A model of smart village ICT behavior: the role of external conditions, perceptions, and attitudes towards ICT use (an empirical evidence from Indonesia)
Bibliographic record
Abstract
Understanding the determinants of smart village ICT behavior is essential for formulating effective policies and intervention strategies to improve the behavior. Unfortunately, research on smart villages from the perspective of ICT behavior remains limited. Thus, this research aims to develop and test a smart village ICT behavior model by examining the influence of external conditions, perceptions, and attitudes towards ICT use. The study was conducted in a smart village in Indonesia using a quantitative approach. Data were collected through a questionnaire-based survey, with a sample of 99 participants. The main variables—smart village ICT behavior, perception, attitude, and external conditions—were measured using multiple indicators. Data were analyzed using Partial Least Squares Structural Equation Modeling with SmartPLS software. The results show that external conditions significantly and positively influence smart village ICT behavior. However, perception and attitude toward ICT use do not have a significant direct effect on behavior. Perception and external conditions, on the other hand, significantly influence attitude. This study contributes theoretically by being the first to integrate perception, attitude, and external conditions into a smart village ICT behavior model. Practically, the findings can support government in designing effective policies and strategies for smart village development.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".