Evaluation of acceptance of information systems in state university with theory of planned behavior and theory of acceptance model approaches
Bibliographic record
Abstract
Development of information systems in state universities, is needed in order to support more effective and efficient performance. This research was conducted to evaluate the factors that influence the intensity and behavior of users when using user systems. The sample are 240 users which were determined by using the convenience sampling method. The result confirms that the intensity of the use of the system by users is influenced by attitudes, subjective norms, and behavioral control. With the Theory of Acceptance Model (TAM) approach, the researchers also find that intensity is positively influenced by users' perceptions of system use and convenience. User intensity will increase their use of the system. In addition, the re-searchers found that the behavior in terms of using the system was also influenced by behavioral control and the user's perception of behavior in using the system. These results also show that the merging of the TAM and TPB models will have a greater impact on both the intensity and the actual behavior of users in the utilization of the system. The study has social implications for system developers, the user's psychological condition and system characteristics need to be considered in developing the system for future studies.
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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 | 0.021 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".