An Empirical Analysis of Perceived Transaction Convenience, Performance Expectancy, Effort Expectancy and Behavior Intention to Mobile Payment of Cambodian Users
Bibliographic record
Abstract
Mobile payment (m-payment) is determined as modern application of electronic commerce. It helps financial institutions to widen the financial services to existing customers in developed countries and to increase financial inclusion in developing and emerging countries. Cambodia is a country with low financial inclusion and National Bank of Cambodia perceives that the usage of m-payment can help to increase financial deepness. However, the lack of empirical evidences is a concern and this study is developed to fill the literature gaps. A research model as proposed in which behavior intention towards m-payment is affected by performance expectancy and effort expectancy. This model involves perceived transaction convenience as direct impact on performance expectancy and effort expectancy. Four research hypotheses were proposed and data was collected from 252 questionnaires. Obtained result showed that three hypotheses were supported. Only the effect of perceived transaction convenience on performance expectancy was not significantly. All factors qualified for reliability test’s requirement. EFA Analysis was conducted to verify the construct between factors and belonged items. Based on empirical results, recommendations and future researches were proposed.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".