A Cybersecurity-Centric Model for Predicting Electronic Health Records System Adoption for Sustainable Healthcare: A SEM-ANN Approach

A Cybersecurity-Centric Model for Predicting Electronic Health Records System Adoption for Sustainable Healthcare: A SEM-ANN Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Cybersecurity-Centric Model for Predicting Electronic Health Records System Adoption for Sustainable Healthcare: A SEM-ANN Approach Muhammed Ibrahim, Mohammed A. Al-Sharafi, Mousa Albashrawi, Moamin A. Mahmoud This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5798963/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Electronic Health Records (EHR) systems are critical for achieving healthcare sustainability, offering benefits such as improving care of the patient, enhanced management of data, and operational efficiency. Despite these advantages, the adoption of EHR systems remains a challenge, influenced by various technological, organizational, and individual factors. This study builds upon the UTAUT2 framework by incorporating cybersecurity considerations to offer a more comprehensive understanding of EHR adoption and its role in promoting sustainable healthcare. Data were collected from 374 healthcare professionals through purposive sampling and analyzed using a hybrid approach combining Structural Equation Modeling (SEM) and Artificial Neural Networks (ANN). The findings demonstrate that EHR use plays a key role in advancing healthcare sustainability by improving organizational efficiency and long-term resilience. Key factors influencing EHR adoption include confidentiality and possession/control, underscoring the importance of data privacy, security, and system ownership. Performance expectancy and social influence significantly impact adoption decisions, reflecting the role of usability, peer influence, and organizational dynamics. Additional factors such as integrity and facilitating conditions showed moderate importance, while hedonic motivation and availability were less critical. This study contributes to EHR adoption research by integrating cybersecurity and user experience factors, offering insights for healthcare organizations and policymakers. The findings highlight the need to prioritize data security and usability to enhance adoption. Future research could explore EHR adoption in diverse settings and examine evolving adoption dynamics. Electronic Health Records Adoption Cybersecurity Healthcare Sustainability UTAUT2 Sustainable Development Goals SEM-ANN Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction The Sustainable Development Goals (SDGs) agenda for 2030 of the United Nations established in 2015, with 17 goals aimed at facilitating an improved and more sustainable future for all [ 1 ]. These goals, together with their 169 targets, tackle some of the most pressing global challenges. They are classified into three primary domains: social, economic, and environment [ 2 ]. Information Technology (IT) has significant potential to enhance access to care, decrease expenses, and optimize operational efficiencies within the healthcare system [ 3 ]. Management of business processes and clinical automation are prominent trends globally impacting both the emerging and established healthcare sectors. The motivation behind these development is to streamline the complicated traditional and paper-based systems, allowing systems of medical care to manage patient data more efficiently, ensure adherence to health regulations, improve data accessibility for enhanced care delivery, and bolster security measures to safeguard patient confidentiality. [ 4 ], [ 5 ]. Technological advancements provide firms, especially those in healthcare, a competitive advantage by enhancing procedures and facilitating more effective strategy implementation [ 6 ]. The healthcare sector has rapidly transitioned from paper-based methods to digital alternatives. These include individual health records, prescriptions electronically, intelligent health devices, wearable tech, AI-powered patient management tools, and telemedicine [ 7 ]–[ 10 ]. The reliance on paper for documenting health information in several healthcare institutions has generated a significant paper trail, prompting many healthcare organizations to consider transformation to electronic health records (EHR) from paper-based health records [ 11 ]. Information and Communication Technologies (ICT) usage in healthcare began in the early 1970s with the commencement of commercial computer utilization. Nonetheless, it underwent a substantial expansion in the 1990s, significantly enhancing access, efficiency, quality, and ultimately the efficacy of provision of medical care service systems [ 12 ]. The primary goal of EHR is to advance the access and efficiency of medical care systems. EHRs encompass a comprehensive array of information on an individual's health, including demographics, diagnoses, laboratory tests and results, medicines, medical imaging, physician's notes, and much more [ 13 ]. The General Practice Research Database (GPRD) in the United Kingdom is among the largest electronic health databases globally. Initially established in 1987 as the VAMP Research Databank, it was acquired by the UK Department of Health in 1994 and subsequently renamed the GPRD. Since that time, it has been meticulously administered to guarantee superior data quality and is utilized for scholarly study. The database contains data from approximately five million patients across 590 primary care clinics in the UK. These procedures conform to a defined methodology for the demographic collection and data of clinical, which are then sent in anonymized form to the database by automated extraction methods [ 14 ]. In Hong Kong, public hospitals provide the majority of in-patient care, covering about 90% of all hospital bed-days, while around 70% of outpatient services are handled by private clinics. The Hospital Authority established the Electronic Health Record Sharing System to link these two sectors, which was initiated in March 2016. This system functions as an electronic platform that enables the exchange of information including patient demographics, clinical data, and prescription profiles, across public and private healthcare institutions [ 15 ]. EHR is a digital version of a medical history of patient's that a medical care provider maintains over time; it includes important details about the patient's care, like their demographics, immunizations, health issues, medications, medical history, progress notes, lab results, vital signs, and radiology reports" [ 11 ]. The deployment of EHR in hospitals and clinics worldwide is increasing, significantly contributing to sustainable healthcare provision. Numerous nations have shown a desire to adopt integrated electronic health records owing to the expected advantages. The American Recovery and Reinvestment Act of 2009 designated $ 27 billion for the digitization of medical care data in the United States, with the objective that most Americans will be integrated into the EHR system by 2015 and 2017 [ 16 ], [ 17 ]. Nonetheless, some challenges persist that hinder effective implementation, particularly in developing economies [ 18 ], [ 19 ]. Certain difficulties pertain to infrastructure, in addition to the security and privacy of patients' data. There has been negligible progress in formulating policies to address the privacy concerns associated with the transformation to integrated EHRs from paper based health records [ 20 ]. The rapid progression of ICT has rendered patients' health data susceptible to security and privacy risks. Presently, there are considerable apprehensions surrounding the security and privacy of guarded health data, which pose substantial barriers to the introduction of EHR. Consequently, healthcare institutions must formulate methods to safeguard the system [ 21 ]. There is a need for investigation regarding healthcare providers direct experiences with EHR to understand the potential challenges, benefits, motivations, and barriers related to EHR adoption because their attitudes and actions will significantly influence the system's efficacy [ 22 ]. Most previous studies in literature have taken an observational or secondary-data analysis approach focusing on outcomes at the hospital level. It remains unclear how EHR implementation impacts healthcare providers daily activities [ 23 ]–[ 26 ]. Only a number of limited research have investigated healthcare providers assessments of how EHR influences their work [ 22 ]. A recent study also suggests the significance of understanding the key factors that either support or hinder healthcare providers EHR usage in order to enhance EHR utilization [ 27 ]. Despite the increasing number of studies on EHRs, there exist a significant knowledge gap remains in the literature concerning studies that empirically identify the factors that influence the acceptance and use of EHR systems [ 6 ], [ 15 ], [ 22 ], [ 23 ], [ 28 ]. This study seeks to address this knowledge gap by examining the factors that influence the healthcare professionals to adopt EHR systems in developing economies, specifically emphasizing usability, cybersecurity, and the contribution of the adoption of EHR in fostering sustainable healthcare practices in resource-limited environments. This research integrates the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) model by [ 29 ] as its foundational framework and incorporates additional factors such as cybersecurity factors, namely confidentiality, integrity, availability, and possession/control [ 30 ]. Furthermore, it introduces healthcare sustainability as a dependent variable, drawing on the framework provided by [ 2 ], [ 31 ]. By combining these variables, the study seeks to develop a comprehensive model for understanding the determinants of the adoption of EHR and use. This approach is expected to provide actionable insights into designing focused procedures and strategies that advance EHR adoption, ultimately serving patients, healthcare providers, and the larger healthcare environment. This study provides three key contributions. First, it advances the theoretical understanding of the EHR adoption process by analyzing its usage and implications for healthcare sustainability. Using the UTAUT2 framework as a base and integrating cybersecurity-related factors, the research offers a novel perspective on healthcare providers’ behaviors and attitudes toward EHR systems. This perspective provides healthcare organizations valuable insights into fostering greater adoption and utilization of these systems. Second, the research makes an important empirical contribution by incorporating healthcare sustainability as a dependent variable in its model. This allows the research to examine the impact of EHR usage on sustainable healthcare practices, highlighting its capacity to enhance the efficiency and resilience of healthcare delivery in resource-restricted settings. Lastly, the methodological contribution is applying a hybrid Structural Equation Modeling-Artificial Neural Network (SEM-ANN) approach for analyzing and testing the proposed model. This innovative methodology enables a more extensive understanding of the complicated interactions among various factors influencing EHR adoption and their implications for sustainable healthcare outcomes. The study is structured as follows: it starts with an introduction, followed by a section providing a theoretical framework for EHR adoption. Next, it establishes the foundation for the study model and the hypotheses formulation. Subsequently, the study method is explained, followed by a discussion on data analysis and findings. The findings are analyzed, emphasizing their significance for theory, methodology, and practice, then conclusions and recommendations for future study areas. 2. Theoretical Background The research proposed a conceptual model built on the integration of the UTAUT2 model developed by [ 29 ] and cybersecurity constructs [ 30 ] to predict the acceptance of EHR systems and examine their role in promoting healthcare sustainability [ 2 ], [ 31 ]. The UTAUT2 model is extensively recognized for its comprehensive approach to explaining behavior of user in technology adoption. Researchers have praised its robust framework for identifying and explaining the behavioral determinants that influence user acceptance and technology use [ 32 ], [ 33 ]. Unlike earlier models that examined technology acceptance in isolation, UTAUT2 is the extension of UTAUT which synthesizes key variables from eight leading technology acceptance theories, including the Technology Acceptance Model [ 34 ], Theory of Reasoned Action [ 35 ], and the Innovation Diffusion Theory [ 32 ], [ 36 ]. This integrative approach provides researchers with a more detailed and comprehensive knowledge of the significant factors that drive the adoption of technology across diverse contexts, including organizational, educational, and consumer settings. Moreover, in the context of EHR systems, it addresses how ease of use (effort expectancy), the pleasure or fun derived (hedonic motivation), and resource availability (facilitating conditions), perceived usefulness (performance expectancy), peer and organizational support (social influence) contribute to adoption decisions. This holistic approach ensures that both individual and organizational factors are considered [ 37 ]. Furthermore, the flexibility of UTAUT2 enables its extension and adaptation to address specific contexts, making it an ideal foundation for this research [ 38 ], [ 39 ]. In this study, two key UTAUT2 constructs which are price value and habit, were omitted due to their limited relevance. Unlike consumer-oriented technologies where individual users bear direct costs, EHR system implementation expenses are typically covered by healthcare organizations or government entities, rendering price value immaterial to healthcare professionals' adoption decisions. Similarly, the habit construct proves insufficient in healthcare settings. The shift from paper-based to digital records is relatively recent, especially in developing countries, where EHR systems have not yet achieved universal implementation. While UTAUT2 comprehensively covers usability and behavioral aspects, it falls short in addressing the critical concerns of security of data, which are specifically relevant in healthcare due to the sensitive nature of patient information. To bridge this gap, the study incorporates cybersecurity constructs (i.e., confidentiality, integrity, availability, and possession/control) into the UTAUT2 model, offering a more holistic perspective on technology adoption within healthcare environments. Studying cybersecurity factors is crucial for several compelling reasons, particularly in healthcare technology adoption. Healthcare information systems, especially EHRs, operate in a uniquely sensitive domain that demands a more comprehensive approach to technology acceptance [ 6 ]. Unlike traditional technology acceptance models that primarily focus on user acceptance and behavioral intention, this extended research model recognizes the significance importance of protection data in medical care settings [ 30 ], [ 40 ], [ 41 ]. The proposed cybersecurity constructs provide a holistic risk mitigation strategy that addresses the multifaceted challenges of the technology acceptance in healthcare. The successful adoption and use of EHR systems are closely linked to healthcare sustainability. EHR systems play a significant role in sustainable healthcare by reducing paper use, improving data accessibility, and streamlining care delivery, which can lead to cost savings and reduced environmental impact [ 42 ]–[ 44 ]. Integrating sustainability as a core objective within the proposed research model highlights the broader implications of EHR adoption. This approach reinforces the importance of usability, security, and long-term effectiveness in fostering sustained use. It also provides valuable insights into how behavioral and cybersecurity factors can align with sustainable healthcare goals. Figure 1 indicates the proposed research model. 2.1 Model and Hypothesis Development 2.1.1 Performance Expectancy Performance expectancy, referring to “the belief that a job performance will be improve by using technology, has been recognized as an important factor that influence the technology adoption” [ 45 ]. This study defined performance expectancy as using electronic health record system by healthcare providers to meet the expectations of the patient. Prior studies, such as those by [ 46 ] and [ 47 ], underscore this construct's impact on healthcare technologies, where perceived improvements in efficiency and patient outcomes drive adoption. In the context of EHR usage, increased efficiency and better data access contribute to healthcare sustainability by improving care quality and optimizing resource allocation. It is an important predictor of intention behavior [ 47 ] and has been found to strongly influence many technologies adoption [ 46 ], [ 48 ]–[ 50 ]. In the EHR adoption domain, performance expectancy was identified as an important factor in healthcare providers' adoption [ 51 ]. This factor also serves an important function in the adoption of medical Internet of Things (IoT) acceptance in medical care [ 52 ]. The research by [ 53 ] on factors influencing the use of AI-based chatbots for sharing of knowledge, performance expectancy is crucial in determining how much these chatbots are utilized. Healthcare providers are more inclined to adopt EHR if they believe it will save time, reduce costs, or enhance access to care. Therefore, the following hypothesis is proposed: H1: There is a strong positive connection between performance expectancy and the actual use of EHR. 2.1.2 Effort Expectancy Effort expectancy pertains to individuals' awareness of how easy technology is to use. This construct aligns with two key concepts from established models: complexity from IDT and perceived ease of use from TAM/TAM2 [ 45 ]. This study defined effort expectancy as the perceived ease with which healthcare providers can use electronic health record systems. Many studies in the literature have found effort expectancy as a significant predictor for the acceptance or adoption of technology [ 49 ], [ 53 ], [ 54 ]. In a research conducted by [ 51 ] regarding the attitudes and factors that influence patients' adoption of accessible EHR in China identified effort expectancy as highly significant and the driver of behavioral intention. Effort expectancy is a key factor impacting the m-health application adoption among senior citizens who are regarded as having strong digital literacy skills [ 18 ]. Study by [ 53 ] on the use of AI-based chatbots for sharing of knowledge found that the PLS-SEM analysis supported that effort expectancy positively influences their adoption for sharing knowledge. If the EHR system is easy to use and requires minimal effort featuring clear instructions, simple navigation, and intuitive interface healthcare providers are likely to feel more at ease when adopting it. Consequently, the following hypothesis is proposed: H2: There is a strong positive connection between effort expectancy and the actual use of EHR. 2.1.3 Facilitating Conditions “Facilitating conditions refer to how much an individual perceive or believes that there is adequate organizational and technical support accessible to help them use a system effectively” [ 45 ]. This study defined facilitating conditions as the extent by which healthcare provider beliefs that there is access to the required resources, infrastructure, and assistance for using EHR systems. Previous research in the literature have demonstrated the significance importance of facilitating conditions on technology adoption [ 55 ]–[ 57 ]. For instance, a study conducted by [ 58 ] of adoption of teachers' ICT-based instruction in the classroom, the findings shows that facilitating conditions significantly and positively influence the actual use of ICT tools for teaching. According to empirical evidence, facilitating conditions is one of the important factors that has significant impact on companies' decisions to adopt blockchain technology in supply chain management [ 59 ]. A strong technological infrastructure featuring compatible software, suitable hardware, and reliable internet access is crucial for the effective operation of EHR systems. Healthcare providers are more likely to adopt EHR systems whenever they are aware that support is available for any technical issues or questions about how to use the system. Thus, the proposed hypothesis is as follows: H4: There is a strong positive connection between facilitating conditions and the actual use of EHR. 2.1.4 Social Influence “Social influence is the degree to which a person feels that key individuals in their life think they should adopt the new technology” [ 45 ]. This study defined social influence as the healthcare providers’ perception that important people around them might influence them to use EHR systems. Previous research in the literature have shown a significant part played by social influence towards the adoption of many technologies [ 48 ], [ 53 ], [ 60 ], [ 61 ]. According to the study by [ 62 ], which analyzes factors influencing the decision to use digital nursing homes for the older adults within a Chinese context the findings shows that social influence plays a significant role in shaping people's behavioral intentions. The study by [ 48 ] on smart meter adoption shows that social influence serve an important role in the adoption of smart meters among consumers in Brazil. Also, in the study by [ 28 ] on enhancing the use of e-health records among Generation Z highlights that social influence is significant factor in encouraging this demographic to adopt such technologies more broadly. Sharing positive experiences of using EHR by friends, colleagues at work, or anyone working together can encourage healthcare providers to use it. Therefore, the following hypothesis is proposed: H4: There is a strong positive connection between social influence and the actual use of EHR. 2.1.5 Hedonic Motivation Hedonic motivation was presented in the UTAUT2 model as a new variable to enhance the original UTAUT model, particularly in connection to usage of technology by a consumer. Hedonic motivation can be defined as the enjoyment or pleasure gained from technology usage, and research has indicated that it significantly influences technology acceptance and usage” [ 29 ]. This study defined hedonic motivation as the extent of the pleasure and fun healthcare provider get while using EHR. Some studies in the literature such as [ 18 ], [ 63 ]–[ 65 ] have found hedonic motivation as a significant predictor that influence positively the technology adoption intention. The findings of the study of [ 18 ] on mobile health application usage among elderly people show the significance important of hedonic motivation and the pleasure they derived while using the application. The study of [ 64 ] found that hedonic motivation has a significant and positive effect on the intention to adopt and use smart meters. Therefore, it makes sense to suggest that hedonic motivation will remain a significant predictor of the adoption and use of technology. As a result, the following hypothesis is proposed: H5: There is a strong positive connection between hedonic motivation and the actual use of EHR. 2.1.6 Confidentiality Confidentiality can be defined as the ability to protect cyberspace from unauthorized access [ 30 ]. Healthcare providers must ensure the confidentiality of patient’s information flows into and out of the EHR through various channels, when essential details of a medical record of a patient like diagnoses or medications, are entered without prioritizing confidentiality, there's a substantial risk of a breach when this information is accessed through a patient portal system. This predicament frequently places healthcare providers in a tough spot, as they must balance meeting their patients' needs with following best clinical practices, navigating different state laws, and dealing with ambiguous institutional policies [ 66 ]. The adoption of a particular technology largely depends on the service providers' capacity to safeguard transaction confidentiality and users' ability to protect their personal data privacy [ 40 ]. This study defined confidentiality as healthcare providers ability to protect patients records from unauthorized access. Information reliability might be compromise if there is a breach of confidentiality [ 41 ]. Confidentiality has being studied in many studies in the literature and have been identified to have a significant impact [ 41 ], [ 67 ]–[ 70 ]. Encryption, access restrictions, and data anonymization techniques are employed in a system to ensure confidentiality [ 68 ]. This factor also plays a significant role and effect in personal health record adoption [ 69 ]. Therefore, healthcare providers are more likely to adopt EHR if they perceive patient records are secured. Thus, the following hypothesis is proposed: H6: There is a strong positive connection between confidentiality and the actual use of EHR. 2.1.7 Integrity Integrity can be as the ability to preserve the consistency, accuracy, and reliability of information in the digital space [ 30 ]. Integrity involves ensuring that data stays accurate and reliable during its entire lifecycle. To safeguard against unauthorized changes, various approaches like checksums, version control systems, and digital signatures are employed [ 41 ]. This study defined integrity as the ability to safe guide patient record accurately without any changes. A blockchain-based system for managing patient-centric EHR is created to provide effective EHR solutions in eHealth environments [ 71 ]. In the study by [ 40 ] it suggests that users' confidence in the security of cryptocurrencies held in a brokerage account believing they are protected from theft, hacking, or loss by third parties strongly influences their attitudes toward using cryptocurrencies. In the study conducted by [ 71 ] demonstrate that the inherent properties of blockchain, which make altering data challenging, guarantee the integrity and consistency of health records. Therefore, it is reasonable to believe that integrity will remain an important factor influencing the acceptance and use of technology moving forward. Thus, the following hypothesis is proposed: H7: There is a strong and positive connection between integrity and the actual use of EHR. 2.1.8 Availability "The ability of allowing authorized users to access cyberspace" [ 30 ]. This study defined availability as the extent of allowing access to patient record to authorize person. Availability emphasizes the constant accessibility of information and systems for authorized users. To guarantee seamless access, it's crucial to have redundancy measures, backup systems, and effective disaster recovery plans in place [ 41 ]. Based on the study of [ 72 ] on smart home adoption the findings indicate that availability significantly influence usage intention. Users ought to have the ability to access their cryptocurrency accounts whenever they need to [ 73 ]. If the patient records in EHR system is available to authorize individual at any given time can make healthcare providers feel more comfortable to adopt. Therefore, we proposed the following hypothesis: H2: There is a strong and positive connection between availability and the actual use of EHR. 2.1.9 Possession/Control " The capability to maintain a sense of control or ownership in the digital space" [ 30 ]. This study defined possession/control as the ability of EHR to control patient record. Possession or control refers to the degree of control individuals have over their assets [ 40 ]. This factor is new explored in a very little study. The study by [ 40 ] shows that users' attitudes toward using cryptocurrencies would be significantly influenced by their ability to manage possession or control. This involves actions like keeping their cryptocurrency account password private, using phone verification services to safeguard passwords, and avoiding the storage of personal data online. The capability of EHR to manage and protect patient information encourages healthcare providers to keep using the system. Consequently, the following hypothesis is proposed: H2: There is a strong and positive connection between possession/control and the actual use of EHR. 2.1.10 Actual Use of EHR Systems and Its Impact on Healthcare Sustainability “The actual use of technology, which can be defined as the acceptance or adoption of new technology into people's lives” [ 74 ]. This study defined the actual use of EHR as how healthcare providers integrate and interact with EHR systems for healthcare delivery. Based on the study of [ 75 ] which indicates that individual intentions are significant in determining user behavior. Behavior is characterized as one's intention to perform a particular actions [ 76 ]. User behavior can be predicted by their intention to act, provided that the individual can voluntarily take action [ 60 ]. The actual use behavior have been used in many studies in the literature of technology adoption [ 2 ], [ 9 ], [ 77 ]. Use behavior is considered an important and significant factor added to the extended UTAUT model to determine individual actual use of technology [ 29 ]. Sustainability in healthcare involves balancing three key aspects economic efficiency, social inclusion, and safeguarding the environment [ 31 ]. The focus of healthcare on enhancing efficiency and service quality has led a significant rise in interest in applying sustainable principles [ 78 ]. Healthcare sustainability involves ensuring that healthcare systems are able to operate and provide essential services over the long term without exhausting natural or economic resources. This trend also includes a growing awareness and emphasis on responsible management of internal resources. Integrating medical guidelines and planning with social, economic and environmental approaches reflects healthcare organizations' broader concerns for organizational survival, ongoing development, and the enhancement of health services [ 78 ]. Healthcare sustainability is the extent of a healthcare systems to maintain or improve the quality and accessibility of healthcare services over time while effectively managing costs and minimizing negative environmental impacts [ 79 ]. This study also defined digital healthcare sustainability as the ability of a healthcare system to use digital technologies in a manner that ensures the practical long-term and effectiveness of healthcare delivery. EHR have impact economic, social, and the environmental sustainability by reducing paper wastage and pollution in the hospital environment, reducing patient waiting time, increase wellbeing to patient, and help reduce the clinical cost attached to hospital management. The study of [ 80 ] on e-health literacy discovered that behavioral intention influences the use behavior of e-health literacy by students in China. This study defined EHR for sustainability as a digital repository of patient health information that impacts sustainability by reducing paper consumption, improving access to care, and enhancing healthcare efficiency through cost savings and operational efficiencies in healthcare delivery. Healthcare providers are more likely to continue using EHR for effective healthcare delivery. Thus, the following hypothesis is proposed: H7: There is a strong and positive connection between the actual use of EHR and healthcare sustainability. 3. Research Methodology 3.1 Sampling and Data Collection A quantitative deductive approach was used in this research to test the hypotheses and validate the proposed research model, as it is an effective method for evaluating hypotheses, assessing relationships within groups, and assessing the interconnections among variables [ 81 ]. This study employed purposive sampling to ensure that participants had relevant experience and knowledge in using EHR systems, which is critical for obtaining accurate insights into factors influencing EHR adoption. Purposive sampling allows for targeted selection of medical care professionals, such as nurses, physicians, pharmacists, and administrative personnel using EHR systems as part of their daily roles. This approach is beneficial in contexts like healthcare where specialized expertise can provide depth in responses and enrich data quality [ 82 ], [ 83 ]. To ensure the participants met the required criteria, a preliminary screening question was added in the survey to authenticate that participant had active experience with EHR systems. This measure guaranteed that only individuals with practical, hands-on familiarity participated in the study. The survey was distributed through WhatsApp groups and professional networks connected to hospitals in Nigeria. A cover letter accompanied the questionnaire, providing details concerning the study's objectives, emphasizing voluntary participation, and assuring respondents of the secrecy of their input. Microsoft Forms was used as platform for distributing and collecting responses efficiently. Respondents were given a particular deadline to finish the survey, after which all submissions were thoroughly reviewed to exclude incomplete or inconsistent entries. Ultimately, 374 valid responses were retained for analysis in this study. 3.2 Instrument of the Study To explore the connections among the factors in the developed model, reliable and valid measurement scales were used, sourced from established literature. The constructs for facilitating conditions, performance expectancy, hedonic motivation, social influence, and effort expectancy were measured using items adapted from research conducted previously [ 7 ], [ 32 ], [ 53 ], [ 57 ], [ 84 ]–[ 86 ]. For the constructs related to confidentiality, integrity, availability, and possession/control items were adapted from [ 30 ], [ 73 ]. Factors measuring Healthcare Sustainability were drawn from sources such as [ 2 ], [ 31 ], [ 78 ], [ 79 ], [ 87 ]. As in previous studies, we used a Likert type 5-point scale to evaluate the items, with responses ranging from 1 (strongly disagree) to 5 (strongly agree). This method was chosen based on its simplicity, easy to use, and cost-efficiency [ 19 ], [ 88 ]. This approach is widely regarded for its strong validity and reliability in capturing responses [ 81 ], [ 89 ]. To confirm the survey item’s reliability, 35 participants were used in a pilot study conducted. The results showed that all the values of Cronbach's alpha were above 0.7, demonstrating strong reliability for all constructs included in the study. 3.3 Data Analysis A two-step analytical approach, combining PLS-SEM with ANN techniques, was used for the analysis of data. This combination leverages the strengths of both techniques, providing a comprehensive framework for studying EHR adoption [ 7 ], [ 73 ], [ 90 ], [ 91 ]. In the initial phase, PLS-SEM was utilized to validate the model proposed and test the hypotheses. PLS-SEM is ideal for testing theoretical models by identifying relationships between latent variables and validating hypothesized paths. It's a robust method that can handle complex causal models, including multiple latent variables and their relationships [ 92 ], [ 93 ]. PLS-SEM is well-suited for predictive research, concentrating on the model predictive power. It's also useful for exploratory research, allowing for the identification of new relationships and hypotheses [ 94 ], [ 95 ]. ANN, on the other hand, captures both linear and nonlinear relationships, uncovering complex patterns that traditional linear models may miss [ 7 ], [ 91 ], [ 96 ]. Unlike traditional linear models, ANN is more flexible since it is less impacted by deviations from statistical assumptions, making it capable of providing a better fit for the data when traditional models may fall short [ 40 ], [ 97 ]. By combining PLS-SEM and ANN, the study benefits from PLS-SEM's ability to confirm hypothesized relationships, while ANN uncovers deeper, nonlinear interactions that conventional methods might miss. This integration results in a more detailed and nuanced knowledge of the factors that influence EHR adoption. 4 Findings 4.1 Assessment of Non-Response and Common Method Bias Similar to research conducted previously [ 90 ], [ 97 ], This study assessed non-response bias by comparing the responses of early and late participants. To assess non-response bias, we performed t-tests comparing the responses of the first 100 participants to the last 100. This method helps identify significant differences between early and late respondents, ensuring that our findings were robust and not influenced by the timing of survey completion, thereby enhancing the validity of our results, following the approach by [ 98 ]. The results of t-test revealed no significant differences among the groups, confirming that non-response bias is absent. Additionally, given that the collected data were from a single source, an assessment for common method bias was conducted. Harman's one-factor test was applied to the study variables, indicating that the maximum variance attributed to a single factor was 37.28%, which is well below the recommended threshold of 50%, as outlined in relevant literature [ 99 ], [ 100 ], this suggests that there is no common method bias present. Additionally, we utilized the variance inflation factor (VIF) as recommended by [ 101 ], and found that all constructs had VIF values below the 3.3 threshold. This further supports the conclusion that common method bias is absent. Consequently, the instrument used in this study is deemed free from such biases. 4.2 Evaluation of the Measurement Model The study commenced with an assessment of the measurement model to verify convergent validity, discriminant validity, and internal consistency reliability prior to testing the hypotheses in the structural model, following the guidelines set by [ 102 ], [ 103 ]. To evaluate construct reliability, we used two metrics: composite reliability (CR) and Cronbach's alpha (CA). The CA values ranged from 0.818 to 0.917, and the CR values ranged from 0.824 to 0.922. Since both of these metrics exceeded the recommended threshold of 0.70, this indicates that the constructs used in the study exhibit strong reliability, as supported by [ 103 ]. This thorough evaluation ensures that the measurement model is robust and trustworthy for further analysis. To establish convergent validity, we confirmed that the outer loadings of our constructs exceeded 0.70 and that the values of average variance extracted (AVE) were above the 0.50 threshold, as recommended by [ 102 ]. This information is detailed in Table 1 . Discriminant validity was assessed using the Heterotrait-Monotrait ratio of correlations (HTMT), with all calculated values remaining below the 0.85 thresholds, consistent with the standards outlined by [ 104 ]The results for HTMT analysis are shown in Table 2 . The variance inflation factor (VIF) values ranged from 1.415 to 2.71, which is well below the recommended maximum of 3.3. This confirms that there were no issues with multicollinearity among the constructs. In summary, this study successfully demonstrated convergent validity, discriminant validity, and internal consistency reliability, ensuring that the measurement model is robust and credible for further hypothesis testing. Table 1 Reliability and convergent validity results Construct Item Outer loadings VIF Cronbach's alpha Composite reliability AVE EHR Use AU: 1 0.794 1.468 0.843 0.855 0.677 AU: 2 0.839 2.18 AU: 3 0.84 2.186 AU: 4 0.818 2.061 Availability AV: 1 0.81 1.706 0.87 0.872 0.719 AV: 2 0.87 2.479 AV: 3 0.846 2.252 AV: 4 0.865 2.321 Confidentiality C: 1 0.805 1.746 0.818 0.824 0.647 C: 2 0.821 1.722 C: 3 0.822 1.831 C: 4 0.768 1.638 Possession / Control Co: 1 0.833 2.037 0.877 0.879 0.731 Co: 2 0.867 2.404 Co: 3 0.857 2.149 Co: 4 0.862 2.289 Effort Expectancy EE: 1 0.779 1.605 0.833 0.834 0.667 EE: 2 0.839 1.956 EE: 3 0.829 1.863 EE: 4 0.818 1.811 Facilitating Conditions FC: 1 0.876 2.43 0.896 0.898 0.762 FC: 2 0.883 2.694 FC: 3 0.873 2.487 FC: 4 0.859 2.359 Hedonic Motivation HM: 1 0.753 1.415 0.847 0.848 0.685 HM: 2 0.869 2.456 HM: 3 0.843 2.295 HM: 4 0.841 2.545 Healthcare Sustainability HS: 1 0.81 2.333 0.917 0.922 0.667 HS: 2 0.803 2.55 HS: 3 0.847 2.71 HS: 4 0.797 2.338 HS: 5 0.843 2.699 HS: 6 0.828 2.609 HS: 7 0.786 2.285 Integrity I: 1 0.875 2.17 0.884 0.907 0.738 I: 2 0.853 2.304 I: 3 0.85 2.536 I: 4 0.858 2.249 Performance Expectancy PE: 1 0.866 2.308 0.882 0.884 0.738 PE: 2 0.87 2.336 PE: 3 0.852 2.252 PE: 4 0.849 2.158 Social Influence SI: 1 0.822 1.805 0.854 0.858 0.695 SI: 2 0.85 2.061 SI: 3 0.825 2.02 SI: 4 0.837 1.948 Table 2 HTMT results. Construct 1 2 3 4 5 6 7 8 9 10 11 1. Availability 2. Confidentiality 0.534 3. EHR Use 0.392 0.595 4. Effort Expectancy 0.506 0.545 0.6 5. Facilitating Conditions 0.371 0.492 0.443 0.479 6. Healthcare Sustainability 0.443 0.511 0.471 0.526 0.482 7. Hedonic Motivation 0.506 0.477 0.399 0.474 0.645 0.508 8. Integrity 0.715 0.456 0.327 0.451 0.385 0.513 0.423 9. Performance Expectancy 0.452 0.504 0.528 0.69 0.51 0.481 0.552 0.418 10. Possesssion / Control 0.707 0.552 0.506 0.499 0.42 0.455 0.432 0.676 0.389 11. Social Influence 0.528 0.566 0.577 0.756 0.36 0.492 0.391 0.511 0.542 0.5 4.3 Structural Model assessment After the validity of the measurement model was confirmed, the next step involved assessing the structural model's predictive power and clarifying the relationships between variables, as outlined by [ 94 ]. To accomplish this, we employed a statistical method called bootstrapping, which involved taking 5,000 samples to ensure our results were robust. This analysis provided important metrics such as R² (coefficient of determination) and the path and t-values. These measures are essential for understanding how the constructs interact with one another, providing insights into the dynamics at play [ 103 ]. The findings, presented in Table 3 and shown in Fig. 2 , largely confirmed all the proposed hypotheses, indicating that the structural model effectively captured the relationships and predictive power among the variables under study, largely supported all the hypotheses proposed. For instance, Performance Expectancy had a significant effect on EHR Use (β = 0.146, t = 2.779, p = 0.003), supporting hypothesis H1. Likewise, Effort Expectancy (β = 0.144, t = 2.548, p = 0.005) and Social Influence (β = 0.184, t = 3.389, p < 0.001) were also positively linked to EHR Use, confirming hypotheses H2 and H4. Facilitating Conditions had a positive, though slightly smaller, impact (β = 0.086, t = 1.733, p = 0.042), supporting H3. Other significant influences on EHR Use included Confidentiality (β = 0.211, t = 4.322, p < 0.001) and Possession/Control (β = 0.23, t = 3.805, p < 0.001), backing hypotheses H6 and H9. Integrity also had a positive but moderate effect (β = 0.108, t = 1.925, p = 0.027), supporting H7. However, Hedonic Motivation (β = 0.001, t = 0.023, p = 0.491) and Availability (β = -0.042, t = 0.721, p = 0.235) did not significantly impact EHR Use, so H5 and H8 were not supported. The link between EHR Use and Healthcare Sustainability was very strong (β = 0.429, t = 9.429, p < 0.001), supporting H10. These relationships were validated through the bootstrapping method, as summarized in Table 3 . Examining the R² values provides insight into how much variance in the dependent variables is explained by the model. Specifically, the model accounts for 43.5% of the variance in EHR Use (R² = 0.435), while it explains 18.4% of the variance in Healthcare Sustainability through EHR Use (R² = 0.184). The study evaluated effect sizes (f²) following the guidelines set by [ 105 ]. According to their framework, effect sizes are categorized as small (0.02), medium (0.15), and large (0.35). This analysis helps in understanding the practical significance of the relationships identified in the model, providing a clearer picture of the impact that EHR Use has on Healthcare Sustainability. It was evaluated to see how much each variable contributed to the outcome. As shown in Table 3 , Performance Expectancy, Social Influence, Confidentiality and Possession/Control had small effects. The relationship between EHR Use and Healthcare Sustainability had a medium effect size (f² = 0.226), indicating a strong impact. However, Effort Expectancy, Hedonic Motivation, Integrity, Facilitating Condition, and Availability were found to have no significant influence on EHR usage. Table 3 Results of the Structural Model Analysis H Path β t-Values p-Values f-square VIF R² Values 1 Performance Expectancy -> EHR Use 0.146 2.779 0.003 0.021 1.83 0.435 2 Effort Expectancy -> EHR Use 0.144 2.548 0.005 0.017 2.17 3 Facilitating Conditions -> EHR Use 0.086 1.733 0.042 0.008 1.68 4 Social Influence -> EHR Use 0.184 3.389 0 0.03 1.97 5 Hedonic Motivation -> EHR Use 0.001 0.023 0.491 0 1.75 6 Confidentiality -> EHR Use 0.211 4.322 0 0.048 1.62 7 Integrity -> EHR Use 0.108 1.925 0.027 0.01 1.98 8 Availability -> EHR Use -0.042 0.721 0.235 0.001 2.15 9 Possession / Control -> EHR Use 0.23 3.805 0 0.047 1.99 10 EHR Use -> Healthcare Sustainability 0.429 9.429 0 0.226 1 0.184 4.4 ANN Analysis Given the existence of nonlinear relationships within the study model, ANN was incorporated into the data analysis. Unlike traditional regression techniques, ANNs have the advantage of capturing both linear and nonlinear relationships without being influenced by distribution assumptions, making them a more robust analytical tool [ 73 ], [ 106 ]. After identifying significant factors through PLS-SEM, these factors were integrated into the ANN model for further analysis. The study employed a multilayer perceptron network, which included two hidden layers, thereby classifying it as a deep learning model. The number of neurons in each hidden layer was determined using a specialized algorithm [ 90 ], [ 97 ]. To avoid overfitting where the model becomes too tailored to the training data, a 10-fold cross-validation technique was utilized. This approach helped ensure the model’s performance would generalize well to new, unseen data. Additionally, the significance of each predictor was evaluated to understand their contributions. The sigmoid function was chosen as the activation function for both the hidden and output layers, as noted in previous literature [ 91 ]. Two distinct ANN models were developed for the analysis shown in Fig. 3 and Fig. 4. The first model focused on predicting the use of EHR systems based on multiple predictors, while the second model examined the relationship between EHR use and healthcare sustainability. As shown in Fig. 3 , Model 1 utilized seven input variables, including confidentiality, effort expectancy, facilitating conditions, integrity, performance expectancy, possession/control, and social influence. These inputs were processed through two hidden layers, identifying complex patterns and nonlinear interactions among the variables. The output layer of Model 1 represented the predicted EHR use, reflecting healthcare providers’ adoption and engagement with the systems. Model 2 was designed to isolate and evaluate the impact of EHR use on healthcare sustainability. The model’s input layer consisted of EHR use as the sole variable, emphasizing its direct contribution to sustainability outcomes. This variable was processed through two hidden layers, ultimately producing healthcare sustainability as the output. The Root Mean Square Error (RMSE) was calculated for ten different networks to assess the model's accuracy, as detailed in Table 4 . The mean RMSE values for Model 1 are 0.11546 for training and 0.11485 for testing, with standard deviations of 0.01295 and 0.01537, respectively. These low RMSE values and minimal standard deviations suggest that Model 1 has high predictive accuracy and generalizability. Model 2 exhibits mean RMSE values of 0.15402 for training and 0.15806 for testing, with standard deviations of 0.01774 and 0.00937, respectively. This high level of accuracy contributed to the model’s overall excellent prediction performance [ 7 ], [ 107 ]. Table 4 ANN Model RMSE Values Network Model 1 Model 2 RMSE (Training) RMSE (Testing) RMSE (Training) RMSE (Testing) 1 0.11622 0.13038 0.15834 0.15627 2 0.12889 0.11683 0.16063 0.15082 3 0.11226 0.12705 0.15435 0.16226 4 0.12361 0.11998 0.16054 0.15910 5 0.08167 0.11784 0.10402 0.18115 6 0.11737 0.07726 0.16144 0.14941 7 0.11744 0.12563 0.15918 0.15582 8 0.12513 0.11518 0.16034 0.15036 9 0.11390 0.12921 0.16369 0.15305 10 0.11816 0.12401 0.15765 0.16240 Mean 0.11546 0.11485 0.15402 0.15806 Standard deviation 0.012951843 0.015372807 0.017742004 0.009373395 4.5 Sensitive analysis The sensitivity analysis for Model 1, which predicts EHR adoption, evaluates the relative importance of seven predictors. The sensitivity analysis helped identify and rank the importance of each predictor according to their normalized values, as presented in Table 5 . This analysis was conducted across ten neural network iterations, with the mean importance and normalized importance for each predictor presented in Table 5 . The normalized importance values indicate that confidentiality (87.31%), effort expectancy (80.47%), and possession/control (79.17%) are the most influential factors in predicting EHR adoption. Social influence (70.54%) and performance expectancy (55.49%) also play significant roles, though to a lesser extent. Facilitating conditions (39.04%) and integrity (27.02%) have comparatively lower impacts on the model's predictions (effort expectancy), and control over information (possession/control) in influencing healthcare professionals' adoption of EHR systems. The prominence of social influence suggests that peer opinions and social networks also significantly affect adoption decisions. In contrast, factors such as facilitating conditions and integrity, while relevant, appear to have a less substantial impact on the decision to adopt EHR systems. Table 5 Sensitivity analysis results Network Confidentiality Effort Expectancy Facilitating Conditions Integrity Performance Expectancy Possesssion/Conrtol Social Influence 1 0.273 0.183 0.032 0.090 0.098 0.219 0.104 2 0.136 0.196 0.148 0.031 0.159 0.105 0.226 3 0.225 0.223 0.063 0.058 0.145 0.192 0.093 4 0.190 0.088 0.107 0.067 0.159 0.190 0.199 5 0.289 0.249 0.108 0.007 0.098 0.144 0.105 6 0.185 0.266 0.052 0.033 0.127 0.236 0.102 7 0.244 0.225 0.076 0.070 0.049 0.154 0.182 8 0.166 0.142 0.099 0.109 0.138 0.159 0.186 9 0.172 0.120 0.127 0.049 0.148 0.189 0.195 10 0.155 0.202 0.055 0.082 0.117 0.220 0.169 Mean Importance 0.2036 0.1894 0.0867 0.0595 0.1237 0.1809 0.1561 Normalized importance % 87.31% 80.47% 39.04% 27.02% 55.49% 79.17% 70.54% Ranking 1 2 6 7 5 3 4 5. Discussion This study combined SEM with ANN to examine the factors that influence EHR adoption in the context of healthcare sustainability. By integrating these two methods, we gained a more thorough understanding of both linear and nonlinear relationships between key factors driving EHR adoption. Based on SEM results the findings revealed that performance expectancy plays a significant role in the adoption of EHRs. This aligns with research by [ 52 ] on medical IoT, and [ 53 ] on factors influencing the use of AI-based chatbots for sharing of knowledge. This shows that technologies perceived to enhance performance are more likely to be embraced. Healthcare professionals are more likely to adopt EHR systems when they believe these tools will boost their efficiency and patient care, ultimately promoting sustainability through improved healthcare delivery. Effort expectancy is another significant factor. Studies by [ 51 ] and [ 18 ] emphasize that effort expectancy is a major driver for adopting healthcare technologies. The results confirm that when EHR systems are easy to use, adoption rates increase. By reducing the cognitive and time burdens on healthcare providers these systems promote more sustainable practices by streamlining workflows and enhancing operational efficiency. Facilitating conditions also contribute positively though its impact is less pronounced. This finding supports [ 59 ], who identify the importance of resources and infrastructure in technology adoption. For EHRs, factors like sufficient training and a supportive infrastructure help ensure the systems are effectively utilized. Social influence has a significant impact as well, consistent with [ 48 ] who highlighted its role in smart meter adoption. In healthcare, peer influence and the perceived acceptance of EHRs among colleagues can drive broader adoption, fostering a collective shift towards integrated and efficient healthcare practices that support sustainability. Interestingly, hedonic motivation does not significantly impact EHR adoption. This did not align with the findings from [ 18 ] and [ 64 ], where enjoyment was a key factor for other technologies like mobile health apps and smart meters. In the professional setting of EHR use, the emphasis is more on functionality and efficiency than enjoyment, which may explain this difference. Confidentiality emerges as a highly significant factor consistent with previous research [ 41 ], [ 67 ]–[ 70 ]. Reflecting the study of [ 71 ], the trust in EHR systems to secure data is crucial for their adoption. Protecting patient privacy and ensuring data security are essential for sustainable healthcare practices. Integrity has a moderate yet important impact, underlining the need for reliable and accurate data. This is vital for protecting user from theft, hacking, or loss by third parties which aligns with existing literature that stresses the importance of security of information [ 40 ], [ 71 ]. Availability was found to be less significant, differing from findings of [ 72 ] who noted its importance in smart home adoption. In healthcare, reliable access to EHR systems is often expected which may explain why it’s not a primary concern for adoption. Finally, Possession/Control is highly significant, aligning with [ 40 ] findings. The ability for healthcare professionals to manage and control data within EHRs builds trust and supports sustainable practices by ensuring proper data handling. The strong link between EHR use and Healthcare Sustainability is clear, as highlighted by [ 80 ]. EHRs play an important role in enhancing data management, improving care quality, and streamlining healthcare operations, all of which are vital for achieving long-term sustainable outcomes in healthcare. The findings of ANN revealed that confidentiality was the most important factor, with a normalized importance of 87.31%, highlighting those concerns about data security and privacy are the main barriers to EHR adoption. This is consistent with prior research emphasizing the critical nature of confidentiality in healthcare technologies, where sensitive patient information is involved. Effort expectancy (80.47%) and possession/control (79.17%) were major factors, indicating that users are more likely to adopt EHR systems if they are easy to use and if users feel a sense of control over the management of data. Social influence (70.54%) played a significant role, suggesting that peer pressure and organizational culture strongly impact the decision to adopt EHR systems. This finding points to the need for collaborative environments and leadership support in promoting EHR technology. Performance expectancy (55.49%) and facilitating conditions (39.04%) were also relevant but less influential, indicating that while users believe in the effectiveness of EHR systems and the availability of supporting resources, these factors are secondary to concerns about confidentiality and usability. The relatively lower importance of hedonic motivation suggests that the enjoyment of using EHR systems is not as significant for healthcare professionals compared to functional concerns such as security and ease of use. Similarly, integrity (27.02%) and availability (48.72%) were less critical but still contributed to the overall adoption of EHR systems, particularly in maintaining data accuracy and ensuring system uptime. The ANN analysis further validated these findings, with low Root Mean Square Error (RMSE) values and minimal standard deviations indicating strong predictive accuracy. The hybrid SEM-ANN approach proved effective in uncovering both linear and nonlinear relationships, providing a more nuanced understanding of EHR adoption. Importantly, our findings show that key predictors like confidentiality and effort expectancy can have outsized effects on EHR adoption, stressing the need for targeted interventions by healthcare administrators and policymakers. 6. Implications of the Study 6.1 Practical Implications The study findings offer useful guidance for healthcare organizations and policymakers aiming to enhance the adoption of EHR. Given that confidentiality emerged as the most critical factor impacting EHR adoption, it is fundamental for healthcare organizations to prioritize strong data security initiatives. This includes investing in advanced security measures such as secure access protocols, encryption, and regular security audits. By effectively addressing privacy concerns, organizations can cultivate trust in EHR systems, making them more appealing to potential users. Moreover, the significance of effort expectancy underscores the need for EHR systems to be user-friendly. Organizations should focus on improving the design and usability of these systems, ensuring they are effortless and easy to navigate. Additionally, providing ongoing training and support can help ease the transition to EHR, making it less daunting for users. The study also highlights the impact of social influence on EHR adoption. Healthcare leaders have an opportunity to encourage greater adoption by fostering a workplace culture that emphasizes the benefits of EHR use. Encouraging peer support and endorsement from influential figures can motivate others to embrace the technology. Addressing the concerns related to possession and control is vital for building trust in EHR systems. By giving users more authority over how their data is managed, healthcare organizations can enhance user confidence and encourage a greater willingness to adopt EHR technologies. For policymakers, it is crucial to focus on the most influential factors, specifically confidentiality and usability when crafting initiatives aimed at boosting EHR adoption. Creating incentives for healthcare organizations to implement best practices in data security could serve as an effective strategy to promote wider acceptance of EHR systems. Overall, these actionable insights can help shape effective strategies for encouraging EHR adoption and improving healthcare sustainability. 6.2 Theoretical Contributions This study makes significant contributions to the theoretical understanding of technology adoption within the healthcare sector, particularly regarding EHR systems. First, it expands the existing literature on EHR adoption by incorporating crucial cybersecurity factors such as confidentiality, integrity, availability, and possession and control. This research enriches traditional technology adoption models by highlighting the often-overlooked yet essential role of data security and user control in the adoption of healthcare technologies. Additionally, by employing a combination of SEM-ANN, the study provides a more nuanced exploration of both linear and nonlinear relationships among different variables. This hybrid approach enhances the robustness of technology acceptance theory, demonstrating how nonlinear factors can significantly influence adoption rates and offering fresh perspectives for future research in this area. The identification of nonlinear relationships among key predictors of EHR adoption challenges the conventional linear assumptions prevalent in technology acceptance studies. The findings indicate that certain factors, particularly confidentiality and effort expectancy, may have a more substantial impact on adoption than previously recognized. This insight calls into question the linear models that have traditionally been applied in technology acceptance research. 6.3 Methodological Contributions This study contributes methodologically by showcasing the benefits of a hybrid SEM-ANN approach to analyze complex adoption phenomena. By integrating SEM and ANN, it provides a novel framework. SEM captures linear relationships, while ANN identifies both linear and nonlinear interactions, making it powerful for understanding technology adoption behaviors. The use of ANN improved predictive accuracy, as reflected in the low Root Mean Square Error (RMSE) values, showing its effectiveness in managing complex variable interactions. Additionally, the study employed 10-fold cross-validation, enhancing the reliability of the ANN model and minimizing overfitting risks, which bolstered the credibility of the findings. This methodological rigor sets a benchmark for future technology adoption studies, stressing the importance of advanced analytical techniques to capture the challenges of adoption behaviors across various contexts. 7. Conclusion This study showed that combining SEM and ANN offers a powerful way to analyze factors affecting EHR adoption. Confidentiality, effort expectancy, and possession/control emerged as the most influential factors, underlining the importance of addressing security concerns and system usability. These findings provide useful guidance for healthcare organizations and policymakers looking to encourage sustainable EHR adoption, contributing to broader efforts to improve healthcare sustainability. While this study offers useful insights, there are some limitations to consider. One key limitation is the use of cross-sectional data, which only captures a snapshot of adoption behavior at a single point in time. To gain a deeper understanding of how attitudes and behaviors towards EHR systems evolve, future studies should take a longitudinal approach, tracking changes over time. Additionally, while we focused on several important predictors, other factors like cultural influences or the impact of regulatory policies were not included in this study but could offer important insights in future research. Exploring how these factors vary across different healthcare settings or regions could also lead to more targeted and context-specific findings. Declarations Ethics Approval : “The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (# 2023-7). Informed consent was obtained from all subjects involved in the study.” Informed Consent : “Informed consent was obtained from all subjects involved in the study.” Data Availability : “Data will be available upon request.” Conflict of Interest : “No potential competing interest was reported by the authors.” Author Contributions: Muhammed Ibrahim; Writing original draft preparation, Data collection, Methodology. Mohammed A. Al-Sharafi ; Conceptualization, Supervision, Formal analysis, Mousa Albashrawi ; Writing, review and editing; Moamin A. Mahmoud ; Writing, review and editing, Supervision. Funding: “This work was supported by the Dato’ Low Tuck Kwong International Energy Transition Grant under the project code of 202202002ETG.” References United Nations General Assembly, “Transforming our world: the 2030 Agenda for Sustainable Development | Department of Economic and Social Affairs,” outcome document of the United Nations summit for the adoption of the post- 2015 agenda , RES/A/70/L.1 . United Nations, New York , 2015. https://sdgs.un.org/2030agenda (accessed Dec. 03, 2024). M. Al-Emran, “Beyond technology acceptance: Development and evaluation of technology-environmental, economic, and social sustainability theory,” Technol. Soc., p. 102383, 2023. A. Kruszyńska-Fischbach, S. Sysko-Romańczuk, T. M. Napiórkowski, A. Napiórkowska, and D. Kozakiewicz, “Organizational e-Health Readiness: How to Prepare the Primary Healthcare Providers’ Services for Digital Transformation,” Int. J. Environ. Res. Public Health, vol. 19, no. 7, 2022, doi: 10.3390/ijerph19073973 . V. Mishra, D. Liebovitz, M. Quinn, L. Kang, T. Yackel, and R. Hoyt, “Factors That Influence Clinician Experience with Electronic Health Records,” Perspect. Heal. Inf. Manag., vol. 19, no. 1, 2022. G. Cline and J. Luiz, “Information technology systems in public sector health facilities in developing countries: the case of South Africa,” BMC Med. Inform. Decis. Mak., vol. 13, p. 13, 2013. A. M. Al-Momani, T. Ramayah, and M. A. Al-Sharafi, “Exploring the impact of cybersecurity on using electronic health records and their performance among healthcare professionals: A multi-analytical SEM-ANN approach,” Technol. Soc. , vol. 77, no. May, p. 102592, Jun. 2024, doi: 10.1016/j.techsoc.2024.102592 . Z. Thabet, S. Albashtawi, H. Ansari, M. Al-Emran, M. A. Al-Sharafi, and A. A. AlQudah,“Exploring the Factors Affecting Telemedicine Adoption by Integrating UTAUT2 and IS Success Model: A Hybrid SEM–ANN Approach,” IEEE Trans. Eng. Manag. , 2023, doi: 10.1109/TEM.2023.3296132. A. D. Tori, “UTAUT Modification Model for the Analysis of User Experience of Telemedicine Application Users in Indonesia,” 2024 3rd Int. Conf. Digit. Transform. Appl. , pp. 33–38, 2024, doi: 10.1109/ICDXA61007.2024.10470732 . S. A. Abebe, “Intention to Use Personal Health Record System and Its Predictors Among Chronic Patients Enrolled at Public Hospitals in Bahir Dar City, Northwest Ethiopia : Using Modified UTAUT-2 Model,” pp. 1–34, 2023. O. Christopher, D. Ang, E. Etu, I. Tenebe, and S. Edo, “International Journal of Information Management Data Insights Why do healthcare workers adopt digital health technologies - A cross-sectional study integrating the TAM and UTAUT model in a developing economy,” Int. J. Inf. Manag. Data Insights, vol. 3, no. 2, p. 100186, 2023, doi: 10.1016/j.jjimei.2023.100186 . I. Keshta and A. Odeh, “Security and privacy of electronic health records: Concerns and challenges,” Egypt. Informatics J., vol. 22, no. 2, pp. 177–183, 2021, doi: 10.1016/j.eij.2020.07.003 . E. Mbwambo, “Acceptance of Interoperable Electronic Health Record (EHRs) Systems: A Tanzanian e-Health Perspective Acceptance of Interoperable Electronic Health Record ( EHRs ) Systems : A Tanzanian e-Health Perspective,” 2022. J. R. Ayala Solares et al. , “Deep learning for electronic health records: A comparative review of multiple deep neural architectures,” J. Biomed. Inform. , vol. 101, no. March 2019, p. 103337, 2020, doi: 10.1016/j.jbi.2019.103337 . Y. C. Chen, J. C. Wu, I. Haschler, A. Majeed, T. J. Chen, and T. Wetter, “Academic impact of a public electronic health database: Bibliometric analysis of studies using the general practice research database,” PLoS One, vol. 6, no. 6, pp. 1–7, 2011, doi: 10.1371/journal.pone.0021404 . J. Huang et al. , “Factors Associated With the Acceptance of an eHealth App for Electronic Health Record Sharing System: Population-Based Study,” J. Med. Internet Res., vol. 24, no. 12, 2022, doi: 10.2196/40370 . E. R. Melnick et al. , “The Association Between Perceived Electronic Health Record Usability and Professional Burnout Among US Physicians,” Mayo Clin. Proc. , vol. 95, no. 3, pp. 476–487, 2020, doi: 10.1016/j.mayocp.2019.09.024 . S. Shi, D. He, L. Li, N. Kumar, M. K. Khan, and K. K. R. Choo, “Applications of blockchain in ensuring the security and privacy of electronic health record systems: A survey,” Comput. Secur., vol. 97, 2020, doi: 10.1016/j.cose.2020.101966 . G. Aydin, “Increasing mobile health application usage among Generation Z members: evidence from the UTAUT model,” vol. 17, no. 3, pp. 353–379, 2023, doi: 10.1108/IJPHM-02-2021-0030 . M. Ibrahim, Y. Kani, and E. Ahmed, “Examining the Impact of Implementation of Electronic Health Record System for Effective Health Management in Katsina State Hospitals, Nigeria,” Int. J. Gastroenterol. , vol. 3, no. 2, pp. 27–34, Dec. 2019, doi: 10.11648/j.ijg.20190302.11 . J. J. Hathaliya and S. Tanwar, “An exhaustive survey on security and privacy issues in Healthcare 4. 0,” Comput. Commun. , vol. 153, no. January, pp. 311–335, 2020, doi: 10.1016/j.comcom.2020.02.018 . K. Miyachi and T. K. Mackey, “hOCBS: A privacy-preserving blockchain framework for healthcare data leveraging an on-chain and off-chain system design,” vol. 58, no. February, 2021, doi: 10.1016/j.ipm.2021.102535 . S. Upadhyay and H. F. Hu, “A Qualitative Analysis of the Impact of Electronic Health Records (EHR) on Healthcare Quality and Safety: Clinicians’ Lived Experiences,” Heal. Serv. Insights, vol. 15, 2022, doi: 10.1177/11786329211070722 . M. D. Kasaye, N. D. Mengestie, S. Beyene, N. Kebede, and H. S. Ngusie, “Acceptance of electronic medical records and associated factor among physicians working in University of Gondar comprehensive specialized hospital: A cross-sectional study,” 2023, doi: 10.1177/20552076231213445 . E. C. O’Brien et al. , “The use of electronic health records for recruitment in clinical trials: a mixed methods analysis of the Harmony Outcomes Electronic Health Record Ancillary Study,” Trials, vol. 22, no. 1, pp. 4–11, 2021, doi: 10.1186/s13063-021-05397-0 . A. Nanjo, H. Evans, K. Direk, A. C. Hayward, A. Story, and A. Banerjee, “Prevalence, incidence, and outcomes across cardiovascular diseases in homeless individuals using national linked electronic health records,” Eur. Heart J., vol. 41, no. 41, pp. 4011–4020, 2020, doi: 10.1093/eurheartj/ehaa795 . K. L. Colborn et al. , “Development and validation of models for detection of postoperative infections using structured electronic health records data and machine learning,” Surg. (United States), vol. 173, no. 2, pp. 464–471, 2023, doi: 10.1016/j.surg.2022.10.026 . K. C. Derecho et al. , “Technology adoption of electronic medical records in developing economies: A systematic review on physicians ’ perspective,” no. 2, 2024, doi: 10.1177/20552076231224605 . G. Aydin and S. Kumru, “Paving the way for increased e-health record use: elaborating intentions of Gen-Z,” Heal. Syst., vol. 12, no. 3, pp. 281–298, 2023, doi: 10.1080/20476965.2022.2129471 . V. Venkatesh, “C ONSUMER A CCEPTANCE AND U SE OF I NFORMATION T ECHNOLOGY: E XTENDING THE U NIFIED T HEORY,” vol. 36, no. 1, pp. 157–178, 2012. I. Arpaci and K. Sevinc, “Development of the cybersecurity scale (CS-S): Evidence of validity and reliability,” Inf. Dev., vol. 38, no. 2, pp. 218–226, 2022, doi: 10.1177/0266666921997512 . T. K. Rattan, M. Joshi, G. Vesty, and S. Sharma, “Sustainability indicators in public healthcare: A factor analysis approach,” J. Clean. Prod., vol. 370, no. August, p. 133253, 2022, doi: 10.1016/j.jclepro.2022.133253 . V. Venkatesh, J. Y. L. Thong, and X. Xu, “Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology,” MIS Q. Manag. Inf. Syst., vol. 36, no. 1, pp. 157–178, 2012, doi: 10.2307/41410412 . Y. K. Dwivedi, N. P. Rana, A. Jeyaraj, M. Clement, and M. D. Williams, “Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model,” Inf. Syst. Front. , vol. 21, no. 3, pp. 719–734, Jun. 2019, doi: 10.1007/s10796-017-9774-y . F. D. Davis, “A technology acceptance model for empirically testing new end-user information systems: Theory and results.” Massachusetts Institute of Technology, 1985. I. Ajzen and M. Fishbein, Understanding attitudes and predicting social behavior . 1980. V. Venkatesh, J. Thong, and X. Xu, “Unified Theory of Acceptance and Use of Technology: A Synthesis and the Road Ahead,” J. Assoc. Inf. Syst., vol. 17, no. 5, pp. 328–376, May 2016, doi: 10.17705/1jais.00428 . M. B. Alazzam, A. S. H. Basari, A. S. Sibghatullah, Y. M. Ibrahim, M. R. Ramli, and M. H. Naim, “Trust in stored data in EHRs acceptance of medical staff: Using UTAUT2,” Int. J. Appl. Eng. Res., vol. 11, no. 4, pp. 2737–2748, 2016. M. A. Al-Sharafi et al. , “Generation Z use of artificial intelligence products and its impact on environmental sustainability: A cross-cultural comparison,” Comput. Human Behav., vol. 143, p. 107708, Jun. 2023, doi: 10.1016/j.chb.2023.107708 . H. Albanna, A. A. Alalwan, and M. Al-Emran, “An integrated model for using social media applications in non-profit organizations,” Int. J. Inf. Manage., vol. 63, p. 102452, Apr. 2022, doi: 10.1016/j.ijinfomgt.2021.102452 . I. Arpaci and M. Bahari, “A complementary SEM and deep ANN approach to predict the adoption of cryptocurrencies from the perspective of cybersecurity,” Comput. Human Behav., vol. 143, p. 107678, Jun. 2023, doi: 10.1016/j.chb.2023.107678 . O. Access, “Cybersecurity Framework for Ensuring Confidentiality, Integrity, and Availability of University Management Systems in,” no. 1, pp. 16–38, 2023, doi: 10.13140/RG.2.2.15091.30241 . B. Wheatley, “Transforming care delivery through health information technology.,” Perm. J., vol. 17, no. 1, pp. 81–86, 2013, doi: 10.7812/TPP/12-030 . P. Poba-Nzaou, N. Kume, and S. Kobayashi, “Governance and Sustainability of an Open Source Electronic Health Record: An Interpretive Case Study of OpenDolphin in Japan,” Stud. Health Technol. Inform. , vol. 264, pp. 739–743, Aug. 2019, doi: 10.3233/SHTI190321 . P. Poba-Nzaou, N. Kume, and S. Kobayashi, “Developing and Sustaining an Open Source Electronic Health Record: Evidence from a Field Study in Japan,” J. Med. Syst., vol. 44, no. 9, pp. 1–10, Sep. 2020, doi: 10.1007/S10916-020-01625-3/FIGURES/3 . Venkatesh et al., “User Acceptance of Information Technology: Toward a Unified View,” Inorg. Chem. Commun., vol. 67, no. 3, pp. 95–98, 2003, doi: 10.1016/j.inoche.2016.03.015 . M. Q. Aldossari and A. Sidorova, “Consumer Acceptance of Internet of Things (IoT): Smart Home Context,” J. Comput. Inf. Syst., vol. 60, no. 6, pp. 507–517, 2020, doi: 10.1080/08874417.2018.1543000 . N. Tomić, Z. Kalinić, and V. Todorović, “Using the UTAUT model to analyze user intention to accept electronic payment systems in Serbia,” Port. Econ. J., vol. 22, no. 2, pp. 251–270, 2023, doi: 10.1007/s10258-022-00210-5 . J. Gumz, D. C. Fettermann, Â. M. O. Sant’Anna, and G. L. Tortorella, “Social Influence as a Major Factor in Smart Meters’ Acceptance: Findings from Brazil,” Results Eng., vol. 15, Sep. 2022, doi: 10.1016/j.rineng.2022.100510 . A. N. Tak, B. Becerik-Gerber, L. Soibelman, and G. Lucas, “A framework for investigating the acceptance of smart home technologies: Findings for residential smart HVAC systems,” Build. Environ., vol. 245, no. August, p. 110935, 2023, doi: 10.1016/j.buildenv.2023.110935 . N. Dwi Andini and K. Adiwijaya, “What Factors Do Affect the Adoption of Internet of Things (Smart Home) in Indonesia,” Int. J. Bus. Technol. Manag., vol. 3, no. 2, pp. 84–97, 2021. J. Liu, X. Gong, M. Weal, W. Dai, and S. Hou, “Attitudes and associated factors of patients ’ adoption of patient accessible electronic health records in China — A mixed methods study,” no. 13, 2023, doi: 10.1177/20552076231174101 . A. Alomari and B. Soh, “Determinants of Medical Internet of Things Adoption in Healthcare and the Role of Demographic Factors Incorporating Modified UTAUT,” vol. 14, no. 7, pp. 17–32, 2023. M. Al-Emran, A. A. AlQudah, G. A. Abbasi, M. A. Al-Sharafi, and M. Iranmanesh, “Determinants of Using AI-Based Chatbots for Knowledge Sharing: Evidence From PLS-SEM and Fuzzy Sets (fsQCA),” IEEE Trans. Eng. Manag., pp. 1–15, 2023, doi: 10.1109/tem.2023.3237789 . A. A. Zaid, D. F. Kakeesh, G. A. Al-weshah, and M. M. Al-debei, “Journal of Open Innovation: Technology, Market, and Complexity Consumer post-adoption of e-wallet : An extended UTAUT2 perspective with trust,” J. Open Innov. Technol. Mark. Complex., vol. 9, no. 3, p. 100113, 2023, doi: 10.1016/j.joitmc.2023.100113 . T. Muchenje and R. A. Botha, “Consumer-centric factors for the implementation of smart meters in South Africa,” South African Comput. J., vol. 33, no. 2, pp. 17–54, 2021, doi: 10.18489/SACJ.V33I2.909 . J. Iqbal and M. Idrees, “Understanding the IOT Adoption for Home Automation in the Perspective of UTAUT2,” Glob. Bus. Rev., 2022, doi: 10.1177/09721509221132058 . G. Cao, Y. Duan, J. S. Edwards, and Y. K. Dwivedi, “Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making,” Technovation, vol. 106, p. 102312, Aug. 2021, doi: 10.1016/j.technovation.2021.102312 . J. Kim and K. S. Lee, “Conceptual model to predict Filipino teachers ’ adoption of ICT-based instruction in class: using the UTAUT model,” Asia Pacific J. Educ., vol. 00, no. 00, pp. 1–15, 2020, doi: 10.1080/02188791.2020.1776213 . L. Wong, G. W. Tan, V. Lee, K. Ooi, and G. W. Tan, “Unearthing the determinants of Blockchain adoption in supply chain management,” Int. J. Prod. Res., vol. 0, no. 0, pp. 1–24, 2020, doi: 10.1080/00207543.2020.1730463 . C. Y. Joa and K. Magsamen-conrad, “Social influence and UTAUT in predicting digital immigrants ’ technology use,” Behav. Inf. Technol., vol. 0, no. 0, pp. 1–19, 2021, doi: 10.1080/0144929X.2021.1892192 . A. Alaiad and L. Zhou, “Patients’ behavioral intentions toward using WSN based smart home healthcare systems: An empirical investigation,” Proc. Annu. Hawaii Int. Conf. Syst. Sci. , vol. 2015-March, pp. 824–833, 2015, doi: 10.1109/HICSS.2015.104 . Z. Ren and G. Zhou, “Analysis of Driving Factors in the Intention to Use the Virtual Nursing Home for the Elderly: A Modified UTAUT Model in the Chinese Context,” 2023. I. B. Hassani and A. Ouiddad, “Impact of hedonic motivation and corporate culture on the adoption of an information system,” vol. 49, no. 5, pp. 1561–1578, 2019, doi: 10.1108/K-01-2019-0040 . F. Große-Kreul, “What will drive household adoption of smart energy? Insights from a consumer acceptance study in Germany,” Util. Policy , vol. 75, no. December 2021, 2022, doi: 10.1016/j.jup.2021.101333 . A. Al-azawei and A. Alowayr, “Technology in Society Predicting the intention to use and hedonic motivation for mobile learning: A comparative study in two Middle Eastern countries,” Technol. Soc., vol. 62, no. June, p. 101325, 2020, doi: 10.1016/j.techsoc.2020.101325 . R. L. Goldstein, A. Anoshiravani, M. V. Svetaz, and J. L. Carlson, “Providers’ Perspectives on Adolescent Confidentiality and the Electronic Health Record: A State of Transition,” J. Adolesc. Heal., vol. 66, no. 3, pp. 296–300, 2020, doi: 10.1016/j.jadohealth.2019.09.020 . S. Rathod, M. D. Salunke, M. Yashwante, M. Bhende, S. R. Rangari, and V. D. Rewaskar, “INTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING Ensuring Optimized Storage with Data Confidentiality and Privacy- Preserving for Secure Data Sharing Model Over Cloud,” pp. 0–3, 2023. R. K. Jha, “Cybersecurity and Confidentiality in Smart Grid for Enhancing Sustainability and Reliability,” no. July, 2023, doi: 10.36548/rrrj.2023.2.001 . M. Abdekhoda, “The effect of confidentiality and privacy concerns on adoption of personal health record from patient ’ s perspective The effect of confidentiality and privacy concerns on adoption of personal health record from patient ’ s perspective,” no. January, 2019, doi: 10.1007/s12553-018-00287-z . J. Raphael, A. Mhina, G. Johar, and M. H. Alkawaz, “The Influence of Perceived Confidentiality Risks and Attitude on Tanzania Government Employees ’ Intention to Adopt Web 2. 0 and Social Media for Work-Related Purposes,” Int. J. Public Adm., vol. 42, no. 7, pp. 558–571, 2018, doi: 10.1080/01900692.2018.1491596 . M. U. CHELLADURAI, D. S. Pandian, and D. K. Ramasamy, “A blockchain based patient centric electronic health record storage and integrity management for e-Health systems,” Heal. Policy Technol., vol. 10, no. 4, p. 100513, 2021, doi: 10.1016/j.hlpt.2021.100513 . A. Shuhaiber, I. Mashal, and O. Alsaryrah, “Smart homes as an IoT application: Predicting attitudes and behaviours,” Proc. IEEE/ACS Int. Conf. Comput. Syst. Appl. AICCSA , vol. 2019-Novem, pp. 1–7, 2019, doi: 10.1109/AICCSA47632.2019.9035295 . I. Arpaci, “A Multi-Analytical SEM-ANN Approach to Investigate the Social Sustainability of AI Chatbots Based on Cybersecurity and Protection Motivation Theory,” IEEE Trans. Eng. Manag., 2023, doi: 10.1109/TEM.2023.3339578 . M. Turner, B. Kitchenham, P. Brereton, S. Charters, and D. Budgen, “Does the technology acceptance model predict actual use ? A systematic literature review,” Inf. Softw. Technol., vol. 52, no. 5, pp. 463–479, 2010, doi: 10.1016/j.infsof.2009.11.005 . X. Wang, C. Lee, J. Jiang, G. Zhang, and Z. Wei, “behavioral sciences Research on the Factors Affecting the Adoption of Smart Aged-Care Products by the Aged in China: Extension Based on UTAUT Model,” 2023. M. Christian et al. , “Generation YZ ’ s E-Healthcare Use Factors Distribution in COVID-19 ’ s Third Year: A UTAUT Modeling,” vol. 7, pp. 117–129, 2023. L. Nguyen, E. Bellucci, and L. T. Nguyen, “Electronic health records implementation: An evaluation of information system impact and contingency factors,” Int. J. Med. Inform., vol. 83, no. 11, pp. 779–796, 2014, doi: 10.1016/j.ijmedinf.2014.06.011 . M. Calabrese, S. Suparaku, S. Santovito, and X. Hysa, “Preventing and developmental factors of sustainability in healthcare organisations from the perspective of decision makers: an exploratory factor analysis,” pp. 1–9, 2023. S. Malone et al. , “The Clinical Sustainability Assessment Tool: measuring organizational capacity to promote sustainability in healthcare,” vol. 2, pp. 1–12, 2021. Y. Ma 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 behaviors of non – smoking college students in China: a cross – sectional investigation,” pp. 1–14, 2023, doi: 10.1186/s12889-023-15644-5 . J. W. Creswell and J. D. Creswell, Research Design Qualitative, Quantitative, and Mixed Methods Approaches , Sixth., vol. 6. SAGE, 2023. Accessed: Dec. 20, 2023. [Online]. Available: https://uk.sagepub.com/en-gb/asi/research-design/book270550#description M. D. C. Tongco, “Purposive sampling as a tool for informant selection,” Ethnobot. Res. Appl., vol. 5, pp. 147–158, 2007, doi: 10.17348/era.5.0.147-158 . E. I. Obilor, “Convenience and Purposive Sampling Techniques: Are they the Same?,” Int. J. Innov. Soc. Sci. Educ. Res., vol. 11, no. 1, pp. 1–7, 2023. B. Basarir-Ozel, H. B. Turker, and V. A. Nasir, “Identifying the Key Drivers and Barriers of Smart Home Adoption: A Thematic Analysis from the Business Perspective,” Sustain., vol. 14, no. 15, 2022, doi: 10.3390/su14159053 . Z. Barua and A. Barua, “Modeling the predictors of mobile health adoption by Rohingya Refugees in Bangladesh: An extension of UTAUT2 using combined SEM-Neural network approach,” J. Migr. Heal., vol. 8, no. July, p. 100201, 2023, doi: 10.1016/j.jmh.2023.100201 . V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, “User acceptance of information technology: Toward a unified view,” MIS Q. Manag. Inf. Syst., vol. 27, no. 3, pp. 425–478, 2003, doi: 10.2307/30036540 . R. Mehra and M. K. Sharma, “Sustainability Analytics and Modeling Measures of Sustainability in Healthcare,” Sustain. Anal. Model. , vol. 1, no. January 2021, p. 100001, 2022, doi: 10.1016/j.samod.2021.100001 . Y. Noh, “A Study on Measuring the Change of the Response Results in Likert 5-Point Scale Measurement * 리커트 5점척도에서 자극에 의한 응답결과의 변화 측정에 관한 연구,” vol. 28, no. 3, pp. 335–353, 2011. J. T. Croasmun and L. Ostrom, “Using Likert-Type Scales in the Social Sciences,” J. Adult Educ., vol. 40, no. 1, pp. 19–22, 2011. M. A. Al-Sharafi, M. Iranmanesh, M. Al-Emran, A. I. Alzahrani, F. Herzallah, and N. Jamil, “Determinants of cloud computing integration and its impact on sustainable performance in SMEs: An empirical investigation using the SEM-ANN approach,” Heliyon , vol. 9, no. 5, p. e16299, May 2023, doi: 10.1016/j.heliyon.2023.e16299 . L. Y. Leong, T. S. Hew, K. B. Ooi, G. W. H. Tan, and A. Koohang, “An SEM-ANN Approach - Guidelines in Information Systems Research,” J. Comput. Inf. Syst. , Mar. 2024, doi: 10.1080/08874417.2024.2329128 . M. Sarstedt, C. M. Ringle, and J. F. Hair, “Partial Least Squares Structural Equation Modeling,” Handb. Mark. Res. , no. July, pp. 587–632, 2021, doi: 10.1007/978-3-319-57413-4_15 . T. Ramayah, C. J. Hwa, F. Chuah, and M. A. Memon, Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS 3.0: An Updated and Practical… , 2nd ed., no. July. Kuala Lumpur, Malaysia: Pearson., 2016. J. F. Hair Jr, G. T. M. Hult, C. M. Ringle, and M. Sarstedt, A primer on partial least squares structural equation modeling (PLS-SEM) . Sage publications, 2021. J. Henseler, C. M. Ringle, and M. Sarstedt, “A new criterion for assessing discriminant validity in variance-based structural equation modeling,” J. Acad. Mark. Sci., vol. 43, no. 1, pp. 115–135, Jan. 2015, doi: 10.1007/s11747-014-0403-8 . K. B. Ooi, V. H. Lee, G. W. H. Tan, T. S. Hew, and J. J. Hew, “Cloud computing in manufacturing: The next industrial revolution in Malaysia?,” Expert Syst. Appl. , vol. 93, pp. 376–394, Mar. 2018, doi: 10.1016/j.eswa.2017.10.009 . A. Khayer, M. S. Talukder, Y. Bao, and M. N. Hossain, “Cloud computing adoption and its impact on SMEs’ performance for cloud supported operations: A dual-stage analytical approach,” Technol. Soc., vol. 60, p. 101225, Feb. 2020, doi: 10.1016/j.techsoc.2019.101225 . J. S. Armstrong and T. S. Overton, “Estimating Nonresponse Bias in Mail Surveys,” J. Mark. Res., vol. 14, no. 3, pp. 396–402, 1977, doi: 10.1177/002224377701400320 . N. Kock, “Harman’s single factor test in PLS-SEM: Checking for common method bias,” Data Anal. Perspect. J. , vol. 2, no. 2, pp. 1–6, 2021, [Online]. Available: https://scriptwarp.com/dapj/2021_DAPJ_2_2/Kock_2021_DAPJ_2_2_HarmansCMBTest.pdf S. G. Rogelberg, “Common Method Variance,” SAGE Encycl. Ind. Organ. Psychol. 2nd Ed. , 2017, doi: 10.4135/9781483386874.n68 . N. Kock, “Common method bias in PLS-SEM: A full collinearity assessment approach,” Int. J. e-Collaboration, vol. 11, no. 4, pp. 1–10, 2015, doi: 10.4018/ijec.2015100101 . J. Hair and A. Alamer, “Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example,” Res. Methods Appl. Linguist., vol. 1, no. 3, 2022, doi: 10.1016/j.rmal.2022.100027 . C. M. Ringle and M. Sarstedt, Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R . 2021. J. Henseler, C. M. Ringle, and M. Sarstedt, “A new criterion for assessing discriminant validity in variance-based structural equation modeling,” J. Acad. Mark. Sci., vol. 43, no. 1, pp. 115–135, Jan. 2015, doi: 10.1007/s11747-014-0403-8 . W. F. Criterion- and C. E. Shannon, “Coding Theorems for a Discrete Source,” pp. 325–350, 1959. A. S. Albahri et al. , “Hybrid artificial neural network and structural equation modelling techniques: a survey,” Complex Intell. Syst., vol. 8, no. 2, pp. 1781–1801, 2022, doi: 10.1007/s40747-021-00503-w . G. A. Alkawsi et al. , “A hybrid SEM-neural network method for identifying acceptance factors of the smart meters in Malaysia: Challenges perspective,” Alexandria Eng. J., vol. 60, no. 1, pp. 227–240, Feb. 2021, doi: 10.1016/j.aej.2020.07.002 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5798963","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":402047798,"identity":"c8f4d40d-c05b-4993-97c7-485db57437de","order_by":0,"name":"Muhammed Ibrahim","email":"","orcid":"","institution":"-\tFaculty of Computing, Nigerian Army University, Biu","correspondingAuthor":false,"prefix":"","firstName":"Muhammed","middleName":"","lastName":"Ibrahim","suffix":""},{"id":402047799,"identity":"a217ceba-83cd-42fd-a655-46ce584d7ed7","order_by":1,"name":"Mohammed A. Al-Sharafi","email":"data:image/png;base64,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","orcid":"","institution":"-\tIRC for Finance and Digital Economy, King Fahd University of Petroleum \u0026 Minerals,","correspondingAuthor":true,"prefix":"","firstName":"Mohammed","middleName":"A.","lastName":"Al-Sharafi","suffix":""},{"id":402047800,"identity":"59dc99e7-9648-4c78-8da5-711253145742","order_by":2,"name":"Mousa Albashrawi","email":"","orcid":"","institution":"-\tDepartment of Information Systems \u0026 Operations Management, KFUPM Business School, King Fahd University of Petroleum and Minerals","correspondingAuthor":false,"prefix":"","firstName":"Mousa","middleName":"","lastName":"Albashrawi","suffix":""},{"id":402047801,"identity":"53d5c64d-6087-465c-9dd6-90f7cf7a7fa2","order_by":3,"name":"Moamin A. Mahmoud","email":"","orcid":"","institution":"-\tCollege of Computing and Informatics, Universiti Tenaga Nasional","correspondingAuthor":false,"prefix":"","firstName":"Moamin","middleName":"A.","lastName":"Mahmoud","suffix":""}],"badges":[],"createdAt":"2025-01-09 20:08:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5798963/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5798963/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74249373,"identity":"d3c0813f-c818-4c05-81a0-4434d6699764","added_by":"auto","created_at":"2025-01-20 10:14:09","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":176890,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResearch model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5798963/v1/0a3236d103e7402a10872d17.jpeg"},{"id":74249980,"identity":"8c833dd4-bdf6-40c3-a2ff-9345e0532b93","added_by":"auto","created_at":"2025-01-20 10:22:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":107904,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStructural model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5798963/v1/87627ef3badcb630bb9642b2.png"},{"id":74247606,"identity":"3cb174a5-27b2-4365-99dc-1401121b0c6c","added_by":"auto","created_at":"2025-01-20 10:06:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":70794,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eANN Model 1\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5798963/v1/8350686a009eae9b3fd9dcaa.png"},{"id":74247609,"identity":"ba12d9e0-47ef-4359-b4a0-febc0de7f891","added_by":"auto","created_at":"2025-01-20 10:06:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":18801,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eANN model 2\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5798963/v1/6d97268b8a2ba0f0f3b2d2b0.png"},{"id":74251502,"identity":"33019c48-ba85-4997-b648-eaa34d39379a","added_by":"auto","created_at":"2025-01-20 10:38:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2261174,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5798963/v1/fdc820d5-d391-49be-9590-ce7ec2689f5d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Cybersecurity-Centric Model for Predicting Electronic Health Records System Adoption for Sustainable Healthcare: A SEM-ANN Approach","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe Sustainable Development Goals (SDGs) agenda for 2030 of the United Nations established in 2015, with 17 goals aimed at facilitating an improved and more sustainable future for all [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. These goals, together with their 169 targets, tackle some of the most pressing global challenges. They are classified into three primary domains: social, economic, and environment [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Information Technology (IT) has significant potential to enhance access to care, decrease expenses, and optimize operational efficiencies within the healthcare system [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Management of business processes and clinical automation are prominent trends globally impacting both the emerging and established healthcare sectors. The motivation behind these development is to streamline the complicated traditional and paper-based systems, allowing systems of medical care to manage patient data more efficiently, ensure adherence to health regulations, improve data accessibility for enhanced care delivery, and bolster security measures to safeguard patient confidentiality. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Technological advancements provide firms, especially those in healthcare, a competitive advantage by enhancing procedures and facilitating more effective strategy implementation [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The healthcare sector has rapidly transitioned from paper-based methods to digital alternatives. These include individual health records, prescriptions electronically, intelligent health devices, wearable tech, AI-powered patient management tools, and telemedicine [\u003cspan additionalcitationids=\"CR8 CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The reliance on paper for documenting health information in several healthcare institutions has generated a significant paper trail, prompting many healthcare organizations to consider transformation to electronic health records (EHR) from paper-based health records [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Information and Communication Technologies (ICT) usage in healthcare began in the early 1970s with the commencement of commercial computer utilization. Nonetheless, it underwent a substantial expansion in the 1990s, significantly enhancing access, efficiency, quality, and ultimately the efficacy of provision of medical care service systems [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The primary goal of EHR is to advance the access and efficiency of medical care systems. EHRs encompass a comprehensive array of information on an individual's health, including demographics, diagnoses, laboratory tests and results, medicines, medical imaging, physician's notes, and much more [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The General Practice Research Database (GPRD) in the United Kingdom is among the largest electronic health databases globally. Initially established in 1987 as the VAMP Research Databank, it was acquired by the UK Department of Health in 1994 and subsequently renamed the GPRD. Since that time, it has been meticulously administered to guarantee superior data quality and is utilized for scholarly study. The database contains data from approximately five million patients across 590 primary care clinics in the UK. These procedures conform to a defined methodology for the demographic collection and data of clinical, which are then sent in anonymized form to the database by automated extraction methods [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In Hong Kong, public hospitals provide the majority of in-patient care, covering about 90% of all hospital bed-days, while around 70% of outpatient services are handled by private clinics. The Hospital Authority established the Electronic Health Record Sharing System to link these two sectors, which was initiated in March 2016. This system functions as an electronic platform that enables the exchange of information including patient demographics, clinical data, and prescription profiles, across public and private healthcare institutions [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. EHR is a digital version of a medical history of patient's that a medical care provider maintains over time; it includes important details about the patient's care, like their demographics, immunizations, health issues, medications, medical history, progress notes, lab results, vital signs, and radiology reports\" [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The deployment of EHR in hospitals and clinics worldwide is increasing, significantly contributing to sustainable healthcare provision. Numerous nations have shown a desire to adopt integrated electronic health records owing to the expected advantages. The American Recovery and Reinvestment Act of 2009 designated \u003cspan\u003e$\u003c/span\u003e27\u0026nbsp;billion for the digitization of medical care data in the United States, with the objective that most Americans will be integrated into the EHR system by 2015 and 2017 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNonetheless, some challenges persist that hinder effective implementation, particularly in developing economies [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Certain difficulties pertain to infrastructure, in addition to the security and privacy of patients' data. There has been negligible progress in formulating policies to address the privacy concerns associated with the transformation to integrated EHRs from paper based health records [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The rapid progression of ICT has rendered patients' health data susceptible to security and privacy risks. Presently, there are considerable apprehensions surrounding the security and privacy of guarded health data, which pose substantial barriers to the introduction of EHR. Consequently, healthcare institutions must formulate methods to safeguard the system [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. There is a need for investigation regarding healthcare providers direct experiences with EHR to understand the potential challenges, benefits, motivations, and barriers related to EHR adoption because their attitudes and actions will significantly influence the system's efficacy [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Most previous studies in literature have taken an observational or secondary-data analysis approach focusing on outcomes at the hospital level. It remains unclear how EHR implementation impacts healthcare providers daily activities [\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Only a number of limited research have investigated healthcare providers assessments of how EHR influences their work [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. A recent study also suggests the significance of understanding the key factors that either support or hinder healthcare providers EHR usage in order to enhance EHR utilization [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite the increasing number of studies on EHRs, there exist a significant knowledge gap remains in the literature concerning studies that empirically identify the factors that influence the acceptance and use of EHR systems [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This study seeks to address this knowledge gap by examining the factors that influence the healthcare professionals to adopt EHR systems in developing economies, specifically emphasizing usability, cybersecurity, and the contribution of the adoption of EHR in fostering sustainable healthcare practices in resource-limited environments.\u003c/p\u003e \u003cp\u003eThis research integrates the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) model by [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] as its foundational framework and incorporates additional factors such as cybersecurity factors, namely confidentiality, integrity, availability, and possession/control [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Furthermore, it introduces healthcare sustainability as a dependent variable, drawing on the framework provided by [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. By combining these variables, the study seeks to develop a comprehensive model for understanding the determinants of the adoption of EHR and use. This approach is expected to provide actionable insights into designing focused procedures and strategies that advance EHR adoption, ultimately serving patients, healthcare providers, and the larger healthcare environment.\u003c/p\u003e \u003cp\u003eThis study provides three key contributions. First, it advances the theoretical understanding of the EHR adoption process by analyzing its usage and implications for healthcare sustainability. Using the UTAUT2 framework as a base and integrating cybersecurity-related factors, the research offers a novel perspective on healthcare providers\u0026rsquo; behaviors and attitudes toward EHR systems. This perspective provides healthcare organizations valuable insights into fostering greater adoption and utilization of these systems. Second, the research makes an important empirical contribution by incorporating healthcare sustainability as a dependent variable in its model. This allows the research to examine the impact of EHR usage on sustainable healthcare practices, highlighting its capacity to enhance the efficiency and resilience of healthcare delivery in resource-restricted settings. Lastly, the methodological contribution is applying a hybrid Structural Equation Modeling-Artificial Neural Network (SEM-ANN) approach for analyzing and testing the proposed model. This innovative methodology enables a more extensive understanding of the complicated interactions among various factors influencing EHR adoption and their implications for sustainable healthcare outcomes.\u003c/p\u003e \u003cp\u003eThe study is structured as follows: it starts with an introduction, followed by a section providing a theoretical framework for EHR adoption. Next, it establishes the foundation for the study model and the hypotheses formulation. Subsequently, the study method is explained, followed by a discussion on data analysis and findings. The findings are analyzed, emphasizing their significance for theory, methodology, and practice, then conclusions and recommendations for future study areas.\u003c/p\u003e"},{"header":"2. Theoretical Background","content":"\u003cp\u003eThe research proposed a conceptual model built on the integration of the UTAUT2 model developed by [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and cybersecurity constructs [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] to predict the acceptance of EHR systems and examine their role in promoting healthcare sustainability [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The UTAUT2 model is extensively recognized for its comprehensive approach to explaining behavior of user in technology adoption. Researchers have praised its robust framework for identifying and explaining the behavioral determinants that influence user acceptance and technology use [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUnlike earlier models that examined technology acceptance in isolation, UTAUT2 is the extension of UTAUT which synthesizes key variables from eight leading technology acceptance theories, including the Technology Acceptance Model [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], Theory of Reasoned Action [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], and the Innovation Diffusion Theory [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. This integrative approach provides researchers with a more detailed and comprehensive knowledge of the significant factors that drive the adoption of technology across diverse contexts, including organizational, educational, and consumer settings. Moreover, in the context of EHR systems, it addresses how ease of use (effort expectancy), the pleasure or fun derived (hedonic motivation), and resource availability (facilitating conditions), perceived usefulness (performance expectancy), peer and organizational support (social influence) contribute to adoption decisions. This holistic approach ensures that both individual and organizational factors are considered [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Furthermore, the flexibility of UTAUT2 enables its extension and adaptation to address specific contexts, making it an ideal foundation for this research [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In this study, two key UTAUT2 constructs which are price value and habit, were omitted due to their limited relevance. Unlike consumer-oriented technologies where individual users bear direct costs, EHR system implementation expenses are typically covered by healthcare organizations or government entities, rendering price value immaterial to healthcare professionals' adoption decisions. Similarly, the habit construct proves insufficient in healthcare settings. The shift from paper-based to digital records is relatively recent, especially in developing countries, where EHR systems have not yet achieved universal implementation.\u003c/p\u003e \u003cp\u003eWhile UTAUT2 comprehensively covers usability and behavioral aspects, it falls short in addressing the critical concerns of security of data, which are specifically relevant in healthcare due to the sensitive nature of patient information. To bridge this gap, the study incorporates cybersecurity constructs (i.e., confidentiality, integrity, availability, and possession/control) into the UTAUT2 model, offering a more holistic perspective on technology adoption within healthcare environments. Studying cybersecurity factors is crucial for several compelling reasons, particularly in healthcare technology adoption. Healthcare information systems, especially EHRs, operate in a uniquely sensitive domain that demands a more comprehensive approach to technology acceptance [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Unlike traditional technology acceptance models that primarily focus on user acceptance and behavioral intention, this extended research model recognizes the significance importance of protection data in medical care settings [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The proposed cybersecurity constructs provide a holistic risk mitigation strategy that addresses the multifaceted challenges of the technology acceptance in healthcare. The successful adoption and use of EHR systems are closely linked to healthcare sustainability. EHR systems play a significant role in sustainable healthcare by reducing paper use, improving data accessibility, and streamlining care delivery, which can lead to cost savings and reduced environmental impact [\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Integrating sustainability as a core objective within the proposed research model highlights the broader implications of EHR adoption. This approach reinforces the importance of usability, security, and long-term effectiveness in fostering sustained use. It also provides valuable insights into how behavioral and cybersecurity factors can align with sustainable healthcare goals. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e indicates the proposed research model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Model and Hypothesis Development\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Performance Expectancy\u003c/h2\u003e \u003cp\u003ePerformance expectancy, referring to \u0026ldquo;the belief that a job performance will be improve by using technology, has been recognized as an important factor that influence the technology adoption\u0026rdquo; [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. This study defined performance expectancy as using electronic health record system by healthcare providers to meet the expectations of the patient. Prior studies, such as those by [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] and [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], underscore this construct's impact on healthcare technologies, where perceived improvements in efficiency and patient outcomes drive adoption. In the context of EHR usage, increased efficiency and better data access contribute to healthcare sustainability by improving care quality and optimizing resource allocation.\u003c/p\u003e \u003cp\u003eIt is an important predictor of intention behavior [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] and has been found to strongly influence many technologies adoption [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], [\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. In the EHR adoption domain, performance expectancy was identified as an important factor in healthcare providers' adoption [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. This factor also serves an important function in the adoption of medical Internet of Things (IoT) acceptance in medical care [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. The research by [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] on factors influencing the use of AI-based chatbots for sharing of knowledge, performance expectancy is crucial in determining how much these chatbots are utilized. Healthcare providers are more inclined to adopt EHR if they believe it will save time, reduce costs, or enhance access to care. Therefore, the following hypothesis is proposed:\u003c/p\u003e \u003cp\u003eH1: There is a strong positive connection between performance expectancy and the actual use of EHR.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Effort Expectancy\u003c/h2\u003e \u003cp\u003eEffort expectancy pertains to individuals' awareness of how easy technology is to use. This construct aligns with two key concepts from established models: complexity from IDT and perceived ease of use from TAM/TAM2 [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. This study defined effort expectancy as the perceived ease with which healthcare providers can use electronic health record systems. Many studies in the literature have found effort expectancy as a significant predictor for the acceptance or adoption of technology [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. In a research conducted by [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] regarding the attitudes and factors that influence patients' adoption of accessible EHR in China identified effort expectancy as highly significant and the driver of behavioral intention. Effort expectancy is a key factor impacting the m-health application adoption among senior citizens who are regarded as having strong digital literacy skills [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Study by [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] on the use of AI-based chatbots for sharing of knowledge found that the PLS-SEM analysis supported that effort expectancy positively influences their adoption for sharing knowledge. If the EHR system is easy to use and requires minimal effort featuring clear instructions, simple navigation, and intuitive interface healthcare providers are likely to feel more at ease when adopting it. Consequently, the following hypothesis is proposed:\u003c/p\u003e \u003cp\u003eH2: There is a strong positive connection between effort expectancy and the actual use of EHR.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.1.3 Facilitating Conditions\u003c/h2\u003e \u003cp\u003e\u0026ldquo;Facilitating conditions refer to how much an individual perceive or believes that there is adequate organizational and technical support accessible to help them use a system effectively\u0026rdquo; [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. This study defined facilitating conditions as the extent by which healthcare provider beliefs that there is access to the required resources, infrastructure, and assistance for using EHR systems. Previous research in the literature have demonstrated the significance importance of facilitating conditions on technology adoption [\u003cspan additionalcitationids=\"CR56\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. For instance, a study conducted by [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] of adoption of teachers' ICT-based instruction in the classroom, the findings shows that facilitating conditions significantly and positively influence the actual use of ICT tools for teaching. According to empirical evidence, facilitating conditions is one of the important factors that has significant impact on companies' decisions to adopt blockchain technology in supply chain management [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. A strong technological infrastructure featuring compatible software, suitable hardware, and reliable internet access is crucial for the effective operation of EHR systems. Healthcare providers are more likely to adopt EHR systems whenever they are aware that support is available for any technical issues or questions about how to use the system. Thus, the proposed hypothesis is as follows:\u003c/p\u003e \u003cp\u003eH4: There is a strong positive connection between facilitating conditions and the actual use of EHR.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.1.4 Social Influence\u003c/h2\u003e \u003cp\u003e\u0026ldquo;Social influence is the degree to which a person feels that key individuals in their life think they should adopt the new technology\u0026rdquo; [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. This study defined social influence as the healthcare providers\u0026rsquo; perception that important people around them might influence them to use EHR systems. Previous research in the literature have shown a significant part played by social influence towards the adoption of many technologies [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. According to the study by [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], which analyzes factors influencing the decision to use digital nursing homes for the older adults within a Chinese context the findings shows that social influence plays a significant role in shaping people's behavioral intentions. The study by [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] on smart meter adoption shows that social influence serve an important role in the adoption of smart meters among consumers in Brazil. Also, in the study by [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] on enhancing the use of e-health records among Generation Z highlights that social influence is significant factor in encouraging this demographic to adopt such technologies more broadly. Sharing positive experiences of using EHR by friends, colleagues at work, or anyone working together can encourage healthcare providers to use it. Therefore, the following hypothesis is proposed:\u003c/p\u003e \u003cp\u003eH4: There is a strong positive connection between social influence and the actual use of EHR.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.1.5 Hedonic Motivation\u003c/h2\u003e \u003cp\u003eHedonic motivation was presented in the UTAUT2 model as a new variable to enhance the original UTAUT model, particularly in connection to usage of technology by a consumer. Hedonic motivation can be defined as the enjoyment or pleasure gained from technology usage, and research has indicated that it significantly influences technology acceptance and usage\u0026rdquo; [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This study defined hedonic motivation as the extent of the pleasure and fun healthcare provider get while using EHR. Some studies in the literature such as [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], [\u003cspan additionalcitationids=\"CR64\" citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e] have found hedonic motivation as a significant predictor that influence positively the technology adoption intention. The findings of the study of [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] on mobile health application usage among elderly people show the significance important of hedonic motivation and the pleasure they derived while using the application. The study of [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] found that hedonic motivation has a significant and positive effect on the intention to adopt and use smart meters. Therefore, it makes sense to suggest that hedonic motivation will remain a significant predictor of the adoption and use of technology. As a result, the following hypothesis is proposed:\u003c/p\u003e \u003cp\u003eH5: There is a strong positive connection between hedonic motivation and the actual use of EHR.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.1.6 Confidentiality\u003c/h2\u003e \u003cp\u003eConfidentiality can be defined as the ability to protect cyberspace from unauthorized access [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Healthcare providers must ensure the confidentiality of patient\u0026rsquo;s information flows into and out of the EHR through various channels, when essential details of a medical record of a patient like diagnoses or medications, are entered without prioritizing confidentiality, there's a substantial risk of a breach when this information is accessed through a patient portal system. This predicament frequently places healthcare providers in a tough spot, as they must balance meeting their patients' needs with following best clinical practices, navigating different state laws, and dealing with ambiguous institutional policies [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. The adoption of a particular technology largely depends on the service providers' capacity to safeguard transaction confidentiality and users' ability to protect their personal data privacy [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. This study defined confidentiality as healthcare providers ability to protect patients records from unauthorized access. Information reliability might be compromise if there is a breach of confidentiality [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Confidentiality has being studied in many studies in the literature and have been identified to have a significant impact [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], [\u003cspan additionalcitationids=\"CR68 CR69\" citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Encryption, access restrictions, and data anonymization techniques are employed in a system to ensure confidentiality [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. This factor also plays a significant role and effect in personal health record adoption [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Therefore, healthcare providers are more likely to adopt EHR if they perceive patient records are secured. Thus, the following hypothesis is proposed:\u003c/p\u003e \u003cp\u003eH6: There is a strong positive connection between confidentiality and the actual use of EHR.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.1.7 Integrity\u003c/h2\u003e \u003cp\u003eIntegrity can be as the ability to preserve the consistency, accuracy, and reliability of information in the digital space [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Integrity involves ensuring that data stays accurate and reliable during its entire lifecycle. To safeguard against unauthorized changes, various approaches like checksums, version control systems, and digital signatures are employed [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. This study defined integrity as the ability to safe guide patient record accurately without any changes. A blockchain-based system for managing patient-centric EHR is created to provide effective EHR solutions in eHealth environments [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. In the study by [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] it suggests that users' confidence in the security of cryptocurrencies held in a brokerage account believing they are protected from theft, hacking, or loss by third parties strongly influences their attitudes toward using cryptocurrencies. In the study conducted by [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e] demonstrate that the inherent properties of blockchain, which make altering data challenging, guarantee the integrity and consistency of health records. Therefore, it is reasonable to believe that integrity will remain an important factor influencing the acceptance and use of technology moving forward. Thus, the following hypothesis is proposed:\u003c/p\u003e \u003cp\u003eH7: There is a strong and positive connection between integrity and the actual use of EHR.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.1.8 Availability\u003c/h2\u003e \u003cp\u003e\"The ability of allowing authorized users to access cyberspace\" [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This study defined availability as the extent of allowing access to patient record to authorize person. Availability emphasizes the constant accessibility of information and systems for authorized users. To guarantee seamless access, it's crucial to have redundancy measures, backup systems, and effective disaster recovery plans in place [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Based on the study of [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e] on smart home adoption the findings indicate that availability significantly influence usage intention. Users ought to have the ability to access their cryptocurrency accounts whenever they need to [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. If the patient records in EHR system is available to authorize individual at any given time can make healthcare providers feel more comfortable to adopt. Therefore, we proposed the following hypothesis:\u003c/p\u003e \u003cp\u003eH2: There is a strong and positive connection between availability and the actual use of EHR.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.1.9 Possession/Control\u003c/h2\u003e \u003cp\u003e\" The capability to maintain a sense of control or ownership in the digital space\" [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This study defined possession/control as the ability of EHR to control patient record. Possession or control refers to the degree of control individuals have over their assets [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. This factor is new explored in a very little study. The study by [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] shows that users' attitudes toward using cryptocurrencies would be significantly influenced by their ability to manage possession or control. This involves actions like keeping their cryptocurrency account password private, using phone verification services to safeguard passwords, and avoiding the storage of personal data online. The capability of EHR to manage and protect patient information encourages healthcare providers to keep using the system. Consequently, the following hypothesis is proposed:\u003c/p\u003e \u003cp\u003eH2: There is a strong and positive connection between possession/control and the actual use of EHR.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.1.10 Actual Use of EHR Systems and Its Impact on Healthcare Sustainability\u003c/h2\u003e \u003cp\u003e\u0026ldquo;The actual use of technology, which can be defined as the acceptance or adoption of new technology into people's lives\u0026rdquo; [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. This study defined the actual use of EHR as how healthcare providers integrate and interact with EHR systems for healthcare delivery. Based on the study of [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e] which indicates that individual intentions are significant in determining user behavior. Behavior is characterized as one's intention to perform a particular actions [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. User behavior can be predicted by their intention to act, provided that the individual can voluntarily take action [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. The actual use behavior have been used in many studies in the literature of technology adoption [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Use behavior is considered an important and significant factor added to the extended UTAUT model to determine individual actual use of technology [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Sustainability in healthcare involves balancing three key aspects economic efficiency, social inclusion, and safeguarding the environment [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The focus of healthcare on enhancing efficiency and service quality has led a significant rise in interest in applying sustainable principles [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Healthcare sustainability involves ensuring that healthcare systems are able to operate and provide essential services over the long term without exhausting natural or economic resources. This trend also includes a growing awareness and emphasis on responsible management of internal resources. Integrating medical guidelines and planning with social, economic and environmental approaches reflects healthcare organizations' broader concerns for organizational survival, ongoing development, and the enhancement of health services [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Healthcare sustainability is the extent of a healthcare systems to maintain or improve the quality and accessibility of healthcare services over time while effectively managing costs and minimizing negative environmental impacts [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. This study also defined digital healthcare sustainability as the ability of a healthcare system to use digital technologies in a manner that ensures the practical long-term and effectiveness of healthcare delivery. EHR have impact economic, social, and the environmental sustainability by reducing paper wastage and pollution in the hospital environment, reducing patient waiting time, increase wellbeing to patient, and help reduce the clinical cost attached to hospital management. The study of [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e] on e-health literacy discovered that behavioral intention influences the use behavior of e-health literacy by students in China. This study defined EHR for sustainability as a digital repository of patient health information that impacts sustainability by reducing paper consumption, improving access to care, and enhancing healthcare efficiency through cost savings and operational efficiencies in healthcare delivery. Healthcare providers are more likely to continue using EHR for effective healthcare delivery. Thus, the following hypothesis is proposed:\u003c/p\u003e \u003cp\u003eH7: There is a strong and positive connection between the actual use of EHR and healthcare sustainability.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Research Methodology","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Sampling and Data Collection\u003c/h2\u003e \u003cp\u003eA quantitative deductive approach was used in this research to test the hypotheses and validate the proposed research model, as it is an effective method for evaluating hypotheses, assessing relationships within groups, and assessing the interconnections among variables [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. This study employed purposive sampling to ensure that participants had relevant experience and knowledge in using EHR systems, which is critical for obtaining accurate insights into factors influencing EHR adoption. Purposive sampling allows for targeted selection of medical care professionals, such as nurses, physicians, pharmacists, and administrative personnel using EHR systems as part of their daily roles. This approach is beneficial in contexts like healthcare where specialized expertise can provide depth in responses and enrich data quality [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e], [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo ensure the participants met the required criteria, a preliminary screening question was added in the survey to authenticate that participant had active experience with EHR systems. This measure guaranteed that only individuals with practical, hands-on familiarity participated in the study. The survey was distributed through WhatsApp groups and professional networks connected to hospitals in Nigeria. A cover letter accompanied the questionnaire, providing details concerning the study's objectives, emphasizing voluntary participation, and assuring respondents of the secrecy of their input. Microsoft Forms was used as platform for distributing and collecting responses efficiently. Respondents were given a particular deadline to finish the survey, after which all submissions were thoroughly reviewed to exclude incomplete or inconsistent entries. Ultimately, 374 valid responses were retained for analysis in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Instrument of the Study\u003c/h2\u003e \u003cp\u003eTo explore the connections among the factors in the developed model, reliable and valid measurement scales were used, sourced from established literature. The constructs for facilitating conditions, performance expectancy, hedonic motivation, social influence, and effort expectancy were measured using items adapted from research conducted previously [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], [\u003cspan additionalcitationids=\"CR85\" citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. For the constructs related to confidentiality, integrity, availability, and possession/control items were adapted from [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Factors measuring Healthcare Sustainability were drawn from sources such as [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e], [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e], [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs in previous studies, we used a Likert type 5-point scale to evaluate the items, with responses ranging from 1 (strongly disagree) to 5 (strongly agree). This method was chosen based on its simplicity, easy to use, and cost-efficiency [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. This approach is widely regarded for its strong validity and reliability in capturing responses [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e], [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. To confirm the survey item\u0026rsquo;s reliability, 35 participants were used in a pilot study conducted. The results showed that all the values of Cronbach's alpha were above 0.7, demonstrating strong reliability for all constructs included in the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Data Analysis\u003c/h2\u003e \u003cp\u003eA two-step analytical approach, combining PLS-SEM with ANN techniques, was used for the analysis of data. This combination leverages the strengths of both techniques, providing a comprehensive framework for studying EHR adoption [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e], [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e], [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]. In the initial phase, PLS-SEM was utilized to validate the model proposed and test the hypotheses. PLS-SEM is ideal for testing theoretical models by identifying relationships between latent variables and validating hypothesized paths. It's a robust method that can handle complex causal models, including multiple latent variables and their relationships [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e], [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. PLS-SEM is well-suited for predictive research, concentrating on the model predictive power. It's also useful for exploratory research, allowing for the identification of new relationships and hypotheses [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e], [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e]. ANN, on the other hand, captures both linear and nonlinear relationships, uncovering complex patterns that traditional linear models may miss [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e], [\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e]. Unlike traditional linear models, ANN is more flexible since it is less impacted by deviations from statistical assumptions, making it capable of providing a better fit for the data when traditional models may fall short [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]. By combining PLS-SEM and ANN, the study benefits from PLS-SEM's ability to confirm hypothesized relationships, while ANN uncovers deeper, nonlinear interactions that conventional methods might miss. This integration results in a more detailed and nuanced knowledge of the factors that influence EHR adoption.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Findings","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Assessment of Non-Response and Common Method Bias\u003c/h2\u003e \u003cp\u003eSimilar to research conducted previously [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e], [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e], This study assessed non-response bias by comparing the responses of early and late participants. To assess non-response bias, we performed t-tests comparing the responses of the first 100 participants to the last 100. This method helps identify significant differences between early and late respondents, ensuring that our findings were robust and not influenced by the timing of survey completion, thereby enhancing the validity of our results, following the approach by [\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e]. The results of t-test revealed no significant differences among the groups, confirming that non-response bias is absent. Additionally, given that the collected data were from a single source, an assessment for common method bias was conducted. Harman's one-factor test was applied to the study variables, indicating that the maximum variance attributed to a single factor was 37.28%, which is well below the recommended threshold of 50%, as outlined in relevant literature [\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e], [\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e], this suggests that there is no common method bias present. Additionally, we utilized the variance inflation factor (VIF) as recommended by [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e], and found that all constructs had VIF values below the 3.3 threshold. This further supports the conclusion that common method bias is absent. Consequently, the instrument used in this study is deemed free from such biases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Evaluation of the Measurement Model\u003c/h2\u003e \u003cp\u003eThe study commenced with an assessment of the measurement model to verify convergent validity, discriminant validity, and internal consistency reliability prior to testing the hypotheses in the structural model, following the guidelines set by [\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e], [\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e]. To evaluate construct reliability, we used two metrics: composite reliability (CR) and Cronbach's alpha (CA). The CA values ranged from 0.818 to 0.917, and the CR values ranged from 0.824 to 0.922. Since both of these metrics exceeded the recommended threshold of 0.70, this indicates that the constructs used in the study exhibit strong reliability, as supported by [\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e]. This thorough evaluation ensures that the measurement model is robust and trustworthy for further analysis.\u003c/p\u003e \u003cp\u003eTo establish convergent validity, we confirmed that the outer loadings of our constructs exceeded 0.70 and that the values of average variance extracted (AVE) were above the 0.50 threshold, as recommended by [\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e]. This information is detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Discriminant validity was assessed using the Heterotrait-Monotrait ratio of correlations (HTMT), with all calculated values remaining below the 0.85 thresholds, consistent with the standards outlined by [\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e]The results for HTMT analysis are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The variance inflation factor (VIF) values ranged from 1.415 to 2.71, which is well below the recommended maximum of 3.3. This confirms that there were no issues with multicollinearity among the constructs. In summary, this study successfully demonstrated convergent validity, discriminant validity, and internal consistency reliability, ensuring that the measurement model is robust and credible for further hypothesis testing.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReliability and convergent validity results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOuter loadings\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCronbach's alpha\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eComposite reliability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eEHR Use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAU: 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.843\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.855\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.677\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAU: 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAU: 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.186\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAU: 4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eAvailability\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAV: 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.87\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.872\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.719\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAV: 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.479\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAV: 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAV: 4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.321\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eConfidentiality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eC: 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.746\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.818\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.824\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.647\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eC: 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.722\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eC: 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.831\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eC: 4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.638\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003ePossession / Control\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCo: 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.877\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.879\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.731\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCo: 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.404\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCo: 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.149\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eCo: 4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eEffort Expectancy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEE: 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.833\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.834\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.667\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEE: 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.956\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEE: 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.863\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEE: 4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.811\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eFacilitating Conditions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFC: 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.896\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.898\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.762\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFC: 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.694\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFC: 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.487\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFC: 4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.359\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eHedonic Motivation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHM: 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.847\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.848\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.685\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHM: 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.456\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHM: 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.295\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHM: 4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.545\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e\u003cb\u003eHealthcare Sustainability\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHS: 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e\u003cb\u003e0.917\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e\u003cb\u003e0.922\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e\u003cb\u003e0.667\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHS: 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHS: 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHS: 4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.338\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHS: 5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.699\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHS: 6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.609\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHS: 7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.285\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eIntegrity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eI: 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.884\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.907\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.738\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eI: 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.304\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eI: 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.536\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eI: 4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.249\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003ePerformance Expectancy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePE: 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.882\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.884\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.738\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePE: 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.336\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePE: 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.252\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePE: 4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.158\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eSocial Influence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSI: 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.854\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.858\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003e0.695\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSI: 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSI: 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSI: 4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.948\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHTMT results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstruct\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Availability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2. Confidentiality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3. EHR Use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4. Effort Expectancy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5. Facilitating Conditions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6. Healthcare Sustainability\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7. Hedonic Motivation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8. Integrity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9. Performance Expectancy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e10. Possesssion / Control\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.707\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e11. Social Influence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Structural Model assessment\u003c/h2\u003e \u003cp\u003eAfter the validity of the measurement model was confirmed, the next step involved assessing the structural model's predictive power and clarifying the relationships between variables, as outlined by [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e]. To accomplish this, we employed a statistical method called bootstrapping, which involved taking 5,000 samples to ensure our results were robust. This analysis provided important metrics such as R\u0026sup2; (coefficient of determination) and the path and t-values. These measures are essential for understanding how the constructs interact with one another, providing insights into the dynamics at play [\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e]. The findings, presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, largely confirmed all the proposed hypotheses, indicating that the structural model effectively captured the relationships and predictive power among the variables under study, largely supported all the hypotheses proposed.\u003c/p\u003e \u003cp\u003eFor instance, Performance Expectancy had a significant effect on EHR Use (β\u0026thinsp;=\u0026thinsp;0.146, t\u0026thinsp;=\u0026thinsp;2.779, p\u0026thinsp;=\u0026thinsp;0.003), supporting hypothesis H1. Likewise, Effort Expectancy (β\u0026thinsp;=\u0026thinsp;0.144, t\u0026thinsp;=\u0026thinsp;2.548, p\u0026thinsp;=\u0026thinsp;0.005) and Social Influence (β\u0026thinsp;=\u0026thinsp;0.184, t\u0026thinsp;=\u0026thinsp;3.389, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were also positively linked to EHR Use, confirming hypotheses H2 and H4. Facilitating Conditions had a positive, though slightly smaller, impact (β\u0026thinsp;=\u0026thinsp;0.086, t\u0026thinsp;=\u0026thinsp;1.733, p\u0026thinsp;=\u0026thinsp;0.042), supporting H3. Other significant influences on EHR Use included Confidentiality (β\u0026thinsp;=\u0026thinsp;0.211, t\u0026thinsp;=\u0026thinsp;4.322, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and Possession/Control (β\u0026thinsp;=\u0026thinsp;0.23, t\u0026thinsp;=\u0026thinsp;3.805, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), backing hypotheses H6 and H9. Integrity also had a positive but moderate effect (β\u0026thinsp;=\u0026thinsp;0.108, t\u0026thinsp;=\u0026thinsp;1.925, p\u0026thinsp;=\u0026thinsp;0.027), supporting H7. However, Hedonic Motivation (β\u0026thinsp;=\u0026thinsp;0.001, t\u0026thinsp;=\u0026thinsp;0.023, p\u0026thinsp;=\u0026thinsp;0.491) and Availability (β = -0.042, t\u0026thinsp;=\u0026thinsp;0.721, p\u0026thinsp;=\u0026thinsp;0.235) did not significantly impact EHR Use, so H5 and H8 were not supported. The link between EHR Use and Healthcare Sustainability was very strong (β\u0026thinsp;=\u0026thinsp;0.429, t\u0026thinsp;=\u0026thinsp;9.429, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), supporting H10. These relationships were validated through the bootstrapping method, as summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eExamining the R\u0026sup2; values provides insight into how much variance in the dependent variables is explained by the model. Specifically, the model accounts for 43.5% of the variance in EHR Use (R\u0026sup2; = 0.435), while it explains 18.4% of the variance in Healthcare Sustainability through EHR Use (R\u0026sup2; = 0.184). The study evaluated effect sizes (f\u0026sup2;) following the guidelines set by [\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e]. According to their framework, effect sizes are categorized as small (0.02), medium (0.15), and large (0.35). This analysis helps in understanding the practical significance of the relationships identified in the model, providing a clearer picture of the impact that EHR Use has on Healthcare Sustainability. It was evaluated to see how much each variable contributed to the outcome. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Performance Expectancy, Social Influence, Confidentiality and Possession/Control had small effects. The relationship between EHR Use and Healthcare Sustainability had a medium effect size (f\u0026sup2; = 0.226), indicating a strong impact. However, Effort Expectancy, Hedonic Motivation, Integrity, Facilitating Condition, and Availability were found to have no significant influence on EHR usage.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the Structural Model Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePath\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-Values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-Values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ef-square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eR\u0026sup2; Values\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePerformance Expectancy -\u0026gt; EHR Use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEffort Expectancy -\u0026gt; EHR Use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFacilitating Conditions -\u0026gt; EHR Use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.733\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSocial Influence -\u0026gt; EHR Use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHedonic Motivation -\u0026gt; EHR Use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eConfidentiality -\u0026gt; EHR Use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIntegrity -\u0026gt; EHR Use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAvailability -\u0026gt; EHR Use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.721\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePossession / Control -\u0026gt; EHR Use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eEHR Use -\u0026gt; Healthcare Sustainability\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.4 ANN Analysis\u003c/h2\u003e \u003cp\u003eGiven the existence of nonlinear relationships within the study model, ANN was incorporated into the data analysis. Unlike traditional regression techniques, ANNs have the advantage of capturing both linear and nonlinear relationships without being influenced by distribution assumptions, making them a more robust analytical tool [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e], [\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e]. After identifying significant factors through PLS-SEM, these factors were integrated into the ANN model for further analysis. The study employed a multilayer perceptron network, which included two hidden layers, thereby classifying it as a deep learning model. The number of neurons in each hidden layer was determined using a specialized algorithm [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e], [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]. To avoid overfitting where the model becomes too tailored to the training data, a 10-fold cross-validation technique was utilized. This approach helped ensure the model\u0026rsquo;s performance would generalize well to new, unseen data. Additionally, the significance of each predictor was evaluated to understand their contributions. The sigmoid function was chosen as the activation function for both the hidden and output layers, as noted in previous literature [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTwo distinct ANN models were developed for the analysis shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;4. The first model focused on predicting the use of EHR systems based on multiple predictors, while the second model examined the relationship between EHR use and healthcare sustainability. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Model 1 utilized seven input variables, including confidentiality, effort expectancy, facilitating conditions, integrity, performance expectancy, possession/control, and social influence. These inputs were processed through two hidden layers, identifying complex patterns and nonlinear interactions among the variables. The output layer of Model 1 represented the predicted EHR use, reflecting healthcare providers\u0026rsquo; adoption and engagement with the systems. Model 2 was designed to isolate and evaluate the impact of EHR use on healthcare sustainability. The model\u0026rsquo;s input layer consisted of EHR use as the sole variable, emphasizing its direct contribution to sustainability outcomes. This variable was processed through two hidden layers, ultimately producing healthcare sustainability as the output. The Root Mean Square Error (RMSE) was calculated for ten different networks to assess the model's accuracy, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The mean RMSE values for Model 1 are 0.11546 for training and 0.11485 for testing, with standard deviations of 0.01295 and 0.01537, respectively. These low RMSE values and minimal standard deviations suggest that Model 1 has high predictive accuracy and generalizability. Model 2 exhibits mean RMSE values of 0.15402 for training and 0.15806 for testing, with standard deviations of 0.01774 and 0.00937, respectively. This high level of accuracy contributed to the model\u0026rsquo;s overall excellent prediction performance [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], [\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eANN Model RMSE Values\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNetwork\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRMSE (Training)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRMSE (Testing)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRMSE (Training)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRMSE (Testing)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.11622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.13038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15627\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.11226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.16226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15910\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.08167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11784\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.18115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.11737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14941\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.11744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15582\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.11390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.16369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15305\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.11816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.12401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.16240\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.11546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15806\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.012951843\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.015372807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.017742004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009373395\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Sensitive analysis\u003c/h2\u003e \u003cp\u003eThe sensitivity analysis for Model 1, which predicts EHR adoption, evaluates the relative importance of seven predictors. The sensitivity analysis helped identify and rank the importance of each predictor according to their normalized values, as presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. This analysis was conducted across ten neural network iterations, with the mean importance and normalized importance for each predictor presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The normalized importance values indicate that confidentiality (87.31%), effort expectancy (80.47%), and possession/control (79.17%) are the most influential factors in predicting EHR adoption. Social influence (70.54%) and performance expectancy (55.49%) also play significant roles, though to a lesser extent. Facilitating conditions (39.04%) and integrity (27.02%) have comparatively lower impacts on the model's predictions (effort expectancy), and control over information (possession/control) in influencing healthcare professionals' adoption of EHR systems. The prominence of social influence suggests that peer opinions and social networks also significantly affect adoption decisions. In contrast, factors such as facilitating conditions and integrity, while relevant, appear to have a less substantial impact on the decision to adopt EHR systems.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSensitivity analysis results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNetwork\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConfidentiality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEffort Expectancy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFacilitating Conditions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIntegrity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePerformance Expectancy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePossesssion/Conrtol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSocial Influence\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.186\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean Importance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.1237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.1809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1561\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormalized importance %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87.31%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80.47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.04%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.02%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55.49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e79.17%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e70.54%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRanking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion","content":"\u003cp\u003eThis study combined SEM with ANN to examine the factors that influence EHR adoption in the context of healthcare sustainability. By integrating these two methods, we gained a more thorough understanding of both linear and nonlinear relationships between key factors driving EHR adoption. Based on SEM results the findings revealed that performance expectancy plays a significant role in the adoption of EHRs. This aligns with research by [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] on medical IoT, and [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] on factors influencing the use of AI-based chatbots for sharing of knowledge. This shows that technologies perceived to enhance performance are more likely to be embraced. Healthcare professionals are more likely to adopt EHR systems when they believe these tools will boost their efficiency and patient care, ultimately promoting sustainability through improved healthcare delivery. Effort expectancy is another significant factor. Studies by [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] and [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] emphasize that effort expectancy is a major driver for adopting healthcare technologies. The results confirm that when EHR systems are easy to use, adoption rates increase. By reducing the cognitive and time burdens on healthcare providers these systems promote more sustainable practices by streamlining workflows and enhancing operational efficiency. Facilitating conditions also contribute positively though its impact is less pronounced. This finding supports [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], who identify the importance of resources and infrastructure in technology adoption. For EHRs, factors like sufficient training and a supportive infrastructure help ensure the systems are effectively utilized. Social influence has a significant impact as well, consistent with [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] who highlighted its role in smart meter adoption. In healthcare, peer influence and the perceived acceptance of EHRs among colleagues can drive broader adoption, fostering a collective shift towards integrated and efficient healthcare practices that support sustainability.\u003c/p\u003e \u003cp\u003eInterestingly, hedonic motivation does not significantly impact EHR adoption. This did not align with the findings from [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], where enjoyment was a key factor for other technologies like mobile health apps and smart meters. In the professional setting of EHR use, the emphasis is more on functionality and efficiency than enjoyment, which may explain this difference. Confidentiality emerges as a highly significant factor consistent with previous research [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], [\u003cspan additionalcitationids=\"CR68 CR69\" citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]\u0026ndash;[\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Reflecting the study of [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e], the trust in EHR systems to secure data is crucial for their adoption. Protecting patient privacy and ensuring data security are essential for sustainable healthcare practices. Integrity has a moderate yet important impact, underlining the need for reliable and accurate data. This is vital for protecting user from theft, hacking, or loss by third parties which aligns with existing literature that stresses the importance of security of information [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Availability was found to be less significant, differing from findings of [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e] who noted its importance in smart home adoption. In healthcare, reliable access to EHR systems is often expected which may explain why it\u0026rsquo;s not a primary concern for adoption. Finally, Possession/Control is highly significant, aligning with [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] findings. The ability for healthcare professionals to manage and control data within EHRs builds trust and supports sustainable practices by ensuring proper data handling. The strong link between EHR use and Healthcare Sustainability is clear, as highlighted by [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. EHRs play an important role in enhancing data management, improving care quality, and streamlining healthcare operations, all of which are vital for achieving long-term sustainable outcomes in healthcare.\u003c/p\u003e \u003cp\u003eThe findings of ANN revealed that confidentiality was the most important factor, with a normalized importance of 87.31%, highlighting those concerns about data security and privacy are the main barriers to EHR adoption. This is consistent with prior research emphasizing the critical nature of confidentiality in healthcare technologies, where sensitive patient information is involved. Effort expectancy (80.47%) and possession/control (79.17%) were major factors, indicating that users are more likely to adopt EHR systems if they are easy to use and if users feel a sense of control over the management of data. Social influence (70.54%) played a significant role, suggesting that peer pressure and organizational culture strongly impact the decision to adopt EHR systems. This finding points to the need for collaborative environments and leadership support in promoting EHR technology. Performance expectancy (55.49%) and facilitating conditions (39.04%) were also relevant but less influential, indicating that while users believe in the effectiveness of EHR systems and the availability of supporting resources, these factors are secondary to concerns about confidentiality and usability. The relatively lower importance of hedonic motivation suggests that the enjoyment of using EHR systems is not as significant for healthcare professionals compared to functional concerns such as security and ease of use. Similarly, integrity (27.02%) and availability (48.72%) were less critical but still contributed to the overall adoption of EHR systems, particularly in maintaining data accuracy and ensuring system uptime.\u003c/p\u003e \u003cp\u003eThe ANN analysis further validated these findings, with low Root Mean Square Error (RMSE) values and minimal standard deviations indicating strong predictive accuracy. The hybrid SEM-ANN approach proved effective in uncovering both linear and nonlinear relationships, providing a more nuanced understanding of EHR adoption. Importantly, our findings show that key predictors like confidentiality and effort expectancy can have outsized effects on EHR adoption, stressing the need for targeted interventions by healthcare administrators and policymakers.\u003c/p\u003e"},{"header":"6. Implications of the Study","content":"\u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Practical Implications\u003c/h2\u003e \u003cp\u003eThe study findings offer useful guidance for healthcare organizations and policymakers aiming to enhance the adoption of EHR. Given that confidentiality emerged as the most critical factor impacting EHR adoption, it is fundamental for healthcare organizations to prioritize strong data security initiatives. This includes investing in advanced security measures such as secure access protocols, encryption, and regular security audits. By effectively addressing privacy concerns, organizations can cultivate trust in EHR systems, making them more appealing to potential users. Moreover, the significance of effort expectancy underscores the need for EHR systems to be user-friendly. Organizations should focus on improving the design and usability of these systems, ensuring they are effortless and easy to navigate. Additionally, providing ongoing training and support can help ease the transition to EHR, making it less daunting for users. The study also highlights the impact of social influence on EHR adoption. Healthcare leaders have an opportunity to encourage greater adoption by fostering a workplace culture that emphasizes the benefits of EHR use. Encouraging peer support and endorsement from influential figures can motivate others to embrace the technology. Addressing the concerns related to possession and control is vital for building trust in EHR systems. By giving users more authority over how their data is managed, healthcare organizations can enhance user confidence and encourage a greater willingness to adopt EHR technologies. For policymakers, it is crucial to focus on the most influential factors, specifically confidentiality and usability when crafting initiatives aimed at boosting EHR adoption. Creating incentives for healthcare organizations to implement best practices in data security could serve as an effective strategy to promote wider acceptance of EHR systems. Overall, these actionable insights can help shape effective strategies for encouraging EHR adoption and improving healthcare sustainability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e6.2 Theoretical Contributions\u003c/h2\u003e \u003cp\u003eThis study makes significant contributions to the theoretical understanding of technology adoption within the healthcare sector, particularly regarding EHR systems. First, it expands the existing literature on EHR adoption by incorporating crucial cybersecurity factors such as confidentiality, integrity, availability, and possession and control. This research enriches traditional technology adoption models by highlighting the often-overlooked yet essential role of data security and user control in the adoption of healthcare technologies. Additionally, by employing a combination of SEM-ANN, the study provides a more nuanced exploration of both linear and nonlinear relationships among different variables. This hybrid approach enhances the robustness of technology acceptance theory, demonstrating how nonlinear factors can significantly influence adoption rates and offering fresh perspectives for future research in this area. The identification of nonlinear relationships among key predictors of EHR adoption challenges the conventional linear assumptions prevalent in technology acceptance studies. The findings indicate that certain factors, particularly confidentiality and effort expectancy, may have a more substantial impact on adoption than previously recognized. This insight calls into question the linear models that have traditionally been applied in technology acceptance research.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e6.3 Methodological Contributions\u003c/h2\u003e \u003cp\u003eThis study contributes methodologically by showcasing the benefits of a hybrid SEM-ANN approach to analyze complex adoption phenomena. By integrating SEM and ANN, it provides a novel framework. SEM captures linear relationships, while ANN identifies both linear and nonlinear interactions, making it powerful for understanding technology adoption behaviors. The use of ANN improved predictive accuracy, as reflected in the low Root Mean Square Error (RMSE) values, showing its effectiveness in managing complex variable interactions. Additionally, the study employed 10-fold cross-validation, enhancing the reliability of the ANN model and minimizing overfitting risks, which bolstered the credibility of the findings. This methodological rigor sets a benchmark for future technology adoption studies, stressing the importance of advanced analytical techniques to capture the challenges of adoption behaviors across various contexts.\u003c/p\u003e \u003c/div\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003eThis study showed that combining SEM and ANN offers a powerful way to analyze factors affecting EHR adoption. Confidentiality, effort expectancy, and possession/control emerged as the most influential factors, underlining the importance of addressing security concerns and system usability. These findings provide useful guidance for healthcare organizations and policymakers looking to encourage sustainable EHR adoption, contributing to broader efforts to improve healthcare sustainability. While this study offers useful insights, there are some limitations to consider. One key limitation is the use of cross-sectional data, which only captures a snapshot of adoption behavior at a single point in time. To gain a deeper understanding of how attitudes and behaviors towards EHR systems evolve, future studies should take a longitudinal approach, tracking changes over time. Additionally, while we focused on several important predictors, other factors like cultural influences or the impact of regulatory policies were not included in this study but could offer important insights in future research. Exploring how these factors vary across different healthcare settings or regions could also lead to more targeted and context-specific findings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e: “The study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board (# 2023-7). Informed consent was obtained from all subjects involved in the study.”\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent\u003c/strong\u003e: “Informed consent was obtained from all subjects involved in the study.”\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e: “Data will be available upon request.”\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e: “No potential competing interest was reported by the authors.”\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions: Muhammed Ibrahim;\u0026nbsp;\u003c/strong\u003eWriting original draft preparation, Data collection, Methodology.\u0026nbsp;\u003cstrong\u003eMohammed A. Al-Sharafi\u003c/strong\u003e\u003cstrong\u003e;\u0026nbsp;\u003c/strong\u003eConceptualization, Supervision, Formal analysis,\u0026nbsp;\u003cstrong\u003eMousa Albashrawi\u003c/strong\u003e\u003cstrong\u003e;\u0026nbsp;\u003c/strong\u003eWriting, review and editing;\u0026nbsp;\u003cstrong\u003eMoamin A. Mahmoud\u003c/strong\u003e\u003cstrong\u003e;\u0026nbsp;\u003c/strong\u003eWriting, review and editing,\u0026nbsp;Supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003e“This work was supported by the Dato’ Low Tuck Kwong International Energy Transition Grant under the project code of 202202002ETG.”\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eUnited Nations General Assembly, \u0026ldquo;Transforming our world: the 2030 Agenda for Sustainable Development | Department of Economic and Social Affairs,\u0026rdquo; \u003cem\u003eoutcome document of the United Nations summit for the adoption of the post-\u003c/em\u003e2015 \u003cem\u003eagenda\u003c/em\u003e, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003eRES/A/70/L.1\u003c/span\u003e\u003cspan address=\"http://RES/A/70/L.1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. \u003cem\u003eUnited Nations, New York\u003c/em\u003e, 2015. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sdgs.un.org/2030agenda\u003c/span\u003e\u003cspan address=\"https://sdgs.un.org/2030agenda\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed Dec. 03, 2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. Al-Emran, \u0026ldquo;Beyond technology acceptance: Development and evaluation of technology-environmental, economic, and social sustainability theory,\u0026rdquo; Technol. Soc., p. 102383, 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA. Kruszyńska-Fischbach, S. Sysko-Romańczuk, T. M. Napi\u0026oacute;rkowski, A. Napi\u0026oacute;rkowska, and D. Kozakiewicz, \u0026ldquo;Organizational e-Health Readiness: How to Prepare the Primary Healthcare Providers\u0026rsquo; Services for Digital Transformation,\u0026rdquo; Int. J. Environ. Res. Public Health, vol. 19, no. 7, 2022, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijerph19073973\u003c/span\u003e\u003cspan address=\"10.3390/ijerph19073973\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eV. Mishra, D. Liebovitz, M. Quinn, L. Kang, T. Yackel, and R. Hoyt, \u0026ldquo;Factors That Influence Clinician Experience with Electronic Health Records,\u0026rdquo; Perspect. Heal. Inf. Manag., vol. 19, no. 1, 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eG. Cline and J. Luiz, \u0026ldquo;Information technology systems in public sector health facilities in developing countries: the case of South Africa,\u0026rdquo; BMC Med. Inform. Decis. Mak., vol. 13, p. 13, 2013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA. M. Al-Momani, T. Ramayah, and M. A. Al-Sharafi, \u0026ldquo;Exploring the impact of cybersecurity on using electronic health records and their performance among healthcare professionals: A multi-analytical SEM-ANN approach,\u0026rdquo; \u003cem\u003eTechnol. Soc.\u003c/em\u003e, vol. 77, no. May, p. 102592, Jun. 2024, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.techsoc.2024.102592\u003c/span\u003e\u003cspan address=\"10.1016/j.techsoc.2024.102592\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZ. Thabet, S. Albashtawi, H. Ansari, M. Al-Emran, M. A. Al-Sharafi, and A. A. AlQudah,\u0026ldquo;Exploring the Factors Affecting Telemedicine Adoption by Integrating UTAUT2 and IS Success Model: A Hybrid SEM\u0026ndash;ANN Approach,\u0026rdquo; \u003cem\u003eIEEE Trans. Eng. Manag.\u003c/em\u003e, 2023, doi: 10.1109/TEM.2023.3296132.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA. D. Tori, \u0026ldquo;UTAUT Modification Model for the Analysis of User Experience of Telemedicine Application Users in Indonesia,\u0026rdquo; 2024 \u003cem\u003e3rd Int. Conf. Digit. Transform. Appl.\u003c/em\u003e, pp. 33\u0026ndash;38, 2024, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/ICDXA61007.2024.10470732\u003c/span\u003e\u003cspan address=\"10.1109/ICDXA61007.2024.10470732\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS. A. Abebe, \u0026ldquo;Intention to Use Personal Health Record System and Its Predictors Among Chronic Patients Enrolled at Public Hospitals in Bahir Dar City, Northwest Ethiopia : Using Modified UTAUT-2 Model,\u0026rdquo; pp. 1\u0026ndash;34, 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO. Christopher, D. Ang, E. Etu, I. Tenebe, and S. Edo, \u0026ldquo;International Journal of Information Management Data Insights Why do healthcare workers adopt digital health technologies - A cross-sectional study integrating the TAM and UTAUT model in a developing economy,\u0026rdquo; Int. J. Inf. Manag. Data Insights, vol. 3, no. 2, p. 100186, 2023, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jjimei.2023.100186\u003c/span\u003e\u003cspan address=\"10.1016/j.jjimei.2023.100186\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eI. Keshta and A. Odeh, \u0026ldquo;Security and privacy of electronic health records: Concerns and challenges,\u0026rdquo; Egypt. Informatics J., vol. 22, no. 2, pp. 177\u0026ndash;183, 2021, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.eij.2020.07.003\u003c/span\u003e\u003cspan address=\"10.1016/j.eij.2020.07.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eE. Mbwambo, \u0026ldquo;Acceptance of Interoperable Electronic Health Record (EHRs) Systems: A Tanzanian e-Health Perspective Acceptance of Interoperable Electronic Health Record ( EHRs ) Systems : A Tanzanian e-Health Perspective,\u0026rdquo; 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. R. Ayala Solares \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Deep learning for electronic health records: A comparative review of multiple deep neural architectures,\u0026rdquo; \u003cem\u003eJ. Biomed. Inform.\u003c/em\u003e, vol. 101, no. March 2019, p. 103337, 2020, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jbi.2019.103337\u003c/span\u003e\u003cspan address=\"10.1016/j.jbi.2019.103337\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eY. C. Chen, J. C. Wu, I. Haschler, A. Majeed, T. J. Chen, and T. Wetter, \u0026ldquo;Academic impact of a public electronic health database: Bibliometric analysis of studies using the general practice research database,\u0026rdquo; PLoS One, vol. 6, no. 6, pp. 1\u0026ndash;7, 2011, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0021404\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0021404\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. Huang \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Factors Associated With the Acceptance of an eHealth App for Electronic Health Record Sharing System: Population-Based Study,\u0026rdquo; J. Med. Internet Res., vol. 24, no. 12, 2022, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2196/40370\u003c/span\u003e\u003cspan address=\"10.2196/40370\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eE. R. Melnick \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;The Association Between Perceived Electronic Health Record Usability and Professional Burnout Among US Physicians,\u0026rdquo; \u003cem\u003eMayo Clin. Proc.\u003c/em\u003e, vol. 95, no. 3, pp. 476\u0026ndash;487, 2020, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.mayocp.2019.09.024\u003c/span\u003e\u003cspan address=\"10.1016/j.mayocp.2019.09.024\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS. Shi, D. He, L. Li, N. Kumar, M. K. Khan, and K. K. R. Choo, \u0026ldquo;Applications of blockchain in ensuring the security and privacy of electronic health record systems: A survey,\u0026rdquo; Comput. Secur., vol. 97, 2020, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cose.2020.101966\u003c/span\u003e\u003cspan address=\"10.1016/j.cose.2020.101966\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eG. Aydin, \u0026ldquo;Increasing mobile health application usage among Generation Z members: evidence from the UTAUT model,\u0026rdquo; vol. 17, no. 3, pp. 353\u0026ndash;379, 2023, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1108/IJPHM-02-2021-0030\u003c/span\u003e\u003cspan address=\"10.1108/IJPHM-02-2021-0030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. Ibrahim, Y. Kani, and E. Ahmed, \u0026ldquo;Examining the Impact of Implementation of Electronic Health Record System for Effective Health Management in Katsina State Hospitals, Nigeria,\u0026rdquo; \u003cem\u003eInt. J. Gastroenterol.\u003c/em\u003e, vol. 3, no. 2, pp. 27\u0026ndash;34, Dec. 2019, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.11648/j.ijg.20190302.11\u003c/span\u003e\u003cspan address=\"10.11648/j.ijg.20190302.11\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. J. Hathaliya and S. Tanwar, \u0026ldquo;An exhaustive survey on security and privacy issues in Healthcare 4. 0,\u0026rdquo; \u003cem\u003eComput. Commun.\u003c/em\u003e, vol. 153, no. January, pp. 311\u0026ndash;335, 2020, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.comcom.2020.02.018\u003c/span\u003e\u003cspan address=\"10.1016/j.comcom.2020.02.018\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eK. Miyachi and T. K. Mackey, \u0026ldquo;hOCBS: A privacy-preserving blockchain framework for healthcare data leveraging an on-chain and off-chain system design,\u0026rdquo; vol. 58, no. February, 2021, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ipm.2021.102535\u003c/span\u003e\u003cspan address=\"10.1016/j.ipm.2021.102535\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS. Upadhyay and H. F. Hu, \u0026ldquo;A Qualitative Analysis of the Impact of Electronic Health Records (EHR) on Healthcare Quality and Safety: Clinicians\u0026rsquo; Lived Experiences,\u0026rdquo; Heal. Serv. Insights, vol. 15, 2022, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/11786329211070722\u003c/span\u003e\u003cspan address=\"10.1177/11786329211070722\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. D. Kasaye, N. D. Mengestie, S. Beyene, N. Kebede, and H. S. Ngusie, \u0026ldquo;Acceptance of electronic medical records and associated factor among physicians working in University of Gondar comprehensive specialized hospital: A cross-sectional study,\u0026rdquo; 2023, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/20552076231213445\u003c/span\u003e\u003cspan address=\"10.1177/20552076231213445\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eE. C. O\u0026rsquo;Brien \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;The use of electronic health records for recruitment in clinical trials: a mixed methods analysis of the Harmony Outcomes Electronic Health Record Ancillary Study,\u0026rdquo; Trials, vol. 22, no. 1, pp. 4\u0026ndash;11, 2021, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13063-021-05397-0\u003c/span\u003e\u003cspan address=\"10.1186/s13063-021-05397-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA. Nanjo, H. Evans, K. Direk, A. C. Hayward, A. Story, and A. Banerjee, \u0026ldquo;Prevalence, incidence, and outcomes across cardiovascular diseases in homeless individuals using national linked electronic health records,\u0026rdquo; Eur. Heart J., vol. 41, no. 41, pp. 4011\u0026ndash;4020, 2020, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/eurheartj/ehaa795\u003c/span\u003e\u003cspan address=\"10.1093/eurheartj/ehaa795\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eK. L. Colborn \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Development and validation of models for detection of postoperative infections using structured electronic health records data and machine learning,\u0026rdquo; Surg. (United States), vol. 173, no. 2, pp. 464\u0026ndash;471, 2023, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.surg.2022.10.026\u003c/span\u003e\u003cspan address=\"10.1016/j.surg.2022.10.026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eK. C. Derecho \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Technology adoption of electronic medical records in developing economies: A systematic review on physicians \u0026rsquo; perspective,\u0026rdquo; no. 2, 2024, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/20552076231224605\u003c/span\u003e\u003cspan address=\"10.1177/20552076231224605\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eG. Aydin and S. Kumru, \u0026ldquo;Paving the way for increased e-health record use: elaborating intentions of Gen-Z,\u0026rdquo; Heal. Syst., vol. 12, no. 3, pp. 281\u0026ndash;298, 2023, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/20476965.2022.2129471\u003c/span\u003e\u003cspan address=\"10.1080/20476965.2022.2129471\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eV. Venkatesh, \u0026ldquo;C ONSUMER A CCEPTANCE AND U SE OF I NFORMATION T ECHNOLOGY: E XTENDING THE U NIFIED T HEORY,\u0026rdquo; vol. 36, no. 1, pp. 157\u0026ndash;178, 2012.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eI. Arpaci and K. Sevinc, \u0026ldquo;Development of the cybersecurity scale (CS-S): Evidence of validity and reliability,\u0026rdquo; Inf. Dev., vol. 38, no. 2, pp. 218\u0026ndash;226, 2022, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/0266666921997512\u003c/span\u003e\u003cspan address=\"10.1177/0266666921997512\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eT. K. Rattan, M. Joshi, G. Vesty, and S. Sharma, \u0026ldquo;Sustainability indicators in public healthcare: A factor analysis approach,\u0026rdquo; J. Clean. Prod., vol. 370, no. August, p. 133253, 2022, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jclepro.2022.133253\u003c/span\u003e\u003cspan address=\"10.1016/j.jclepro.2022.133253\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eV. Venkatesh, J. Y. L. Thong, and X. Xu, \u0026ldquo;Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology,\u0026rdquo; MIS Q. Manag. Inf. Syst., vol. 36, no. 1, pp. 157\u0026ndash;178, 2012, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2307/41410412\u003c/span\u003e\u003cspan address=\"10.2307/41410412\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eY. K. Dwivedi, N. P. Rana, A. Jeyaraj, M. Clement, and M. D. Williams, \u0026ldquo;Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model,\u0026rdquo; \u003cem\u003eInf. Syst. Front.\u003c/em\u003e, vol. 21, no. 3, pp. 719\u0026ndash;734, Jun. 2019, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10796-017-9774-y\u003c/span\u003e\u003cspan address=\"10.1007/s10796-017-9774-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eF. D. Davis, \u0026ldquo;A technology acceptance model for empirically testing new end-user information systems: Theory and results.\u0026rdquo; Massachusetts Institute of Technology, 1985.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eI. Ajzen and M. Fishbein, \u003cem\u003eUnderstanding attitudes and predicting social behavior\u003c/em\u003e. 1980.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eV. Venkatesh, J. Thong, and X. Xu, \u0026ldquo;Unified Theory of Acceptance and Use of Technology: A Synthesis and the Road Ahead,\u0026rdquo; J. Assoc. Inf. Syst., vol. 17, no. 5, pp. 328\u0026ndash;376, May 2016, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.17705/1jais.00428\u003c/span\u003e\u003cspan address=\"10.17705/1jais.00428\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. B. Alazzam, A. S. H. Basari, A. S. Sibghatullah, Y. M. Ibrahim, M. R. Ramli, and M. H. Naim, \u0026ldquo;Trust in stored data in EHRs acceptance of medical staff: Using UTAUT2,\u0026rdquo; Int. J. Appl. Eng. Res., vol. 11, no. 4, pp. 2737\u0026ndash;2748, 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. A. Al-Sharafi \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Generation Z use of artificial intelligence products and its impact on environmental sustainability: A cross-cultural comparison,\u0026rdquo; Comput. Human Behav., vol. 143, p. 107708, Jun. 2023, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.chb.2023.107708\u003c/span\u003e\u003cspan address=\"10.1016/j.chb.2023.107708\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH. Albanna, A. A. Alalwan, and M. Al-Emran, \u0026ldquo;An integrated model for using social media applications in non-profit organizations,\u0026rdquo; Int. J. Inf. Manage., vol. 63, p. 102452, Apr. 2022, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ijinfomgt.2021.102452\u003c/span\u003e\u003cspan address=\"10.1016/j.ijinfomgt.2021.102452\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eI. Arpaci and M. Bahari, \u0026ldquo;A complementary SEM and deep ANN approach to predict the adoption of cryptocurrencies from the perspective of cybersecurity,\u0026rdquo; Comput. Human Behav., vol. 143, p. 107678, Jun. 2023, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.chb.2023.107678\u003c/span\u003e\u003cspan address=\"10.1016/j.chb.2023.107678\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO. Access, \u0026ldquo;Cybersecurity Framework for Ensuring Confidentiality, Integrity, and Availability of University Management Systems in,\u0026rdquo; no. 1, pp. 16\u0026ndash;38, 2023, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.13140/RG.2.2.15091.30241\u003c/span\u003e\u003cspan address=\"10.13140/RG.2.2.15091.30241\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eB. Wheatley, \u0026ldquo;Transforming care delivery through health information technology.,\u0026rdquo; Perm. J., vol. 17, no. 1, pp. 81\u0026ndash;86, 2013, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.7812/TPP/12-030\u003c/span\u003e\u003cspan address=\"10.7812/TPP/12-030\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP. Poba-Nzaou, N. Kume, and S. Kobayashi, \u0026ldquo;Governance and Sustainability of an Open Source Electronic Health Record: An Interpretive Case Study of OpenDolphin in Japan,\u0026rdquo; \u003cem\u003eStud. Health Technol. Inform.\u003c/em\u003e, vol. 264, pp. 739\u0026ndash;743, Aug. 2019, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3233/SHTI190321\u003c/span\u003e\u003cspan address=\"10.3233/SHTI190321\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP. Poba-Nzaou, N. Kume, and S. Kobayashi, \u0026ldquo;Developing and Sustaining an Open Source Electronic Health Record: Evidence from a Field Study in Japan,\u0026rdquo; J. Med. Syst., vol. 44, no. 9, pp. 1\u0026ndash;10, Sep. 2020, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/S10916-020-01625-3/FIGURES/3\u003c/span\u003e\u003cspan address=\"10.1007/S10916-020-01625-3/FIGURES/3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVenkatesh et al., \u0026ldquo;User Acceptance of Information Technology: Toward a Unified View,\u0026rdquo; Inorg. Chem. Commun., vol. 67, no. 3, pp. 95\u0026ndash;98, 2003, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.inoche.2016.03.015\u003c/span\u003e\u003cspan address=\"10.1016/j.inoche.2016.03.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. Q. Aldossari and A. Sidorova, \u0026ldquo;Consumer Acceptance of Internet of Things (IoT): Smart Home Context,\u0026rdquo; J. Comput. Inf. Syst., vol. 60, no. 6, pp. 507\u0026ndash;517, 2020, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/08874417.2018.1543000\u003c/span\u003e\u003cspan address=\"10.1080/08874417.2018.1543000\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eN. Tomić, Z. Kalinić, and V. Todorović, \u0026ldquo;Using the UTAUT model to analyze user intention to accept electronic payment systems in Serbia,\u0026rdquo; Port. Econ. J., vol. 22, no. 2, pp. 251\u0026ndash;270, 2023, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10258-022-00210-5\u003c/span\u003e\u003cspan address=\"10.1007/s10258-022-00210-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. Gumz, D. C. Fettermann, \u0026Acirc;. M. O. Sant\u0026rsquo;Anna, and G. L. Tortorella, \u0026ldquo;Social Influence as a Major Factor in Smart Meters\u0026rsquo; Acceptance: Findings from Brazil,\u0026rdquo; Results Eng., vol. 15, Sep. 2022, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.rineng.2022.100510\u003c/span\u003e\u003cspan address=\"10.1016/j.rineng.2022.100510\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA. N. Tak, B. Becerik-Gerber, L. Soibelman, and G. Lucas, \u0026ldquo;A framework for investigating the acceptance of smart home technologies: Findings for residential smart HVAC systems,\u0026rdquo; Build. Environ., vol. 245, no. August, p. 110935, 2023, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.buildenv.2023.110935\u003c/span\u003e\u003cspan address=\"10.1016/j.buildenv.2023.110935\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eN. Dwi Andini and K. Adiwijaya, \u0026ldquo;What Factors Do Affect the Adoption of Internet of Things (Smart Home) in Indonesia,\u0026rdquo; Int. J. Bus. Technol. Manag., vol. 3, no. 2, pp. 84\u0026ndash;97, 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. Liu, X. Gong, M. Weal, W. Dai, and S. Hou, \u0026ldquo;Attitudes and associated factors of patients \u0026rsquo; adoption of patient accessible electronic health records in China \u0026mdash; A mixed methods study,\u0026rdquo; no. 13, 2023, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/20552076231174101\u003c/span\u003e\u003cspan address=\"10.1177/20552076231174101\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA. Alomari and B. Soh, \u0026ldquo;Determinants of Medical Internet of Things Adoption in Healthcare and the Role of Demographic Factors Incorporating Modified UTAUT,\u0026rdquo; vol. 14, no. 7, pp. 17\u0026ndash;32, 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. Al-Emran, A. A. AlQudah, G. A. Abbasi, M. A. Al-Sharafi, and M. Iranmanesh, \u0026ldquo;Determinants of Using AI-Based Chatbots for Knowledge Sharing: Evidence From PLS-SEM and Fuzzy Sets (fsQCA),\u0026rdquo; IEEE Trans. Eng. Manag., pp. 1\u0026ndash;15, 2023, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/tem.2023.3237789\u003c/span\u003e\u003cspan address=\"10.1109/tem.2023.3237789\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA. A. Zaid, D. F. Kakeesh, G. A. Al-weshah, and M. M. Al-debei, \u0026ldquo;Journal of Open Innovation: Technology, Market, and Complexity Consumer post-adoption of e-wallet : An extended UTAUT2 perspective with trust,\u0026rdquo; J. Open Innov. Technol. Mark. Complex., vol. 9, no. 3, p. 100113, 2023, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.joitmc.2023.100113\u003c/span\u003e\u003cspan address=\"10.1016/j.joitmc.2023.100113\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eT. Muchenje and R. A. Botha, \u0026ldquo;Consumer-centric factors for the implementation of smart meters in South Africa,\u0026rdquo; South African Comput. J., vol. 33, no. 2, pp. 17\u0026ndash;54, 2021, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.18489/SACJ.V33I2.909\u003c/span\u003e\u003cspan address=\"10.18489/SACJ.V33I2.909\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. Iqbal and M. Idrees, \u0026ldquo;Understanding the IOT Adoption for Home Automation in the Perspective of UTAUT2,\u0026rdquo; Glob. Bus. Rev., 2022, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/09721509221132058\u003c/span\u003e\u003cspan address=\"10.1177/09721509221132058\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eG. Cao, Y. Duan, J. S. Edwards, and Y. K. Dwivedi, \u0026ldquo;Understanding managers\u0026rsquo; attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making,\u0026rdquo; Technovation, vol. 106, p. 102312, Aug. 2021, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.technovation.2021.102312\u003c/span\u003e\u003cspan address=\"10.1016/j.technovation.2021.102312\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. Kim and K. S. Lee, \u0026ldquo;Conceptual model to predict Filipino teachers \u0026rsquo; adoption of ICT-based instruction in class: using the UTAUT model,\u0026rdquo; Asia Pacific J. Educ., vol. 00, no. 00, pp. 1\u0026ndash;15, 2020, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/02188791.2020.1776213\u003c/span\u003e\u003cspan address=\"10.1080/02188791.2020.1776213\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL. Wong, G. W. Tan, V. Lee, K. Ooi, and G. W. Tan, \u0026ldquo;Unearthing the determinants of Blockchain adoption in supply chain management,\u0026rdquo; Int. J. Prod. Res., vol. 0, no. 0, pp. 1\u0026ndash;24, 2020, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/00207543.2020.1730463\u003c/span\u003e\u003cspan address=\"10.1080/00207543.2020.1730463\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eC. Y. Joa and K. Magsamen-conrad, \u0026ldquo;Social influence and UTAUT in predicting digital immigrants \u0026rsquo; technology use,\u0026rdquo; Behav. Inf. Technol., vol. 0, no. 0, pp. 1\u0026ndash;19, 2021, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/0144929X.2021.1892192\u003c/span\u003e\u003cspan address=\"10.1080/0144929X.2021.1892192\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA. Alaiad and L. Zhou, \u0026ldquo;Patients\u0026rsquo; behavioral intentions toward using WSN based smart home healthcare systems: An empirical investigation,\u0026rdquo; \u003cem\u003eProc. Annu. Hawaii Int. Conf. Syst. Sci.\u003c/em\u003e, vol. 2015-March, pp. 824\u0026ndash;833, 2015, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/HICSS.2015.104\u003c/span\u003e\u003cspan address=\"10.1109/HICSS.2015.104\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZ. Ren and G. Zhou, \u0026ldquo;Analysis of Driving Factors in the Intention to Use the Virtual Nursing Home for the Elderly: A Modified UTAUT Model in the Chinese Context,\u0026rdquo; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eI. B. Hassani and A. Ouiddad, \u0026ldquo;Impact of hedonic motivation and corporate culture on the adoption of an information system,\u0026rdquo; vol. 49, no. 5, pp. 1561\u0026ndash;1578, 2019, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1108/K-01-2019-0040\u003c/span\u003e\u003cspan address=\"10.1108/K-01-2019-0040\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eF. Gro\u0026szlig;e-Kreul, \u0026ldquo;What will drive household adoption of smart energy? Insights from a consumer acceptance study in Germany,\u0026rdquo; \u003cem\u003eUtil. Policy\u003c/em\u003e, vol. 75, no. December 2021, 2022, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jup.2021.101333\u003c/span\u003e\u003cspan address=\"10.1016/j.jup.2021.101333\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA. Al-azawei and A. Alowayr, \u0026ldquo;Technology in Society Predicting the intention to use and hedonic motivation for mobile learning: A comparative study in two Middle Eastern countries,\u0026rdquo; Technol. Soc., vol. 62, no. June, p. 101325, 2020, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.techsoc.2020.101325\u003c/span\u003e\u003cspan address=\"10.1016/j.techsoc.2020.101325\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR. L. Goldstein, A. Anoshiravani, M. V. Svetaz, and J. L. Carlson, \u0026ldquo;Providers\u0026rsquo; Perspectives on Adolescent Confidentiality and the Electronic Health Record: A State of Transition,\u0026rdquo; J. Adolesc. Heal., vol. 66, no. 3, pp. 296\u0026ndash;300, 2020, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jadohealth.2019.09.020\u003c/span\u003e\u003cspan address=\"10.1016/j.jadohealth.2019.09.020\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS. Rathod, M. D. Salunke, M. Yashwante, M. Bhende, S. R. Rangari, and V. D. Rewaskar, \u0026ldquo;INTELLIGENT SYSTEMS AND APPLICATIONS IN ENGINEERING Ensuring Optimized Storage with Data Confidentiality and Privacy- Preserving for Secure Data Sharing Model Over Cloud,\u0026rdquo; pp. 0\u0026ndash;3, 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR. K. Jha, \u0026ldquo;Cybersecurity and Confidentiality in Smart Grid for Enhancing Sustainability and Reliability,\u0026rdquo; no. July, 2023, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.36548/rrrj.2023.2.001\u003c/span\u003e\u003cspan address=\"10.36548/rrrj.2023.2.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. Abdekhoda, \u0026ldquo;The effect of confidentiality and privacy concerns on adoption of personal health record from patient \u0026rsquo; s perspective The effect of confidentiality and privacy concerns on adoption of personal health record from patient \u0026rsquo; s perspective,\u0026rdquo; no. January, 2019, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s12553-018-00287-z\u003c/span\u003e\u003cspan address=\"10.1007/s12553-018-00287-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. Raphael, A. Mhina, G. Johar, and M. H. Alkawaz, \u0026ldquo;The Influence of Perceived Confidentiality Risks and Attitude on Tanzania Government Employees \u0026rsquo; Intention to Adopt Web 2. 0 and Social Media for Work-Related Purposes,\u0026rdquo; Int. J. Public Adm., vol. 42, no. 7, pp. 558\u0026ndash;571, 2018, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/01900692.2018.1491596\u003c/span\u003e\u003cspan address=\"10.1080/01900692.2018.1491596\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. U. CHELLADURAI, D. S. Pandian, and D. K. Ramasamy, \u0026ldquo;A blockchain based patient centric electronic health record storage and integrity management for e-Health systems,\u0026rdquo; Heal. Policy Technol., vol. 10, no. 4, p. 100513, 2021, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.hlpt.2021.100513\u003c/span\u003e\u003cspan address=\"10.1016/j.hlpt.2021.100513\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA. Shuhaiber, I. Mashal, and O. Alsaryrah, \u0026ldquo;Smart homes as an IoT application: Predicting attitudes and behaviours,\u0026rdquo; \u003cem\u003eProc. IEEE/ACS Int. Conf. Comput. Syst. Appl. AICCSA\u003c/em\u003e, vol. 2019-Novem, pp. 1\u0026ndash;7, 2019, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/AICCSA47632.2019.9035295\u003c/span\u003e\u003cspan address=\"10.1109/AICCSA47632.2019.9035295\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eI. Arpaci, \u0026ldquo;A Multi-Analytical SEM-ANN Approach to Investigate the Social Sustainability of AI Chatbots Based on Cybersecurity and Protection Motivation Theory,\u0026rdquo; IEEE Trans. Eng. Manag., 2023, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/TEM.2023.3339578\u003c/span\u003e\u003cspan address=\"10.1109/TEM.2023.3339578\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. Turner, B. Kitchenham, P. Brereton, S. Charters, and D. Budgen, \u0026ldquo;Does the technology acceptance model predict actual use ? A systematic literature review,\u0026rdquo; Inf. Softw. Technol., vol. 52, no. 5, pp. 463\u0026ndash;479, 2010, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.infsof.2009.11.005\u003c/span\u003e\u003cspan address=\"10.1016/j.infsof.2009.11.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eX. Wang, C. Lee, J. Jiang, G. Zhang, and Z. Wei, \u0026ldquo;behavioral sciences Research on the Factors Affecting the Adoption of Smart Aged-Care Products by the Aged in China: Extension Based on UTAUT Model,\u0026rdquo; 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. Christian \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Generation YZ \u0026rsquo; s E-Healthcare Use Factors Distribution in COVID-19 \u0026rsquo; s Third Year: A UTAUT Modeling,\u0026rdquo; vol. 7, pp. 117\u0026ndash;129, 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL. Nguyen, E. Bellucci, and L. T. Nguyen, \u0026ldquo;Electronic health records implementation: An evaluation of information system impact and contingency factors,\u0026rdquo; Int. J. Med. Inform., vol. 83, no. 11, pp. 779\u0026ndash;796, 2014, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ijmedinf.2014.06.011\u003c/span\u003e\u003cspan address=\"10.1016/j.ijmedinf.2014.06.011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. Calabrese, S. Suparaku, S. Santovito, and X. Hysa, \u0026ldquo;Preventing and developmental factors of sustainability in healthcare organisations from the perspective of decision makers: an exploratory factor analysis,\u0026rdquo; pp. 1\u0026ndash;9, 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS. Malone \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;The Clinical Sustainability Assessment Tool: measuring organizational capacity to promote sustainability in healthcare,\u0026rdquo; vol. 2, pp. 1\u0026ndash;12, 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eY. Ma \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Using the Unified Theory of Acceptance and Use of Technology (UTAUT) and e \u0026ndash; health literacy ( e \u0026ndash; HL ) to investigate the tobacco control intentions and behaviors of non \u0026ndash; smoking college students in China: a cross \u0026ndash; sectional investigation,\u0026rdquo; pp. 1\u0026ndash;14, 2023, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12889-023-15644-5\u003c/span\u003e\u003cspan address=\"10.1186/s12889-023-15644-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. W. Creswell and J. D. Creswell, \u003cem\u003eResearch Design Qualitative, Quantitative, and Mixed Methods Approaches\u003c/em\u003e, Sixth., vol. 6. SAGE, 2023. Accessed: Dec. 20, 2023. [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://uk.sagepub.com/en-gb/asi/research-design/book270550#description\u003c/span\u003e\u003cspan address=\"https://uk.sagepub.com/en-gb/asi/research-design/book270550#description\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. D. C. Tongco, \u0026ldquo;Purposive sampling as a tool for informant selection,\u0026rdquo; Ethnobot. Res. Appl., vol. 5, pp. 147\u0026ndash;158, 2007, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.17348/era.5.0.147-158\u003c/span\u003e\u003cspan address=\"10.17348/era.5.0.147-158\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eE. I. Obilor, \u0026ldquo;Convenience and Purposive Sampling Techniques: Are they the Same?,\u0026rdquo; Int. J. Innov. Soc. Sci. Educ. Res., vol. 11, no. 1, pp. 1\u0026ndash;7, 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eB. Basarir-Ozel, H. B. Turker, and V. A. Nasir, \u0026ldquo;Identifying the Key Drivers and Barriers of Smart Home Adoption: A Thematic Analysis from the Business Perspective,\u0026rdquo; Sustain., vol. 14, no. 15, 2022, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/su14159053\u003c/span\u003e\u003cspan address=\"10.3390/su14159053\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZ. Barua and A. Barua, \u0026ldquo;Modeling the predictors of mobile health adoption by Rohingya Refugees in Bangladesh: An extension of UTAUT2 using combined SEM-Neural network approach,\u0026rdquo; J. Migr. Heal., vol. 8, no. July, p. 100201, 2023, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jmh.2023.100201\u003c/span\u003e\u003cspan address=\"10.1016/j.jmh.2023.100201\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eV. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, \u0026ldquo;User acceptance of information technology: Toward a unified view,\u0026rdquo; MIS Q. Manag. Inf. Syst., vol. 27, no. 3, pp. 425\u0026ndash;478, 2003, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2307/30036540\u003c/span\u003e\u003cspan address=\"10.2307/30036540\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eR. Mehra and M. K. Sharma, \u0026ldquo;Sustainability Analytics and Modeling Measures of Sustainability in Healthcare,\u0026rdquo; \u003cem\u003eSustain. Anal. Model.\u003c/em\u003e, vol. 1, no. January 2021, p. 100001, 2022, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.samod.2021.100001\u003c/span\u003e\u003cspan address=\"10.1016/j.samod.2021.100001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eY. Noh, \u0026ldquo;A Study on Measuring the Change of the Response Results in Likert 5-Point Scale Measurement * 리커트 5점척도에서 자극에 의한 응답결과의 변화 측정에 관한 연구,\u0026rdquo; vol. 28, no. 3, pp. 335\u0026ndash;353, 2011.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. T. Croasmun and L. Ostrom, \u0026ldquo;Using Likert-Type Scales in the Social Sciences,\u0026rdquo; J. Adult Educ., vol. 40, no. 1, pp. 19\u0026ndash;22, 2011.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. A. Al-Sharafi, M. Iranmanesh, M. Al-Emran, A. I. Alzahrani, F. Herzallah, and N. Jamil, \u0026ldquo;Determinants of cloud computing integration and its impact on sustainable performance in SMEs: An empirical investigation using the SEM-ANN approach,\u0026rdquo; \u003cem\u003eHeliyon\u003c/em\u003e, vol. 9, no. 5, p. e16299, May 2023, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.heliyon.2023.e16299\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2023.e16299\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL. Y. Leong, T. S. Hew, K. B. Ooi, G. W. H. Tan, and A. Koohang, \u0026ldquo;An SEM-ANN Approach - Guidelines in Information Systems Research,\u0026rdquo; \u003cem\u003eJ. Comput. Inf. Syst.\u003c/em\u003e, Mar. 2024, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/08874417.2024.2329128\u003c/span\u003e\u003cspan address=\"10.1080/08874417.2024.2329128\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. Sarstedt, C. M. Ringle, and J. F. Hair, \u0026ldquo;Partial Least Squares Structural Equation Modeling,\u0026rdquo; \u003cem\u003eHandb. Mark. Res.\u003c/em\u003e, no. July, pp. 587\u0026ndash;632, 2021, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-3-319-57413-4_15\u003c/span\u003e\u003cspan address=\"10.1007/978-3-319-57413-4_15\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eT. Ramayah, C. J. Hwa, F. Chuah, and M. A. Memon, \u003cem\u003ePartial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS 3.0: An Updated and Practical\u0026hellip;\u003c/em\u003e, 2nd ed., no. July. Kuala Lumpur, Malaysia: Pearson., 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. F. Hair Jr, G. T. M. Hult, C. M. Ringle, and M. Sarstedt, \u003cem\u003eA primer on partial least squares structural equation modeling (PLS-SEM)\u003c/em\u003e. Sage publications, 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. Henseler, C. M. Ringle, and M. Sarstedt, \u0026ldquo;A new criterion for assessing discriminant validity in variance-based structural equation modeling,\u0026rdquo; J. Acad. Mark. Sci., vol. 43, no. 1, pp. 115\u0026ndash;135, Jan. 2015, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11747-014-0403-8\u003c/span\u003e\u003cspan address=\"10.1007/s11747-014-0403-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eK. B. Ooi, V. H. Lee, G. W. H. Tan, T. S. Hew, and J. J. Hew, \u0026ldquo;Cloud computing in manufacturing: The next industrial revolution in Malaysia?,\u0026rdquo; \u003cem\u003eExpert Syst. Appl.\u003c/em\u003e, vol. 93, pp. 376\u0026ndash;394, Mar. 2018, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.eswa.2017.10.009\u003c/span\u003e\u003cspan address=\"10.1016/j.eswa.2017.10.009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA. Khayer, M. S. Talukder, Y. Bao, and M. N. Hossain, \u0026ldquo;Cloud computing adoption and its impact on SMEs\u0026rsquo; performance for cloud supported operations: A dual-stage analytical approach,\u0026rdquo; Technol. Soc., vol. 60, p. 101225, Feb. 2020, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.techsoc.2019.101225\u003c/span\u003e\u003cspan address=\"10.1016/j.techsoc.2019.101225\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. S. Armstrong and T. S. Overton, \u0026ldquo;Estimating Nonresponse Bias in Mail Surveys,\u0026rdquo; J. Mark. Res., vol. 14, no. 3, pp. 396\u0026ndash;402, 1977, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/002224377701400320\u003c/span\u003e\u003cspan address=\"10.1177/002224377701400320\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eN. Kock, \u0026ldquo;Harman\u0026rsquo;s single factor test in PLS-SEM: Checking for common method bias,\u0026rdquo; \u003cem\u003eData Anal. Perspect. J.\u003c/em\u003e, vol. 2, no. 2, pp. 1\u0026ndash;6, 2021, [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://scriptwarp.com/dapj/2021_DAPJ_2_2/Kock_2021_DAPJ_2_2_HarmansCMBTest.pdf\u003c/span\u003e\u003cspan address=\"https://scriptwarp.com/dapj/2021_DAPJ_2_2/Kock_2021_DAPJ_2_2_HarmansCMBTest.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eS. G. Rogelberg, \u0026ldquo;Common Method Variance,\u0026rdquo; SAGE Encycl. Ind. Organ. Psychol. \u003cem\u003e2nd Ed.\u003c/em\u003e, 2017, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4135/9781483386874.n68\u003c/span\u003e\u003cspan address=\"10.4135/9781483386874.n68\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eN. Kock, \u0026ldquo;Common method bias in PLS-SEM: A full collinearity assessment approach,\u0026rdquo; Int. J. e-Collaboration, vol. 11, no. 4, pp. 1\u0026ndash;10, 2015, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4018/ijec.2015100101\u003c/span\u003e\u003cspan address=\"10.4018/ijec.2015100101\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. Hair and A. Alamer, \u0026ldquo;Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example,\u0026rdquo; Res. Methods Appl. Linguist., vol. 1, no. 3, 2022, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.rmal.2022.100027\u003c/span\u003e\u003cspan address=\"10.1016/j.rmal.2022.100027\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eC. M. Ringle and M. Sarstedt, \u003cem\u003ePartial Least Squares Structural Equation Modeling (PLS-SEM) Using R\u003c/em\u003e. 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ. Henseler, C. M. Ringle, and M. Sarstedt, \u0026ldquo;A new criterion for assessing discriminant validity in variance-based structural equation modeling,\u0026rdquo; J. Acad. Mark. Sci., vol. 43, no. 1, pp. 115\u0026ndash;135, Jan. 2015, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11747-014-0403-8\u003c/span\u003e\u003cspan address=\"10.1007/s11747-014-0403-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eW. F. Criterion- and C. E. Shannon, \u0026ldquo;Coding Theorems for a Discrete Source,\u0026rdquo; pp. 325\u0026ndash;350, 1959.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA. S. Albahri \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Hybrid artificial neural network and structural equation modelling techniques: a survey,\u0026rdquo; Complex Intell. Syst., vol. 8, no. 2, pp. 1781\u0026ndash;1801, 2022, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s40747-021-00503-w\u003c/span\u003e\u003cspan address=\"10.1007/s40747-021-00503-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eG. A. Alkawsi \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;A hybrid SEM-neural network method for identifying acceptance factors of the smart meters in Malaysia: Challenges perspective,\u0026rdquo; Alexandria Eng. J., vol. 60, no. 1, pp. 227\u0026ndash;240, Feb. 2021, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.aej.2020.07.002\u003c/span\u003e\u003cspan address=\"10.1016/j.aej.2020.07.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Electronic Health Records Adoption, Cybersecurity, Healthcare Sustainability, UTAUT2, Sustainable Development Goals, SEM-ANN","lastPublishedDoi":"10.21203/rs.3.rs-5798963/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5798963/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eElectronic Health Records (EHR) systems are critical for achieving healthcare sustainability, offering benefits such as improving care of the patient, enhanced management of data, and operational efficiency. Despite these advantages, the adoption of EHR systems remains a challenge, influenced by various technological, organizational, and individual factors. This study builds upon the UTAUT2 framework by incorporating cybersecurity considerations to offer a more comprehensive understanding of EHR adoption and its role in promoting sustainable healthcare. Data were collected from 374 healthcare professionals through purposive sampling and analyzed using a hybrid approach combining Structural Equation Modeling (SEM) and Artificial Neural Networks (ANN). The findings demonstrate that EHR use plays a key role in advancing healthcare sustainability by improving organizational efficiency and long-term resilience. Key factors influencing EHR adoption include confidentiality and possession/control, underscoring the importance of data privacy, security, and system ownership. Performance expectancy and social influence significantly impact adoption decisions, reflecting the role of usability, peer influence, and organizational dynamics. Additional factors such as integrity and facilitating conditions showed moderate importance, while hedonic motivation and availability were less critical. This study contributes to EHR adoption research by integrating cybersecurity and user experience factors, offering insights for healthcare organizations and policymakers. The findings highlight the need to prioritize data security and usability to enhance adoption. Future research could explore EHR adoption in diverse settings and examine evolving adoption dynamics.\u003c/p\u003e","manuscriptTitle":"A Cybersecurity-Centric Model for Predicting Electronic Health Records System Adoption for Sustainable Healthcare: A SEM-ANN Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-20 10:06:05","doi":"10.21203/rs.3.rs-5798963/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f48985f7-b832-4ba4-9276-a350b2818c10","owner":[],"postedDate":"January 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-24T04:53:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-20 10:06:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5798963","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5798963","identity":"rs-5798963","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}