An Empirical Examination of Factors Influencing the Intention to Use Tawakkalna App
Bibliographic record
Abstract
In recent years, a global surge in mobile application adoption has led to the development of innovative digital solutions spanning multiple domains. The Tawakkalna application, particularly significant during periods of curfew, is one such innovation, offering manifold benefits that support users in their daily activities. This study aims to investigate the determinants influencing the continuous usage intention of Tawakkalna app users within the theoretical framework of the Technology Acceptance Model (TAM). A purposive sample of 320 users of the Tawakkalna app was employed for the study. The data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Findings indicate that the simplicity of the application's features emerged as a pivotal determinant of the users' intention to persistently use and endorse the app. Moreover, the research highlights that application simplicity influences user intention through two key mechanisms - Perceived Ease-to-Use and Perceived Usefulness. These insights offer valuable guidance to developers and stakeholders, underscoring the need to prioritize user-friendly design and utility in the iterative development of mobile applications to ensure sustained user engagement and recommendation.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".