System Service Quality Factors and Their Impact on User Satisfaction and Continuance Intention to Use M-Health: The Moderating Influence of Monetary Cost
Bibliographic record
Abstract
Mobile health (M-health) is widely recognized as a powerful technological driver for developing health systems.Service quality factors have been identified as critical indicators of user satisfaction and the intention to continue using M-health.However, the relationship between these factors varies across studies, leaving a research gap, particularly in the Middle Eastern region where limited studies have been conducted.This study aimed to investigate the impact of quality system factors on user satisfaction and the continuance intention to use Mhealth.A survey was conducted among 292 diabetes patients in the UAE.The results revealed that both interaction quality (ITQ) and system quality (SQ) significantly influenced user satisfaction and the intention to continue using M-health.In contrast, information quality (IFQ) had a significant impact on user satisfaction but not on continuance intention.Additionally, the study found that the relationship between user satisfaction and the intention to continue using M-health was moderated by monetary cost.These findings extend the information system model by offering new insights into the role of the IS success model in predicting Mhealth continuance intention.
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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 | 0.002 | 0.011 |
| 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) | 0.005 | 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".