Factors Influencing Citizen's Adoption of M-government: The Case of Saudi Arabia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Governments across the world are pushed towards the provisioning of mobile government (m-government) services. However, the development of m-government services will not drive the expected benefits unless citizens’ accept the use of these services. Literature shows that there is a paucity of studies on factors impacting citizens’ acceptance and use of m-government services in Saudi Arabia. This paper proposed a conceptual model extending the Unified Theory of Acceptance and Use of Technology (UTAUT) model to consider other relevant factors, such as awareness and information quality, that can impact citizens’ adoption of m-government applications. A survey questionnaire was developed and a total of 264 responses of Saudi citizens were collected and analysed using Partial Least square (PLS). The results indicate that social influence, performance expectancy, and effort expectancy are the factors that have significant impact on citizens behavioural intention to use m-government services, accounting for 57% of the variability, while citizens’ awareness and information quality have no impact. Our findings can be used to stimulate the use of m-government services. The findings of this study suggest that decision makers on governments agencies and developers of m-government services should emphasis the role of social strategies to allow people to incentivise each other to use m-government services, clarify the benefits of using m-government services, and reduce the effort required for using m-government services.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it