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. 2024 Aug 3;24(1):889.
doi: 10.1186/s12913-024-11378-1.

Understanding the predictors of health professionals' intention to use electronic health record system: extend and apply UTAUT3 model

Habtamu Setegn Ngusie  1 , Sisay Yitayih Kassie  2 , Alemu Birara Zemariam  3 , Agmasie Damtew Walle  4 , Ermias Bekele Enyew  5 , Mulugeta Desalegn Kasaye  5 , Binyam Tariku Seboka  6 , Shegaw Anagaw Mengiste  7
Affiliations

Understanding the predictors of health professionals' intention to use electronic health record system: extend and apply UTAUT3 model

Habtamu Setegn Ngusie et al. BMC Health Serv Res. .

Abstract

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: The proposed model outperformed previous UTAUT models, explaining 84.5% (squared multiple correlations (R2) = 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.

Keywords: Behavioral intention; Electronic health record; Health professionals; Mediation effect; Moderation effect; Structural equation modeling; Technology acceptance; UTAUT; UTAUT2; UTAUT3.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
The proposed research model. NB: TSE = technology self-efficacy, EE = effort expectancy, PE = performance expectancy, PI = personal innovativeness, FC = facilitating condition, SI = social influence, HM = Hedonic motivation, TAN = technology anxiety, EHR = Electronic health record
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