjective norm was non-significant. The study’s findings have significant implications for scholars, mobile payments’ service providers, marketers, policymakers, and banks.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| 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".