The Role of Personality and Top Management Support in Continuance Intention to Use Electronic Health Record Systems among Nurses

Abstract

This study examines nurses’ Continuance Intention (CI) to use electronic health records (EHRs) through a combination of three conceptual frameworks: the Unified Theory of Acceptance and Use of Technology (UTAUT), the theory of expectation-confirmation (ECT), and the Five-Factor Model (FFM). A model is developed to examine and predict the determinants of nurses’ CI to use EHRs, including top management support (TMS) and the FFM’s five personality domains. Data were collected from a survey of 497 nurses, which were analyzed using partial least squares. No significant relationship was found between TMS and CI. The study revealed that performance expectancy significantly mediated the influences of two different hypotheses of two predictors: agreeableness and openness to testing CI. A significant moderating impact of conscientiousness was found on the relationship between performance expectancy and CI and the relationship between social influence and CI. The findings of this study indicated that rigorous attention to the personality of individual nurses and substantial TMS could improve nurses’ CI to use EHRs. A literature gap was filled concerning the mediating effects of performance expectancy on the FFM-CI relationship, and the moderation effects of Conscientiousness on UTAUT constructs and CI are another addition to the literature. The results are expected to assist government agencies, health policymakers, and health institutions all over the globe in their attempts to understand the post-adoption use of EHRs.

Promoting Healthcare Workers’ Adoption Intention of Artificial-Intelligence-Assisted Diagnosis and Treatment: The Chain Mediation of Social Influence and Human–Computer Trust

“…This study explored the adoption intention theoretical model of AI-assisted diagnosis and treatment by integrating the UTAUT model and HCT theory. The findings revealed that expectancy (performance expectancy and effort expectancy) positively influenced healthcare workers’ adoption intention of AI-assisted diagnosis and treatment, corroborating well-established evidence in previous UTAUT studies [ 20 , 23 , 25 , 27 , 28 , 29 ]. Notably, effort expectancy had a relatively smaller impact in determining healthcare workers’ adoption intention of AI-assisted diagnosis and treatment compared with performance expectancy.…”

“…First, this study enriches theoretical research on the application of medical AI scenarios. Previous research on healthcare workers’ intention to adopt technology focused on technologies such as the EHR [ 23 , 25 , 26 ], telemedicine [ 24 , 29 ], and the HIS [ 28 ]. However, limited research has been conducted on AI-assisted diagnosis and treatment.…”

“…Adenuga et al (2017) posited that performance expectancy and effort expectancy exerted significant effects on Nigerian clinicians’ intention to adopt the telemedicine systems [ 29 ]. Regarding the adoption of the EHR [ 23 , 25 ] and the health information system (HIS) [ 28 ], studies have confirmed that performance expectancy and effort expectancy are positively related to physicians’ adoption intention. Hence, the following hypotheses are proposed:…”

“…Research on technology-adoption intention in healthcare can be divided into three categories according to the different subjects of adoption: healthcare recipients (e.g., patients), healthcare workers (e.g., doctors, nurses), and healthcare institutions (e.g., hospitals, clinics). For different adopters, there are different factors influencing the intention to adopt technology, and the research models also differ [ 20 , 21 , 22 , 23 , 24 , 25 ]. This study compares the relevant literature, summarizes the theoretical basis and factors of healthcare workers’ intention to adopt technology, and lays the foundation for subsequent research on healthcare workers’ adoption intention of AI-assisted treatment technology (see Table 1 ).…”

“…This study contributes to the extant research literature in two ways. First, previous studies primarily used a single technology-adoption model to examine the electronic health record (EHR) [ 23 , 25 , 26 ] and telemedicine [ 24 , 29 ] by healthcare workers’ adoption intention. This study proposes an integrated model of the UTAUT model and HCT theory to determine what factors affect the intention of healthcare workers to adopt AI-assisted diagnosis and treatment, enriches the theoretical research of the UTAUT model, and expands the application scenarios of medical AI.…”