Understanding the Factors Influencing Mobile Health (mHealth) Adoption among Healthcare Professionals in Punjab, Pakistan

Abstract

This study was carried out with objectives to identify the predictors of mobile health (mHealth) technology adoption among healthcare professionals in limited-resource settings. study examines the predictors of mHealth adoption among doctors and nurses working in public and private hospitals across South Punjab, Pakistan, using an extended version of the Unified Theory of Acceptance and Use of Technology (UTAUT2). A cross-sectional survey was administered to 423 healthcare professionals, and the resulting data were analysed through confirmatory factor analysis (CFA) and structural equation modelling (SEM) across an eleven-construct model comprising Performance Expectancy (PE), Effort Expectancy(EE), Social Influence (SI), Facilitating Conditions (FC), Self-Concept (SC), Hedonic Motivation, Price Value, Habit, Digital Literacy (DL), Behavioural Intention (BI), Digital Health Adoption (DHA), and Health Disparity Reduction (HDR). The measurement model demonstrated an acceptable fit to the data (RMSEA = 0.033; CFI = 0.925; IFI = 0.926; TLI = 0.915). Results indicate that Effort Expectancy and Digital Literacy were statistically significant positive predictors of Behavioural Intention, while Performance Expectancy, Facilitating Conditions, Hedonic Motivation, Social Influence, Habit, and Price Value did not reach significance. Behavioral Intention, in turn, significantly predicted Digital Health Adoption, which subsequently predicted reductions in health disparity. These findings suggest that, contrary to assumptions embedded in adoption models developed in high-resource settings, perceived usefulness alone is not sufficient to drive mHealth uptake among healthcare professionals in low-resource contexts; ease of use and digital competence carry far greater weight. The study proposes a scientificallly proven basis for policymakers, health officers, and technology designers in search of to plan and scale mHealth involvements that meaningfully decrease healthcare access gaps.