Mixed reality training for medical first responders: system evaluation and recommendations

BackgroundSmart insole technologies that provide biofeedback on foot health can support foot-care in adults with diabetes. However, the factors that influence patient uptake and acceptance of this technology are unclear. Therefore, the aim of this mixed-methods study was to use an established theoretical framework to determine a model of psychosocial factors that best predicts participant intention to use smart insoles.MethodsFifty-three adults with diabetes from regional Australia completed the validated Unified Theory of Acceptance and Use of Technology (UTAUT) questionnaire. Multiple regression analysis was used to determine the psychosocial factors that best predict behavioural intention to adopt a smart insole. Additionally, a focus group was conducted and thematic analysis was performed to explore barriers and enablers to adopting this technology.ResultsThe multiple regression model that best predicted intention to adopt the smart insole (adjusted R2 = 0.51, p < 0.001) identified that self-efficacy (β = 0.67, p = 0.001) and attitude (β = 0.72, p < 0.001) were significant predictors of behavioural intention, while effort expectancy (β = − 0.52, p = 0.003) and performance expectancy (β = − 0.40, p = 0.040) were moderating factors. Thematic analysis illustrates the importance of attitude and self-efficacy on participants’ behavioural intentions, influenced by participant’s belief in the device’s clinical efficacy and anticipated effort expectancy.ConclusionsThis mixed-methods study demonstrates that attitude, self-efficacy, performance expectancy and effort expectancy combine to predict intention to adopt smart insole technology. Clinicians should consider these psychosocial factors when they prescribe and implement smart soles with patients at high risk of foot ulceration.

The growing integration of artificial intelligence (AI) in public sector governance has significantly transformed tax administration by enhancing efficiency, accuracy, and taxpayer engagement. In India, the adoption of AI based tax filing systems marks a shift toward automated, data-driven compliance mechanisms aimed at reducing errors and administrative burden. However, taxpayer acceptance of such systems depends on multiple technological and behavioral factors. This study examines the determinants influencing the adoption of AI-based tax filing systems among Indian taxpayers using an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework. A quantitative, cross-sectional research design was employed, with primary data collected from 300 Indian taxpayers through a structured questionnaire. Key constructs analyzed include performance expectancy, effort expectancy, social influence, facilitating conditions, and trust in AI-based tax systems, with behavioral intention as the dependent variable. Data analysis was conducted using SPSS and AMOS, applying reliability analysis, exploratory and confirmatory factor analysis, and structural equation modeling. The results indicate that performance expectancy and trust in AI systems are the most significant predictors of behavioral intention, followed by facilitating conditions and effort expectancy. Social influence also shows a positive but comparatively weaker effect. The proposed model explains 68 percent of the variance in behavioral intention. The study highlights that effective implementation of AI-based tax filing systems requires not only technological sophistication but also institutional trust, transparency, and digital readiness. The findings offer valuable implications for policymakers and tax authorities in designing inclusive, citizen-centric AI-enabled tax governance frameworks.

Public speaking anxiety (PSA) is a common issue among the youth due to a lack of confidence, fear of strict evaluation, and concerns about self-image. This can cause negative effects, such as shyness, reduced social skills, and limited career goals. Past researchers asserted that virtual reality (VR) simulation with a virtual public speaking environment for practicing increases confidence. Moreover, other researchers applied artificial intelligence (AI) to enhance communication and foster student learning. To our understanding, there are currently few studies on integrating large language models (LLMs) in VR within the public speaking realm. In addition, analysis of behavioral intention to use the simulation is also essential before widely applying new technologies in educational environments. Therefore, we conducted a study based on the Unified Theory of Acceptance and Use of Technology (UTAUT) approach, using an LLM-integrated VR simulation system. In this study, we recruited 30 students from two public speaking sections and asked them to experience and rate the system. Results revealed that effort expectancy, facilitating conditions, and hedonic motivation are significant predictors of behavioral intention. Gender and prior VR experience were not moderating factors for connections between UTAUT constructs and behavioral intention. In addition, our results also highlighted the critical role of two antecedent variables: academic major and GPA levels, which had a significant effect on performance expectancy, effort expectancy, and hedonic motivation. Our research findings offer beneficial theoretical and practical implications for applying novel technologies to students.

A smart banking service can be characterized as a convergence point at which technology can be adopted through mobile applications. The study examines the factors that have an impact on Pakistani bank customers' intentions to adopt smart banking services (SBS). This study used the unified theory of acceptance and use of technology (UTAUT2) as a base model and extended it by incorporating perceived self-expressiveness, which may influence individual usage of SBS. A total of 324 banking customers from five major banks in Pakistan completed a survey questionnaire in person using convenient sampling approach. The measurement and structural analysis were carried out using Smart PLS3 software. This study found that UTAUT2 significantly explained the behavioral intention (BI) to use SBS. The significant predictors of BI were facilitating conditions (FC), habits (HT), performance expectancy (PE), hedonic motivation (HM), effort expectancy (EE) and perceived self-expressiveness (SE). However, price value (PV) and social influence (SI) were not significant. FC, HT, and BI were also identified as significant factors influencing use behavior (UB). Indeed, this study has novel contribution in extending the UTAUT2 model with self-expressiveness to understand SBS adoption for the first time in Pakistan as a developing country.

This paper examines the impacts of AI-powered assistive technologies (AIATs) on the academic success of higher education university students with visual impairments. As digital learning contexts become progressively more prevalent in higher education institutions, it is critical to understand how these technologies foster the academic success of university students with blindness or low vision. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model, the study conducted a quantitative research approach and collected data from 390 visually impaired students who were enrolled in different universities across Saudi Arabia (SA). Employing Partial Least Squares Structural Equation Modeling (PLS-SEM), the paper tested the influences of four UTAUT dimensions—Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC)—on Academic Performance (AP), while also evaluating the mediating role of Behavioral Intention (BI). The results revealed a significant positive relationship between the implementation of AI-based assistive tools and students’ academic success. Particularly, BI emerged as a key mediator in these intersections. The results indicated that PE (β = 0.137, R2 = 0.745), SI (β = 0.070, R2 = 0.745), and BI (β = 0.792, R2 = 0.745) significantly affected AP. In contrast, EE (β = −0.041, R2 = 0.745) and FC (β = −0.004, R2 = 0.745) did not have a significant effect on AP. Concerning predictors of BI, PE (β = 0.412, R2 = 0.317), SI (β = 0.462, R2 = 0.317), and EE (β = 0.139, R2 = 0.317) were all positively associated with BI. However, FC had a significant negative association with BI (β = −0.194, R2 = 0.317). Additionally, the analysis revealed that EE, SI, and PE can all indirectly enhance Academic Performance by influencing BI. The findings provide practical insights for higher education policymakers, higher education administrators, and AI designers, emphasizing the need to improve the accessibility and usability of sustainable and long-term assistive technologies to better accommodate learners with visual impairments in higher education contexts.

We present a novel Mixed Reality (MR) remote collaboration system that integrates a telepresence drone to overcome viewpoint limitations in previous approaches. Current remote collaboration systems often restrict remote users to the local user's perspective or a fixed viewpoint, which may reduce spatial awareness and collaboration effectiveness. Our proposed system enables a remote user wearing a VR headset to independently navigate the local environment through a drone while guiding a local AR headset user. The drone represents the remote user physically and virtually with an AR avatar. The system provides the remote user with a 3D reconstruction of the local environment, the local user's avatar, and real-time drone camera video to facilitate drone operation and collaboration. A user study comparing our drone-enhanced MR system with 2D video and 360-degree video MR systems during inspection tasks revealed that the telepresence drone significantly improved collaboration efficiency and enhanced users' social presence and spatial presence. However, the remote users perceived a higher workload. Our findings demonstrate the benefits of independent spatial navigation through drones in remote collaboration and offer insights for future drone-enhanced MR remote collaboration systems.

Purpose This paper aims to analyse the adoption of fitness wearables by using the unified theory of acceptance and use of technology (UTAUT). The study analyses the relative weights and causal combinations of antecedent variables on use and intention to use fitness wearables. Design/methodology/approach The study design involves two stages: first, from the perspective of variable-oriented analysis, a structural equation model is tested using partial least squares (PLS) technique on a sample of 176 adopters and a second sample of 187 non-adopters. Second, from the perspective of case-oriented analysis, a fuzzy set qualitative comparative analysis (fsQCA) identifies causal combinations of variables that lead to use of wearables by adopters and intention to use by non-adopters. Findings PLS results show that performance expectancy and effort expectancy have high net effects on use and intention to use for adopters. FsQCA analysis shows that current users follow a streamlined path to adoption. High beliefs on performance expectancy and effort expectancy are the main influences of intention to use a fitness wearable for non-adopters. In contrast to adopters, non-adopters may follow a number of paths to intention to use through performance expectancy, effort expectancy or facilitating conditions. This insight was apparent only after analysing the data sets by using fsQCA. Research limitations/implications For sake of parsimony, this paper tested UTAUT model instead of the more complex unified theory of acceptance and use of technology 2. Practical implications Marketers in the fitness category can enhance use and intention to use by utilising not one but a combination of causal factors such as performance expectancy, effort expectancy and facilitating conditions. Wide societal deployment of wearables depends on performance and expectations. Social implications The widespread use of mobile devices depends on performance expectancy and effort expectancy. To transit to a real knowledge economy, co-creation should occur at early stages of product development so that these expectations are shared and better products be developed. Originality/value This paper offers a nuanced understanding of fitness wearable adoption by analysing adopters and non-adopters through variable- and case-oriented techniques. It complements the one-linear-path perspective with a number of alternative causal combinations of variables that lead to use and intention to use fitness wearables. While the causal path for adopters is unique, there are a number of causal combinations of antecedents that lead to high intention to use in potential adopters.

Accurately detecting when a user begins interaction with virtual objects is necessary for compelling multi-sensory experiences in mixed reality. To address inherent sensing, computation, display, and actuation latency, we propose to predict when a user will begin touch interaction with a virtual object before it occurs. We hypothesize that the sequence of hand poses when approaching an object, combined with object pose, contain sufficient information to predict when the user will begin contact. By leveraging this information, we could reduce or eliminate latency in providing haptic feedback during virtual object interaction. We focus on small time horizons, on the order of 100 ms, to overcome sense-to-actuation latency for haptic feedback in mixed reality systems. We use a time series of tracked hand poses, along with virtual object geometry to perform our prediction. By calculating minimum hand-object distance and feeding those along with hand poses to a self-attention-based network, we achieve approximately 52.8 ms of timing error for a 100 ms prediction horizon. Additionally, we test our system against different levels of tracking and hand-object alignment noise, finding minimal change in timing error. By contrast, when only extrapolating joint and hand velocities, we find that timing error consistently exceeds the prediction horizon.

PURPOSE This study aimed to explore the structural relationships between technology-related factors and the intention to use baseball data, drawing upon the Unified Theory of Acceptance and Use of Technology (UTAUT) and technology readiness (TR). METHODS Survey data from 203 Korean professional baseball players were used in frequency, reliability, correlation, and confirmatory factor analyses as well as structural equation modeling. RESULTS Positive TR positively influenced performance expectancy, effort expectancy, social influence, and conditions facilitating baseball data use. Negative TR did not significantly impact performance expectancy, effort expectancy, social influence, and conditions facilitating baseball data use. Performance expectancy, effort expectancy, and facilitating conditions were found to positively influence data use intention, while social influence did not significantly impact data use intention. CONCLUSIONS The findings suggest that increasing performance expectancy, effort expectancy, and facilitating condition factors could be key to enhancing the intention to utilize baseball data.

Pharmaceutical care can be improved via mobile health (mHealth) applications (apps); however, the engagement of these apps depends on healthcare providers' acceptance. Thus, identifying barriers and facilitators to utilizing mHealth apps is needed to develop and facilitate their use. The objective of this research was to investigate the various elements that influence the acceptability and utilization of mHealth apps in the delivery of pharmaceutical care services, as perceived by pharmacists working in the government health sector of Kuwait. A cross-sectional survey was conducted, and the results were used in the mobile health technology acceptance model (m-TAM) to measure the behavioral intentions of pharmacists with regard to the acceptance and use of mHealth apps. Multiple elements influence the behavioral intention of pharmacists to use mHealth apps, including Compatibility (CO), Performance Expectancy (PE), Personal Innovativeness (PI) and Effort Expectancy (EE) were statistically significant predictors. The mediating role of PE was found to be statistically significant in the link between CO and BI and in the association between EE and BI. The substantial mediation effects of Effort Expectancy (EE) and Performance Expectancy (PE) were seen in the three associations between job-related mental demands (MA) and burnout (BI), professional identity (PI) and burnout (BI), and perceived social support at work (PSA) and Behavioural Intention (BI). The results further demonstrate that EE substantially mediates the connections between MA and PE, MSE and PE, PI and PE, and PSA and PE. These results suggest that the acceptance of mHealth apps by pharmacists in Kuwait is multifaceted and requires evaluating a chain of effects. As a result, developing apps that provide pharmaceutical care requires consideration of each one of these factors. Results can direct policymakers and stakeholders in their efforts to implement mHealth in Kuwait.

Tangible physical systems are more intuitive than Intangible virtual Systems. Mixed reality systems are considered as an alternative to virtual systems, bringing advantages of tangible systems into an interaction. However, past research has mainly focussed on technical aspects of incorporating pervasive-ness and immersive-ness in the virtual systems. This paper reports on an empirical study of intuitive Interaction in a Mixed Reality game system for children and the design aspects that could facilitate intuitive Interaction in such systems. A related samples Friedman’s test showed that the Mixed Reality game system demonstrated more intuitive interactions than non‐intuitive Interactions. A linear regression analysis further established that the variation in intuitive Interaction in the Mixed Reality system could be statistically significantly explained primarily by physical affordances offered by the Mixed Reality system and to a lesser extent by the perceived affordances in the system. Design guidelines to develop intuitive Mixed Reality systems are discussed. These guidelines should allow designers to exploit the wonders of advances in technology and at the same time allow users to directly interact with the physical real world. This will allow users to access maximal physical affordances, which are primary contributors to intuitive interaction in Tangible and Mixed Reality systems. Keywords: Intuitive Interaction; Mixed Reality Systems; Tangibles; Child Computer Interaction

Inserting Lego sets in education have several benefits inside the classroom not only for the teaching of disciplines, but also it develops several skills such as problem solving, teamwork, and creativity. In Jordan, teachers face many struggles in teaching, starting from the tradition way in teaching, including decreasing levels of motivation among students, not enough teaching ways of reaching goals in the subjects, and missing problem solving strategies used by the teachers, and students alike. In this paper, we identified the factors influencing teacher's acceptance of the use of Lego sets in education in light of the unified theory of acceptance and use of technology (UTAUT), to evaluate if there is a relationship between performance expectancy, effort expectancy, social influence, facilitating conditions, and the intention of computer and science school's teachers to use Lego sets in education. Online questionnaire was created based on UTAUT survey instruments to collect data. 116 respondents were participated from computer and science teachers in Jordan. Statistical Package for the Social Sciences (SPSS) used to analyze the data collected. Multiple regression analysis was used to measure the correlations between variables, and the final results. The model was able to predict 60 % of the variation of the intention to use Lego sets in education. The final results found that performance expectancy and effort expectancy were statically significant. Performance expectancy and effort expectancy were the predictors and had the biggest impact.

Using the frameworks of the Theory of Use and Gratification (UGT) and the Unified Theory of Acceptance and Use of Technology (UTAUT2), this study explores the factors that influence individual behavior and behavioral goals in the adoption of digital banking. Partial Least Square-Structural Equation Modeling (PLS-SEM) is used in the analysis of research data using the program SmartPLS 3.2.9 professional. There are 432 people in the research sample that filled out questionnaires. The results show that behavioral intentions are strongly influenced by the integration of UGT-UTAUT2 by 60.3%. Performance and effort expectations are influenced by cognitive needs, effort expectations are influenced by affective needs, and social influence is impacted by social needs. Behavioral intentions for the use of digital banking are shaped by a combination of factors such as price value, hedonic motivation, habits, facilitating conditions, and effort expectations. The relationship between behavioral intentions, affective and cognitive needs is mediated by effort expectations. In the context of using digital banking, habits and behavioral intentions are important factors that influence behavior; in contrast, cognitive needs, affective needs, performance expectations, and social influence have no direct effect on behavioral intentions.

Digital inspection technologies are increasingly being adopted in the construction industry to improve efficiency, decision quality, and sustainability performance. Mixed reality (MR) systems can reduce rework, minimise human error, and support resource-efficient inspection processes. However, empirical evidence on how perceptions of efficiency and safety influence professional acceptance of MR technologies remains limited. This study investigates the adoption of MR for construction inspection using an extended technology acceptance model (TAM) that incorporates task efficiency and safety perception as domain-specific human factors. A within-subjects scenario-based experimental design was applied, in which 103 construction professionals evaluated four inspection modalities: HoloLens MR, smart glasses, tablet-based systems, and traditional paper-based methods. Data was analysed using linear mixed-effects models, structural equation modelling, mediation analysis, and dominance analysis. The results show that HoloLens MR achieved the highest perceived efficiency and safety perception, while imposing the lowest cognitive demand. Perceived efficiency was a strong predictor of device preference and significantly predicted perceived usefulness (β = 0.322, p &lt; 0.001), which fully mediated its effect on behavioural intention. Safety perception accounted for a substantial proportion of the variance in user evaluations (η2 = 0.237). These findings indicate that sustainable adoption of MR in construction inspection depends on combined perceptions of efficiency gains, usability, and safety support.

Extended-Reality Technologies: An Overview of Emerging Applications in Medical Education and Clinical Care.