Examining the technology acceptance model using cloud-based accounting software of Vietnamese enterprises
Bibliographic record
Abstract
Along with the rapid development of information technology, cloud computing has brought many benefits to users in handling work via the internet, especially in the field of accounting. However, Vietnamese enterprises are still at early stage of cloud computing accounting implementation. The purpose of this study was to apply the Technology Acceptance Model (TAM) in the applications of cloud computing technology in accounting in Vietnamese enterprises. Data was collected through a Structured questionnaire from 112 accountants and managers in Vietnamese enterprises through purposive method. After collecting, the data is synthesized by excel file, processed by SPSS 20 software with descriptive statistics and multiple regression analysis. The research model was established with 4 factors effecting the intention to use cloud-based accounting software: (1) Perceived usefulness, (2) Perceived ease of use, (3) Perceived convenience, (4) Perceived safety and privacy. The result indicates that perceived usefulness and perceived ease of use had positive impacts on the enterprises' intentions to use cloud-based accounting software. Additionally, the study found a positive relationship between perceived convenience and perceived ease of use on perceived usefulness; perceived convenience also had a positive impact on perceived ease of use. However, perceived safety and privacy did not significantly affect the intention of cloud-based accounting software use.
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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 | 0.004 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".