Mobile government public value model for assessing the public institution’s services: Evidence through the context of Jordan
Bibliographic record
Abstract
Having a citizen's opinion is considered the most important method to evaluate a public institution's performance. As a contemporary theory regarding public institutions and private organizations, public value theory provides an alternative approach to evaluating organizations' performance. The current research has provided a new insight to assess the Mobile-government applications (m-gov app) by proposing a new model entitled ‘Mobile Government Public Value (MGPV)’ to measure the performance of m-gov apps in developing country settings, specifically Jordan. Depending on several theories engaged with information technology, many determinants have been selected to draw the line for evaluating MGPV in Jordan. Measuring the level of m-gov apps’ usage was estimated depending on its perceived need, awareness, perceived security, social influence and self-efficacy to gauge the weather of creating or increasing the public value of the m-gov app from a citizen's perspective. In the current research, Structural Equation Model (SEM) was selected to obtain the research objectives. The results have indicated that the m- gov apps perceived need, m- gov apps' awareness, m- gov apps perceived security, and m- gov apps social factors played an essential role in creating public value of m- gov apps through the mediation role of m- gov apps use factor. While m-gov apps' self-efficacy factor did not provide a positive effect on creating the public value of m-government apps.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".