Antecedents of user attitude toward e-government services use: Empirical study on department of lands and survey
Bibliographic record
Abstract
This qualitative study examined the impact of social media characteristics on the attitude of users toward the use of e-services by the Department of Lands and Survey. The study population comprised users of Department of Lands and Survey e-service, while the study sample comprised 407 users. Data from respondents were analyzed using SEM run using Amos (23). Results showed reliability, security, website design, ease of use, awareness, and digital divide as direct significant antecedents of user attitude toward the use of e-services from the Department of Lands and Survey. Also, all antecedents showed direct significant relationships with user attitude. Results showed a direct significant relationship between user attitude and the use of e-services from the Department of Lands and Survey. Through mediation of user attitude, five indirect significant relationships between the antecedents and the use of e-services from the Department of Lands and Survey were found. The inclusion of new antecedents such as privacy and site content may enhance the understanding of how users use e-services provided by the Department of Lands and Survey.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".