Antecedents of perceived usefulness on the use of electronic hospital management information systems

Abstract

Research aims: This study aims to analyze the factors that influence SIMRS and examine the mediating effect of perceived ease of use on the factors that affect SIMRS. Design/Methodology/Approach: This explanatory research used a quantitative approach through survey methods. The population in this study was doctors practicing in the outpatient polyclinic of XYZ Hospital, totaling 80 doctors using the total sampling. Data collection techniques employed a scale and analyzed utilizing Smart Partial Least Square (PLS). Research findings: According to the research findings, all examined indicators were found to be positively and substantially correlated with perceived ease of use by the following variables: computer self-efficacy, trustworthiness, technological risk, facilitating conditions, and degree of openness. Although all but the degree of openness exhibited a significant correlation with perceived usefulness, the remaining indicators did not. In relation to perceived usefulness, all factors exhibited a positive and statistically significant association with perceived ease of use as a mediator.Practical and Theoretical Contribution/Originality: In terms of practical and theoretical contributions, these findings offer valuable insights for hospitals, emphasizing the importance of enhancing doctors' knowledge, openness, and confidence in adopting SIMRS technology. Recommendations offered include increasing computer literacy among doctors, mitigating risks associated with SIMRS, and improving access to healthcare services.Research limitation: In this case, the research limitation stems from the need to explore further additional characteristics, such as computer self-efficacy, trustworthiness, technological risk, facilitating conditions, and perceived ease of use, as they may impact perceived usefulness across various variables. Future research should expand its scope and population, employing comprehensive data collection techniques to maximize research outcomes.