Transformational Leadership and Telemedicine Acceptance: Predicting Providers’ Intention to Use Emergency Teleophthalmology

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This study examines the role of transformational leadership in technology acceptance of live inter-provider teleophthalmology. Methods A cross-sectional online survey of 254 emergency and eye care professionals in the United States assessed perceived usefulness (PU), intention to use (IU), subjective norms (SN), self-efficacy (SE), and transformational leadership (TL) using validated scales. Confirmatory factor analysis and covariance-based structural equation modeling evaluated a modified Technology Acceptance Model in which leadership and subjective norms were specified as contextual factors influencing IU directly and indirectly through PU, with self-efficacy specified as an antecedent of PU. Results Of the 244 respondents included in analysis (96.1% completion rate), 51% were female, 31% were emergency physicians, and 19% were eye care specialists. The mean factor-based score for intention to use teleophthalmology was 3.74 (± 0.95) on a 5-point scale. The modified TAM demonstrated acceptable model fit and explained 67.5% of the variance in IU. Perceived usefulness was the strongest predictor of intention to use (β = 0.66). Transformational leadership (β = 0.37) and subjective norms (β = 0.47) showed significant positive effects on IU, largely mediated through PU. Self-efficacy was not significantly associated with PU (β = 0.18, p = 0.105). Conclusions The model predicted intention to use live teleophthalmology and identifies transformational leadership as an important driver of acceptance through both direct and indirect effects. Leadership engagement may play a supportive role in broader acceptance of teleophthalmology technology in emergency eye care. Critical Care & Emergency Medicine Ophthalmology Emergency Teleophthalmology Structural Equation Modeling Technology Acceptance Model Telemedicine Adoption Transformational Leadership Figures Figure 1 Figure 2 INTRODUCTION Telehealth is rapidly transitioning from a peripheral service to a mainstream component of modern healthcare (Tuckson et al., 2017 ). Its growth is especially significant in acute care, where tele-emergency systems have reduced transfer rates, improved specialist access, and stabilized costs (Muller et al., 2014 ). Ophthalmology represents a crucial test case—timely remote triage can prevent irreversible vision loss (Bahaadinbeigy & Yogesan, 2012 ; Maa et al., 2017 ). Inter-provider teleophthalmology, involving live audio-visual consultation between emergency and ophthalmology teams, offers a practical response to disparities in access (Rathi et al., 2017 ). Although telemedicine’s feasibility is well established in screening (Prathiba & Rema, 2011) and outpatient care (Flumignan et al., 2019 ), its acceptance among frontline providers and the influence of leadership on adoption in emergency settings remain unclear (Kruse et al., 2016 ). Technology adoption in healthcare is commonly explained by frameworks such as the Technology Acceptance Model (TAM) (Davis, 1989 ) and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh & Bala, 2008 ), which consistently identify perceived usefulness as the main predictor of behavioral intention (Holden & Karsh, 2010 ; Harst et al., 2019 ). This association holds across clinical specialties (Abushaar & Ismail, 2017 ; Asua et al., 2012 ; Gagnon et al., 2012 ; Rho et al., 2014 ). Social influences also shape clinicians’ behavior, as subjective norms from colleagues and supervisors can reinforce adoption (Taylor & Todd, 1995 ; Schepers et al., 2005 ; Lee & Wan, 2010 ). Within this context, transformational leadership is critical in defining vision and legitimizing digital norms (Lavoie-Tremblay et al., 2016 ; Carrara et al., 2016 ). Despite growing evidence, key gaps persist in understanding telemedicine adoption in acute care. Over one million annual emergency visits for eye conditions require urgent specialist input (Channa et al., 2016 ; Gibson, 2016 ), yet a gap remains between available technology and its perceived utility (Rademacher et al., 2019 ). Little empirical data exist on provider acceptance of emergency teleophthalmology (Kilduff et al., 2020 ; Mirza et al., 2020 ). Although leadership’s role in technology adoption is well documented, its direct and mediated effects within emergency clinical environments remain underexplored. Moreover, contextual factors such as workflow and experience influence adoption, but their structural relationships with core acceptance constructs—especially how leadership and social support shape perceived usefulness—require further study (Scott Kruse et al., 2018 ; Van Dyk, 2014 ). The purpose of this study is to test a theoretical structural model predicting frontline providers’ acceptance of teleophthalmology for inter-provider consultations in emergency departments. Specifically, it aims (1) to identify the key determinants of providers’ intention to use emergency teleophthalmology and (2) to examine transformational leadership as a modifiable organizational factor influencing technology acceptance within an extended technology acceptance framework. METHODS Study Design and Participants We employed a cross-sectional survey design as a complementary part of a larger dissertation project evaluating the impact of transformational leadership constructs on technology acceptance. Data was collected via a national, web-based survey platform between May and June 2020. Eligible participants were U.S. healthcare professionals actively engaged in emergency eye care, including emergency physicians (EPs), Advanced Practice Providers (APPs, like NPs and PAs), emergency nurses, technicians, ophthalmologists (MDs), and optometry doctors (ODs). These roles were included because live ED teleophthalmology requires coordinated input from both ED teams and consulting specialists (Host et al., 2018 ). Sampling and Sample Size We utilized a pragmatic, nonprobability purposive sampling strategy, aligning with telehealth evaluation guidance that emphasizes context and implementation in early-stage studies (Van Dyk, 2014 ). Recruitment combined assisted crowdsourcing via a mobile/web platform with professional-network outreach. The approach prioritized heterogeneity across roles and U.S. regions to illuminate adoption barriers and facilitators in real-world settings where formal sampling frames for interprofessional telehealth are limited. The final sample of N = 244 exceeded the ‘a priori’ requirement of N = 229 participants needed to detect medium effects with 80% power (Kyriazos, 2018 ). Electronic consent preceded the survey launch, and platform safeguards minimized inattentive or fraudulent responses (Supplementary Materials: Figure S1). Key Terms and Constructs For this study, acceptance refers to individual-level attitudes toward teleophthalmology, measured as Perceived Usefulness (PU) and Intention to Use (IU) following TAM/UTAUT frameworks (Davis, 1989 ; Venkatesh & Bala, 2008 ). Adoption denotes organizational-level integration into ED workflows, influenced by factors like infrastructure, reimbursement, and staff readiness (Tuckson et al., 2017 ; Van Dyk, 2014 ). Live teleophthalmology is defined as real-time, synchronous video consultation between ED teams and eye-care specialists, in contrast to store-and-forward models. Advanced practice providers (APPs) include nurse practitioners (NPs) and physician assistants (PAs), grouped together in analyses. Research Framework and Hypotheses The primary purpose was to predict providers' Intention to Use (IU) emergency teleophthalmology, focusing on the roles of Transformational Leadership (TL), Subjective Norms (SN), and Self-Efficacy (SE) as determinants of Perceived Usefulness (PU). We hypothesized that SN (H1), SE (H2), and TL (H3) would positively influence PU. Furthermore, we predicted that PU would positively influence IU (H4), and that both SN (H5) and TL (H6) would positively influence IU, both directly and indirectly through PU. The conceptual framework guiding these tests is presented in Fig. 1 . Survey Instruments And Measures The self-administered online questionnaire (Supplement A) utilized 5-point Likert scales for all constructs. Technology Acceptance items for Perceived Usefulness (PU) and Intention to Use (IU) were drawn from established scales (Davis, 1989 ; Taylor & Todd, 1995 ). Wording was adapted to specific, prior real-time teleophthalmology programs to ensure face validity for emergency use-cases (Host et al., 2018 ) and informed by telehealth adoption guidance pertinent to workflow and governance (Van Dyk, 2014 ). Subjective Norms (SN) and Self-Efficacy (SE) were similarly adapted from established measures (Compeau & Higgins, 1995 ). Transformational Leadership (TL) was measured using the 7-item Global Transformational Leadership (GTL) scale (Carless et al., 2000 ). The GTL scale assesses leadership behaviors related to vision, inspiration, and intellectual stimulation, demonstrating strong psychometric properties and wide use in organizational and healthcare research (Van Beveren et al., 2017 ). Control variables included professional role, ED caseload, on-call ophthalmology availability, and prior telemedicine exposure. Additionally, the questionnaire included checklist items on perceived challenges and opportunities, along with an open-ended prompt on implementation, which were core to the study's qualitative aims. Detailed items are provided in the Supplementary Materials (Tables S1 and S2). Statistical Analysis All analyses were performed using covariance-based Structural Equation Modeling (CB-SEM) in Stata 16 (StataCorp, 2019 ), a methodology selected for its robustness in testing established theory (Aimran et al., 2016 ; Kline, 2015 ). The analysis followed a two-step approach. First, Confirmatory Factor Analysis (CFA) assessed the measurement model. Construct reliability was confirmed using Composite Reliability (CR), and validity was established by assessing factor loadings, Average Variance Extracted (AVE), and discriminant validity (Hair et al., 2017 ). Second, the structural model was tested to evaluate the hypothesized paths. Model fit was evaluated against stringent criteria, including the Chi-Square divided by degrees of freedom ratio (Chi-Square/df), Comparative Fit Index (CFI), Tucker–Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA with p-close), and Standardized Root Mean Square Residual (SRMR) (Schreiber et al., 2006 ). Additional tests for nonnormality (Cain et al., 2017 ; Das & Imon, 2016 ) and missing data (Carter et al., 2006 ) were conducted. Control variables were included to test for associations and potential moderating effects (Fig. 1 ). Ethical Considerations The Institutional Review Board of Georgia Southern University approved the study (H19418). Informed consent was obtained electronically before participation. RESULTS Sample Characteristics The final analytic sample comprised 244 respondents representing 36 U.S. states and the District of Columbia. The largest proportions were from California (20.5%), Florida (9.8%), New York (7.4%), Illinois (6.6%), Georgia (5.7%), North Carolina (4.5%), and Texas (4.1%). The mean age was 43.4 years (SD = 11.2; range = 20–81), and 51% were female. By professional role, the sample was dominated by ophthalmologists or optometrists (19%), emergency physicians (31%), and emergency nurses or technicians (21%). Approximately 35% reported previous experience using video-based telemedicine (Table 1 ). The sample had a relatively proportionate distribution of professional categories in the target population, though it overrepresents nurses and optometrists. Latent Variables The latent variables appeared slightly skewed to the right (-0.33 to -0.84), but all showed mesokurtic (close to + 3.6) distribution ranging from 2.8 to 3.0. The recommended range is between -2 to + 2 for skewness and ‐7 to + 7 for kurtosis (Hair, 2010; Bryne, 2010). Hence, all latent variables showed symmetrical distribution fulfilling the multivariate normality prerequisite for structural equation modeling (Table 2 ). The Measurement Model: Reliability, Validity, and Fit. Confirmatory factor analysis demonstrated strong psychometric properties across all constructs, with standardized factor loadings generally ≥ 0.70, indicating good item reliability. Internal consistency was high, as Cronbach’s α values exceeded 0.75 for all scales, and composite reliability (CR) values were all ≥ 0.75, reflecting excellent construct reliability and unidimensionality (Hair et al., 2017 ). Convergent validity was supported by average variance extracted (AVE) values ≥ 0.50, indicating that each latent factor explained more than half of the variance in its indicators. Discriminant validity was satisfactory overall, as the square root of each construct’s AVE exceeded inter-construct correlations; however, the correlation between perceived usefulness (PU) and intention to use (IU) slightly surpassed this threshold, suggesting conceptual proximity between these theoretically linked constructs. Detailed reliability and validity measures are provided as supplementary materials (Table S3). The modified TAM demonstrated acceptable model fit, with χ²/df ranging from 1.55–1.70, CFI = 0.97, TLI = 0.96, RMSEA = 0.048 (90% CI 0.036–0.060, p -close > 0.05), and SRMR = 0.036— all exceeding recommended cut-off thresholds for good fit. Model fit indices for alternative structural models are provided as supplementary materials (Tables S4). The covariance matrix of latent variables used in the measurement and structural models is provided as supplementary materials (Table S6). In SEM, the coefficient of determination (CD = 0.995) indicated that 99.5% of the variance was explained by the model, though this high value likely reflects the strong correlation between PU and IU and residual noise from numerous items. A simpler regression with IU as the dependent variable and SN, TL, and PU as predictors explained 66.7% of the variance ( adjusted R² = 0.6665 ), which provides a more comparable measure across studies. Table 1 Distribution Of The Study’s Sample by Age And Gender Provider Categories, US, 2020. Provider Category Group Proportion Mean Age (years) Proportion of Females Population Sample Population Sample Population Sample • Ophthalmologists & Optometrists (n = 47) 32% 19.3% 48 40.5 *** 40.0% 42.6% • Emergency Physicians (n = 76) 37% 31.1% 47 42 *** 27.6% 42.0% • Physician Assistants (n = 27) 10% 11.1% 38 39.4 72.0% 44.0% * • Nurse Practitioners (n = 45) 14% 18.4% 47 44.3 90.0% 64.0% *** • Nurses & Techs (n = 49) 17% 21.1% 43 50.6 *** 78.0% 67.0% • Total (n = 244) 100% 100% 45.4 43.4** 50% 51% * = Statistically significant difference * p < 0.05. ** p < 0.01. *** p < 0.001. Table 2 Mean Scores Of Latent Variables And Measures Of Normal Distribution, Emergency Eyecare Providers, USA, 2020. Latent Variables (n = 244) Items Raw Score (M, SD) * Factor-Based Score (M, SD*) Refined Factor Score (M, SD) Skewness Kurtosis Self-Efficacy 3 3.46 (.93) 3.44 (.94) -5.39e-16 (0.8) -0.33 2.8 Subjective Norms 3 3.73 (.86) 3.72 (.86) -4.05e-16 (0.75) -0.50 3.0 Transformational Leadership 7 3.91 (.87) 3.85 (.86) -1.13e-15 (0.76) -0.84 3.4 Perceived Usefulness 4 3.52 (.97) 3.52 (.97) -4.44e-15 (0.86) -0.43 2.6 Intention to Use 4 3.74 (.95) 3.74 (.95) -1.52e-15 (0.91) -0.61 2.9 Notes: * p value > 0.05 (t-test for two means, Raw vs Factor based) Mean = Mean, SD = Standard Deviation # Normal Skewness = -2 to 2. Normal Kurtosis = -7 to 7 (Hair, 2010) . The Structural Model: Path Analysis Hypothesis testing assessed the proposed relationships among the latent variables, estimating their direct, indirect, and total effects on intention to use (IU). Path coefficients significantly different from zero ( p < 0.05) were considered supported. The data confirmed all hypothesized paths except for the effect of self-efficacy (SE) (Table 3 ). Table 3 Path Coefficients and Hypothesis Test Results From The Modified Structural Model Of Teleophthalmology Acceptance By Emergency Providers, USA, 2020. Research Hypothesis Path Effect Stdzd. Coef. Path Coef. Stand. Error Z Stat. P > z Std. Hypothesis Supported? H1 SN⇒PU Direct 0.44 0.51 0.14 3.77 0.000 Supported H5d SN ⇒ IU Direct 0.17 0.21 0.10 2.17 0.030 Supported H5i (via PU) Indirect 0.29 0.36 0.101 3.57 0.000 Supported H5t Total 0.45 0.57 0.12 4.71 0.000 Supported H2 SE⇒PU Direct 0.18 0.18 0.11 1.62 0.105 Not Supported H3 TL⇒PU Direct 0.30 0.34 0.08 4.45 0.000 Supported H6d TL⇒ IU Direct 0.18 0.21 0.07 3.09 0.002 Supported H6i (via PU) Indirect 0.20 0.24 0.06 3.95 0.000 Supported H6t Total 0.37 0.46 0.08 5.7 0.000 Supported H4 PU ⇒ IU Direct 0.66 0.70 0.10 7.28 0.000 Supported Notes: H = Hypothesis. Coef = coefficient. Stdzd= standardized. Stand = Standard. Perceived usefulness (PU) was the strongest predictor of IU (β = 0.66, p < .001). Transformational leadership (TL) positively influenced both PU (β = 0.30–0.34, p < .001) and IU directly (β = 0.18–0.21, p ≤ .03), yielding a total effect of approximately 0.37. Subjective norm (SN) also strongly predicted PU (β = 0.44–0.51, p < .001) and IU (β = 0.17–0.21, p ≤ .03), with a total effect near 0.45. The effect of SE on PU was small and nonsignificant (β = 0.18, p = .105). Overall, the model explained 66.7% of the variance in IU (Fig. 2 ). Contextual Factors Associations Most demographic and organizational characteristics showed weak or no association with perceived usefulness (PU) or intention to use (IU) teleophthalmology ( p > .05). However, several contextual factors demonstrated meaningful relationships. Providers serving mainly as consultation seekers (e.g., emergency clinicians) reported lower PU (r = − 0.19, p = .003) and IU (r = − 0.22, p = .001) than consulting ophthalmologists or optometrists, while those in leadership roles showed higher PU (r = 0.20, p = .002) and IU (r = 0.18, p = .004). Prior video-telemedicine exposure was modest but positively associated with PU and IU ( p < .05). Organizational context also mattered: higher ocular caseloads, availability of on-call ophthalmologists, and existing telemedicine systems in the ED correlated positively with both PU and IU ( p < .01). In regression analyses, only the availability of telemedicine in the ED remained significant for PU ( p = .008), though its inclusion did not improve model fit. Correlation details for control and contextual variables are provided in the Supplementary (Table S5). DISCUSSION This study tested a structural model predicting frontline providers’ acceptance of teleophthalmology for inter-provider consultation in emergency departments. The analysis identified key determinants of intention to use, evaluated transformational leadership as a modifiable influence within an extended technology acceptance framework, and clarified pathways linking leadership to behavioral intention. Three main findings emerged. Perceived usefulness (PU) was the central driver of acceptance, explaining the largest share of variance in intention to use (IU) emergency teleophthalmology. Transformational leadership (TL) and supportive subjective norms (SN) were significant, modifiable levers of acceptance, exerting their influence primarily through PU. In contrast, self-efficacy (SE) did not contribute additional explanatory power, suggesting that provider confidence alone is insufficient without demonstrable, workflow-integrated clinical value. These findings align with the Technology Acceptance Model (TAM) tradition, confirming PU as the dominant predictor of behavioral intention (Holden et al., 2010; Davis, 1989 ). The strong association between PU and IU (β = 0.66) and the high explained variance are consistent with prior health IT studies (Chau et al., 2002; Abushaar et al., 2017; Kamal et al., 2018 ; Saigi Rubió et al., 2016; Harst et al., 2019 ), extending this evidence to emergency teleophthalmology. Subjective norms influenced both PU and IU, reinforcing that peer and supervisory endorsement is most effective when it translates into tangible clinical value (Schepers et al., 2005 ; Taylor et al., 1995). Transformational leadership was also associated with higher PU and IU, extending leadership theory into acute care by demonstrating that leaders who articulate vision and foster innovation can promote technology acceptance (Rezvani et al., 2017 ). The non-significant relationship between SE and PU contrasts with some prior findings (Rho et al., 2014 ), suggesting that in specialized emergency settings, confidence alone may be insufficient. Future models may benefit from incorporating perceived ease of use to better reflect operational demands (Venkatesh et al., 1998). Contextual factors such as higher ocular caseloads and prior telemedicine exposure were associated with greater PU and IU, though these effects were modest, indicating that acceptance was driven more by cognitive appraisals and leadership than by demographic or institutional characteristics. Implications These findings yield actionable implications for implementation (Van Dyk, 2014 ). Because adoption depends on perceived clinical value, implementation strategies should be leader-enabled and utility-focused rather than capability-driven. Leaders should articulate a clear clinical use case, positioning teleophthalmology as a solution for managing high-risk, low-volume ocular emergencies to support patient safety (Lokken et al., 2020 ). Adoption may be further supported by embedding teleconsultation into standard workflows and reinforcing supportive subjective norms, particularly given the lack of association between general self-efficacy and PU. Institutions with existing tele-emergency services may benefit from prioritizing rollout in high-volume emergency departments with frequent ophthalmology demand (Wedekind et al., 2016). At the policy level, sustained reimbursement parity and integration of telemedicine competencies into training curricula are needed to support long-term viability (Hall et al., 2014; Faden et al., 2020 ). Limitations This study has several strengths, including a theory-driven design using validated psychometric instruments and established measures of technology acceptance and transformational leadership. Nationwide participation across diverse provider roles enhances robustness. Limitations include nonprobability sampling, which may affect generalizability, and reliance on behavioral intention rather than actual use. Self-reported measures collected in a single survey raise the possibility of common method variance. The cross-sectional design also limits causal inference, with SEM identifying probabilistic rather than deterministic relationships. Conclusions The structural model predicted emergency providers’ intention to use live teleophthalmology, confirming perceived usefulness as the central mechanism in technology acceptance. Transformational leadership emerged as an important influence, shaping intention through direct effects and indirect pathways via perceived usefulness and subjective norms. These findings highlight leadership behaviors as practical, modifiable levers for translating technical capability into clinically meaningful utility. Future research should link behavioral intention with actual utilization, efficiency, and patient outcomes. Longitudinal and multilevel studies may clarify how leadership, organizational readiness, and unit culture sustain teleophthalmology over time. Declarations Acknowledgments: The authors are very grateful to the clinicians who participated in the survey for their time and insights. We also thank Yousuf M. Khalifa, MD, and Hany H. Atallah, MD, of Emory University at Grady Memorial Hospital for their clinical perspectives, as well as Moges S. Ido, MD, PhD, of the Georgia Department of Health for statistical support. 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NEJM Catalyst Innovations Care Delivery 1(4):1–12 Maa AY, Evans C, DeJean S, Patel P (2017) A sensitive, specific and safe system for automated detection of diabetic retinopathy. Diabetes Care 40(12):1817–1824. https://doi.org/10.2337/dc17-0883 Memon MA, Ting H, Ramayah T, Chuah F, Cheah JH (2017) A review of the methodological misconceptions and guidelines related to the application of structural equation modeling: A Malaysian scenario. J Appl Struct Equation Model 1(1):1–13 Mirza RG, Zafar S, Breazzano MP (2020) Teleophthalmology in the COVID-19 era: Opportunities and challenges. Asia-Pacific J Ophthalmol 9(3):210–214. https://doi.org/10.1097/APO.0000000000000308 Muller KJ, Potter AJ, MacKinney AC, Ward MM (2014) Lessons from tele-emergency: Improving care quality and health outcomes by expanding support for rural care systems. Health Aff 33(2):228–234. https://doi.org/10.1377/hlthaff.2013.1010 Rademacher NJ, Cole G, Psoter KJ, Kelen G, Fan JW, Gordon D, Razzak J (2019) Use of telemedicine to screen patients in the emergency department: Matched cohort study evaluating efficiency and patient safety. JMIR Med Inf 7(2):e11233. https://doi.org/10.2196/11233 Rathi S, Tsui E, Mehta N, Zahid S, Schuman JS (2017) The current state of teleophthalmology in the United States. Ophthalmology 124(12):1729–1734. https://doi.org/10.1016/j.ophtha.2017.05.032 Rezvani A, Dong L, Khosravi P (2017) Promoting the continuing usage of strategic information systems: The role of supervisory leadership in the successful implementation of enterprise systems. Int J Inf Manag 37(5):417–430. https://doi.org/10.1016/j.ijinfomgt.2017.04.008 Rezvani A, Khosravi P, Dong L (2017) Motivating users toward continued usage of information systems: Self-determination theory perspective. Comput Hum Behav 76:263–275. https://doi.org/10.1016/j.chb.2017.07.032 Rho MJ, Choi IY, Lee J, Choi IY (2014) Factors influencing the acceptance of telemedicine for diabetes management. Cluster Comput 17(3):759–766. https://doi.org/10.1007/s10586-013-0319-5 Rockwell KL, Gilroy AS (2020) Incorporating telemedicine as part of COVID-19 outbreak response systems. Am J Managed Care 26(4):147–148 Saigi-Rubió F, Torrent-Sellens J, Jiménez-Zarco AI (2016) Drivers of telemedicine use: Comparative evidence from physicians in Catalonia and the United States. Telemedicine e-Health 22(5):396–404. https://doi.org/10.1089/tmj.2015.0126 Schepers J, Wetzels M, de Ruyter K (2005) Leadership styles in technology acceptance: Do followers practice what leaders preach? Managing Service Qual 15(6):496–508. https://doi.org/10.1108/09604520510633998 Schreiber JB, Nora A, Stage FK, Barlow EA, King J (2006) Reporting structural equation modeling and confirmatory factor analysis results: A review. J Educational Res 99(6):323–338. https://doi.org/10.3200/JOER.99.6.323-338 Scott Kruse C, Karem P, Shifflett K, Vegi L, Ravi K, Brooks M (2018) Evaluating barriers to adopting telemedicine worldwide: A systematic review. J Telemed Telecare 24(1):4–12. https://doi.org/10.1177/1357633X16674087 Sharma R, Nachum S, Davidson KW, Nochomovitz M (2020) It’s not just FaceTime: Core competencies for the medical virtualist. NEJM Catalyst 6(1):1–11 StataCorp (2019) Stata Statistical Software: Release 16. StataCorp LLC Taylor S, Todd P (1995) Understanding information technology usage: A test of competing models. Inform Syst Res 6(2):144–176. https://doi.org/10.1287/isre.6.2.144 Tuckson RV, Edmunds M, Hodgkins ML (2017) Telehealth. N Engl J Med 377(16):1585–1592. https://doi.org/10.1056/NEJMsr1503323 Van Beveren P, Dimas ID, Lourenço PR, Rebelo T (2017) Psychometric properties of the Portuguese version of the Global Transformational Leadership (GTL) scale. Revista de Psicología del Trabajo y de las Organizaciones, 33(2), 109–114. https://doi.org/10.1016/j.rpto.2017.02.001 Van Dyk L (2014) A review of telehealth service implementation frameworks. Int J Environ Res Public Health 11(2):1279–1298. https://doi.org/10.3390/ijerph110201279 Venkatesh V, Bala H (2008) Technology Acceptance Model 3 and a research agenda on interventions. Decis Sci 39(2):273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x Additional Declarations The authors declare no competing interests. Supplementary Files SuppleLeaderTelemedSEM.docx 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. 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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-8942270","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":595318918,"identity":"e5887988-1620-4cab-a482-438731dd3d26","order_by":0,"name":"Assegid Roba","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYBADHobjDQzMJGo5c4BELQwMNxKI1MLfv/jxxx8VtTJ8N98Yfi6osGHgb+9OwKtF4sYzM2meM8d5JG/nGEvPOJPGIHHm7AYC7jlgxszYdozH4HaOgTRv22EGA4lc/Frkbxz//PHnP6CWm2eMfxOlxeB8j4EEb0MNj8ENHjPibDG8wVMmzXPsAI/kmbQya54zaTwE/SJ3/vjmjz9q6uz5jh/efJunwkaOv72XgPclEkDkYSDmMACxePArBwH+AyCyDojZHxBWPQpGwSgYBSMSAAAiRUtoxXxnXQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-7661-3856","institution":"Grady Health Systems","correspondingAuthor":true,"prefix":"","firstName":"Assegid","middleName":"","lastName":"Roba","suffix":""},{"id":595318919,"identity":"bc3699a7-eb50-4abb-bc57-3acefedb5d66","order_by":1,"name":"Gulzar Shah","email":"","orcid":"https://orcid.org/0000-0002-8390-1730","institution":"Georgia Suthern University","correspondingAuthor":false,"prefix":"","firstName":"Gulzar","middleName":"","lastName":"Shah","suffix":""},{"id":595318920,"identity":"297cad40-7ad2-47af-aacc-a626322afdaf","order_by":2,"name":"William Mase","email":"","orcid":"","institution":"Georgia Suthern University","correspondingAuthor":false,"prefix":"","firstName":"William","middleName":"","lastName":"Mase","suffix":""},{"id":595318921,"identity":"29464b43-bd26-45e3-9482-e10e3204ed10","order_by":3,"name":"Bettye Apenteng","email":"","orcid":"","institution":"Georgia Suthern University","correspondingAuthor":false,"prefix":"","firstName":"Bettye","middleName":"","lastName":"Apenteng","suffix":""}],"badges":[],"createdAt":"2026-02-23 02:33:19","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8942270/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8942270/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103308043,"identity":"f8ddecab-69d8-43fe-8a7a-eb2df1fac006","added_by":"auto","created_at":"2026-02-24 09:35:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":54298,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eThe Study Framework based on Extended Technology Acceptance Models\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8942270/v1/78a3888d0f98db816aa2d246.png"},{"id":103308045,"identity":"9ad45d46-ebdb-4a89-bf55-181cbc7ebb59","added_by":"auto","created_at":"2026-02-24 09:35:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":346872,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eThe Corrected Structural Model of Teleophthalmology Technology Acceptance.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8942270/v1/463a9b970760e4f203cec882.png"},{"id":103506131,"identity":"98910b4a-7489-426e-acda-7938eadc18d6","added_by":"auto","created_at":"2026-02-26 13:34:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1542321,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8942270/v1/c2413db6-1531-466c-9558-87945d10c356.pdf"},{"id":103308044,"identity":"c8cc64da-c739-4469-9220-9bef343afc87","added_by":"auto","created_at":"2026-02-24 09:35:51","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":65017,"visible":true,"origin":"","legend":"","description":"","filename":"SuppleLeaderTelemedSEM.docx","url":"https://assets-eu.researchsquare.com/files/rs-8942270/v1/bee77d595bb825aac217fd60.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eTransformational Leadership and Telemedicine Acceptance: Predicting Providers’ Intention to Use Emergency Teleophthalmology\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eTelehealth is rapidly transitioning from a peripheral service to a mainstream component of modern healthcare (Tuckson et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Its growth is especially significant in acute care, where tele-emergency systems have reduced transfer rates, improved specialist access, and stabilized costs (Muller et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Ophthalmology represents a crucial test case\u0026mdash;timely remote triage can prevent irreversible vision loss (Bahaadinbeigy \u0026amp; Yogesan, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Maa et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Inter-provider teleophthalmology, involving live audio-visual consultation between emergency and ophthalmology teams, offers a practical response to disparities in access (Rathi et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Although telemedicine\u0026rsquo;s feasibility is well established in screening (Prathiba \u0026amp; Rema, 2011) and outpatient care (Flumignan et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), its acceptance among frontline providers and the influence of leadership on adoption in emergency settings remain unclear (Kruse et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTechnology adoption in healthcare is commonly explained by frameworks such as the Technology Acceptance Model (TAM) (Davis, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) and the Unified Theory of Acceptance and Use of Technology (UTAUT) (Venkatesh \u0026amp; Bala, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), which consistently identify perceived usefulness as the main predictor of behavioral intention (Holden \u0026amp; Karsh, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Harst et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This association holds across clinical specialties (Abushaar \u0026amp; Ismail, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Asua et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Gagnon et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Rho et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Social influences also shape clinicians\u0026rsquo; behavior, as subjective norms from colleagues and supervisors can reinforce adoption (Taylor \u0026amp; Todd, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Schepers et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Lee \u0026amp; Wan, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Within this context, transformational leadership is critical in defining vision and legitimizing digital norms (Lavoie-Tremblay et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Carrara et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite growing evidence, key gaps persist in understanding telemedicine adoption in acute care. Over one million annual emergency visits for eye conditions require urgent specialist input (Channa et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Gibson, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), yet a gap remains between available technology and its perceived utility (Rademacher et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Little empirical data exist on provider acceptance of emergency teleophthalmology (Kilduff et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mirza et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although leadership\u0026rsquo;s role in technology adoption is well documented, its direct and mediated effects within emergency clinical environments remain underexplored. Moreover, contextual factors such as workflow and experience influence adoption, but their structural relationships with core acceptance constructs\u0026mdash;especially how leadership and social support shape perceived usefulness\u0026mdash;require further study (Scott Kruse et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Van Dyk, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe purpose of this study is to test a theoretical structural model predicting frontline providers\u0026rsquo; acceptance of teleophthalmology for inter-provider consultations in emergency departments. Specifically, it aims (1) to identify the key determinants of providers\u0026rsquo; intention to use emergency teleophthalmology and (2) to examine transformational leadership as a modifiable organizational factor influencing technology acceptance within an extended technology acceptance framework.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Participants\u003c/h2\u003e \u003cp\u003eWe employed a cross-sectional survey design as a complementary part of a larger dissertation project evaluating the impact of transformational leadership constructs on technology acceptance. Data was collected via a national, web-based survey platform between May and June 2020. Eligible participants were U.S. healthcare professionals actively engaged in emergency eye care, including emergency physicians (EPs), Advanced Practice Providers (APPs, like NPs and PAs), emergency nurses, technicians, ophthalmologists (MDs), and optometry doctors (ODs). These roles were included because live ED teleophthalmology requires coordinated input from both ED teams and consulting specialists (Host et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSampling and Sample Size\u003c/h3\u003e\n\u003cp\u003eWe utilized a pragmatic, nonprobability purposive sampling strategy, aligning with telehealth evaluation guidance that emphasizes context and implementation in early-stage studies (Van Dyk, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Recruitment combined assisted crowdsourcing via a mobile/web platform with professional-network outreach. The approach prioritized heterogeneity across roles and U.S. regions to illuminate adoption barriers and facilitators in real-world settings where formal sampling frames for interprofessional telehealth are limited. The final sample of N\u0026thinsp;=\u0026thinsp;244 exceeded the \u0026lsquo;a priori\u0026rsquo; requirement of N\u0026thinsp;=\u0026thinsp;229 participants needed to detect medium effects with 80% power (Kyriazos, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Electronic consent preceded the survey launch, and platform safeguards minimized inattentive or fraudulent responses (Supplementary Materials: Figure S1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eKey Terms and Constructs\u003c/h3\u003e\n\u003cp\u003eFor this study, acceptance refers to individual-level attitudes toward teleophthalmology, measured as Perceived Usefulness (PU) and Intention to Use (IU) following TAM/UTAUT frameworks (Davis, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Venkatesh \u0026amp; Bala, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Adoption denotes organizational-level integration into ED workflows, influenced by factors like infrastructure, reimbursement, and staff readiness (Tuckson et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Van Dyk, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Live teleophthalmology is defined as real-time, synchronous video consultation between ED teams and eye-care specialists, in contrast to store-and-forward models. Advanced practice providers (APPs) include nurse practitioners (NPs) and physician assistants (PAs), grouped together in analyses.\u003c/p\u003e\n\u003ch3\u003eResearch Framework and Hypotheses\u003c/h3\u003e\n\u003cp\u003eThe primary purpose was to predict providers' Intention to Use (IU) emergency teleophthalmology, focusing on the roles of Transformational Leadership (TL), Subjective Norms (SN), and Self-Efficacy (SE) as determinants of Perceived Usefulness (PU). We hypothesized that SN (H1), SE (H2), and TL (H3) would positively influence PU. Furthermore, we predicted that PU would positively influence IU (H4), and that both SN (H5) and TL (H6) would positively influence IU, both directly and indirectly through PU. The conceptual framework guiding these tests is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eSurvey Instruments And Measures\u003c/h3\u003e\n\u003cp\u003eThe self-administered online questionnaire (Supplement A) utilized 5-point Likert scales for all constructs. Technology Acceptance items for Perceived Usefulness (PU) and Intention to Use (IU) were drawn from established scales (Davis, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Taylor \u0026amp; Todd, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Wording was adapted to specific, prior real-time teleophthalmology programs to ensure face validity for emergency use-cases (Host et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and informed by telehealth adoption guidance pertinent to workflow and governance (Van Dyk, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Subjective Norms (SN) and Self-Efficacy (SE) were similarly adapted from established measures (Compeau \u0026amp; Higgins, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTransformational Leadership (TL) was measured using the 7-item Global Transformational Leadership (GTL) scale (Carless et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). The GTL scale assesses leadership behaviors related to vision, inspiration, and intellectual stimulation, demonstrating strong psychometric properties and wide use in organizational and healthcare research (Van Beveren et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Control variables included professional role, ED caseload, on-call ophthalmology availability, and prior telemedicine exposure. Additionally, the questionnaire included checklist items on perceived challenges and opportunities, along with an open-ended prompt on implementation, which were core to the study's qualitative aims. Detailed items are provided in the Supplementary Materials (Tables S1 and S2).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll analyses were performed using covariance-based Structural Equation Modeling (CB-SEM) in Stata 16 (StataCorp, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), a methodology selected for its robustness in testing established theory (Aimran et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kline, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The analysis followed a two-step approach. First, Confirmatory Factor Analysis (CFA) assessed the measurement model. Construct reliability was confirmed using Composite Reliability (CR), and validity was established by assessing factor loadings, Average Variance Extracted (AVE), and discriminant validity (Hair et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Second, the structural model was tested to evaluate the hypothesized paths. Model fit was evaluated against stringent criteria, including the Chi-Square divided by degrees of freedom ratio (Chi-Square/df), Comparative Fit Index (CFI), Tucker\u0026ndash;Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA with p-close), and Standardized Root Mean Square Residual (SRMR) (Schreiber et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Additional tests for nonnormality (Cain et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Das \u0026amp; Imon, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and missing data (Carter et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) were conducted. Control variables were included to test for associations and potential moderating effects (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003e The Institutional Review Board of Georgia Southern University approved the study (H19418). Informed consent was obtained electronically before participation.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSample Characteristics\u003c/h2\u003e \u003cp\u003eThe final analytic sample comprised 244 respondents representing 36 U.S. states and the District of Columbia. The largest proportions were from California (20.5%), Florida (9.8%), New York (7.4%), Illinois (6.6%), Georgia (5.7%), North Carolina (4.5%), and Texas (4.1%). The mean age was 43.4 years (SD\u0026thinsp;=\u0026thinsp;11.2; range\u0026thinsp;=\u0026thinsp;20\u0026ndash;81), and 51% were female. By professional role, the sample was dominated by ophthalmologists or optometrists (19%), emergency physicians (31%), and emergency nurses or technicians (21%). Approximately 35% reported previous experience using video-based telemedicine (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The sample had a relatively proportionate distribution of professional categories in the target population, though it overrepresents nurses and optometrists.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eLatent Variables\u003c/h2\u003e \u003cp\u003eThe latent variables appeared slightly skewed to the right (-0.33 to -0.84), but all showed mesokurtic (close to +\u0026thinsp;3.6) distribution ranging from 2.8 to 3.0. The recommended range is between -2 to +\u0026thinsp;2 for skewness and ‐7 to +\u0026thinsp;7 for kurtosis (Hair, 2010; Bryne, 2010). Hence, all latent variables showed symmetrical distribution fulfilling the multivariate normality prerequisite for structural equation modeling (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eThe Measurement Model: Reliability, Validity, and Fit.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eConfirmatory factor analysis demonstrated strong psychometric properties across all constructs, with standardized factor loadings generally\u0026thinsp;\u0026ge;\u0026thinsp;0.70, indicating good item reliability. Internal consistency was high, as Cronbach\u0026rsquo;s α values exceeded 0.75 for all scales, and composite reliability (CR) values were all \u0026ge;\u0026thinsp;0.75, reflecting excellent construct reliability and unidimensionality (Hair et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Convergent validity was supported by average variance extracted (AVE) values\u0026thinsp;\u0026ge;\u0026thinsp;0.50, indicating that each latent factor explained more than half of the variance in its indicators. Discriminant validity was satisfactory overall, as the square root of each construct\u0026rsquo;s AVE exceeded inter-construct correlations; however, the correlation between perceived usefulness (PU) and intention to use (IU) slightly surpassed this threshold, suggesting conceptual proximity between these theoretically linked constructs. Detailed reliability and validity measures are provided as supplementary materials (Table S3).\u003c/p\u003e \u003cp\u003eThe modified TAM demonstrated acceptable model fit, with χ\u0026sup2;/df ranging from 1.55\u0026ndash;1.70, CFI\u0026thinsp;=\u0026thinsp;0.97, TLI\u0026thinsp;=\u0026thinsp;0.96, RMSEA\u0026thinsp;=\u0026thinsp;0.048 (90% CI 0.036\u0026ndash;0.060, \u003cem\u003ep\u003c/em\u003e-close\u0026thinsp;\u0026gt;\u0026thinsp;0.05), and SRMR\u0026thinsp;=\u0026thinsp;0.036\u0026mdash; all exceeding recommended cut-off thresholds for good fit. Model fit indices for alternative structural models are provided as supplementary materials (Tables S4).\u003c/p\u003e \u003cp\u003eThe covariance matrix of latent variables used in the measurement and structural models is provided as supplementary materials (Table S6). In SEM, the coefficient of determination (CD\u0026thinsp;=\u0026thinsp;0.995) indicated that 99.5% of the variance was explained by the model, though this high value likely reflects the strong correlation between PU and IU and residual noise from numerous items. A simpler regression with IU as the dependent variable and SN, TL, and PU as predictors explained 66.7% of the variance (\u003cem\u003eadjusted R\u0026sup2; = 0.6665\u003c/em\u003e), which provides a more comparable measure across studies.\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\u003eDistribution Of The Study\u0026rsquo;s Sample by Age And Gender Provider Categories, US, 2020.\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eProvider Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGroup Proportion\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMean Age (years)\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eProportion of Females\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePopulation\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSample\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ePopulation\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eSample\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003ePopulation\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eSample\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026bull; Ophthalmologists\u003c/p\u003e \u003cp\u003e\u0026amp; Optometrists (n\u0026thinsp;=\u0026thinsp;47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e19.3%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e40.5 ***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026bull; Emergency Physicians (n\u0026thinsp;=\u0026thinsp;76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e31.1%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e42 ***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026bull; Physician Assistants (n\u0026thinsp;=\u0026thinsp;27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e72.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e44.0% *\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026bull; Nurse Practitioners (n\u0026thinsp;=\u0026thinsp;45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e64.0% ***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026bull; Nurses \u0026amp; Techs (n\u0026thinsp;=\u0026thinsp;49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e50.6 ***\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e78.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e67.0%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026bull; \u003cb\u003eTotal (n\u0026thinsp;=\u0026thinsp;244)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e100%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e100%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e45.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e43.4**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e50%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e51%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e* = Statistically significant difference * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01. *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\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\u003eMean Scores Of Latent Variables And Measures Of Normal Distribution, Emergency Eyecare Providers, USA, 2020.\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLatent Variables\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;244)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRaw Score\u003c/p\u003e \u003cp\u003e(M, SD) *\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFactor-Based Score (M, SD*)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRefined Factor Score (M, SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eKurtosis\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\u003eSelf-Efficacy\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.46 (.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.44 (.94)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.39e-16\u003c/p\u003e \u003cp\u003e(0.8)\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSubjective Norms\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.73 (.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.72 (.86)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.05e-16\u003c/p\u003e \u003cp\u003e(0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTransformational Leadership\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.91 (.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.85 (.86)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.13e-15\u003c/p\u003e \u003cp\u003e(0.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerceived Usefulness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.52 (.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.52 (.97)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.44e-15\u003c/p\u003e \u003cp\u003e(0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIntention to Use\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.74 (.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.74 (.95)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.52e-15\u003c/p\u003e \u003cp\u003e(0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNotes: *\u003cem\u003ep value\u0026thinsp;\u0026gt;\u0026thinsp;0.05 (t-test for two means, Raw vs Factor based) Mean\u0026thinsp;=\u0026thinsp;Mean, SD\u0026thinsp;=\u0026thinsp;Standard Deviation\u003c/em\u003e \u003csup\u003e\u003cb\u003e#\u003c/b\u003e\u003c/sup\u003e \u003cem\u003eNormal Skewness = -2 to 2.\u003c/em\u003e \u003cem\u003eNormal Kurtosis = -7 to 7 (Hair, 2010)\u003c/em\u003e.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eThe Structural Model: Path Analysis\u003c/h2\u003e \u003cp\u003eHypothesis testing assessed the proposed relationships among the latent variables, estimating their direct, indirect, and total effects on intention to use (IU). Path coefficients significantly different from zero (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were considered supported. The data confirmed all hypothesized paths except for the effect of self-efficacy (SE) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\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\u003ePath Coefficients and Hypothesis Test Results From The Modified Structural Model Of Teleophthalmology Acceptance By Emergency Providers, USA, 2020.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResearch Hypothesis\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\u003eEffect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStdzd. Coef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePath Coef.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStand. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003cp\u003eStat.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;z Std.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHypothesis\u003c/p\u003e \u003cp\u003eSupported?\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\u003eH1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSN\u0026rArr;PU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDirect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.44\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.51\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH5d\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSN \u0026rArr; IU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDirect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH5i\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(via PU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndirect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH5t\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSE\u0026rArr;PU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDirect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eNot Supported\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTL\u0026rArr;PU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDirect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH6d\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTL\u0026rArr; IU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDirect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH6i\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(via PU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndirect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH6t\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePU \u0026rArr; IU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDirect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.66\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cem\u003eNotes: H\u0026thinsp;=\u0026thinsp;Hypothesis. Coef\u0026thinsp;=\u0026thinsp;coefficient. Stdzd= standardized. Stand\u0026thinsp;=\u0026thinsp;Standard.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePerceived usefulness (PU) was the strongest predictor of IU (β\u0026thinsp;=\u0026thinsp;0.66, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001). Transformational leadership (TL) positively influenced both PU (β\u0026thinsp;=\u0026thinsp;0.30\u0026ndash;0.34, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) and IU directly (β\u0026thinsp;=\u0026thinsp;0.18\u0026ndash;0.21, \u003cem\u003ep\u003c/em\u003e \u0026le; .03), yielding a total effect of approximately 0.37. Subjective norm (SN) also strongly predicted PU (β\u0026thinsp;=\u0026thinsp;0.44\u0026ndash;0.51, \u003cem\u003ep\u003c/em\u003e \u0026lt; .001) and IU (β\u0026thinsp;=\u0026thinsp;0.17\u0026ndash;0.21, \u003cem\u003ep\u003c/em\u003e \u0026le; .03), with a total effect near 0.45. The effect of SE on PU was small and nonsignificant (β\u0026thinsp;=\u0026thinsp;0.18, \u003cem\u003ep\u003c/em\u003e = .105). Overall, the model explained 66.7% of the variance in IU (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eContextual Factors Associations\u003c/h2\u003e \u003cp\u003eMost demographic and organizational characteristics showed weak or no association with perceived usefulness (PU) or intention to use (IU) teleophthalmology (\u003cem\u003ep\u003c/em\u003e \u0026gt; .05). However, several contextual factors demonstrated meaningful relationships. Providers serving mainly as consultation seekers (e.g., emergency clinicians) reported lower PU (r = \u0026minus;\u0026thinsp;0.19, \u003cem\u003ep\u003c/em\u003e = .003) and IU (r = \u0026minus;\u0026thinsp;0.22, \u003cem\u003ep\u003c/em\u003e = .001) than consulting ophthalmologists or optometrists, while those in leadership roles showed higher PU (r\u0026thinsp;=\u0026thinsp;0.20, \u003cem\u003ep\u003c/em\u003e = .002) and IU (r\u0026thinsp;=\u0026thinsp;0.18, \u003cem\u003ep\u003c/em\u003e = .004). Prior video-telemedicine exposure was modest but positively associated with PU and IU (\u003cem\u003ep\u003c/em\u003e \u0026lt; .05).\u003c/p\u003e \u003cp\u003eOrganizational context also mattered: higher ocular caseloads, availability of on-call ophthalmologists, and existing telemedicine systems in the ED correlated positively with both PU and IU (\u003cem\u003ep\u003c/em\u003e \u0026lt; .01). In regression analyses, only the availability of telemedicine in the ED remained significant for PU (\u003cem\u003ep\u003c/em\u003e = .008), though its inclusion did not improve model fit. Correlation details for control and contextual variables are provided in the Supplementary (Table S5).\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study tested a structural model predicting frontline providers\u0026rsquo; acceptance of teleophthalmology for inter-provider consultation in emergency departments. The analysis identified key determinants of intention to use, evaluated transformational leadership as a modifiable influence within an extended technology acceptance framework, and clarified pathways linking leadership to behavioral intention. Three main findings emerged. Perceived usefulness (PU) was the central driver of acceptance, explaining the largest share of variance in intention to use (IU) emergency teleophthalmology. Transformational leadership (TL) and supportive subjective norms (SN) were significant, modifiable levers of acceptance, exerting their influence primarily through PU. In contrast, self-efficacy (SE) did not contribute additional explanatory power, suggesting that provider confidence alone is insufficient without demonstrable, workflow-integrated clinical value.\u003c/p\u003e \u003cp\u003eThese findings align with the Technology Acceptance Model (TAM) tradition, confirming PU as the dominant predictor of behavioral intention (Holden et al., 2010; Davis, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). The strong association between PU and IU (β\u0026thinsp;=\u0026thinsp;0.66) and the high explained variance are consistent with prior health IT studies (Chau et al., 2002; Abushaar et al., 2017; Kamal et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Saigi Rubi\u0026oacute; et al., 2016; Harst et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), extending this evidence to emergency teleophthalmology. Subjective norms influenced both PU and IU, reinforcing that peer and supervisory endorsement is most effective when it translates into tangible clinical value (Schepers et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Taylor et al., 1995).\u003c/p\u003e \u003cp\u003eTransformational leadership was also associated with higher PU and IU, extending leadership theory into acute care by demonstrating that leaders who articulate vision and foster innovation can promote technology acceptance (Rezvani et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The non-significant relationship between SE and PU contrasts with some prior findings (Rho et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), suggesting that in specialized emergency settings, confidence alone may be insufficient. Future models may benefit from incorporating perceived ease of use to better reflect operational demands (Venkatesh et al., 1998). Contextual factors such as higher ocular caseloads and prior telemedicine exposure were associated with greater PU and IU, though these effects were modest, indicating that acceptance was driven more by cognitive appraisals and leadership than by demographic or institutional characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eImplications\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThese findings yield actionable implications for implementation (Van Dyk, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Because adoption depends on perceived clinical value, implementation strategies should be leader-enabled and utility-focused rather than capability-driven. Leaders should articulate a clear clinical use case, positioning teleophthalmology as a solution for managing high-risk, low-volume ocular emergencies to support patient safety (Lokken et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Adoption may be further supported by embedding teleconsultation into standard workflows and reinforcing supportive subjective norms, particularly given the lack of association between general self-efficacy and PU. Institutions with existing tele-emergency services may benefit from prioritizing rollout in high-volume emergency departments with frequent ophthalmology demand (Wedekind et al., 2016). At the policy level, sustained reimbursement parity and integration of telemedicine competencies into training curricula are needed to support long-term viability (Hall et al., 2014; Faden et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study has several strengths, including a theory-driven design using validated psychometric instruments and established measures of technology acceptance and transformational leadership. Nationwide participation across diverse provider roles enhances robustness. Limitations include nonprobability sampling, which may affect generalizability, and reliance on behavioral intention rather than actual use. Self-reported measures collected in a single survey raise the possibility of common method variance. The cross-sectional design also limits causal inference, with SEM identifying probabilistic rather than deterministic relationships.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe structural model predicted emergency providers\u0026rsquo; intention to use live teleophthalmology, confirming perceived usefulness as the central mechanism in technology acceptance. Transformational leadership emerged as an important influence, shaping intention through direct effects and indirect pathways via perceived usefulness and subjective norms. These findings highlight leadership behaviors as practical, modifiable levers for translating technical capability into clinically meaningful utility.\u003c/p\u003e \u003cp\u003eFuture research should link behavioral intention with actual utilization, efficiency, and patient outcomes. Longitudinal and multilevel studies may clarify how leadership, organizational readiness, and unit culture sustain teleophthalmology over time.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eThe authors are very grateful to the clinicians who participated in the survey for their time and insights. We also thank Yousuf M. Khalifa, MD, and Hany H. Atallah, MD, of Emory University at Grady Memorial Hospital for their clinical perspectives, as well as Moges S. Ido, MD, PhD, of the Georgia Department of Health for statistical support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding and Disclosures:\u003c/strong\u003e No external funding was received for this study. The authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eA. Roba conceptualized and led the study, including survey design, data analysis, and manuscript drafting. Drs. Mase, Apenteng, and Shah contributed to the study design, methodology, interpretation of findings, and critical revision of the manuscript. Dr. Shah provided overall guidance and oversight.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbushaar SA, Ismail AI (2017) Physicians\u0026rsquo; acceptance of telemedicine in practice: An application of the Technology Acceptance Model. 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Decis Sci 39(2):273\u0026ndash;315. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1540-5915.2008.00192.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1540-5915.2008.00192.x\" 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":true,"hideJournal":true,"highlight":"","institution":"Georgia Southern University","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":"Emergency Teleophthalmology, Structural Equation Modeling, Technology Acceptance Model, Telemedicine Adoption, Transformational Leadership","lastPublishedDoi":"10.21203/rs.3.rs-8942270/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8942270/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eDeterminants of telemedicine adoption among emergency eye care providers remain poorly understood. This study examines the role of transformational leadership in technology acceptance of live inter-provider teleophthalmology.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional online survey of 254 emergency and eye care professionals in the United States assessed perceived usefulness (PU), intention to use (IU), subjective norms (SN), self-efficacy (SE), and transformational leadership (TL) using validated scales. Confirmatory factor analysis and covariance-based structural equation modeling evaluated a modified Technology Acceptance Model in which leadership and subjective norms were specified as contextual factors influencing IU directly and indirectly through PU, with self-efficacy specified as an antecedent of PU.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOf the 244 respondents included in analysis (96.1% completion rate), 51% were female, 31% were emergency physicians, and 19% were eye care specialists. The mean factor-based score for intention to use teleophthalmology was 3.74 (\u0026plusmn;\u0026thinsp;0.95) on a 5-point scale. The modified TAM demonstrated acceptable model fit and explained 67.5% of the variance in IU. Perceived usefulness was the strongest predictor of intention to use (β\u0026thinsp;=\u0026thinsp;0.66). Transformational leadership (β\u0026thinsp;=\u0026thinsp;0.37) and subjective norms (β\u0026thinsp;=\u0026thinsp;0.47) showed significant positive effects on IU, largely mediated through PU. Self-efficacy was not significantly associated with PU (β\u0026thinsp;=\u0026thinsp;0.18, p\u0026thinsp;=\u0026thinsp;0.105).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe model predicted intention to use live teleophthalmology and identifies transformational leadership as an important driver of acceptance through both direct and indirect effects. Leadership engagement may play a supportive role in broader acceptance of teleophthalmology technology in emergency eye care.\u003c/p\u003e","manuscriptTitle":"Transformational Leadership and Telemedicine Acceptance: Predicting Providers’ Intention to Use Emergency Teleophthalmology","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-24 09:35:41","doi":"10.21203/rs.3.rs-8942270/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":"b1fa49e2-f23d-4bde-82d2-f882b51a6bdd","owner":[],"postedDate":"February 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63344298,"name":"Critical Care \u0026 Emergency Medicine"},{"id":63344299,"name":"Ophthalmology"}],"tags":[],"updatedAt":"2026-02-24T09:35:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-24 09:35:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8942270","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8942270","identity":"rs-8942270","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}