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Analyzing healthcare professionals’ perceptions of mHealth application adoption: task-technology fit model approach

Year: 2026
Type: article
Study design: Observational Study
Abstract: This study investigated the determinants of mobile health (mHealth) adoption among healthcare professionals in Algeria and its impact on perceived quality of care. Drawing on the Task–Technology Fit (TTF) framework, we developed a structural model integrating task and technology characteristics, self-efficacy, and utilization behavior. A cross-sectional study was conducted across three urban districts in Algeria. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), we analyzed survey data from 149 healthcare professionals. The constructs were modeled reflectively and formatively using an extended repeated indicators approach. Validity, reliability, multicollinearity, and path significance were assessed through bootstrapping (5,000 samples). TTF significantly influenced PQoC ( \(\beta = 0.676,\ p < 0.001\) ), whereas its relationship with utilization was not significant. Technology characteristics strongly predicted both TTF and utilization. Self-efficacy positively impacted TTF but did not affect utilization. Utilization had no significant effect on PQoC. The model explained 54.2% of the variance in PQoC and demonstrated strong predictive validity (Q 2 = 0.378). Findings extend the TTF framework by showing that perceived fit—rather than utilization—is the primary driver of perceived quality-of-care in resource-constrained settings. For policymakers and managers, this implies that effective mHealth adoption depends less on usage frequency and more on alignment with clinical tasks, supported by appropriate regulation, digital infrastructure, and targeted capacity-building.
DOI: 10.1186/s12913-026-15151-4
Language: English
Open Access status: gold
Estimated APC paid: $3,290
OpenAlex ID: W7202128668
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