Can digital transformation improve the transparency and accountability of Indonesian public governance?
Bibliographic record
Abstract
The development of information technology is currently growing rapidly, affecting all aspects of life. There are various benefits obtained from advances in information technology, one of which is the ease of communicating and accessing information. This transformation is an opportunity to increase accessibility in various sectors, such as government, health, education, social, economic and other sectors. This research aims to analyze the relationship between digital transformation and public accountability, digital transformation with public transparency and transparency with public accountability. The research uses quantitative methods to test the relationship between variables. The respondents of this research were 478 government public service office employees who were determined using a simple random sampling method. Research data analysis uses structural equation modelling (SEM) partial least squares (PLS) with research data analysis tools using SmartPLS 3.0 software. Based on the results of data analysis, it is concluded that digital transformation had a positive and significant relationship to public accountability, digital transformation had a positive and significant relationship to public transparency and transparency had a positive and significant relationship to public accountability. The process of digital transformation in public administration greatly influences how public services are delivered and how government functions. Leveraging digital technology is an opportunity to increase accessibility in various fields such as government, health, education, economics, social and politics. Public administration can increase transparency, efficiency, public participation and data-based decision-making. Organizations pursuing digital transformation must be flexible, innovative and able to adapt quickly. Several important benefits of transparency include preventing corruption, making it easier to identify weaknesses and strengths of a policy, and increasing accountability in the delivery of public services by government agencies. In addition, a transparent attitude will increase trust in government institutions to decide on certain policies, as well as being able to encourage a conducive investment climate and increase business certainty.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".