Factors influencing customers’ attitude to adopt e-government mobile applications
Bibliographic record
Abstract
Establishing new relations with new customers and managing customers’ relations with the existing ones is important and considered challenges for each firm nowadays. Thus, studying and understanding different psychological factors like consumers’ emotions and happiness, motivates and attitudes are important while it helps in how to do more business with customers and push them to stay longer especially when adopting e-government mobile applications. This study used a quantitative research approach with survey data collection from different users of government mobile applications (GMA). The sample is around 340 from different backgrounds and levels of using different government services mobile apps. The results revealed the standardized regression and coefficients interpret the direct association between the study variables, hence confirmed the hypothesized model that included several factors such as perceived usefulness, perceived ease of use, perceived skills readiness, and perceived security toward attitudes to GMA and use of GMA effects on customer happiness and positive emotion. Moreover, the conclusion and implications confirmed the literature of this research field and elucidated most of the stated factors that significantly influence the customers’ emotion and happiness. For further study, research directions are given to expand the extant understanding of new different factors with other outcomes of mobile applications use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".