Analyzing technology acceptance model for collaborative governance in public administration: Empirical evidence of digital governance and perceived ease of use
Bibliographic record
Abstract
This research was conducted with the primary aim to investigate digital governance by examining empirical evidence regarding the application of Technology Acceptance Model (TAM) in public administration. The antecedents of TAM were explored to estimate behavioral intention and actual use in electronic public service in public administration in Indonesia. The research was conducted in Semarang, Central Java, Indonesia by employing simple random sampling techniques to collect a total of 182 respondents. By using Structural Equation Modeling–Partial Least Square (PLS-SEM), the results showed significant effects on perceived usefulness and attitude toward use. The variable of perceived usefulness was also empirically proven to have a significant effect on attitude toward use and behavioral intention. The findings found that attitude toward use had a significant effect on behavioral intention, and then behavioral intention was empirically proven to have an effect on actual use. Mediating analysis from the variables of perceived usefulness, attitude toward use and behavioral intention also found the mediating roles. Theoretically, these findings contribute to the digital governance framework by providing empirical evidence strengthening the relevance and affirming the application of the Technology Acceptance Model (TAM) in the context of public administration. Practically, these findings have managerial implications that the application of TAM in the public administration sector is relevant to be explored with a professional management model and a user-based approach in the development of digital applications and websites.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".