The impact of construction stakeholder’s readiness and acceptance of technology on the success of Indonesian digital government transformation in construction sector
Bibliographic record
Abstract
Despite its potential to significantly improve the efficiency, effectiveness, transparency, and accountability of construction sector services, the implementation of digital government transformation in many countries has often failed to achieve desired results due to low user adoption. This study introduces a factor model to predict technology readiness and acceptance behavior in the implementation of digital government transformation within construction business licensing and procurement services by integrating the Technology Readiness and Technology Acceptance Models. Survey data collected from construction companies, experts, project managers, and procurement committees in Indonesia were used to test the model through Structural Equation Modelling (SMART-PLS). The findings reveal that positive technological readiness has a significant impact on perceived usefulness and users' intention to use the system. Additionally, it was discovered that a one-point increase in the intention to use led to a 0.625 increase in achievement value. This research contributes to addressing the slow adoption of digital government transformation by exploring intention-to-use behavior in the construction sector.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".