Choosing a Mobile Wallet: Motives and Attitudes of Saudi Consumers toward the Adoption of Apple Pay
Bibliographic record
Abstract
Purpose: The mobile payment system is widely used globally. However, this notion is not shared by all consumers in Saudi Arabia, and there is still prevailed prejudice or lack of trust among consumers towards using this unorthodox method of paying. Which raises the question of: What are the reasons that are hindering towards usage of mobile wallet method such as ‘Apple pay’ in Saudi Consumers. This study aims to find motives and attitudes of the Saudi consumers toward the adoption of Apple pay. Methodology: A correlation study design was adopted to answer the research question using a meta-UTAUT method. The study recruited 315 participants through social media to fulfil the questionnaire. Cronbach’s Alpha test was done to test the reliability of the test. Findings: This study resulted that performance expectancy, effort expectancy, personal innovativeness, trust and anxiety factors influences the attitude of Saudi customers towards adapting Apple pay method (p value > 0.05). Whereas, attitude affects behavioral intentions. Furthermore, performance expectancy and grievance redressal affect user behavior (p value > 0.05). Alternatively, social influence and behavioral condition has no significant relationship with behavioral intentions (p value < 0.05). Similarly, performance expectation is also not influencing user behavior (p value < 0.05). In conclusion, these factors will help the marketers and the manufacturers to understand the user demands of Saudi customers and its attention will ultimately help the consumers. Originality: This study will help understand the perception and attitude of Saudi consumers toward the adoption of Apple pay.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".