Extending Technology Acceptance Model to EPV Application
Bibliographic record
Abstract
This study intends to determine the acceptance of the technology in making ethical performance among auditors. We have reviewed current technology acceptance theories and formulates a unified model for the context of EPV application based on several constructs which is: performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, motivating and impeding factor and perceive risk. Hypothesis are generated and their validity is analyzed by using Pearson Correlation and Multiple Linear Regression and a model has been produces for findings of the study namely Technology Acceptance towards EPV application (TAEPV). From the model, hedonic motivation has been excluded because it shows no significant relationship towards technology acceptance which means it is not suitable to be a factor to determine technology acceptance of EPV application. The respondents are tertiary students that are undergoing study in accounting, as they are the future pool of talent for the workforce. The results show that TAEPV outperforms current technology acceptance model by significantly improving the R-squares. Thus, this study makes modest theoretical and empirical contributions to the professional sector to (1) analyse existing tools in measuring ethical performance of individuals, (2) propose a unified model of technology acceptance toward EPV application and (3) evaluate technology acceptance in implementing EPV application. As for recommendation, it is hoped that TAEPV theories can be then evaluated by the financial auditors to assess their acceptance and use behavior of EPV application, the pioneer of professional judgment 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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| 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".