ArticleBMC health services research2021
Nurses and the acceptance of innovations in technology-intensive contexts: the need for tailored management strategies.
Article in BMC health services research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
36 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Determinants of Health Care Technology Adoption Using an Integrated Unified Theory of Acceptance and Use of Technology and Task Technology Fit Model: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Acceptance of telemedicine among care personnel in inpatient and outpatient elderly care: a systematic review.BMC geriatrics · 2025Pooled it
- Factors affecting nurses' acceptance of a digital triage platform in primary health care in Sweden: an extended UTAUT analysis.Scandinavian journal of primary health care · 2026Article
- The Impact of Digital Transformation on Nursing Professional Experience: Critical Interpretive Synthesis Review Developing the Fragmented Professional Transition in Nursing (FPT-N) Model.Asian/Pacific Island nursing journal · 2026Review
- Development and Validation of the Professional Self-Concept Scale for Hospital Nurses (PSCS-HN).Research in nursing & health · 2026Article
- Recalibrating identity and adapting roles: ageing at work among senior nurses in Saudi Arabia.BMC nursing · 2026Article
- Organizational Tensions in the Implementation of Modifiable Off-the-Shelf Technologies in a University Hospital: Qualitative Multimethod Study.JMIR human factors · 2026Article
- Knowledge, Attitude, Benefits, Risks, Barriers, Professional Impact, and Preparedness of Nursing Students Toward the Utilization of Artificial Intelligence in Healthcare.Nursing reports (Pavia, Italy) · 2026Article
- Insights Into Factors Affecting Nurses' Knowledge of and Attitudes Toward AI and Implications for Successful AI Integration in Critical Care: Cross-Sectional Study.JMIR nursing · 2026Article
- Artificial Intelligence in Nursing Education: A Bibliometric Analysis.Nursing research and practice · 2026Article
- Are caregivers for older adults satisfied with the care robots in the workplace? A cross-sectional study in China.Frontiers in public health · 2026Article
- Factors Influencing ICU Nurses' Proficiency in Point-of-Care Gastrointestinal Ultrasound: A National Cross-Sectional Study.Journal of nursing management · 2026Article
- Acceptance of Electronic Medical Records Among Practitioner Nurses and Nurse Managers Using the UTAUT Framework in Hospital Settings: A Scoping Review.Journal of multidisciplinary healthcare · 2026Review
- Applications of machine learning and deep learning in precision medicine: Opportunities and challenges in genomics, oncology and clinical integration (Review).Biomedical reports · 2025Review
- Neonatal nurses' e-health literacy and technology‑mediated clinical practice: a cross-sectional analysis of digital health competencies and practice patterns.BMC nursing · 2025Article
- Nurse-Assisted Remote Patient Monitoring for Self-Management Support to Patients With Long-Term Illness-A Qualitative Multimethod Study.Journal of advanced nursing · 2025Article
- Article
- Nursing activities and associated workload of nurses in virtual care centres: A multicentre observational study.PLOS digital health · 2025Article
- New technologies and related changes at work as triggers for professional development in the nursing domain: an exploratory interview study.BMC nursing · 2025Article
- Device Functionalities and Technology Acceptance for Innovations in Neonatal Ventilation and Enhanced, Immediate Newborn Care: International, Multicenter, Web-Based Survey Study.JMIR human factors · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSeveral technological innovations have been introduced in healthcare over the years, and their implementation proved crucial in addressing challenges of modern health. Healthcare workers have frequently been called upon to become familiar with technological innovations that pervade every aspect of their profession, changing their working schedule, habits, and daily actions. PURPOSE: An in-depth analysis of the paths towards the acceptance and use of technology may facilitate the crafting and adoption of specific personnel policies taking into consideration definite levers, which appear to be different in relation to the age of nurses. APPROACH: The strength of this study is the application of UTAUT model to analyse the acceptance of innovations by nurses in technology-intensive healthcare contexts. Multidimensional Item Response Theory is applied to identify the main dimensions characterizing the UTAUT model. Paths are tested through two stage regression models and validated using a SEM covariance analysis.
resultsThe age is a moderator for the social influence: social influence, or peer opinion, matters more for young nurse.
conclusionThe use of MIRT to identify the most important items for each construct of UTAUT model and an in-depth path analysis helps to identify which factors should be considered a leverage to foster nurses' acceptance and intention to use new technologies (o technology-intensive devices). PRACTICAL IMPLICATIONS: Young nurses may benefit from the structuring of shifts with the most passionate colleagues (thus exploiting the social influence), the participation in ad hoc training courses (thus exploiting the facilitating conditions), while other nurses could benefit from policies that rely on the stressing of the perception of their expectations or the downsizing of their expectancy of the effort in using new technologies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.