Users’ Behavioral Intention to Use E-Payment Service in Nepal: Based on SEM Analysis
Bibliographic record
Abstract
Background: Digital payments were revolutionized after 1990 in Nepal’s payment system, and the rise of ICT and mobile tech since 2010 led customers to embrace e-payments. While e-payment adoption grows, progress is needed to make bank cards default for online purchases and enhance user awareness and trust for secure digital transactions. Further, understanding user perspectives and influencing factors is crucial. Objectives: This study aims to analyze the intention of users to use e-payment services in the Nepalese context. Methods: Explanatory research design is used to meet the purpose of the study. A total of 295 respondents were selected as users of the e-payment services are increasing and there is no exact number of users identified. A structured questionnaire was developed and a pretest was carried out on 10% of the sample. Data is collected from the survey through the structured questionnaire and used the Kobo Toolbox and Interviewed from Key Informants Interview (KII) method. Results: The study shows mobile banking users are highly increasing yearly, followed by internet banking with growing users. The SEM result depicts the significant relationship between behavioral intention on user satisfaction (β = 0.191, P < 0.05), perceived usefulness (β = 0.099, P < 0.05), perceived ease of use (β = 0.084, P < 0.05), social influence (β = 0.064, P < 0.05) and perceived credibility (β =0.096, P < 0.05). Furthermore, improved credibility and ease in e-payment functions can enhance customer satisfaction. Conclusion: The study concluded that Perceived Credibility (PC), Perceived Usefulness (PU), Perceived Ease of and Social Influence (SCI) have an impact on user satisfaction and Behavioral Intention (BI) to use the e-payment system. Additionally, User Satisfaction was also found to be related to Behavioral Intention (BI) to use an e-payment system.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".