This study investigates the adoption of Generative AI text-to-image tools in the creative industry using an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model. The objective is ...

Scientific Reports - Analyzing blockchain adoption in management operations through an extended UTAUT model in the Libyan context

The current study aims to explore the extended UTAUT model by incorporating new dimensions, such as perceived trust and regulatory support, as independent variables. This research develops an innovative modified conceptual framework of the extended UTAUT model to examine blockchain...

Traditional social media platforms face controversies, leading users to decentralized alternatives like Steemit and Hive, which offer better privacy and financial incentives. A study extended the UTAU...

Existing research has developed several models to explain technology acceptance, such as the Theory of Planned Behavior (TPB), the Technology Acceptance Model (TAM), and UTAUT. Among these, the UTAUT model has been widely recognized as the most comprehensive one. As...

This study investigates the adoption of Generative AI text-to-image tools in the creative industry using an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model. The objective is ...

Existing literature on chatbot acceptance has largely relied on the Technology Acceptance Model (TAM) or Unified Theory of Acceptance and Use of Technology (UTAUT), often neglecting the broader pol...

Background Extended reality (XR) technologies which include virtual, augmented, and mixed reality have significant potential in surgical training, because they can help to eliminate the limitations of traditional methods. This umbrella review aimed to investigate factors that influence the acceptance and use of XR in surgical training using the unified theory of acceptance and use of technology (UTAUT) model. Methods An umbrella review was conducted in 2024 by searching various databases until the end of 2023. Studies were selected based on the predefined eligibility criteria and analyzed using the components of the UTAUT model. The quality and risk of bias of the selected studies were assessed, and the findings were reported descriptively. Results A total of 44 articles were included in this study. In most studies, XR technologies were used for surgical training of orthopedics, neurology, and laparoscopy. Based on the UTAUT model, the findings indicated that XR technologies improved surgical skills and procedural accuracy while simultaneously reducing risks and operating room time (performance expectancy). In terms of effort expectancy, user-friendly systems were accessible for the trainees with various levels of expertise. From a social influence standpoint, XR technologies enhanced learning by providing positive feedback from experienced surgeons during surgical training. In addition, facilitating conditions emphasized the importance of resource availability and addressing technical and financial limitations to maximize the effectiveness of XR technologies in surgical training. Conclusions XR technologies significantly improve surgical training by increasing skills and procedural accuracy. Although adoption is facilitated by designing user-friendly interfaces and positive social influences, financial and resource challenges must be overcome, too. The successful integration of XR into surgical training necessitates careful curriculum design and resource allocation. Future research should focus on overcoming these barriers, so that XR can fully realize its potential in surgical training.

The proliferation of generative AI (genAI) has sparked debate on its integration into university curricula, particularly in engineering education. This paper examines the adoption of genAI-based te...

This study examined how perceptions of artificial intelligence (AI) relate to technostress in healthcare professionals, testing whether AI acceptance mediates this relationship and whether self-efficacy moderates the formation of acceptance. Seventy-one participants completed measures of Perceptions of AI (Shinners), AI Acceptance (UTAUT), Self-Efficacy, and four technostress outcomes: Technostress Overall, Techno-Overload, Techno-Complexity/Insecurity, and Techno-Uncertainty. Conditional process analyses (PROCESS Model 7; 5000 bootstrap samples) were performed controlling for sex, age (years), and professional role (radiology residents, attending radiologists, PhD researchers). Perceptions of AI were directly and positively associated with Technostress Overall (b = 0.57, p = 0.003), Techno-Overload (b = 0.58, p = 0.008), and Techno-Complexity/Insecurity (b = 0.83, p < 0.001), but not with Techno-Uncertainty (b = −0.02, p = 0.930). AI Acceptance negatively predicted the same three outcomes (e.g., Technostress Overall b = −0.55, p = 0.004), and conditional indirect effects indicated significant negative mediation at low, mean, and high self-efficacy for these three outcomes. Self-efficacy moderated the Perceptions → Acceptance path (interaction b = −0.165, p = 0.028), with a stronger X→M effect at lower self-efficacy, but indices of moderated mediation were not significant for any outcome. The results suggest that perceptions of AI exert both demand-like direct effects and buffering indirect effects via acceptance; implementation should therefore foster acceptance, build competence, and address workload and organizational clarity.

Background: The technology acceptance model (TAM) and the unified theory of acceptance and use of technology (UTAUT) are widely used to examine #HealthCare technology acceptance among older adults. However, existing literature exhibits considerable heterogeneity, making it difficult to determine consistent predictors of acceptance and behavior. Objective: We aimed to (1) determine the influence of perceived usefulness (PU), perceived ease of use (PEOU), and social influence (SI) on the behavioral intention (BI) to use #HealthCare technology among older adults and (2) assess the moderating effects of age, gender, geographic region, type of #HealthCare technology, and presence of visual demonstrations. Methods: A systematic search was conducted across Google Scholar, #Web of Science, Scopus, IEEE Xplore, and ProQuest databases on March 15, 2024, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Of the 1167 initially identified studies, 41 studies (11,574 participants; mean age 67.58, SD 4.76 years; and female:male ratio=2.00) met the inclusion criteria. The studies comprised 12 mobile health, 12 #Online or #Tele#Medicine, 9 #Wearable, and 8 home or institution hardware investigations, with 23 studies from Asia, 7 from Europe, 7 from African-Islamic regions, and 4 from the United States. Studies were eligible if they used the TAM or UTAUT, examined #HealthCare technology adoption among older adults, and reported zero-order correlations. Two independent reviewers screened studies, extracted data, and assessed methodological quality using the Newcastle-Ottawa Scale, evaluating selection, comparability, and outcome assessment with 34% (14/41) of studies rated as good quality and 66% (27/41) as satisfactory. Results: Random-effects meta-analysis revealed significant positive correlations for PU-BI (r=0.607, 95% CI 0.543-0.665; P