The Acceptance of Tax Office Automation System (VEDOP) By Employees: Factorial Validation of Turkish Adapted Technology Acceptance Model (TAM)
Bibliographic record
Abstract
In this study, Davis’s (1989) The Technology Acceptance Model (TAM) is used as a theoretical framework to extend and complement extant tax officials by acceptance of technological components of Tax Office Automation System (VEDOP) in Turkey. Relying on the basic TAM model, we examine the extent to which perceived usefulness (PU), perceived ease of use (PE), and attitudes (AT) toward VEDOP affect behavior intentions (BI). The data set of the study was obtained from the survey applied to 185 individual tax officials in the city of Zonguldak. Consistent with the hypotheses, the results in general provided that the core constructs of TAM namely PU, PE and AT are positively and significantly determine BI of automation system used by tax officials. As predicted, these three factors explained a large proportion of variance in Behavioral Intention to use the VEDOP system. Internet and VEDOP training experience have not found to effect significantly.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".