An Extended Expectation-Confirmation Model for Mobile Nursing Information System Continuance.

Nursing is critical in health care systems and comprises the planning, execution, and documentation of nursing care. To better manage health care information during patient care, the use of a mobile nursing information system (MNIS) provides more time to care for inpatients by reducing time-consuming and redundant paperwork. The purpose of this study was to extend the expectation-confirmation model and explore the roles of nursing professional competency (skill in use), habit (customary use), satisfaction (with use), and frequency of prior use in the context of MNIS continuance usage. We randomly chose 3 hospitals from among 14 hospitals in Taiwan that had indicated they used an MNIS. We conducted a field survey of nurses who had experience using the MNIS. We used a valid sample of 90 nurses to test the research model, using structural equation modeling with the partial least squares method. The results show that habit and frequency of prior use had a significant impact on MNIS continuance usage. Satisfaction and frequency of prior use had a significant impact on habit. Nurses' professional competence is crucial to perceived usefulness and, thus, is relevant in the context of MNIS continuance usage. When habit weakens over time, the continuance intention predicts continuance usage. This study showed that the extended expectation-confirmation model effectively predicts nurses' MNIS continuance usage and provides implications. Academics and practitioners should understand how nurses' habits form and how they affect continued MNIS use. Understanding the antecedents of habits can help nursing managers identify and manipulate habit formation.

Healthcare professionals' use of health clouds: Integrating technology acceptance and status quo bias perspectives.

PURPOSE: Cloud computing technology has recently been seen as an important milestone in medical informatics development. Despite its great potential, there are gaps in our understanding of how users evaluate change in relation to the health cloud and how they decide to resist it. Integrating technology acceptance and status quo bias perspectives, this study develops an integrated model to explain healthcare professionals' intention to use the health cloud service and their intention to resist it. METHODS: A field survey was conducted in Taiwan to collect data from healthcare professionals; a structural equation model was used to examine the data. A valid sample of 209 healthcare professionals was collected for data analysis. RESULTS: The results show that healthcare professionals' resistance to the use of the health cloud is the result of regret avoidance, inertia, perceived value, switching costs, and perceived threat. Attitude, subjective norm, and perceived behavior control are shown to have positive and direct effects on healthcare professionals' intention to use the health cloud. The results also indicate a significant negative effect in the relationship between healthcare professionals' intention and resistance to using the health cloud. CONCLUSION: Our study illustrates the importance of incorporating user resistance in technology acceptance studies in general and in health technology usage studies in particular. This study also identifies key factors for practitioners and hospitals to make adoption decisions in relation to the health cloud. Further, the study provides a useful reference for future studies in this subject field.

Physicians' acceptance of electronic medical records exchange: an extension of the decomposed TPB model with institutional trust and perceived risk.

PURPOSE: Electronic medical records (EMRs) exchange improves clinical quality and reduces medical costs. However, few studies address the antecedent factors of physicians' intentions to use EMR exchange. Based on institutional trust and perceived risk integrated with the decomposed theory of planned behavior (TPB) model, we propose a theoretical model to explain the intention of physicians to use an EMR exchange system. METHODS: We conducted a field survey in Taiwan to collect data from physicians who had experience using the EMR exchange systems. A valid sample of 191 responses was collected for data analysis. To test the proposed research model, we employed structural equation modeling using the partial least squares method. RESULTS: The study findings show that the following five factors have a significant influence on the physicians' intentions to use EMR exchange systems: (a) attitude; (b) subjective norm; (c) perceived behavior control; (d) institutional trust; and (e) perceived risk. These five factors are predictable by perceived usefulness, perceived ease of use, and compatibility, interpersonal and governmental influence, facilitating conditions and self-efficacy, situational normality and structural assurance, and institutional trust, respectively. The results also indicate that institutional trust and perceived risk integrated with the decomposed TPB model improve the prediction of physician's intentions to use EMR exchange. CONCLUSION: The results of this study indicate that our research model effectively predicts the intention of physicians to use EMR exchange, and provides valuable implications for academics and practitioners.