The role of e-government, human resource competency and good corporate governance on the financial performance of the government companies
Bibliographic record
Abstract
Research on e-government and good governance is still rarely carried out, even though e-government and good governance are important factors in government companies. This research aims to analyze the relationship between e-government and financial performance, the relationship between employee competency variables on financial performance, and the relationship that good governance variables have on financial performance. The method of this research is quantitative through surveys, research data was obtained by distributing online questionnaires to 590 managers of government companies who were selected using a simple random sampling method, and an online questionnaire was designed using statements item with a Likert scale from 1 to 7. Data analysis used Structural Equation Modelling (SEM) with the SmartPLS 3.0 software tool to analyze research data. The stages of data analysis are validity testing, reliability testing, and significance testing of hypothesis testing. The results of this research show that e-government had a positive and significant effect on financial performance, and employee competence had a positive and significant effect on financial performance. Moreover, good governance had a positive and significant effect on financial performance. The novelty of this research is the creation of a new model of the relationship between e-government and financial performance, employee competence and financial performance, and good governance and financial performance which has not existed in previous studies. The practical implication of this research is that to improve the financial performance of government companies, we must implement e-government by increasing employee competency and implementing good governance.
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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 | 0.003 | 0.010 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".