Theses (Master of Business Administration in Healthcare Management)

Aims: With the rapid proliferation of data in healthcare it has provided an opportune platform creation of Artificial Intelligence (AI). AI has brought a paradigm shift for healthcare professionals, promising improvement in delivery and quality. This study aims to determine the perception of healthcare personnel on perceived ease of use, perceived usefulness, and subjective norm toward attitude for artificial intelligence acceptance. Methods: This is a cross-sectional single institutional study of employees’ perception of adopting AI in the hospital. The study population is all healthcare personnel of a private hospital. The inclusion criteria include all healthcare personnel involved with clinical management such as doctors, nurses, and others. The survey was conducted using a questionnaire adapted from Technology Acceptance Model and a four-point Likert scale was used for the measurement variables in this study. Data were analysed using IBM Statistical Package for the Social Sciences (SPSS) Version 26. Results: There were 96 respondents. This study started off with three hypotheses but has found that only two hypotheses have been accepted and one hypothesis has been rejected. H2 to H3 perceived usefulness and subjective norm respectively, showed positive relationship between their perception on artificial intelligence acceptance and were accepted However, H1 which was ease of use, was rejected as it did not generate a positive relationship between ease of use on attitude and artificial intelligence acceptance. Conclusion: This study has shown the significant relationship and the importance of ease of use, perceived usefulness, and subjective norm to the acceptance of AI. In the study results, there are determining factor and mostly those with most interaction with the patients and clinical management have a strong acceptance of AI in their practices. Key words: Artificial Intelligence, Acceptance, Healthcare, Healthcare Professionals

The rapid advancement of Artificial Intelligence (AI) is transforming healthcare systems worldwide, offering significant potential to enhance efficiency, accuracy, and patient-centred care. Within nursing practice, AI applications, such as predictive analytics, clinical decision support, and automated documentation, can enhance clinical decision-making, reduce workload, and support professional development. However, integrating AI into nursing is not without challenges, as issues related to ethical considerations, job security, technological readiness, and the preservation of compassionate care must be addressed. This study aims to examine the opportunities and challenges of AI integration in nursing practice, with a focus on nurses' perceptions, preparedness, and behavioural intention towards adopting AI tools. A cross-sectional quantitative research design will be employed among registered nurses in Klang Valley to assess perceived usability, benefits, and challenges of AI adoption. The findings are expected to provide evidence-based insights into nurses’ readiness for AI adoption, inform ethical and practical strategies for integration, and guide policy recommendations for a sustainable digital transformation in healthcare. Ultimately, the research seeks to contribute to the advancement of nursing education, practice, and innovation while ensuring that the humanistic essence of care remains central in an AI-driven healthcare environment.

This study investigated the determinants of patient acceptance of healthcare Artificial Intelligence (AI) chatbots in Malaysia using the Unified Theory of Acceptance and Use of Technology (UTAUT). A cross-sectional survey was conducted among 300 Malaysian adults recruited via digital convenience sampling. The instrument measured Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Behavioural Intention (BI), and Use Behavior (UB). Descriptive results showed uniformly high acceptance levels with low variance across constructs. Regression analyses demonstrated that PE, EE, and SI were all highly significant predictors of BI. Furthermore, BI significantly predicted UB, and FC also showed a statistically significant positive effect on UB. All moderation hypotheses involving age and gender were non-significant, signalling the diminishing influence of demographic variables in digitally mature populations. The results highlight the presence of ceiling effects and model saturation, suggesting that traditional UTAUT predictors may have limited explanatory power in contexts of widespread digital literacy in healthcare. The findings support expanding acceptance models to incorporate AI-specific constructs such as trust, perceived diagnostic accuracy, algorithmic transparency, and risk perception. The study provides practical insights for healthcare providers, developers, and policymakers aiming to implement safe, credible, and user-centred AI tools within Malaysia’s evolving digital health ecosystem. Key words: AI Chatbots, Healthcare Technology, UTAUT, Behavioural Intention, Use Behavior, Malaysia, Technology Adoption, Digital Health, Patient Acceptance

Technological evolution has revolutionized surgical procedures with robotic surgery emerging as a minimally invasive approach that offers a greater precision and faster recovery. However, the acceptance of robotic surgery in the Southeast Asia including Malaysia lags behind United States and Europe countries. Misconceptions and limited understanding still persisting, particularly among generation X individuals facing increasing health challenges. Concerns about costs, fears and scepticism lead to the preference for traditional surgery. The study aims to identify the determinants influencing the actual acceptance of robotic surgery. Technology Acceptance Model (TAM), Theory of Planned Behaviour (TPB) and Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) were used to identify determinates influencing actual acceptance. An online Google survey was distributed via purposive sampling to Generation X individuals with medical conditions and surgical history, particularly those visiting private hospitals. Results showed a positive correlation between modern technology use and perceived benefits with intention to accept robotic surgery, while familiarity and perceived concern had a negative impact (p<0.05). Behavioural intentions significantly influenced actual acceptance (p<0.05) and moderated relationships between variables. Greater use of modern technology, perceived benefits, and intention to accept are linked to increased acceptance of robotic surgery, while higher familiarity and perceived concerns may reduce acceptance. Healthcare organizations and policymakers should address safety, malfunctions, and costing concerns to enhance acceptance, especially as robotic surgery is still in early stages in Malaysia. Keywords: Behavioural intentions, Actual acceptance, Robotic surgery, Generation X, Healthcare