Exploring the moderating role of age and gender in adopting mHealth services
Bibliographic record
Abstract
Purpose Unfortunately, mHealth has not reached the level of adoption that providers had expected, as healthcare end-users still face barriers. An in-depth understanding of the factors affecting this adoption is vital for its successful implementation. Thus, this study aims to explore the moderating role of age and gender in adopting mHealth services in a developing country context. Design/methodology/approach A quantitative strategy was adopted and a total of 338 general mHealth users were selected as the study participants. A conceptual framework was constructed based on the widely accepted technology adoption model named unified theory of acceptance and use of technology (UTAUT) model. Perceived reliability, price value, technology anxiety and self-efficacy were incorporated to the UTAUT as new factors reflecting the user’s mHealth adoption. However, a cross sectional survey was employed to collect primary data from 338 general mHealth users in Bangladesh. Findings Results explored that performance expectancy, effort expectancy, social influence, facilitating conditions, perceived reliability, price value, technology anxiety and self-efficacy had significant impact on mHealth adoption. Moreover, the relationship between facilitating conditions and technology anxiety while adopting mHealth is moderated by the role of age and gender. Practical implications This study could insightfully benefit mHealth services providers, policymakers and top marketing managers in implementing more effective marketing strategies to increase the acceptability of this service. Originality/value This is the first initiative to investigate the moderating role of age and gender in a single model in the context of mHealth services.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".