Mobile Healthcare Adoption among Patients in a Developing Country Environment: Exploring the Influence of Age and Gender Differences
Bibliographic record
Abstract
This research was motivated by two considerations: (1) Mobile-based technologies have the potential to improve the delivery process of healthcare services, and (2) limited research has been implemented worldwide to focus on patients’ perceptions towards the adoption of mobile healthcare, particularly in developing country environment. This research proposes an extended TAM model as a research framework to better understand the adoption process of mobile healthcare among patients. To serve the objectives of this study, a paper questionnaire was employed to collect data. Previously validated set of measurement items were used to develop the survey instrument. The proposed research model was validated using the PLS-SEM approach (WarpPLS 4.0) with a sample of 366 respondents. The results of the current study have provided adequate statistical support for the extended TAM model. With the exception of cost, all external variables incorporated in this model (including perceived ease of use, perceived usefulness, social influence, trust, and security/privacy) are found influential in shaping the patients’ perceptions towards the adoption of mobile-healthcare technology. In addition, the current study demonstrates that demographic variables of age and gender have considerable moderating influence on the adoption of mobile technologies in healthcare systems in Jordan. The current research model can serve as a blueprint for future expansion of research in this vital field of study. Theoretical contributions, practical implications and future research directions of the study are also addressed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".