How nurses' technology readiness drives innovative work behavior in AI-enabled healthcare: The roles of AI self-efficacy, trust, and digital literacy

Ahmed Mohamed Hasanein; Hazem Ahmed Khairy; Ahmed Mohamed Hasanein; Hazem Ahmed Khairy

doi:10.3934/publichealth.2026053

AIMS Public Health

2026, Volume 13, Issue 4: 1014-1033. doi: 10.3934/publichealth.2026053

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Research article

How nurses' technology readiness drives innovative work behavior in AI-enabled healthcare: The roles of AI self-efficacy, trust, and digital literacy

Ahmed Mohamed Hasanein ^{1
,
,},
Hazem Ahmed Khairy ^{2
,
,}

1.
Management Department, College of Business Administration, King Faisal University, 380 Al-Ahsa, Saudi Arabia
2.
Hotel Management Department, Faculty of Tourism and Hotels, University of Sadat City, 32897, Egypt

Received: 23 June 2026 Revised: 02 September 2026 Accepted: 17 September 2026 Published: 14 October 2026

Aim
This study aimed to examine how nurses' technology readiness influences innovative work behavior in AI-enabled healthcare environments, focusing on the sequential mediating roles of AI self-efficacy and trust in AI, and the moderating role of digital literacy.
Design
A cross-sectional, theory-driven quantitative design was employed to test a moderated serial mediation model among staff nurses working in AI-enabled hospital settings.
Methods
Data were collected from nurses working in hospitals in Saudi Arabia utilizing AI-enabled clinical decision support systems across key departments, including intensive care units, emergency departments, radiology, general wards, and telehealth units. Established validated scales were used to measure nurses' technology readiness, AI self-efficacy, trust in AI, digital literacy, and innovative work behavior. Data were analyzed using advanced multivariate techniques to test direct, moderating, and serial mediation effects.
Results
The findings revealed that nurses' technology readiness significantly and positively affects innovative work behavior. Technology readiness also positively influences AI self-efficacy, which in turn enhances trust in AI. Trust in AI was found to positively predict innovative work behavior. Digital literacy significantly moderates the relationship between technology readiness and AI self-efficacy, strengthening this relationship when digital literacy is high. Furthermore, AI self-efficacy and trust in AI sequentially mediate the relationship between technology readiness and innovative work behavior.
Conclusion
The study concludes that nurses' innovative behavior in AI-enabled healthcare is shaped by a sequential psychological process driven by technology readiness, AI self-efficacy, and trust in AI, and strengthened by digital literacy. This highlights the importance of integrating psychological and competency-based factors in understanding innovation in digital healthcare environments.
Implications for the profession and/or patient care
The findings suggest that healthcare organizations should enhance nurses' technology readiness, strengthen AI self-efficacy through targeted training, build trust in AI systems through transparency and involvement, and invest in digital literacy development. These interventions are essential for fostering innovation among nurses, improving clinical decision-making, and enhancing the quality and efficiency of patient care in AI-enabled healthcare systems.
Impact
This study advances nursing and healthcare informatics research by providing a comprehensive explanatory model of how individual readiness and digital competencies translate into innovative work behavior in AI-driven clinical environments. It offers actionable insights for healthcare leaders aiming to successfully integrate AI technologies into nursing practice while promoting innovation and patient-centered care.
- nurses',
- technology readiness,
- artificial intelligence,
- AI self-efficacy,
- trust in AI,
- innovative work behavior,
- digital literacy,
- cross-sectional study
Citation: Ahmed Mohamed Hasanein, Hazem Ahmed Khairy. How nurses' technology readiness drives innovative work behavior in AI-enabled healthcare: The roles of AI self-efficacy, trust, and digital literacy[J]. AIMS Public Health, 2026, 13(4): 1014-1033. doi: 10.3934/publichealth.2026053

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Abstract

Aim

This study aimed to examine how nurses' technology readiness influences innovative work behavior in AI-enabled healthcare environments, focusing on the sequential mediating roles of AI self-efficacy and trust in AI, and the moderating role of digital literacy.

Design

A cross-sectional, theory-driven quantitative design was employed to test a moderated serial mediation model among staff nurses working in AI-enabled hospital settings.

Methods

Data were collected from nurses working in hospitals in Saudi Arabia utilizing AI-enabled clinical decision support systems across key departments, including intensive care units, emergency departments, radiology, general wards, and telehealth units. Established validated scales were used to measure nurses' technology readiness, AI self-efficacy, trust in AI, digital literacy, and innovative work behavior. Data were analyzed using advanced multivariate techniques to test direct, moderating, and serial mediation effects.

Results

The findings revealed that nurses' technology readiness significantly and positively affects innovative work behavior. Technology readiness also positively influences AI self-efficacy, which in turn enhances trust in AI. Trust in AI was found to positively predict innovative work behavior. Digital literacy significantly moderates the relationship between technology readiness and AI self-efficacy, strengthening this relationship when digital literacy is high. Furthermore, AI self-efficacy and trust in AI sequentially mediate the relationship between technology readiness and innovative work behavior.

Conclusion

The study concludes that nurses' innovative behavior in AI-enabled healthcare is shaped by a sequential psychological process driven by technology readiness, AI self-efficacy, and trust in AI, and strengthened by digital literacy. This highlights the importance of integrating psychological and competency-based factors in understanding innovation in digital healthcare environments.

Implications for the profession and/or patient care

The findings suggest that healthcare organizations should enhance nurses' technology readiness, strengthen AI self-efficacy through targeted training, build trust in AI systems through transparency and involvement, and invest in digital literacy development. These interventions are essential for fostering innovation among nurses, improving clinical decision-making, and enhancing the quality and efficiency of patient care in AI-enabled healthcare systems.

Impact

This study advances nursing and healthcare informatics research by providing a comprehensive explanatory model of how individual readiness and digital competencies translate into innovative work behavior in AI-driven clinical environments. It offers actionable insights for healthcare leaders aiming to successfully integrate AI technologies into nursing practice while promoting innovation and patient-centered care.

Funding

This work was supported through the Annual Funding track by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [Project No. KFU262259].

Authors' contribution

Conceptualization, A.M.H., and H.A.K; methodology, H.A.K.; software, H.A.K.; validation, A.M.H., H.A.K ; formal analysis, H.A.K.; investigation H.A.K; resources, A.M.H.; data curation, H.A.K; writing—original draft preparation, A.M.H., H.A.K; writing—review and editing, A.M.H., H.A.K; visualization, A.M.H., H.A.K; supervision, A.M.H., H.A.K.; project administration, A.M.H., H.A.K.; funding acquisition, A.M.H.

Conflict of interest

The authors declare no conflict of interest.

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