article · BMC Health Services Research
Implementing electronic health record systems remains a significant operational challenge in low-income settings, where staff acceptance is vital. This study examined the factors influencing healthcare workers' intentions to adopt these systems by extending the Unified Theory of Acceptance and Use of Technology 3 model. A survey of 423 health professionals in Southwest Ethiopia evaluated direct, indirect, and moderating variables using structural equation modelling. The findings reveal that attitude, performance expectancy, and personal innovativeness directly drive the intention to use digital records. Furthermore, technology anxiety, hedonic motivation, and performance expectancy exert significant indirect effects. The analysis also demonstrates that gender moderates how personal innovativeness and social influence affect user intention. Addressing these behavioural factors through tailored strategies can improve the adoption of electronic health records in medical facilities.
Digital transformation in healthcare often fails when end users resist new platforms. By identifying the behavioural drivers that encourage health professionals to adopt digital systems, healthcare administrators can design targeted interventions. Understanding how factors like anxiety, perceived benefits, and gender dynamics shape technology adoption helps healthcare organisations deploy digital tools more effectively in resource-limited environments.
This research provides an applied behavioural framework that digital health software vendors, implementers, and healthcare administrators can use to evaluate user readiness before deploying electronic health records. The findings are at an applied stage, offering design criteria for change-management programmes and user training rather than a commercial product. The abstract indicates no specific product or direct commercialisation pathway.
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BACKGROUND: The implementation of Electronic Health Record (EHR) systems is a critical challenge, particularly in low-income countries, where behavioral intention plays a crucial role. To address this issue, we conducted a study to extend and apply the Unified Theory of Acceptance and Use of Technology 3 (UTAUT3) model in predicting health professionals' behavioral intention to use EHR systems. METHODS: A quantitative research approach was employed among 423 health professionals in Southwest Ethiopia. We assessed the validity of the proposed model through measurement and structural model statistics. Analysis was done using SPSS AMOS version 23. Hypotheses were tested using structural equation modeling (SEM) analysis, and mediation and moderation effects were evaluated. The associations between exogenous and endogenous variables were examined using standardized regression coefficients (β), 95% confidence intervals, and p-values, with a significance level of p-value < 0.05. RESULTS: ) = 0.845) of the variance in behavioral intention to use EHR systems. Personal innovativeness (β = 0.215, p-value < 0.018), performance expectancy (β = 0.245, p-value < 0.001), and attitude (β = 0.611, p-value < 0.001) showed significant associations to use EHR systems. Mediation analysis revealed that performance expectancy, hedonic motivation, and technology anxiety had significant indirect effects on behavioral intention. Furthermore, moderation analysis indicated that gender moderated the association between social influence, personal innovativeness, and behavioral intention. CONCLUSION: The extended UTAUT3 model accurately predicts health professionals' intention to use EHR systems and provides a valuable framework for understanding technology acceptance in healthcare. We recommend that digital health implementers and concerned bodies consider the comprehensive range of direct, indirect, and moderating effects. By addressing personal innovativeness, performance expectancy, attitude, hedonic motivation, technology anxiety, and the gender-specific impact of social influence, interventions can effectively enhance behavioral intention toward EHR systems. It is crucial to design gender-specific interventions that address the differences in social influence and personal innovativeness between males and females.
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DOI: 10.1186/s12913-024-11378-1
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