Year :
2025
| Month :
January
| Volume :
19
| Issue :
1
| Page :
LC17 - LC22
Full Version
Artificial Intelligence in Nursing Education: A Cross-sectional UTAUT Analysis Study
Published: January 1, 2025 | DOI: https://doi.org/10.7860/JCDR/2025/76894.20521
Latifah H Alenazi
1. PhD Candidate, College of Nursing, King Saud University, Riyadh, Saudi Arabia.
Correspondence Address :
Dr. Latifah H Alenazi,
PhD Candidate, College of Nursing, King Saud University, Riyadh, Saudi Arabia.
E-mail: lalmodiani@ksu.edu.sa; 443203341@student.ksu.edu.sa
Abstract 
Introduction: Artificial Intelligence (AI) is a transformative force in nursing education, applicable in academic and clinical settings. It equips nursing students with skills to evaluate and apply AI in future patient care, preparing the nursing workforce for a healthcare landscape increasingly supported by AI. However, lack of studies focus on nursing students as AI users and the behavioural intention to accept and utilise AI.
Aim: This study investigated the factors influencing nursing students’ acceptance and use of AI based on the Unified Theory of Acceptance and Use of Technology (UTAUT).
Materials and Methods: A cross-sectional study was conducted at one of the oldest and most prominent universities, collecting data from April to May 2022. The survey included 213 nursing students and aimed to evaluate the influence of the four UTAUT constructs- Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC)- on behavioural intention and usage behaviour. Additionally, the study explored the moderating effects of age and gender on the UTAUT model. Data were analysed using Statistical Package for the Social Sciences (SPSS) version 29.0 for descriptive statistics and SmartPLS version 4 for Partial Least Squares (PLS) structural equation modeling.
Results: The findings indicated that PE positively influenced the behavioural intention of nursing students to adopt and use AI in nursing education. Regarding moderation effects, age moderated the relationship between PE and behavioural intention, whereas gender did not exhibit any moderation effect.
Conclusion: This study provides a foundation for its integration to enhance learning outcomes and prepare students for technology-driven healthcare. It highlights the importance of evidence-based strategies tailored to meet diverse educational needs, ensuring effective adoption and utilisation.
Keywords
Behavioural intention, Nursing students, Use behaviour
Introduction
The AI represents a transformative paradigm in nursing education, revolutionising how knowledge and competencies are imparted to prepare nurses for the evolving healthcare landscape. The advent of the Fourth Industrial Revolution- anchored in the Internet of Things, cyber-physical systems, and AI- marks a significant leap from the late 20th century’s information and technological revolution powered by computers and the Internet (1). AI leverages advanced technologies to create cognitive systems capable of learning, adapting, self-improving and expanding their functionalities (2). Theoretical approaches within AI aim to replicate and enhance human intelligence (2), leading to its comprehensive definition as “the theory and development of computer systems are able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making and translation between languages” (3).
AI has already begun intellectualising healthcare services, becoming crucial across various sectors, including mobile health applications, virtual patient education programs, intelligent medical robots and systems for measuring and analysing human physiological parameters (4). Its transformative potential is expected to redefine healthcare delivery, significantly influencing healthcare professionals, particularly nurses. This necessitates a reevaluation of core nursing competencies and educational requirements to integrate AI effectively into nursing practice (5). Nurses, as frontline healthcare providers, must possess the skills to understand and implement AI technologies in clinical settings (5),(6).
Furthermore, there are concerns regarding patients’ legal rights, personal health information and patient safety and criticism regarding the uncertainty of the long-term effects of changes in the informatisation process (7),(8). Adopting AI will change nurses’ roles and the delivery of patient care (9). Therefore, the nursing curricula should be designed to develop competencies that prepare them for the future and specific abilities required within healthcare systems and their professional field. The educational paradigm must be transformed to keep abreast with evolving trends (5),(10),(4).
AI’s impact on nursing education extends to instructional techniques, healthcare practices and learning outcomes, underscoring the need for curricula that address the dynamic healthcare environment (11). For instance, Buchanan C et al. and Seibert K et al., explore AI’s potential to enhance clinical decision-making and documentation processes (5),(12). Moreover, Ronquillo CE et al., emphasise the importance of collaboration between AI developers and nursing practitioners to address opportunities and challenges. The Nursing and Artificial Intelligence Leadership Collaborative highlights critical gaps requiring attention to integrate AI effectively in health systems, advocating for its inclusion in nursing education (13).
Integrating AI into nursing education necessitates a thorough understanding of nursing students’ perceptions, including their attitudes and awareness of this emerging technology (14). Without such insights, efforts to integrate AI into curricula may fail to align with students’ readiness, hindering effective engagement with these innovations. Examining these perceptions is essential to ensure that AI tools and methodologies are adopted effectively, fostering both educational and clinical proficiency. To address this critical gap, the study explores the factors influencing nursing students’ acceptance and use of AI by employing the UTAUT as a guiding framework. This model is pivotal in identifying and predicting behavioural intentions, focusing on four key constructs: PE, EE, SI and FC. By applying the UTAUT framework, the study seeks to uncover how these factors shape nursing students’ willingness to adopt AI in their educational journey.
Understanding how users accept new technologies is fundamental for their successful adoption. The theory of reasoned action provides a foundational model to analyse the variables influencing user acceptance of technology, that individuals’ beliefs shape their attitudes, which subsequently drive their intentions and behaviours (15). Building on this foundation, the technology acceptance model asserts that users’ attitudes directly impact their intention to use technology, ultimately influencing their behaviour (16). Although widely utilised, this model has certain limitations, such as its inability to fully capture the relationships among external factors and to analyse complex interdependencies in technology adoption. Nonetheless, it has been instrumental in explaining user acceptance of various information technologies (17),(18).
UTAUT is recognised to have a greater potential for explaining behavioural intention and use, many researchers used it as a framework for their study. According to a study examining how using the UTAUT model affected students’ acceptance of mobile learning applications in higher education, essential factors included perceived security, self-efficacy, consistency, and trust. They perceived awareness in addition to information quality (19).
Furthermore, a study that compared the features of utilising and adopting mobile learning in higher education in developed and developing nations using the UTAUT as a theoretical model discovered that the features and backgrounds of the two groups of countries which had a substantial impact on the use of mobile learning (20). Study showed how students in Ghana intend to accept and use e-counselling by studying an empirical approach that applies the UTAUT model. As a result of the study, performance expectancy and social influence are proposed as the influencing constructions (factors) that affect students' behavioural intention to adopt and use video counselling (21). According to these earlier studies on the intention to adopt novel technologies, using the UTAUT model has produced beneficial outcomes. As mentioned earlier, AI represents a paradigm shift in nursing education. AI in nursing education is essential because it enables nurses to lead in technological advancements rather than following behind, underlining the significance of promoting education in this area (22).
The UTAUT, synthesises key constructs from multiple foundational theories to provide a comprehensive framework for understanding user acceptance of information technologies. UTAUT identifies four primary constructs- PE, EE, SI and FCs- that influence behavioural intention and use behaviour (18). This study adopts the UTAUT framework while considering age and gender as moderating variables. However, voluntariness and experience, which are often included in UTAUT-based studies, have been excluded in this context, as AI represents a novel and optional technology for nursing students. Voluntariness, which is a significant factor in organisational contexts, is not applicable here since AI adoption is self-directed and optional for the participants.
By highlighting the specific drivers and barriers to AI adoption, the study equips educators and policymakers with actionable knowledge to design AI-based interventions that align with students’ expectations and needs. Addressing this gap is essential not only to prepare nursing students for an AI-driven healthcare landscape but also to ensure that their educational experiences foster confidence and competence in leveraging AI tools. Ultimately, understanding nursing students’ perceptions of AI is a strategic step toward crafting a future-ready nursing workforce capable of thriving in an increasingly technology-integrated environment.
Hypotheses: The study tested several hypotheses derived from the UTAUT model:
Direct effects on behavioural intention and use behaviour:
• H1: PE positively influences behavioural intention.
• H2: EE positively influences behavioural intention.
• H3: SI positively influences behavioural intention.
• H4: FCs positively influences both behavioural intention and use behaviour.
• H5: Behavioural intention positively predicts use behaviour.
Mediating effects:
H6a-H6d: Behavioural intention mediates the relationships between UTAUT constructs (PE, EE, SI, FC) and use behaviour.
Moderating effects:
• H7a-H7h: Gender moderates the relationships between UTAUT constructs (PE, EE, SI and FCs) and behavioural intention/use behaviour. It is hypothesised that gender facilitates these relationships by influencing perceptions and attitudes toward technology adoption.
• H8a-H8h: Age moderates the relationships between UTAUT constructs and behavioural intention/use behaviour. It is hypothesised that age may weaken these relationships, as generational differences could impact adaptability and comfort with emerging technologies.
Material and Methods
The present study was a cross-sectional study, conducted at the oldest and most prominent university in Saudi Arabia, from April to May 2022. This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (IRB) of King Saud University (KSU-HE;23-838, Date of approval: 5 April 2022). All participants provided informed consent prior to participation, ensuring ethical compliance and the confidentiality of all data collected. Nursing students enrolled in the bachelor’s program were recruited as participants.
Inclusion criteria: Male and female nursing students pursuing a bachelor’s degree were included in the study.
Exclusion criteria: Participants with internship program or with academic issues were excluded from the study.
Sample size and selection: A convenience sampling method was used to recruit participants. Based on Kline’s (2016) rule of thumb, a minimum sample size of 200 was deemed necessary. To account for potential nonresponses or missing data, the sample size was increased to 213 students (23).
Data collection tool: A validated questionnaire developed by Venkatesh V et al., was used to measure the constructs of the UTAUT model (18). This questionnaire has been extensively validated across diverse contexts, including Germany, Jordan (24), Finland (25) and Indonesia, with Cronbach’s Alpha (CA) consistently exceeding 0.7 (26). The questionnaire has also been employed in Iran (CA > 0.83) (27) and Germany (CA = 0.92) (28) for similar applications.
The questionnaire consisted of two sections:
1. Demographic data: Age, gender, Grade Point Average (GPA) and years of study.
2. UTATAUT constructs: PE, EE, SI and FCs.
Responses were captured on a five-point Likert scale ranging from “strongly agree” to “strongly disagree.” The questionnaire was distributed electronically via university email, with official approval obtained from the original author. Completion of the survey required approximately 10 minutes.
Variables: Independent variables included PE, EE, SI and FC. Behavioural intention was considered both an independent and mediating variable, while age and gender were included as moderating variables. Use behaviour was the primary dependent variable.
Statistical Analysis
Data were analysed using SPSS version 29.0 for descriptive statistics and SmartPLS version 4 for PLS analysis. PLS, a statistical approach based on structural equation modeling, was employed to test and validate the proposed model by assessing both measurement and structural models. Measurement model assessment included Confirmatory Factor Analysis (CFA) to evaluate convergent validity, discriminant validity and internal consistency reliability (29). Specific methods included cross-loadings, the Fornell-Larcker criterion, the Heterotrait-Monotrait (HTMT) ratio of correlation, outer loadings, Average Variance Extracted (AVE), Composite Reliability (CR) and CA. The structural model was assessed through collinearity diagnostics, path coefficient values, p-values, t-statistics and Confidence Interval Bias-Corrected (CIBC) estimates (29).
Results
Demographic characteristics of the participants, which included age, gender, GPA and level of study are presented in (Table/Fig 1).
Measurement Model
The measurement model was evaluated using outer loadings, internal consistency reliability, convergent validity and discriminant validity, following established guidelines (29). Factor loadings for all items representing the constructs were assessed and found to exceed the acceptable threshold of 0.50. Consequently, no items were excluded from the analysis. These results are summarised in (Table/Fig 2).
Internal consistency reliability was assessed using CA and standardised CR. All constructs demonstrated CA values above the recommended threshold of 0.708 (30), except for behavioural intention, which scored 0.698. This value was deemed acceptable given the CR and AVE values fell within acceptable ranges (31).
Convergent validity was confirmed by examining the AVE values for all constructs, with all values exceeding the 0.50 criterion (31). These results indicate that the measurement model reliably captures the constructs under investigation, as presented in (Table/Fig 2).
Discriminant validity was evaluated using the Heterotrait-Monotrait (HTMT) ratio and the Fornell and Larcker Criterion to seek to confirm the indicators of the measurement model of each construct. (Table/Fig 3) indicates that all indicators obtained discriminant validity since the HTMT ratio values were less than 0.8.
Moreover, discriminant validity was confirmed using the Fornell and Larcker Criterion by ensuring each construct has a square root of the AVE that exceeds the correlation value for another construct (31),(32), as shown in (Table/Fig 4).
Structural Model
The structural model was assessed using the path coefficient (β), t-statistics, the collinearity assessment (Variance Inflated Factor (VIF) and coefficients of determination (R2 values) (33). Since every item in the model has a VIF of less than 5, there is no collinearity issue (33), as shown in (Table/Fig 5). The hypothesised relationship between the constructs in the model was assessed through path coefficient (β), (Table/Fig 6) indicates that PE ->BI and BI -> UB had stronger positive relationships while the EE ->BI, SI ->BI and FC ->BI had insignificant relationships; moreover, the t-values were lower than 1.960, providing support for their lack of statistical significance (34). As (Table/Fig 7) shows, The R2 values for UB and BI are 0.257 and 0.419, respectively. A moderate link between the independent and dependent constructs is indicated by BI’s R2 value of 0.419. This indicates that the model’s independent variable(s) can account for about 41.9% of the variance in the dependent variable. Even though compared to BI, UB’s R2 score of 0.257 suggests a lesser link. It indicates that the model’s independent construct may account for roughly 25.7% of the variance in the dependent construct (33),(35) (Table/Fig 8) shows each construct with its related loadings.
Hypotheses Testing
Direct effects on behavioural intention and use behaviour: The result from (Table/Fig 6) showed PE -> BI (t-statistics=2.985, β=0.371 p-value <0.001) and BI -> UB (t-statistics=6.409, β=0.507 p-value <0.001) were significant. while the hypothesis EE -> BI (t-statistics=0.676, β=0.087 p-value=0.499), SI -> BI (t-statistics=1.633, β=0.219 p-value=0.102) and FC -> BI (t-statistics=0.903, β=0.110 p-value=0.467). were insignificant, thereby rejecting the hypothesis.
Mediating role of behavioural intention: The result in (Table/Fig 9)a,b shows that the relationship between the independent variable (PE) and the dependent variable (use behaviour) was mediated by the behavioural intention of nursing students to accept and use AI in nursing education. The lower CIBC and upper CIBC do not contain zero (33). Therefore, mediation of behavioural intention is confirmed among supported variables. Since the direct relationship between the aforementioned independent variables is significant with use behaviour, as shown in (Table/Fig 9)a, it can be affirmed that all the significant mediated effects of behavioural intention exist between the relationship: (i) PE and use behaviour; in conclusion, H6c were supported, while H6a, H6b and H6d were not.
Moderation effect of age and gender: The results in (Table/Fig 10) show that age and gender do not have a moderate effect on any of the relationships hypothesised, except age moderates the relationship between PE and behavioural intention. To verify the acclaimed moderation effect, the lower and upper CIBC was used and it was confirmed that the moderation effect is significant since the distance between lower and upper CIBC contains zero (33).
Discussion
This study investigates the factors influencing nursing students’ behavioural intention to adopt and use AI in education, employing the UTAUT as a guiding framework. The constructs of PE, EE, SI and FC were analysed, with behavioural intention serving as a mediator and age and gender as moderators. The findings provide critical insights into the complex dynamics of AI adoption in nursing education.
PE was identified as the most significant predictor of behavioural intention, consistent with prior research (36),(37),(38),(39). Nursing students were more likely to adopt AI when they perceived it as a tool that enhances learning outcomes and academic performance. Furthermore, behavioural intention mediated the relationship between PE and use behaviour, underscoring its central role. These results align with studies by Williams MD et al. and Ma Y et al., which emphasise the dominance of PE in technology adoption (38),(39). Contrary to expectations, EE did not significantly influence behavioural intention, suggesting that nursing students, as digital natives, may inherently find AI tools manageable and less intimidating. Bouznif MM, Basaran S and Daganni AM, Amanuail S et al., similarly noted that high technological literacy among students reduces the perceived difficulty of using advanced tools (40),(41),(42),(43).
SI also did not significantly impact behavioural intention. A unique characteristic of the student population enrolling in nursing programs today is their familiarity with technology. Further evidence of technology’s critical role in contemporary nursing education comes from research conducted by Van Houwelingen CTM et al., which emphasises the importance of improving nursing students’ ability to use technology for educational purposes (44). This study’s findings align with previous research that revealed no significant relationship between SI and usage intention within the UTAUT model (45), reflecting findings by Erjavec J and Manfreda A, Alharbi et al., and Andersen BL et al., which suggest that intrinsic motivation often outweighs social pressures in technology adoption decisions (46),(47),(48).
FCs, representing organisational and technical support, was not significantly related to behavioural intention or use behaviour. This finding challenges earlier studies that emphasised the importance of external resources in technology adoption (49),(50),(51). It suggests that nursing students’ willingness to use AI is more closely tied to their intrinsic motivation and perceived benefits rather than institutional support.
Behavioural intention demonstrated a significant direct effect on use behaviour, confirming its reliability as a strong predictor of actual technology use (47),(48),(49). However, the R² values for behavioural intention (41.9%) and use behaviour (25.7%) indicate that additional factors beyond the UTAUT constructs may influence AI adoption. Variables such as trust, ethical concerns and individual differences are likely to play a significant role. Lockey S et al., highlight trust- specifically perceived security, accuracy and dependability- as a key determinant in AI adoption (52). Ethical concerns, including privacy and data security, may also influence nursing students’ attitudes toward AI as noted by Acquisti A et al., (53).
Age was found to moderate the relationship between PE and behavioural intention, indicating that younger students may perceive AI’s benefits more positively, as they tend to be more adaptable to emerging technologies (54),(55),(56),(57). However, gender did not moderate behavioural intention or use behaviour, consistent with studies suggesting that AI’s intuitive and accessible design fosters gender-neutral adoption patterns (58),(59),(60),(61).
These findings highlight the critical role of PE in driving AI adoption in nursing education, emphasising that students’ perceptions of utility and benefits are central. Addressing students’ specific needs and motivations can support effective integration into nursing curricula, contributing to improved educational and clinical outcomes.
Limitation(s)
This study had limitations. It examined UTAUT constructs, excluding factors such as trust, ethical considerations and contextual variables, which may also play a significant role in AI adoption. The cross-sectional design restricts the ability to determine causality and the findings may lack generalisability due to the sample being limited to a single institution. Furthermore, reliance on self-reported data may have introduced response bias, impacting the accuracy of the results.
Conclusion
PE significantly influences nursing students’ behavioural intention to adopt AI, while EE, SI and FCs showed no significant effects. Age moderates the relationship between PE and behavioural intention, whereas gender does not. Behavioural intention mediates the relationship between PE and use behaviour, underscoring its critical role in AI adoption. These findings highlight the importance of aligning AI tools with nursing students’ perceived utility to enhance acceptance and integration effectively.
Acknowledgement
The author thanks the Deanship of Scientific Research, College of Nursing Research Centre at King Saud University for facilitating the research.
Reference
|
| | 1. | Kwak Y, Seo YH, Ahn JW. Nursing students’ intent to use AI-based healthcare technology: Path analysis using the unified theory of acceptance and use of technology. Nurse Educ Today. 2022;119:105541.
[ CrossRef] [ PubMed] | | 2. | Markus AF, Kors JA, Rijnbeek PR. The role of explainability in creating trustworthy artificial intelligence for health care: A comprehensive survey of the terminology, design choices, and evaluation strategies. J Biomed Inform. 2021;113:103655.
[ CrossRef] [ PubMed] | | 3. | Mathur P, Burns ML. Artificial intelligence in critical care. Int Anesthesiol Clin. 2019;57:89-102.
[ CrossRef] [ PubMed] | | 4. | Pepito JA, Locsin R. Can nurses remain relevant in a technologically advanced future? Int J Nurs Sci. 2019;6:106-10.
[ CrossRef] [ PubMed] | | 5. | Buchanan C, Howitt ML, Wilson R, Booth RG, Risling T, Bamford M. Nursing in the age of artificial intelligence: Protocol for a scoping review. JMIR Res Protoc. 2020;9(4):e17490.
[ CrossRef] [ PubMed] | | 6. | Carroll WM. Artificial intelligence, nurses and the quadruple aim. Online J Nurs Inform. 2018;2:22. Available from: http://www.himss.org/ojni.
| | 7. | Barbosa SFF, Abbott P, Dal Sasso GTM. Nursing in the digital health era. J Nurs Scholarsh. 2021;53(1):05-06. Available from: https://doi.org/10.4037/aacna.
[ CrossRef] [ PubMed] | | 8. | Erikson H, Salzmann-Erikson M. Future challenges of robotics and artificial intelligence in nursing: What can we learn from monsters in popular culture? Perm J. 2016;20(3):15-243.
[ CrossRef] [ PubMed] | | 9. | Robert N. How artificial intelligence is changing nursing. Nurs Manage. 2019;50(9):30-39. Doi: 10.1097/01.NUMA.0000578988.56622.21.
[ CrossRef] [ PubMed] | | 10. | Kim E. An exploratory study on the pre-service teachers’ perception of education paradigm in the Fourth Industrial Revolution era. J Korea Contents Assoc. 2018;18(9):248-59. Available from: https://doi.org/10.5392/JKCA.2018.18.09.248.
| | 11. | Jeong GH. Artificial intelligence, machine learning, and deep learning in women’s health nursing. Korean J Women Health Nurs. 2020;26(1):05-09.
[ CrossRef] [ PubMed] | | 12. | Seibert K, Domhoff D, Bruch D, Schulte-Althoff M, Fürstenau D, Biessmann F, et al. Application scenarios for artificial intelligence in nursing care: Rapid review. J Med Internet Res. 2021;23(11):e26522.
[ CrossRef] [ PubMed] | | 13. | Ronquillo CE, Peltonen LM, Pruinelli L, Chu CH, Bakken S, Beduschi A, et al. Artificial intelligence in nursing: Priorities and opportunities from an international invitational think-tank of the Nursing and Artificial Intelligence Leadership Collaborative. J Adv Nurs. 2021;77(9):3707-17.
[ CrossRef] [ PubMed] | | 14. | Abuzaid MM, Elshami W, McConnell J, Tekin HO. An extensive survey of radiographers from the Middle East and India on artificial intelligence integration in radiology practice. Health Technol (Berl). 2021;11(5):1045-50.
[ CrossRef] [ PubMed] | | 15. | Fishbein M, Ajzen I. Belief, attitude, intention, and behaviour: An introduction to theory and research. Addison-Wesley Publishing Company, 1975.
| | 16. | Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989;13(3):319-39.
[ CrossRef] | | 17. | Kim JM. Study on intention and attitude of using artificial intelligence technology in healthcare. Converg Soc SMB. 2017;7(4):53-60. Available from: http://www. earticle.net/article.aspx?sn=307601.
[ CrossRef] | | 18. | Venkatesh V, Smith RH, Morris MG, Davis GB, Davis FD, Walton SM. User acceptance of information technology: Toward a unified view. MIS Quarterly. 2003;27(3)425-78.
[ CrossRef] | | 19. | Almaiah MA, Alamri MM, Al-Rahmi W. Applying the UTAUT model to explain the students’ acceptance of mobile learning system in higher education. IEEE Access. 2019;7:174673-86.
[ CrossRef] | | 20. | Kaliisa R, Palmer E, Miller J. Mobile learning in higher education: A comparative analysis of developed and developing country contexts. Br J Educ Technol. 2019;50(2):546-61.
[ CrossRef] | | 21. | Kolog EA, Sutinen E, Suhonen J, Anohah E, Vanhalakka-Ruoho M. Towards students’ behavioural intention to adopt and use e-counseling: An empirical approach of using UTAUT model. In: IEEE Africon Conf. 2015. pp. 1-6. Doi: 10.1109/AFRCON.2015.7331926.
[ CrossRef] | | 22. | Rony MKK, Parvin MR, Ferdousi S. Advancing nursing practice with artificial intelligence: Enhancing preparedness for the future. Nurs Open. 2024;11(1):10.1002/nop2.2070.
[ CrossRef] [ PubMed] | | 23. | Kline, R. B. (2016). Principles and Practice of Structural Equation Modeling. (4th Ed.). New York: Guilford Press.
| | 24. | Pearson JM, Setterstrom A. Internet banking and customers’ acceptance in Jordan: The unified model’s perspective. 2017. Available from: http://aisel.aisnet. org/cais.
| | 25. | Koivumäki T, Ristola A, Kesti M. The perceptions towards mobile services: An empirical analysis of the role of use facilitators. Pers Ubiquit Comput. 2008;67-75.
[ CrossRef] | | 26. | Nurhayati S, Anandari D, Ekowati W. Unified Theory of Acceptance and Usage of Technology (UTAUT) model to predict health information system adoption. J Kesehatan Masyarakat. 2019;15(1):89-97.
[ CrossRef] | | 27. | Sharifian R, Askarian F, Nematolahi M, Farhadi P. Factors influencing nurses’ acceptance of hospital information systems in Iran: Application of the Unified Theory of Acceptance and Use of Technology. Health Inf Manag J. 2014;43(3):23-28.
[ CrossRef] [ PubMed] | | 28. | Floruss J, Vahlpahl N. Artificial Intelligence in healthcare: Acceptance of AI-based support systems by healthcare professionals [Internet] [Dissertation]. 2020. Available from: https://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-48572.
| | 29. | Sarstedt M, Ringle CM, Smith D, Reams R, Hair JF. Partial Least Squares Structural Equation Modeling (PLS-SEM): A useful tool for family business researchers. J Fam Bus Strategy. 2014;5(1):105-15.
[ CrossRef] | | 30. | Hair JF, Ringle CM, Sarstedt M. Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Plan. 2013;46:01-12.
[ CrossRef] | | 31. | Hair JF, Black WC, Babin BJ, Anderson RE, Tatham RL. Multivariate data analysis. 5th ed. Upper Saddle River: Prentice Hall; 1998.
| | 32. | Fornell C, Larcker DF. Evaluating structural equation models with unobservable variables and measurement error. J Mark Res. 1981;18:39-50.
[ CrossRef] | | 33. | Hair JF, Tomas G, Hult M, Ringle CM, Sarstedt M. A primer on partial least squares structural equation modeling (PLS-SEM). 3rd edition, SAGE Publications, Inc 2022. Available from: https://www.researchgate.net/publication/354331182.
| | 34. | Cho HC, Abe S. Is two-tailed testing for directional research hypotheses legitimate? J Bus Res. 2013;66(9):1261-66.
[ CrossRef] | | 35. | Hair J, Sarstedt M, Ringle CM, Gudergan SS. Advanced issues in partial least squares structural equation modeling. SAGE Publications, Second Edition 2017.
[ CrossRef] | | 36. | Dwivedi YK, Rana NP, Jeyaraj A, Clement M, Williams MD. Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a revised theoretical model. Inf Syst Front. 2019;21(3):719-34.
[ CrossRef] | | 37. | Chao CM. Factors determining the behavioural intention to use mobile learning: An application and extension of the UTAUT model. Front Psychol. 2019;10:1652. Doi: 10.3389/fpsyg.2019.01652.
[ CrossRef] [ PubMed] | | 38. | Williams MD, Rana NP, Dwivedi YK. The unified theory of acceptance and use of technology (UTAUT): A literature review. J Enterp Inf Manag. 2015;28:443-48.
[ CrossRef] | | 39. | Ma Y, Zhou M, Yu W, Zou Z, Ge P, Ma ZF, et al. Using the Unified Theory of Acceptance and Use of Technology (UTAUT) and e-health literacy (e-HL) to investigate the tobacco control intentions and behaviours of non-smoking college students in China: A cross-sectional investigation. BMC Public Health. 2023;23(1):765.
[ CrossRef] [ PubMed] | | 40. | Bouznif MM. Business students’ continuance intention toward Blackboard usage: An empirical investigation of UTAUT model. Int J Bus Manag. 2017;13(1):120.
[ CrossRef] | | 41. | Basaran S, Daganni AM. Learning analytics tool adoption by university students. Int J Adv Comput Sci Appl. 2020;11:76-97.
[ CrossRef] | | 42. | Morchid N. The determinants of use and acceptance of mobile-assisted language learning: The case of EFL students in Morocco. Arab World Engl J. 2019;5:76-97.
[ CrossRef] | | 43. | Amanuail S, Parveen K, Afzal M. Perceptions and expectations of nursing students regarding diverse technology-based nursing education. Int J Health Med Nurs Pract. 2021;3(2):1-16. Available from: www.carijournals.org.
[ CrossRef] | | 44. | Van Houwelingen CTM, Ettema RGA, Antonietti MGEF, Kort HSM. Understanding older people’s readiness for receiving telehealth: mixed-method study. J Med Internet Res. 2018;20(4):e123.
[ CrossRef] [ PubMed] | | 45. | Fianu E, Blewett C, Ampong GOA, Ofori KS. Factors affecting MOOC usage by students in selected Ghanaian universities. Educ Sci. 2018;8(2):70.
[ CrossRef] | | 46. | Erjavec J, Manfreda A. Online shopping adoption during COVID-19 and social isolation: Extending the UTAUT model with herd behaviour. J Retail Consum Serv. 2022;65:102867.
[ CrossRef] [ PubMed] | | 47. | Alharbi A, Aljojo N, Zainol A, Alshutayri A, Alharbi B, Aldhahri E, et al. Identification of critical factors affecting the students’ acceptance of Learning Management System (LMS) in Saudi Arabia. Int J Innov. 2021;9(2):353-88.
[ CrossRef] | | 48. | Andersen BL, Jørnø RL, Nortvig AM. Blending adaptive learning technology into nursing education: A scoping review. Contemp Educ Technol. 2022;14:ep333.
[ CrossRef] | | 49. | Almaiah MA, Al-Khasawneh A, Althunibat A. Exploring the critical challenges and factors influencing the E-learning system usage during COVID-19 pandemic. Educ Inf Technol. 2020;25(6):5261-80.
[ CrossRef] [ PubMed] | | 50. | García Botero G, Questier F, Zhu C. Self-directed language learning in a mobile-assisted, out-of-class context: Do students walk the talk? Comput Assist Lang Learn. 2019;32(1-2):71-97.
[ CrossRef] | | 52. | Venkatesh V, Bala H. Technology acceptance model 3 and a research agenda on interventions. Decis Sci. 2008;39(2):273-315.
[ CrossRef] | | 52. | Lockey S, Gillespie N, Holm D, Someh IA. A review of trust in artificial intelligence: Challenges, vulnerabilities and future directions. In: Proc Annu Hawaii Int Conf Syst Sci. 2021;5463-72.
[ CrossRef] | | 53. | Acquisti A, Brandimarte L, Loewenstein G. Privacy and human behaviour in the age of information. 2015;347(6221):509-14.
[ CrossRef] [ PubMed] | | 54. | Raza SA, Qazi W, Khan KA, Salam J. Social isolation and acceptance of the Learning Management System (LMS) in the time of COVID-19 pandemic: An expansion of the UTAUT model. J Educ Comput Res. 2021;59(2):183-208.
[ CrossRef] [ PubMed] | | 55. | Schukat S, Heise H. Towards an understanding of the behavioural intentions and actual use of smart products among German farmers. Sustainability. 2021;13(12):6666.
[ CrossRef] | | 56. | Mannheim I, Varlamova M, van Zaalen Y, Wouters EJM. The role of ageism in the acceptance and use of digital technology. J Appl Gerontol. 2023;42(6):1283-94.
[ CrossRef] [ PubMed] | | 57. | Wibowo DT, Munawar LA, Nisa S. The interest of technology adoption in e-commerce mobile apps using modified Unified Theory of Acceptance and Use of Technology 2 in Indonesia. Int J Appl Bus Int Manag. 2021;6(3):35-45.
[ CrossRef] | | 58. | Hmoud BI, Várallyai L. Artificial intelligence in human resources information systems: Investigating its trust and adoption determinants. Int J Eng Manag Sci. 2020;5(1):749-65.
[ CrossRef] | | 59. | Bela LR, Riani AL, Indriayu M. The role of gender moderates the effect of entrepreneurial mindsets on student intention. Dinamika Pendidikan. 2021;16(2):134-42.
[ CrossRef] | | 60. | Engin M, Gürses F. Adoption of hospital information systems in public hospitals in Turkey: An analysis with the Unified Theory of Acceptance and Use of Technology model. Int J Innov Technol Manag. 2019;16(6):1950043.
[ CrossRef] | | 61. | Handarkho YD. Impact of social experience on customer purchase decision in the social commerce context. J Syst Inf Technol. 2020;22(4):47-71. [ CrossRef] |
DOI: 10.7860/JCDR/2025/76894.20521
Date of Submission: Nov 19, 2024
Date of Peer Review: Dec 07, 2024
Date of Acceptance: Dec 27, 2024
Date of Publishing: Jan 01, 2025
AUTHOR DECLARATION:
• Financial or Other Competing Interests: None
• Was Ethics Committee Approval obtained for this study? Yes
• Was informed consent obtained from the subjects involved in the study? Yes
• For any images presented appropriate consent has been obtained from the subjects. Yes
PLAGIARISM CHECKING METHODS:
• Plagiarism X-checker: Nov 22, 2024
• Manual Googling: Dec 22, 2024
• iThenticate Software: Dec 25, 2024 (9%)
ETYMOLOGY: Author Origin
EMENDATIONS: 5
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