A Jasri, S Aljasmi, A Aburayya, Employing PLS-SEM Analysis to Examine the Mediation Role of Artificial Intelligence in Physician Experience. An Empirical Study of the Effect of the Medical Smartwatch on Physician Satisfaction. SEEJPH 2022. Posted: 30-07-2022. Page 1 ORIGINAL RESEARCH Employing PLS-SEM Analysis to Examine the Mediation Role of Artificial Intelligence in Physician Experience. An Empirical Study of the Effect of the Medical Smartwatch on Physician Satisfaction Ammar Jasri1, Shaima Aljasmi1, Ahmad Aburayya2 1Senior Specialist Registrar, Dubai Academic Health Corporation, Dubai, UAE 2Assistant Professor, MBA Department, Business College, City University Ajman, Ajman, UAE Corresponding author: Dr. Shaima Aljasmi Senior Specialist Registrar, Dubai Academic Health Corporation, Dubai, UAE Email: Shaima_aljasmi@yahoo.com A Jasri, S Aljasmi, A Aburayya, Employing PLS-SEM Analysis to Examine the Mediation Role of Artificial Intelligence in Physician Experience. An Empirical Study of the Effect of the Medical Smartwatch on Physician Satisfaction. SEEJPH 2022. Posted: 30-07-2022. Page 2 Abstract Objective: The rapid advancements in the Internet of Things (IoT) have allowed end users to enjoy restriction-free access to information. One of the notable developments in IoT is the introduction of wearable technologies, such as smartwatches. The growing popularity of wearable technology has made it possible for users to receive health and fitness data regardless of time or place. This study aims to examine the mediation role of artificial intelligence in physician experience toward using the medical smartwatch, particularly examining the effect of the medical smartwatch on physician satisfaction. Methods: This study utilized a deductive research approach employing a cross-sectional design. Data was collected through online questionnaires from healthcare providers, particularly physicians in the United Arab Emirates (UAE). The Structural Equation Modelling analysis (SEM) was employed to evaluate the theoretical and final path models. This study further assessed the theoretical model using the Partial Least Squares (PLS) as it offers concurrent analysis for evaluating the structural model and enhancing result accuracy. Results: Artificial Intelligence (AI) experience significantly influenced physicians’ satisfaction. Additionally, the study provided supporting, satisfying evidence for the mediating effects of AI experience. Conclusion: The study provided supporting evidence for the mediating effects of AI experience on physicians’ satisfaction. This study bridges the gap in the literature regarding the absence of studies examining physicians’ perceptions of medical smartwatch usage in the medical domain by providing a profound understanding of physicians’ satisfaction and perceptions regarding smartwatch usage in the UAE. This study bridges the gap in the literature regarding the absence of studies examining physicians’ perceptions of medical smartwatch usage by providing a profound understanding of physicians’ satisfaction and perceptions regarding smartwatch usage in the UAE. Keywords: Artificial Intelligence; Medical Smartwatch; Health Wearable Technology; Healthcare; Physician Satisfaction; Structural Equation Modelling; Partial Least Squares. A Jasri, S Aljasmi, A Aburayya, Employing PLS-SEM Analysis to Examine the Mediation Role of Artificial Intelligence in Physician Experience. An Empirical Study of the Effect of the Medical Smartwatch on Physician Satisfaction. SEEJPH 2022. Posted: 30-07-2022. Page 3 1. Introduction Recently, technology has proven to have vast effects on human lives, individually and collectively, influencing nearly every facet (1-4). The most recent edition of technology is Artificial Intelligence (AI) (5). Over time, AI has witnessed massive and rapid developments that saw it being integrated into numerous sectors, including healthcare services within government sectors. AI is “the natural predispositions, genetic inheritance or learned skillsets forming the core of individual personalities” (5,6). AI implicates the use of machine-learning techniques and algorithms integrated within computer systems intended to mimic the functions of a human brain to make decisions (4,6). AI bridges the gap between patients and medical professionals (4,7). Whereas Physicians try to address patients’ demands, and patients seek to satisfy their own. The rapid advancements in the Internet of Things (IoT) have allowed end users to enjoy restriction-free access to information. One of the notable developments in IoT is the introduction of wearable technologies, such as smartwatches (7). The growing popularity of wearable technology has made it possible for users to receive health and fitness data regardless of time or place (4,8). Due to the advantages they offer regarding the health and fitness data of users, smartwatches’ development and sales have seen rapid growth. The ease of connectivity to mobile phones via Bluetooth technology has led to people’s mass adoption of smartwatches, where they can access several mobile features from their wrists, such as reading notifications, making phone calls, and tracking their physical activities (5, 7). There has been significant growth in demand for wearable technologies in the medical field. These devices allow users to monitor vital physical well-being indicators such as physical activity and step count, blood pressure, blood oxygen, and glucose levels. The increased need for a technology that can help people in observing health-cantered features and the rise in health awareness have ignited a notable and rapid shift of the innovation in smartwatch research and development (9). Research performed by Counterpoint’s Global Smartwatch Tracker indicated that there had been a 37% growth worldwide in smartwatch shipments in the second quarter of 2018, 41% of which was dominated by Apple (10). Additionally, since 90% of A Jasri, S Aljasmi, A Aburayya, Employing PLS-SEM Analysis to Examine the Mediation Role of Artificial Intelligence in Physician Experience. An Empirical Study of the Effect of the Medical Smartwatch on Physician Satisfaction. SEEJPH 2022. Posted: 30-07-2022. Page 4 smartwatches sold currently lack cellular capabilities, smartwatch manufacturers are attempting to integrate cellular connectivity capabilities for standalone use cases (10). Although numerous researchers have conducted studies on the usage and adaptation factors of smartwatches (9, 11- 13), none of them have studied how physicians’ perceptions of medical smartwatches—including their quality, adequacy, appropriateness, relevance, usability, brand, and AI experience—are influenced by these factors. This study intends to bridge this gap and provide a profound understanding of physicians’ perceptions regarding smartwatch usage in the medical field in the United Arab Emirates (UAE). Essentially, this research study utilizes the integrated and innovative research approach to examine the factors that influence physicians’ satisfaction with the use of Medical Smartwatches (MSW) through the role Al experience played as a mediator in this context. More specifically, the integrated and innovative research model integrates the diffusion of innovation (DOI) model, Technology Acceptance Model (TAM), and the flow model to assess the predictors of physicians’ satisfaction with MSW and the impact of amplified embracing of this technology. Moreover, the model explores how Al’s experience influences physicians’ satisfaction as a mediating factor. The primary objective of this study is to evaluate the success of MSW in the medical domain since a successful implementation of this technology is equally important to doctors and patients. MSW’s success relies on end- users’ decisions and a feedback loop of relevant information within a network of patients and doctors, which can influence attitudes and behaviors. This research builds upon previous studies investigating exogenous variables as determining factors of MSW use. Prior studies like (8-13) have explored the effect of external factors like availability and mobility on using MSW. However, this study focuses on external factors such as richness of content and innovations as the primary determinants of physicians’ satisfaction with MSW. While several studies have explored the degree of acceptance of MSW, this study is the first to evaluate physicians’ satisfaction with MSW use, specifically within the UAE medical field, employing an integrated research model. A Jasri, S Aljasmi, A Aburayya, Employing PLS-SEM Analysis to Examine the Mediation Role of Artificial Intelligence in Physician Experience. An Empirical Study of the Effect of the Medical Smartwatch on Physician Satisfaction. SEEJPH 2022. Posted: 30-07-2022. Page 5 2. Methods and Materials This study utilized a deductive research approach employing a cross-sectional design. Data was collected through online questionnaires from healthcare providers in the UAE, i.e., from seven hospitals and ten primary healthcare clinics from December 14, 2021, to January 14, 2022. The researchers contacted the targeted physicians through official emails and social media platforms, including WhatsApp, to administer the questionnaires. The collected data were judged reliable as it was obtained from participants working in a healthcare setting, which is the most suitable environment for gathering information on healthcare practices (14). Moreover, the study followed recommendations from healthcare service management studies in selecting the targeted population as the component of analysis [14- 16]. The chosen sample units had sufficient familiarity with their organization’s procedures and health quality [14-15]. In this study, non-probability sampling, specifically convenience sampling, was employed due to difficulties accessing comprehensive physician lists in the selected units of analysis. Moreover, convenience sampling was cost-effective and time-efficient and enabled a larger sample size to be utilized [14,16]. The authors randomly distributed two thousand questionnaires. Respondents replied to 1418 out of the initial 2000 questionnaires distributed. Ten questionnaires were rejected due to missing values resulting in 1408 completed questionnaires ready for analysis, leading to a 70.4% response rate. The sample size of 1408 in this study exceeded the suggested sample size of 323 for a population of 2000 [17]; thus, the use of Structural Equation Modelling (SEM) to test the hypotheses was justified [19]. Although the hypotheses were built upon existing models, they were structured within the framework of MSW. SEM was employed to evaluate the theoretical model and the final path model. This study assessed the theoretical model using the PLS-SEM as it offers concurrent analysis for evaluating the structural model and enhancing result accuracy [15,19]. Furthermore, PLS-SEM also helps examine the predictors of the conceptual model, contributing to information systems research as a complementary multi-analytical method. This research study will be one of the few in A Jasri, S Aljasmi, A Aburayya, Employing PLS-SEM Analysis to Examine the Mediation Role of Artificial Intelligence in Physician Experience. An Empirical Study of the Effect of the Medical Smartwatch on Physician Satisfaction. SEEJPH 2022. Posted: 30-07-2022. Page 6 the medical field using PLS-SEM to examine physicians’ satisfaction with using MSW. The survey instruments used to test for the hypotheses consisted of 47 items intended to measure ten constructs included in the questionnaire. The questions within the survey were revised and restructured, building upon previous studies (14-16, 20) to improve the generalizability of the study. To measure the 47 items, a 5-point Likert scale (1= strongly disagree; 5 = strongly agree) was used. A pilot study was conducted with 75 randomly selected participants (5.3% of the total sample) to assess the reliability of the questionnaire items. To test for the internal reliability of measurement instruments, IBM SPSS Statistics version 23 was used to perform Cronbach’s alpha test. According to social science studies (9,19,21), a Cronbach’s alpha coefficient of 0.7 is sufficient for questionnaire items. The performed Cronbach’s alpha analysis indicated that the measurement items were adequate. Furthermore, a single-factor Harman’s test was conducted with seven factors to confirm that the data were not affected by Common Method Bias (CMB) (19,21). The seven factors were then loaded into a single factor, which explained 26.54% of the variation, but fell below the 50% threshold recommended [9], indicating the absence of CMB in the collected data. 3. Data Analysis and Results 3.1 Demographic information Table 1 presents the demographic characteristics of the study participants. The table indicates that the sample consisted of individuals from diverse genders, age groups, educational backgrounds, and work experience in various sectors. Regarding gender, 29.9% of the respondents were female, while 70.1% were male. The table also reveals that most participants (43.6%) were aged 41 to 50. Furthermore, most of the participants had achieved a high level of education. According to Table 1, 60.4% held bachelor’s degrees, 22.2% had master’s degrees, and 17.4% completed doctoral education. Additionally, the table indicates that 77.9% of the sample had work experience ranging from 1 to 10 years. A Jasri, S Aljasmi, A Aburayya, Employing PLS-SEM Analysis to Examine the Mediation Role of Artificial Intelligence in Physician Experience. An Empirical Study of the Effect of the Medical Smartwatch on Physician Satisfaction. SEEJPH 2022. Posted: 30-07-2022. Page 7 Table 1. The Profile of Respondents. Frequency Percent (%) Gender Male 987 70.1 Female 421 29.9 Age 20-30 101 7.2 31-40 456 32.4 41-50 614 43.6 Over 51 237 16.8 Education Level Bachelor Degree 851 60.4 Master Degree 312 22.2 PhD Degree 245 17.4 Years of Experience Less than 1 Year 41 2.9 1-5 Years 798 56.7 6-10 Years 298 21.2 More than 10 Years 271 19.2 Total 1408 100.0 3.2 Convergent validity To assess the reliability of the measurement model, Cronbach’s alpha test and composite reliability were conducted. Additionally, the discriminant and convergent validity of the model were examined (21). Table 2 presents Cronbach’s alpha coefficients, which range from 0.701 to 0.878, surpassing the threshold of 0.7 (22). Additionally, the composite reliability (CR) values in Table 2 range from 0.677 to 0.776, exceeding the threshold of 0.7 (23). These results confirmed the reliability of the constructs while being measurement- error-free. The average variance extracted (AVE) and factor loading were performed to evaluate Convergent validity (21). The factor loading values shown in Table 2 surpass the threshold of 0.5. However, the AVE values ranging from 0.529 to 0.845 fall short of the required 0.5 thresholds. Nevertheless, the results suggest that all constructs in the study demonstrate satisfactory convergent validity. Table 2. Factor Loading & Cronbach’s Alpha coefficient Construct & Item No. Cron- bach’s Alpha CR & AVE Factor Loadings F1 F2 F3 F4 F5 F6 F7 F8 F9 Relevance (REL) 5 .831 0.748 & 0.854 .725 .827 .619 .728 A Jasri, S Aljasmi, A Aburayya, Employing PLS-SEM Analysis to Examine the Mediation Role of Artificial Intelligence in Physician Experience. An Empirical Study of the Effect of the Medical Smartwatch on Physician Satisfaction. SEEJPH 2022. Posted: 30-07-2022. Page 8 .884 Timeliness (TL) 4 .712 0.736 & 0.529 .712 .754 .854 .762 Sufficiency (SUF) 6 .843 0.765 & 0.801 .664 .803 .876 .799 .837 .789 Perceived Convenience (PC) 5 .878 0.758 & 0.554 .765 .899 .654 .786 .578 Product Quality (PQ) 5 .782 0.677 & 0.689 .723 .738 .879 .808 .823 Service Quality (SQ) 5 .772 0.776 & 0.673 .754 .852 .654 .879 .811 Usability (US) 6 .838 0.748 & 0.776 .607. .741 .723 .760 .782 .796 Perceived Ease of Use (PEU) 5 .759 0.761 & 0.663 .778 .761 .886 .951 .977 AI Experiencee (AI E) 5 .701 0.756 & 0.548 .665 .762 .774 .639 .773 3.3 Causal Model Analysis & Hypotheses Testing The present study utilized a hybrid Partial Least Squares Structural Equation Modeling (PLS-SEM) methodology using Smart PLS to assess the hypothesized relationships among variables in the proposed research model. PLS-SEM was selected due to the exploratory nature of the theoretical model A Jasri, S Aljasmi, A Aburayya, Employing PLS-SEM Analysis to Examine the Mediation Role of Artificial Intelligence in Physician Experience. An Empirical Study of the Effect of the Medical Smartwatch on Physician Satisfaction. SEEJPH 2022. Posted: 30-07-2022. Page 9 and the lack of prior related research. The study followed the general guidelines for using PLS-SEM in information systems (IS) research. It used a two-step technique that involves measurement and structural models as put forward by previous literature (24). IPMA was also used to evaluate the model’s constructs in terms of relevance and practicality. The coefficient of determination (R²) and path coefficients were used to assess the structural model’s predictive accuracy. The literature indicates that R² is an indicator for determining a model’s predictive accuracy (25). Table 3 shows p-values, t- values, and path analysis coefficients for all the hypotheses, revealing that all the hypotheses (H1 to H9) were empirically supported through the data analysis (Refer to Fig. 1). The coefficient of determination R² was used to evaluate the obtained structural models where the correlation between the predicted dependent variable and the actual values were squared (25). Subsequently, R² indicates the degree of variation in the dependent construct. The obtained R² value of 0.743 indicated that the model had a high level of prediction for physicians’ satisfaction towards the use of MSW, explaining 74% of the variation in satisfaction. The results illustrate that REL, TL, and SUF had a significant effect on PC (β = 0.578, p < .05), (β = 0.478, p = .018), and (β =. 299, p < .05) respectively (see Table 3). Accordingly, H1, H2, and H3 are supported. The results also reveal that PC, PEU, PQ, SQ, and US significantly influenced AI Experience (β = 0.502, p < .01), (β =. 637, p = ,000), (β = 0.311, p < .01), (β = 0.539, p < .01), and (β = 0.662, p = .000) respectively. Thus, the results support H4, H5, H6, H7, and H8. Furthermore, the results also reveal that AI Experience significantly affects physicians’ satisfaction with using MSW. Consequently, H9 was confirmed (See Figure 1). Table 3. Hypotheses-testing of the research model. Association Hypothesis Path Coefficient t- Value P- Value Decision REL PC H1 0.378 3.887 0.041 Supported* TL PC H2 0.487 4.682 0.018 Supported* SUF PC H3 0.299 3.211 0.035 Supported* PC AIE H4 0.502 7.549 0.001 Supported** PEU AIE H5 0.637 12.479 0.000 Supported*** PQ AIE H6 0.311 3.305 0.032 Supported* SQ AIE H7 0.539 7.367 0.002 Supported** US AIE H8 0.662 13.802 0.000 Supported*** A Jasri, S Aljasmi, A Aburayya, Employing PLS-SEM Analysis to Examine the Mediation Role of Artificial Intelligence in Physician Experience. An Empirical Study of the Effect of the Medical Smartwatch on Physician Satisfaction. SEEJPH 2022. Posted: 30-07-2022. Page 10 AIE PST H9 0.789 18.778 0.000 Supported*** Note: Relevance (REL); Timeliness (TL); Sufficiency (SUF); Perceived Convenience (PC); Product Quality (PQ); Service Quality (SQ); Usability (US); Perceived Ease of Use (PEU); AI Experiencee (AI E); Physician Satisfaction (PST). *If a p-value is less than 0.05, it is flagged with one star (*). If a p-value is less than 0.01, it is flagged with 2 stars (**). If a p-value is less than 0.001, it is flagged with three stars (***). Figure 1. Path Test of the Research Theoretical Model. 4. Discussion The primary objective of this research was to examine the utilization of AI-powered smartwatches for self-health monitoring and assess how physician experience affects satisfaction by employing a novel model built upon the integrated and innovative theory to explore the factors that determine physicians’ satisfaction with AI-driven MSW, with a particular focus on the mediating role played by AI experience. Data analysis revealed significant impacts of REL, TL, SUF, PC, PQ, SQ, US, and PEU on physicians’ satisfaction. Furthermore, AI experience was found to influence physicians’ satisfaction significantly. Additionally, the study provided supporting evidence for the mediating effects of AI experience on physicians’ satisfaction. This study bridges the gap in the literature regarding the absence of studies examining physicians’ perceptions of MSW usage in the medical domain by providing a profound understanding of physicians’ satisfaction and perceptions regarding smartwatch usage in the UAE. The study contributes to the literature on smartwatch studies that previously examined external variables as contributing factors for MSW adoption. Several studies [9,26,27] have investigated the influence of external factors such as availability and mobility on MSW. In contrast, this study focuses on external factors like content richness and A Jasri, S Aljasmi, A Aburayya, Employing PLS-SEM Analysis to Examine the Mediation Role of Artificial Intelligence in Physician Experience. An Empirical Study of the Effect of the Medical Smartwatch on Physician Satisfaction. SEEJPH 2022. Posted: 30-07-2022. Page 11 innovations as the primary determinants affecting physician satisfaction with MSW. This research study has established empirical evidence of a positive correlation between content richness and MSW, demonstrating that content richness plays a crucial role in achieving high satisfaction with smartwatches. Furthermore, the research findings indicate that content richness influences the increased usage of smartwatches. Specifically, content richness shows a significant positive impact on PC, which in turn affects MSW positively. These findings also align with prior studies that have emphasized the influence of quality content on PC (28,29). Additionally, this study revealed a significant impact of PQ and SQ on the levels of users’ satisfaction and experience. These findings are consistent with prior research that underscores the importance of product and service quality in ensuring positive user experiences, trust, and satisfaction when utilizing such devices (30,31,32). Essentially, a product’s quality reduces the disparity between the received and expected products. In the case of smartwatches, product quality encompasses various aspects, including the device’s functionalities and performance. These qualities are reflected in the smartwatches’ personalized content abilities, instantaneous support, up-to-date information, and seamless user interaction (9, 33). On the other hand, service quality pertains to the reliability with which smartwatches are designed to handle users’ personal and health information and their capability to accurately detect and monitor conditions like hypertension, diabetes, irregular heart rates, and other diseases safely and securely (34). In terms of the reduced time and effort required for usage, this study revealed that PC significantly influences physicians’ trust, experience, and satisfaction. These findings are consistent with the research conducted by Pham et al. (35). Additionally, several studies have emphasized that physicians’ satisfaction is positively correlated with the convenience of having readily available medical information and services (36), real-time information accessibility and assistance, as well as advanced guidance (9,14,15). Such information’s time and place restriction-free features become crucial in emergencies like the COVID-19 pandemic. Enhancing the overall user experience is achieved through the positive contributions offered by self- service, real-time, and time-saving A Jasri, S Aljasmi, A Aburayya, Employing PLS-SEM Analysis to Examine the Mediation Role of Artificial Intelligence in Physician Experience. An Empirical Study of the Effect of the Medical Smartwatch on Physician Satisfaction. SEEJPH 2022. Posted: 30-07-2022. Page 12 convenience (14-16). Moreover, service convenience is crucial in inspiring users to actively utilize products such as smartwatches to attain a good and memorable experience (37,38). In addition to facilitating a favorable experience, the convenience offered by AI-powered smartwatches fosters a sense of trust among smartwatch users. By eliminating barriers, promoting a sense of satisfaction for users, and evaluating service efficacy, user trust is cultivated, which as in turn, encourages users to utilize AI-powered devices such as smartwatches (33-38). Regarding perceived ease of use (EEU), the study’s results affirmed its significance concerning user satisfaction, trust, and user experience. These findings align with previous studies conducted by Jarrahi (39). Having older users who may have lower proficiency with technology highlights the importance of the perceived ease of use construct. Users, especially older ones, feel more at ease using devices like smartwatches if they are designed to be user-friendly, simple to handle, and require minimal effort for maintenance. Thoughtfully crafted layouts, intuitive navigation, and visually pleasing designs that prioritize simplicity and user-friendliness help minimize physical effort and reduce mental strain, ultimately resulting in positive user experiences (9-16). With repeated use, users become more familiar with the technology, enhancing their confidence and fostering trust. Moreover, the synchronization function with physicians’ smartphones proves to be more practical than a fitness tracker, especially when they need to navigate traffic congestion while riding a motorcycle. This feature allows them to determine whether an incoming call is significant enough to warrant pulling over safely. Our data analysis confirmed the mediating role of AI experience in influencing physicians’ satisfaction. The study revealed significant PQ, SQ, PC, US, and PEU effects on both experience and user satisfaction. Furthermore, the study demonstrated that these predictors also had substantial indirect effects on satisfaction when mediated by AI experience. In other words, experience acted as a partial mediator in these relationships. Positive experiences with AI-powered smartwatches in the user AI experience context directly enhance the relationships between predictors (PQ, SQ, PC, US, and PEU) and physician satisfaction. These results are consistent with previous empirical A Jasri, S Aljasmi, A Aburayya, Employing PLS-SEM Analysis to Examine the Mediation Role of Artificial Intelligence in Physician Experience. An Empirical Study of the Effect of the Medical Smartwatch on Physician Satisfaction. SEEJPH 2022. Posted: 30-07-2022. Page 13 research studies that have recognized the significance of AI experience construct as a mediator (40). In healthcare, empirical evidence indicates that prior experience significantly reinforces the relationships between several factors, including satisfaction with using AI-powered devices [31-40). From a practical standpoint, individuals who have had positive past experiences using AI-powered smartwatches to monitor their health conditions and receiving endorsements from medical professionals regarding such devices will likely experience improved satisfaction. Finally, it is essential to acknowledge the limitations of this study. First, the use of a purposive sampling technique restricts the findings’ generalizability to 40 years old or above users, where the sample was dominated by male respondents (41-49). Additionally, respondents were selected from just three emirates within UAE. Future studies should utilize random sampling techniques and include a more comprehensive range of locations, including developing countries. Expanding the sampling coverage to encompass a broader range of individuals would ensure a more accurate population representation. It is also crucial to provide a more balanced picture of both male and female respondents in the sample. Additionally, future research should consider incorporating alternative theories, such as the Unified Theory of Acceptance and Use of Technology (UTAUT). Lastly, future models can explore the impact of Al- enabled smartwatches on various types of diseases and the potential for device personalization. 5. Conclusion This study holds significant importance in shedding light on using the e-doctor concept through AI-powered smart devices, including smartwatches. The findings further contribute to the body of literature aiming to understand physicians’ satisfaction and optimal utilization of AI-enabled devices in healthcare and related domains. 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