EXPLORING THE FACTORS AFFECTING THE DEPLOYMENT OF THE INTERNET OF THINGS IN HEALTHCARE ORGANIZATIONS IN THE UAE USING THE UTAUT MODEL

Journal of Theoretical and Applied Information Technology 31st May 2025. Vol.103. No.10 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4399 EXPLORING THE FACTORS AFFECTING THE DEPLOYMENT OF THE INTERNET OF THINGS IN HEALTHCARE ORGANIZATIONS IN THE UAE USING THE UTAUT MODEL 1NAHIL ABDALLAH 1Information Technology and Computer Science Department, Murdoch University, United Arab Emirates E-mail: [email protected] ABSTRACT Despite the growing interest in digital healthcare, the adoption of Internet of Things (IoT) technologies in healthcare organizations, remains limited and underexplored, particularly from the patients' perspective. This research investigates the key factors influencing the deployment of IoT-based healthcare devices among end users in public hospitals across the UAE. Drawing from the Unified Theory of Acceptance and Use of Technology (UTAUT), enhanced with constructs identified from existing literature, the study proposes a predictive adoption model. Data was gathered from 231 participants, and structural equation modeling was used to validate both the measurement and structural components of the model. The findings highlight that technological complexity, social influence, perceived health risks, facilitating conditions, perceived security and privacy, and relative advantages significantly shape users' attitudes (ATT), which in turn affect their behavioral intentions (BI) to adopt IoT healthcare devices. The study concludes that addressing these factors is critical for successful IoT implementation in healthcare. It contributes to Information Systems (IS) research by integrating new variables into the UTAUT model and offers practical insights for healthcare decision-makers and technology providers aiming to boost IoT adoption. Keywords: Healthcare, Internet of Things, Privacy, Security, UTAUT 1. INTRODUCTION The Internet of Things (IoT) represents a system comprising interconnected physical items, vehicles, gadgets, and various objects equipped with sensors, software, and connectivity to network [1]. These devices can collect and exchange data, enabling them to communicate and interact with each other autonomously. The overarching goal of IoT is to create a seamlessly connected environment where objects can share information, make decisions, and perform tasks to enhance efficiency, convenience, and productivity across various domains, including home automation, healthcare, transportation, and industrial processes. Positioned at the brink of a technological revolution, the assimilation of IoT is transforming sectors, communities, and the fundamental aspects of our day-to-day existence. The incorporation of IoT into healthcare infrastructures has surfaced as a groundbreaking influence, offering the potential to drastically change the operations and service delivery of healthcare institutions [2]. According to Bhatt et al., [3], utilizing IoT as a viable technology offers a solution to the issue of overcrowded public hospitals. Wearable medical devices enabled by IoT have the capability to monitor a patient's condition and relay pertinent health data to hospital staff. This is particularly beneficial for individuals managing chronic conditions who frequently require medical consultations. Incorporating diverse IoT wearable gadgets like watches or smartphones not only has the potential to enhance patient outcomes but also to alleviate the burden on public hospital resources. The IoT possesses remarkable potential to yield superior outcomes through the utilization of cuttingedge technologies. In the medical field, it emerges as a groundbreaking concept, offering top-notch services to COVID-19 patients and facilitating precise surgical procedures [4]. IoT represents a promising technology capable of alleviating the strain on public hospital capacity. Wearable medical devices enabled by IoT can monitor patients' conditions continuously, transmitting pertinent health data to hospital physicians. This holds particular significance for individuals managing chronic illnesses who Journal of Theoretical and Applied Information Technology 31st May 2025. Vol.103. No.10 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4400 frequently require medical consultations [5]. By leveraging a range of IoT-enabled wearable devices, such as smartwatches or smartphones, lives can potentially be saved, while concurrently easing the burden on public hospital resources. While there's a recognized necessity for public health organizations to adopt this approach, its implementation remains limited and is at an early stage of development [5]. Nevertheless, despite numerous benefits, a recent comprehensive analysis of IoT adoption indicates that the integration and embrace of IoT in healthcare remain relatively limited [6]. Key challenges include: Behavioral resistance among healthcare professionals and patients, technological complexity and interoperability issues, Privacy and security concerns hindering trust in IoT-based healthcare solutions, and Lack of policy frameworks for IoT integration in public healthcare systems [10]. While prior studies have examined technical aspects of IoT adoption, there is insufficient empirical research on behavioral factors influencing end-user acceptance, particularly in the local context. Without addressing these barriers, healthcare institutions may struggle to leverage IoT’s full potential for improving patient outcomes and operational efficiency. As per findings by Al-Rawashdeh et al., [7], the integration of IoT applications among healthcare end users remains notably limited. Healthcare professionals pose significant obstacles to the effective implementation of IoT for delivering healthcare services. Although various studies have provided valuable perspectives on IoT adoption in healthcare, there remains a necessity for a comprehensive systematic review of the influential factors driving IoT adoption. The literature predominantly reflects contributions from developed nations equipped with the requisite infrastructure and expertise to leverage IoT technology [8]. Furthermore, scholarly attention is primarily directed towards the technical facets of integrating IoT healthcare devices, encompassing aspects such as connectivity, sensors, networking, and programming, with ongoing exploration into individual adoption and utilization of such technologies [9]. Literature suggests that current research predominantly focuses on the technical aspects of user adoption and interaction with IoT technologies, while the behavioral perspective remains relatively understudied [5]. There exists a notable gap in understanding regarding how the IoT influences individual and societal acceptance, with studies lacking clarity on this matter. Investigations into user experiences with IoT are still at an early stage, necessitating further exploration to identify factors that could encourage widespread adoption of IoT technologies. Limited research has examined the issue of IoT adoption in developing countries, as noted by Maswadi et al., [10]. Furthermore, the reluctance to embrace change and integrate new technologies, along with non-compliance among healthcare personnel and deficiencies in policies for introducing and implementing IT-based solutions, have exacerbated an existing challenge, impeding the introduction of more intricate eHealth solutions like IoT [7]. Therefore, a clear understanding of behavioral factors influencing IoT adoption in healthcare is essential for designing user-centered solutions that address real concerns and encourage uptake. In the context of the UAE, where digital transformation in healthcare is a national priority, understanding user behavior can help policymakers and practitioners ensure the effective deployment of IoT-based solutions. This study adopts an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework to explore the behavioral intentions towards adopting IoT-based healthcare devices. Using data collected from 231 participants in UAE public hospitals, the study employs structural equation modeling (SEM) to evaluate both the measurement and structural models. By identifying the key determinants influencing IoT adoption, this research provides empirical evidence to inform both academic discourse and practical strategies for healthcare innovation. The integration of security, social influence, and facilitating conditions into the UTAUT model will significantly improve the prediction of IoT adoption in healthcare organizations compared to the original UTAUT framework. This paper is organized into sections that provide an overview of existing literature, the theoretical framework, the research model and methodology, the results and hypothesis testing, followed by a discussion of findings, conclusions, practical implications, and limitations. 2. IOT IN HEALTHCARE SYSTEMS The integration of IoT technologies within healthcare systems has emerged as a significant avenue for innovation, promising to revolutionize patient care, operational efficiency, and overall healthcare delivery [11]. IoT in healthcare involves the interconnected network of devices, sensors, and systems that collect, exchange, and analyze data to facilitate intelligent decision-making and Journal of Theoretical and Applied Information Technology 31st May 2025. Vol.103. No.10 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4401 automation. IoT applications in healthcare span a wide range of areas, including remote patient monitoring, telemedicine, medication adherence, predictive analytics, and smart hospital management. Remote monitoring devices enable continuous tracking of vital signs and health parameters, empowering healthcare providers to deliver personalized care and intervene proactively in case of emergencies. Telemedicine platforms leverage IoT technology to facilitate virtual consultations, enabling patients to access healthcare services remotely and reducing the burden on traditional healthcare facilities. The adoption of IoT in healthcare holds numerous benefits, including improved patient outcomes, enhanced patient engagement and empowerment, reduced healthcare costs, and optimized resource utilization. By leveraging realtime data insights and predictive analytics, healthcare organizations can identify trends, anticipate patient needs, and optimize treatment protocols, leading to better clinical outcomes and operational efficiencies [12]. However, the widespread adoption of IoT in healthcare is not without challenges and barriers. Concerns regarding data privacy and security, interoperability issues among disparate systems and devices, regulatory compliance, and the need for robust infrastructure and technical expertise present significant hurdles to overcome. Addressing these challenges is crucial to ensure the successful implementation and scalability of IoT solutions in healthcare settings. Looking ahead, the future of IoT in healthcare appears promising, with ongoing advancements in sensor technology, artificial intelligence, and data analytics driving innovation and transformation. Emerging trends such as edge computing, blockchain, and 5G connectivity hold the potential to overcome existing limitations and unlock new opportunities for IoT-driven healthcare delivery models. 3. THEORETICAL FRAMEWORK AND HYPOTHESES DEVELOPMENT When considering the acceptance of new technology, several theories are available to analyze individuals' choices and determine which innovations to adopt. Among these, the UTAUT model stands out as particularly effective. Ever since UTAUT's inception, it has been widely employed across diverse disciplines to investigate and elucidate technology adoption, primarily on an individual basis. Furthermore, studies have indicated that UTAUT's explanatory power is approximately 70%, surpassing the performance of its eight predecessors. These prior models had explained between 17% and 53% of the variance in behavioral intention (BI) before UTAUT was formulated [6]. This study employs UTAUT2, which suggests that technology adoption hinges on anticipated benefits and the expected physical and mental efforts involved [13]. UTAUT also encompasses additional factors such as social influence and facilitating conditions. The complexity of technology may impede its adoption, while perceived playfulness, especially in the case of wearable devices like IoT, can influence perceptions of effort. Additionally, the influence of others may alter users' perceptions of the benefits of new technologies such as IoT. Consequently, this research posits that the complexity of such technologies plays a significant role in defining effort expectancy (EE), while the social impact and perceived importance of the technology may determine performance expectancy (PE). UTAUT suggests that PE and EE affect technology adoption. Criticisms of UTAUT have highlighted its insufficient consideration of technological factors. To address this critique, this research considers the level of security provided by IoT as a crucial variable. Additionally, privacy (PV), which is integral to technological considerations, is significant in discussions involving technologies like IoT [14]. Furthermore, facilitating conditions are essential predictors in UTAUT, though their moderating role has been minimally explored. The study proposed that attitude (ATT) is influenced by social influence (SI), perceived security and privacy (PSP), technological complexity (TC), perceived health risk (PHR), relative advantage (re), price value (PV), and facilitating conditions (FC). It was expected that both FCs would directly influence behavioral intention to adopt IoT in healthcare. The subsequent section elaborates on the hypotheses of this study. Refer to Figure 1 for the conceptual framework. Journal of Theoretical and Applied Information Technology 31st May 2025. Vol.103. No.10 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4402 Figure 1: Conceptual Research Model. Attitude (ATT) Attitude (ATT) is defined as an individual's psychological inclination towards expressing preference or aversion towards an object following an assessment [15]. ATT plays a significant role in influencing behavior across various contexts. The Technology Acceptance Model (TAM), the Theory of Reasoned Action (TRA), and the Theory of Planned Behavior (TPB) all indicate that ATT serves as a predictor of Behavioral Intention (BI) to use a technology. Studies have examined the impact of healthcare professionals' ATT towards IoT on their BI to adopt the technology. Thus, an individual's ATT towards emerging technologies significantly affects their BI. Given that IoT-based healthcare devices represent a novel technology, we propose the following hypothesis: H1: ATT towards IoT-based healthcare devices will influence end users’ IoT usage. Technological complexity (TC) The theory of diffusion of innovation (DOI) posits that the complexity of technology (TC) plays a role in shaping user intention toward adopting new technology. Individuals who lack awareness or understanding of technology may feel hesitant or uncertain about using it, as suggested by Pal et al. [16]. Complexity refers to “the degree to which an innovation is perceived as relatively difficult to understand and use” [17]. Essentially, innovations perceived as less complex tend to garner greater acceptance and adoption. The influence of perceived ease of use on IoT acceptance and adoption remains a subject of debate. According to Lu [18], most research findings indicate favorable impacts of users' attitudes toward IoT stemming from perceived ease or complexity of use. The adoption of IoT in healthcare settings is not without its challenges, particularly concerning technological complexity factors that significantly affect user intention [19]. Technological complexity is a significant challenge in the adoption of IoT. IoT systems comprise a complex ecosystem of interconnected devices, sensors, networks, and data processing mechanisms. The integration of diverse technologies, communication protocols, and data formats contributes to the inherent complexity of IoT systems. Users are often required to navigate through intricate setup procedures, configure device settings, and troubleshoot connectivity issues, which can lead to frustration and resistance toward adoption. Literature illustrates that technology complexity and anxiety, particularly in the context of computer-related systems and information services are very common [5]. A study by Aghdam et al., [20] highlights interoperability as a key challenge in IoT adoption, particularly in healthcare, where the integration of diverse medical devices and systems is essential for delivering comprehensive patient care. Integrating IoT devices with existing systems while maintaining data integrity and interoperability requires careful planning and investment. Therefore, it is speculated: H2: Technological Complexity influences users' attitudes (ATT) toward IoT healthcare usage. Perceived security and privacy (PSP) Perceived privacy and security play pivotal roles in shaping the adoption of IoT technology in healthcare. As healthcare systems increasingly integrate IoT devices to enhance patient care and operational efficiency, concerns regarding the confidentiality of personal health information and the security of connected devices have become paramount. The term "perceived security" (PS) refers to how people feel about the IoT's security and reliability (Zhang et al., 2014). Numerous studies have underscored the importance of PS in the context of IoT applications [21]. Similarly, Chouk and Mani,[22] identified a positive relationship between PS and the utilization of smart services. Their findings suggest that increased PS contributes Journal of Theoretical and Applied Information Technology 31st May 2025. Vol.103. No.10 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4403 to the broader adoption of IoT. Conversely, PV pertains to the confidentiality of IoT use and users' concerns regarding the safeguarding of their personal information [23]. A high level of perceived privacy has been shown to significantly impact IoT usage. Perceived privacy, referring to individuals' perceptions of the confidentiality of their personal information when using IoT devices, has been shown to significantly influence adoption behaviors. In a study by Kayali and Alaaraj [23], high levels of perceived privacy were found to be positively associated with the adoption of IoT technology. Healthcare professionals and patients alike value the assurance that their sensitive health data remains private and protected from unauthorized access or disclosure. Therefore, perceptions of privacy directly impact the willingness of users to embrace IoT solutions in healthcare settings. Similarly, perceived security, which encompasses individuals' beliefs about the reliability and protection of IoT systems against cyber threats and breaches, is a critical determinant of adoption. Research by Al-Rawashdeh et al., [24] highlights the importance of perceived security in influencing users' attitudes towards IoT technology. In the context of healthcare, where the integrity and availability of medical data are paramount, concerns about the security of IoT devices can significantly hinder adoption efforts. Dutta et al., [21] further emphasize the significance of perceived security in IoT applications, underscoring its role in fostering trust and confidence among users. Furthermore, studies have demonstrated a positive correlation between perceived privacy and security and the adoption of IoT technology across various domains. Chouk and Mani [22] found that increased perceived security was associated with higher usage of smart services, indicating the broader implications of security perceptions on technology adoption. Similarly, in the healthcare sector, where the stakes are particularly high due to the sensitive nature of patient data, perceptions of privacy and security exert a significant influence on adoption decisions. This study posits that elevated levels of perceived privacy and security will have a positive effect on the usage of IoT in healthcare. H3: Perceived security and privacy influence users' behavioral intentions (BI) toward IoThealthcare usage. Social Influence (SI) Social influence pertains to how individuals perceive important figures who influence their decision-making processes. UTAUT suggested that social influence is a crucial factor capable of impacting behavioral intentions [14]. This concept is deeply rooted in social psychology, where individuals are prone to conform to the beliefs and behaviors of those they consider significant. In healthcare settings, where collaboration and professional networks are integral, Social Influence manifests through various channels such as peer interactions, organizational culture, and leadership endorsements. According to Alomari and Soh [6], most prospective users lack adequate information about IoT. Thus, the impact of SI is even amplified in the decision-making process. The attitudes of healthcare professionals towards IoT adoption are significantly influenced by the opinions and behaviors of their peers, superiors, and opinion leaders within their professional communities. The mechanism through which Social Influence affects attitudes towards IoT adoption in healthcare is multifaceted. Firstly, social validation and peer approval reinforce positive attitudes towards technology adoption. When healthcare professionals observe their colleagues embracing IoT solutions and experiencing benefits, they are more likely to develop favorable attitudes toward incorporating similar technologies into their practice. Secondly, normative expectations within healthcare organizations contribute to the influence of Social Influence on attitudes. Healthcare professionals tend to conform to perceived norms and expectations regarding technology adoption established by their peers and organizational leaders. When IoT implementation is endorsed as a standard practice within the healthcare setting, individuals are more inclined to adopt positive attitudes towards its adoption to align with prevailing norms. Several studies have explored the role of Social Influence in shaping attitudes towards IoT adoption in healthcare. For instance, a study by Arfi et al., [25] investigated the factors influencing physicians' intentions to adopt IoT-enabled medical devices. The findings revealed that perceived social pressure from colleagues and superiors significantly impacted physicians' attitudes towards using IoT devices in clinical practice. Similarly, research by Al-Rawashdeh et al., [7] demonstrated that social endorsement from influential peers positively influenced nurses' attitudes toward IoT-based patient monitoring systems. Hence, it is hypothesized that: Journal of Theoretical and Applied Information Technology 31st May 2025. Vol.103. No.10 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4404 H4: SI significantly influences users' attitudes (ATT) toward IoT healthcare usage. Perceived health risk (PH) Perceived risk plays a crucial role in consumer decision-making, particularly within the service industry [26]. Perceived health risk (PH) is a crucial factor influencing the adoption of IoT technology in healthcare settings. As healthcare organizations increasingly leverage IoT devices to monitor patients remotely, deliver personalized care, and optimize treatment outcomes, concerns about the potential health risks associated with these technologies have become increasingly salient. Perceived health risk refers to individuals' perceptions of the potential adverse effects on their health resulting from the use of IoT devices in healthcare contexts [27]. These concerns may stem from worries about data accuracy, reliability of medical diagnoses, or potential harm caused by device malfunctions or errors. Research has shown that perceived health risk significantly impacts individuals' attitudes and behaviors towards adopting IoT technology in healthcare. A study by Alraja et al., [28] investigated the influence of perceived health risk on the adoption of IoT-based health monitoring systems. The findings revealed that individuals who perceived higher health risks associated with using IoT devices were less likely to adopt them for healthcare purposes. This suggests that concerns about the safety and reliability of IoT technology can act as barriers to adoption, particularly in healthcare contexts where the consequences of inaccurate or unreliable data can have serious implications for patient well-being. Moreover, perceptions of health risk may be influenced by factors such as device accuracy and regulatory compliance. For example, concerns about the accuracy of vital sign measurements or the security of patient data stored and transmitted by IoT devices can amplify perceived health risks among healthcare professionals and patients. Addressing these concerns through rigorous testing, certification, and compliance with healthcare regulations is essential for fostering trust and confidence in IoT-enabled healthcare solutions. Additionally, the perception of health risk may vary depending on individual characteristics such as age, health status, and previous experiences with technology. Older adults or individuals with chronic health conditions may be more cautious about adopting IoT devices due to heightened concerns about potential health risks. Al-Rawashdeh et al., [7] concluded that specialists are worried about the health risk if the technology is used in their health activities. Therefore, healthcare organizations and technology developers need to tailor their communication strategies and educational materials to address the specific concerns and needs of different user groups. Hence, the following hypothesis is formulated: H5: Perceived health risk influences users' attitudes (ATT) toward IoT healthcare usage. Relative advantage (RA) Relative advantage, a key concept in the theory of diffusion of innovations, refers to the extent to which a new technology is perceived as superior to existing alternatives [17]. In the context of healthcare, relative advantage plays a significant role in shaping the adoption of IoT technologies, which promise to revolutionize patient care delivery, enhance clinical decision-making, and improve operational efficiency [21]. Healthcare organizations are increasingly turning to IoT solutions to address various challenges, such as remote patient monitoring, chronic disease management, and hospital resource optimization. The perceived relative advantage of IoT technology over traditional approaches, such as manual monitoring or paper-based record-keeping, is a critical factor influencing adoption decisions among healthcare professionals and organizations. Research by Lu [18] examined the factors influencing the adoption of IoT in healthcare settings. The study found that healthcare professionals perceived IoT technologies as offering significant advantages over conventional methods, such as real-time data monitoring, improved patient outcomes, and enhanced efficiency. These perceived benefits were instrumental in driving the adoption of IoT solutions across different healthcare domains. Moreover, relative advantage extends beyond clinical benefits to encompass organizational and economic advantages. For healthcare providers, IoT technology offers opportunities to streamline workflows, reduce administrative burden, and optimize resource utilization. Studies have shown that healthcare organizations that adopt IoT solutions experience improvements in efficiency, cost-effectiveness, and patient satisfaction [27]. Additionally, the perceived relative advantage of IoT in healthcare is influenced by factors such as usability, interoperability, and integration with existing systems. User-friendly interfaces, seamless integration with electronic health records, and Journal of Theoretical and Applied Information Technology 31st May 2025. Vol.103. No.10 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4405 compatibility with other medical devices contribute to the perceived value proposition of IoT solutions [27]. H6: RA has an influence on the ATT toward IoTbased healthcare devices. Facilitating conditions (FC) The successful integration of IoT technology in healthcare hinges on various factors, among which facilitating conditions (FCs) play a pivotal role. The term "FCs" refers to the availability of necessary resources and infrastructure for utilizing technology [29]. FCs encompass the resources and infrastructure necessary for the effective utilization of IoT devices in healthcare settings. Understanding FCs in this context entails recognizing the intricate web of elements, including device reliability, internet connectivity, data management systems, and user support mechanisms, all of which collectively shape users' experiences and perceptions of IoT technology in healthcare. According to Alarefi, [5], FC is proposed by UTAUT to have a direct effect on the BI. Users' attitudes towards adopting IoT in healthcare are intricately intertwined with their perceptions of the ease of use, usefulness, and compatibility of the technology with their needs. FCs exert a profound influence on these perceptions. For instance, the availability of robust infrastructure and technical support can instill confidence in users regarding the reliability and effectiveness of IoT devices for healthcare applications. Conversely, inadequate FCs, such as unreliable internet connectivity or insufficient training, may breed skepticism and hinder users' willingness to adopt IoT solutions in healthcare. Behavioral intention, referring to individuals' readiness to engage in a specific behavior, is also shaped by FCs [27]. Users' perceptions of the feasibility of adopting IoT technology are heavily influenced by the presence or absence of facilitating conditions. When FCs are readily available and conducive to use, users are more likely to express a strong intention to adopt IoT solutions in their healthcare routines. Conversely, barriers posed by insufficient FCs may deter users from embracing IoT technology, despite recognizing its potential benefits. Empirical evidence from previous research corroborates the hypothesis that FCs influence both attitude and behavioral intention toward IoT usage in healthcare. Studies have consistently demonstrated positive correlations between the availability of FCs and users' perceptions of technology usefulness and intention to adopt. Moreover, addressing FC-related barriers has emerged as a critical component of strategies aimed at promoting the successful implementation and acceptance of IoT solutions in healthcare settings. Therefore, the impact of facilitating conditions on attitude and behavioral intention to use IoT in healthcare is substantial and multifaceted. Recognizing the pivotal role of FCs in shaping user perceptions and intentions is paramount for designing effective strategies to foster the adoption and utilization of IoT technology in healthcare. By addressing FC-related challenges and fostering an environment conducive to innovation, stakeholders can harness the transformative potential of IoT to enhance healthcare delivery and improve patient outcomes [21]. Hence, it is hypothesized that: H7: FCs significantly influence users' attitudes (ATT) toward IoT healthcare usage. Furthermore, studies have shown that FCs significantly influence users' behavioral intentions (BI) to adopt new technologies [7]. Previous research has demonstrated the impact of FCs on BI toward various technologies such as mobile banking, mobile payment systems, and food delivery applications. Therefore, the following hypothesis is proposed: H8: FCs influence users' behavioral intentions (BI) toward IoThealthcare usage. 4. RESEARCH DESIGN AND METHODOLOGY This research adopts a quantitative, survey-based research design to investigate the links between independent factors and IoT usage in healthcare. Similar approaches have been used in IoT adoption studies across healthcare [7], smart homes [10], and fintech [23]. Unlike exploratory qualitative designs, this method enables statistical validation of hypothesized relationships. The use of SEM follows precedents in UTAUT extensions [21], but diverges by incorporating healthcarespecific factors. To collect data for the study, a survey instrument with 25 items was created. The survey serves as the cornerstone of the research methodology, employing cross-sectional techniques for data collection .The study population consists of patients attending medical facilities in the UAE. Convenience sampling is employed to gather data due to the absence of a comprehensive database on individuals with chronic illnesses. A questionnaire serves as the primary research instrument, comprising questions sourced from various prior studies. The survey comprises two segments: the Journal of Theoretical and Applied Information Technology 31st May 2025. Vol.103. No.10 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4406 first delves into participant demographics, while the second evaluates IoT adoption through a five-point Likert scale. To collect field data, the administration of four public hospitals was approached to aid in distributing the questionnaire. A total of 346 surveys were sent out, with follow-up reminder emails sent to encourage additional responses. Consequently, 231 questionnaire responses were received and valid, and a response rate of 64.4% has been achieved. With a sample size of 231 participants, the study aimed for a reliable analysis using Structural Equation Modelling (SEM). Following suggestions by Kline [30], a sample size of approximately 200 (refer to Table 2) was deemed suitable for SEM. Sample size determination also considered the type of test applied in data analysis, with larger samples generally reducing error rates in result generalization [31]. Prior to administering the official survey, pilot testing was conducted to verify the reliability and validity of the items. The reliability of the questionnaire was assessed using Cronbach's Alpha, yielding a high reliability. Table 1 presents the Cronbach’s alpha values for the instrument's measures. The majority of the measures achieved an alpha value above 0.74, indicating strong reliability. As a result, no modifications or refinements to the questionnaire were necessary to enhance the alpha coefficients. Table 1: Reliability Tests Factor No Items Cronbach’s alpha Attitude 1 0.82 Technological Complexity 3 0.81 Perceived Security & privacy 4 0.74 Social Influence 4 0.78 Perceived Health risk 4 0.80 Relative Advantage 4 0.81 Facilitating Conditions 5 0.83 The data underwent analysis via SEM to examine initial data, validate measurements, and evaluate structural elements of the proposed model. Analysis findings were utilized to estimate indicator weights, loadings, and path coefficients between exogenous and endogenous variables. Additionally, tests were conducted for convergent and discriminant validity to affirm the survey instrument's validity. Survey reliability was assessed using Cronbach's alpha and composite reliability tests, facilitated by the SPSS statistical tool. Table 2: Respondents demographic data Measure Item Frequency Percentage Gender Male Female 155 76 67% 33% Age 20-40 41-60 > 60 18 168 45 7.7% 72.7% 19.4% Education Level High School Bachelor Higher Degrees 59 71 101 25.5% 30.7% 43.7% Employment Self Employed Private Sector Public Sector 114 48 69 49.3% 20.7% 29.8% 5. DATA ANALYSIS AND FINDINGS This section discusses the results obtained from evaluating the proposed model in this study. The study employed Structural Equation Modeling (SEM) to assess the model's efficacy in adopting mobile cloud computing and evaluating its performance. SEM was chosen due to its capability to analyze multiple relationships simultaneously, unlike other statistical methods such as multiple regression or multivariate analysis of variance, which can only analyze relationships between individual variables. Typically, SEM follows a twostep process, comprising the measurement model and structural model. Initially, exploratory factor analysis (EFA) is carried out to enhance the measurement model. Subsequently, confirmatory factor analysis (CFA) is employed to evaluate the structural model. The analysis of data through SEM was conducted using the AMOS software, chosen for its accessibility and suitability for this study. The subsequent sections present the findings of the proposed model analysis, categorized into two types of analyses: measurement analysis and structural model analysis. 5.1 Exploratory factor analysis (EFA) To evaluate the reliability of the measurement model, this study utilized Exploratory Factor Analysis (EFA) in conjunction with Principal Components Analysis (PCA) to determine factor loading and reliability using Cronbach's alpha (α) for each construct within the proposed model. EFA, aimed at identifying the most relevant items for each construct, played a crucial role in this assessment. Table 3 provides an overview of the results, including Cronbach's alpha (α) for reliability analysis and factor loading values for all Journal of Theoretical and Applied Information Technology 31st May 2025. Vol.103. No.10 © Little Lion Scientific ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 4407 constructs. Initially, for reliability analysis, Cronbach's alpha (α) was employed to gauge internal consistency among items within the same construct, following Byrne’s [32] recommendation of considering values above 0.7 as acceptable. As shown in Table 2, all latent constructs exhibited satisfactory reliability, with Cronbach's alpha exceeding 0.7. Subsequently, Principal Components Analysis (PCA) with Varimax rotation was conducted to uncover the underlying structure for each factor in the research model. PCA relied on factor loading values, with each item demonstrating factor loadings surpassing 0.7, as suggested by Campbell and Fiske [33]. Any item with a factor loading below 0.7 was considered for removal from the construct's structure. The outcomes presented in Table 2 confirmed that all items were appropriately loaded onto the factors, each displaying loadings above 0.7, thereby affirming the identification of all constructs. Table 3: Internal consistency analysis using Exploratory Factor Analysis Constructs Items Factor loadings (>0.7) Cronbach’s Alpha (α ⩾ 0.70) Attitude ATT1 0.814 0.842 Technological Complexity TC1 TC2 TC3 0.811 0.723 0.812 0.823 Perceived Security & privacy PSP1 PSP2 PSP3 PSP4 0.785 0.723 0.814 0.894 0.723 Social Influence SI1 SI2 SI3 SI4 0.890 0.823 0.713 0.81 4 0.875 Perceived Health risk PH1 PH2 PH3 PH4 0.823 0.804 0.823 0.843 0.898 Relative Advantage RA1 RA2 RA3 RA4 0.843 0.893 0.825 0.803 0.804 Facilitating Conditions RA1 RA2 RA3 RA4 RA5 0.856 0.743 0.835 0.802 0.834 0.882 5.2 Measurement analysis Ensuring research precision requires scrutinizing the instrument's reliability and measurement quality before embarking on the main analysis. This process entails evaluating the relationships between the factors outlined in the proposed model and the corresponding measurement items through two analytical methods: reliability and validity analysis. Composite reliability, which measures the consistency among items within the same construct, is considered highly reliable if it surpasses 0.7, and acceptable if it falls between 0.6 and 0.7 [34]. As shown in Table 3, all of the constructs in this study exceeded the minimum threshold for composite reliability, indicating high overall reliability. Convergent validity was assessed using Average Variance Extracted (AVE), with an acceptable value set at 0.5 or higher as recommended by Hair et al. [35]. Based on the recommendation of Hair et al., [34], the best results of convergent validity can be obtained if standardized loading estimates are 0.7 or higher, the estimation of AVE is greater than 0.5 and the estimation of reliability is above 0.7. Following the above-mentioned recommendation, this research study assumed the minimum cut-off criteria for factor loadings, AVE, and composite reliability as 0.7 > 0.5 > 0.7 respectively, in assessing the convergent validity Results from Table 4 reveal that all AVE values for the constructs met or exceeded this threshold. Conversely, discriminant validity was evaluated by comparing the square root of AVE with correlations between the constructs. For discriminant validity to be established, the square root of AVE for each latent construct should exceed the estimated correlation between the constructs [36]. The results presented in Table 5 demonstrate that the square root of AVE for all constructs exceeded the correlations between them, providing sufficient evidence of discriminant validity. Overall, the study offers reliable and valid measurements for the proposed model. Table 4: Results of composite reliability and convergent validity Constructs Composite Reliability (CR > 0.7) Average Variance Extracted (AVE > 0.5) Attitude 0.85 0.71 Technological Complexity 0.89 0.67 Perceived Security & privacy 0.92 0.73 Social Influence 0.82 0.69 Perceived Health risk 0.81 0.63 Relative Advantage 0.88 0.73 Facilitating Conditions 0.86 0.69