Assessing Patient Use of and Attitudes toward eHealth Services for Communication with Primary Care Centers in Saudi Arabia and Factors Affecting Usage.

BACKGROUND: This study investigates patients' use of eHealth services, their awareness of the availability of these services, and their intention to use them in primary care. It also examines patient characteristics and factors that influence the use of these services. METHODS: A cross-sectional design using questionnaires was conducted. Based on the unified theory of acceptance and use of technology (UTAUT), the participants rated the two most common services. Descriptive analyses and linear correlation analyses were performed. A simple linear regression was conducted to identify factors influencing the participants' intention to use eHealth services. RESULTS: In total, 1203 participants with an average age of 43.7 years were surveyed. The participants' usage rates varied, with the lowest at 2.4%, for measuring vital signs, and the highest at 47.4%, for booking appointments. The intentions to use the services ranged from 22.5%, for video consultations, to 46.6%, for prescription refill requests. Approximately 20% of the respondents were unaware of each service's availability. Positive associations were found between all the constructs and the intention to use online services, with a younger age being the most significant factor. CONCLUSIONS: The use of and intention to use eHealth services varied greatly. The participants were often unaware of the availability of these services. Promoting the availability and benefits of eHealth services could enhance patient engagement in primary care settings.

Acceptance of artificial intelligence in university contexts: A conceptual analysis based on UTAUT2 theory.

This systematic review examined, through the UTAUT2 model, the factors influencing the acceptance of artificial intelligence (AI) applications in university contexts. A total of 50 scientific texts published between 2018 and 2023 were analyzed and selected after a rigorous search of specialized databases. These findings confirm the versatility of UTAUT2 in elucidating technological adoption processes in higher education. Performance expectancy and hedonic motivation emerged as significant predictors of intentions and effective use among students, faculty, and administrative staff. Among students, perceived ease of use and social influence were also relevant. The analysis revealed differences in adoption patterns between STEM and non-STEM disciplines and between public and private institutions. Despite widespread positive perceptions of AI's potential, barriers such as distrust and lack of knowledge persist. The research also identified moderating and mediating factors, such as prior technology experience and technological self-efficacy. These results have important implications for the implementation of AI in higher education, suggesting the need for differentiated approaches according to the characteristics of each group and institutional context. It is recommended to develop strategies that address the identified barriers and leverage facilitators, with an emphasis on training, ethical design, and contextual adaptation of AI applications. Future research should explore the longitudinal evolution of these factors and examine AI adoption in non-STEM disciplines in greater depth.

Unraveling the dynamics of ChatGPT adoption and utilization through Structural Equation Modeling.

ChatGPT, an advanced Artificial Intelligence tool, is getting considerable attention in higher education. ChatGPT significantly changes the student learning experience through its AI-aided support, personalized study assistance and effective educational experiences, and it has become an object of particular interest in this context. This research aimed to build a technology acceptance and usage model that encapsulates the elements influencing students' adoption and utilization of ChatGPT, drawing on constructs from the 'Unified Theory of Acceptance and Use of Technology' and 'Flow Theory'. The proposed model was found valid and prolific, with the credibility of the results relying on the self-reported surveys of 505 students from three universities in Pakistan. Structural Equation Modelling (SEM) was used to analyze data that confirmed the robustness and validity of the proposed model of the study. The study findings supported nine out of the ten proposed hypotheses. Perceived playfulness was declared the paramount predictor of behavioral intention, while perceived values and performance expectancy were the next-level predictors. Additionally, behavioral attention was a high and inspiring determinant of ChatGPT usage behavior, followed by attention focus. This analysis demonstrates a need for a thorough investigation of AI tools like ChatGPT in higher education.

How are US hospitals adopting artificial intelligence? Early evidence from 2022.

US hospitals are rapidly adopting artificial intelligence (AI), but there is a lack of knowledge about AI-adopting hospitals' characteristics, trends, and spread. This study aims to fill this gap by analyzing the 2022 American Hospital Association (AHA) data. The novel Hospital AI Adoption Model (HAIAM) is developed to categorize hospitals based on their AI adoption characteristics in the fields of (1) predicting patient demand, (2) optimizing workflow, (3) automating routine tasks, (4) staff scheduling, and (5) predicting staffing needs. Nearly one-fifth of US hospitals (1107 or 18.70%) have adopted some form of AI by 2022. The HAIAM shows that only 3.82% of hospitals are high adopters, followed by 6.22% moderate and 8.67% low adopters. Artificial intelligence adoption rates are highest in optimizing workflow (12.91%), while staff scheduling (9.53%) has the lowest growth rate. Hospitals with large bed sizes and outpatient surgical departments, private not-for-profit ownership, teaching status, and part of health systems are more likely to adopt different forms of AI. New Jersey (48.94%) is the leading hospital AI-adopting state, whereas New Mexico (0%) is the most lagging. These data can help policymakers better understand variations in AI adoption by hospitals and inform potential policy responses.

The role of social interaction in farmers' water-saving irrigation technology adoption: testing farmers' interaction mechanisms.

In rural areas, neighborly relationships are complex, and farmers' behaviors are largely influenced by neighborly interactions. The promotion of agricultural technologies should not overlook the social interactions between farmers. Based on survey data from farmers in Minqin, China, this paper explores the role of overall social interaction and its various dimensions in farmers' adoption of water-saving irrigation technology, focusing on the testing of three interactive mechanisms during the technology adoption process. The goal is to provide scientific policy suggestions for government when promoting agricultural technologies. The results show the following: social interaction promotes the adoption of water-saving irrigation technology among farmers; among the four dimensions of social interaction, the depth and frequency of social interaction facilitate the adoption of these technologies; social interaction promotes technology adoption through endogenous interaction mechanism, situational interaction mechanism, and social norm mechanism, with situational interaction mechanism and social norm mechanism playing a more significant role; social interaction has a stronger impact on farmers with longer farming experience and higher irrigation costs. Therefore, the government should emphasize the important role of social interaction in the adoption of agricultural technologies and accelerate the diffusion of these technologies through fostering technical exchanges among farmers, cultivating demonstration households, and implementing differentiated promotion strategies.

Can digital technology adoption promote environmental innovation in small and medium-sized enterprises (SMEs)? Evidence from China.

Despite the widespread acknowledgment in both industry and academia of the substantial potential of digital technology for fostering innovation and sustainable development, there remains uncertainty surrounding whether integrating these technologies can improve environmental innovation in small and medium-sized enterprises (SMEs) due to the liability of smallness. This paper utilizes a panel dataset comprising 1036 Chinese manufacturing SMEs listed between 2011 and 2020 to investigate the influence of different digital technology adoption strategies-specifically, digital technology adoption breadth (DTAB) and digital technology adoption depth (DTAD)-on SMEs' environmental innovation. Additionally, we examine the moderating effects of government subsidies and industrial agglomeration on these relationships. The results reveal that (1) DTAB has a negative effect on SMEs' environmental innovation; (2) DTAD positively affects SMEs' environmental innovation; and (3) both government R&D subsidies and environmental subsidies can positively moderate the relationships among DTAB, DTAD, and environmental innovation within SMEs. These findings are extended and upheld after undergoing a series of further analyses, endogeneity analyses, and robustness tests. Our research suggests that SMEs are not as suited to the broad adoption of digital technologies as large firms; instead, they should focus on the deep application of specific digital technologies to promote environmental innovation.

Integrated Approach Using Intuitionistic Fuzzy Multicriteria Decision-Making to Support Classifier Selection for Technology Adoption in Patients with Parkinson Disease: Algorithm Development and Validation.

Background: Parkinson disease (PD) is reported to be among the most prevalent neurodegenerative diseases globally, presenting ongoing challenges and increasing burden on health care systems. In an effort to support patients with PD, their carers, and the wider health care sector to manage this incurable condition, the focus has begun to shift away from traditional treatments. One of the most contemporary treatments includes prescribing assistive technologies (ATs), which are viewed as a way to promote independent living and deliver remote care. However, the uptake of these ATs is varied, with some users not ready or willing to accept all forms of AT and others only willing to adopt low-technology solutions. Consequently, to manage both the demands on resources and the efficiency with which ATs are deployed, new approaches are needed to automatically assess or predict a user's likelihood to accept and adopt a particular AT before it is prescribed. Classification algorithms can be used to automatically consider the range of factors impacting AT adoption likelihood, thereby potentially supporting more effective AT allocation. From a computational perspective, different classification algorithms and selection criteria offer various opportunities and challenges to address this need. Objective: This paper presents a novel hybrid multicriteria decision-making approach to support classifier selection in technology adoption processes involving patients with PD. Methods: First, the intuitionistic fuzzy analytic hierarchy process (IF-AHP) was implemented to calculate the relative priorities of criteria and subcriteria considering experts' knowledge and uncertainty. Second, the intuitionistic fuzzy decision-making trial and evaluation laboratory (IF-DEMATEL) was applied to evaluate the cause-effect relationships among criteria/subcriteria. Finally, the combined compromise solution (CoCoSo) was used to rank the candidate classifiers based on their capability to model the technology adoption. Results: We conducted a study involving a mobile smartphone solution to validate the proposed methodology. Structure (F5) was identified as the factor with the highest relative priority (overall weight=0.214), while adaptability (F4) (D-R=1.234) was found to be the most influencing aspect when selecting classifiers for technology adoption in patients with PD. In this case, the most appropriate algorithm for supporting technology adoption in patients with PD was the A3 - J48 decision tree (M3=2.5592). The results obtained by comparing the CoCoSo method in the proposed approach with 2 alternative methods (simple additive weighting and technique for order of preference by similarity to ideal solution) support the accuracy and applicability of the proposed methodology. It was observed that the final scores of the algorithms in each method were highly correlated (Pearson correlation coefficient >0.8). Conclusions: The IF-AHP-IF-DEMATEL-CoCoSo approach helped to identify classification algorithms that do not just discriminate between good and bad adopters of assistive technologies within the Parkinson population but also consider technology-specific features like design, quality, and compatibility that make these classifiers easily implementable by clinicians in the health care system.

Democrats and Republicans choose solar panels in very similar ways.

Introduction: Appealing to individuals' social identity is a powerful form of social influence, capable of changing the way people process information, the information they think about, and how they evaluate other individuals. The purpose of this study is to explore the idea that Democrat and Republican environmental norms may impact the attributes and strategies partisans use when choosing whether to have solar panels on a house. Methods: An online study with N = 363 participants was conducted to examine these possible effects through multi-attribute decision making, applying predefined decision process models to participant behavior to test which attribute-based models best describe participants' decision making. A choice task was combined with an experimental manipulation of political affiliation salience to examine whether the norms of political groups would have influence on decision behavior. Results: Results of the study show remarkable similarities between political parties in their strategies for choosing solar panels. Members of both political parties appeared to use similar strategies and similar attributes for the formation of their decisions. Discussion: Recommendations are made that science communicators and policy makers avoid polarizing language so as not to create unnecessary polarization where ideological gaps may not currently exist.

Evolutionary Trends in the Adoption, Adaptation, and Abandonment of Mobile Health Technologies: Viewpoint Based on 25 Years of Research.

Over the past quarter-century, mobile health (mHealth) technologies have experienced significant changes in adoption rates, adaptation strategies, and instances of abandonment. Understanding the underlying factors driving these trends is essential for optimizing the design, implementation, and sustainability of interventions using these technologies. The evolution of mHealth adoption has followed a progressive trajectory, starting with cautious exploration and later accelerating due to technological advancements, increased smartphone penetration, and growing acceptance of digital health solutions by both health care providers and patients. However, alongside widespread adoption, challenges related to usability, interoperability, privacy concerns, and socioeconomic disparities have emerged, necessitating ongoing adaptation efforts. While many mHealth initiatives have successfully adapted to address these challenges, technology abandonment remains common, often due to unsustainable business models, inadequate user engagement, and insufficient evidence of effectiveness. This paper utilizes the Nonadoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) framework to examine the interplay between the academic and industry sectors in patterns of adoption, adaptation, and abandonment, using 3 major mHealth innovations as examples: health-related SMS text messaging, mobile apps and wearables, and social media for health communication. Health SMS text messaging has demonstrated significant potential as a tool for health promotion, disease management, and patient engagement. The proliferation of mobile apps and devices has facilitated a shift from in-person and in-clinic practices to mobile- and wearable-centric solutions, encompassing everything from simple activity trackers to advanced health monitoring devices. Social media, initially characterized by basic text-based interactions in chat rooms and online forums, underwent a paradigm shift with the emergence of platforms such as MySpace and Facebook. This transition ushered in an era of mass communication through social media. The rise of microblogging and visually focused platforms such as Twitter(now X), Instagram, Snapchat, and TikTok, along with the integration of live streaming and augmented reality features, exemplifies the ongoing innovation within the social media landscape. Over the past 25 years, there have been remarkable strides in the adoption and adaptation of mHealth technologies, driven by technological innovation and a growing recognition of their potential to revolutionize health care delivery. Each mobile technology uniquely enhances public health and health care by catering to different user needs. SMS text messaging offers wide accessibility and proven effectiveness, while mobile apps and wearables provide comprehensive functionalities for more in-depth health management. Social media platforms amplify these efforts with their vast reach and community-building potential, making it essential to select the right tool for specific health interventions to maximize impact and engagement. Nevertheless, continued efforts are needed to address persistent challenges and mitigate instances of abandonment, ensuring that mHealth interventions reach their full potential in improving health outcomes and advancing equitable access to care.

Assessing AI adoption in developing country academia: A trust and privacy-augmented UTAUT framework.

The rapid evolution of Artificial Intelligence (AI) and its widespread adoption have given rise to a critical need for understanding the underlying factors that shape users' behavioral intentions. Therefore, the main objective of this study is to explain user perceived behavioral intentions and use behavior of AI technologies for academic purposes in a developing country. This study has adopted the unified theory of acceptance and use of technology (UTAUT) model and extended it with two dimensions: trust and privacy. Data have been collected from 310 AI users including teachers, researchers, and students. This study finds that users' behavioral intention is positively and significantly associated with trust, social influence, effort expectancy, and performance expectancy. Privacy, on the other hand, has a negative yet significant relationship with behavioral intention unveiling that concerns over privacy can deter users from intending to use AI technologies which is a valuable insight for developers and educators. In determining use behavior, facilitating condition, behavioral intention, and privacy have significant positive impact. This study hasn't found any significant relationship between trust and use behavior elucidating that service providers should have unwavering focus on security measures, credible endorsements, and transparency to build user confidence. In an era dominated by the fourth industrial revolution, this research underscores the pivotal roles of trust and privacy in technology adoption. In addition, this study sheds light on users' perspective to effectively align AI-based technologies with the education system of developing countries. The practical implications encompass insights for service providers, educational institutions, and policymakers, facilitating the smooth adoption of AI technologies in developing countries while emphasizing the importance of trust, privacy, and ongoing refinement.

E-commerce operations and technology perceptions in promoting farmers' adoption of green production technologies: Evidence from rural China.

Agricultural green production is vital for ensuring product quality, safety, and mitigating environmental issues. E-commerce operations have emerged as a key driver of green production transformation. Based on a sample of 704 farm households in Jiangsu Province, this study employs a two-way fixed-effects model, Propensity Score Matching (PSM), Instrumental Variable Probit (IVProbit), and Extended Regression Model (ERM) to address endogeneity, alongside stepwise regression to test the mediating role of technology cognition. The results show that e-commerce operations significantly and positively influence the adoption of green production technologies among farmers, with technology cognition acting as a positive mediator. The impact of e-commerce on technology adoption varied across different age groups and geographical terrains, with older farmers and those in plain regions benefiting the most. Additionally, e-commerce played a crucial role in the adoption of green technologies, particularly in the use of organic fertilizers. To promote the broader adoption of green technologies among farmers, it is recommended that governments strengthen e-commerce support systems, enhance technical training for farmers, improve the inclusivity of e-commerce platforms, and address gaps in the availability of green production technologies.

Understanding the heterogeneous effect of microcredit access on agricultural technology adoption by rural farmers in Ethiopia: A meta-analysis.

Though the Ethiopian economy is predominantly agriculture-based, the adoption of agricultural technologies has been very low. The results of a previous study had shown that microcredit access was one of the factors affecting the adoption of agricultural technology in Ethiopia. However, its effect has not yet been analyzed at the meta-level. Therefore, this study employed meta-analysis to understand the heterogeneous effect of microcredit access among farmers adopting agricultural technologies. We used subgroup analysis and meta-regression analysis to identify the heterogeneity level of credit access on technology adoption using the random-effects (RE) model. The study observed that there was a positive effect of microcredit access on agricultural technology adoption with a log odds ratio of 1.59. The subgroup analysis revealed a 93.2 % overall variation ( I 2 ) with a p-value of 0.000, signifying a significant level of microcredit access within the between-groups heterogeneity of agricultural technology adoption studies conducted in Ethiopia. Notably, this was reflected by the adoption of improved livestock technologies, fertilizers, seed varieties, multiple agriculture, and irrigation technologies, with rates of heterogeneity of 94.9 %, 94.4 %, 94.3 %, 85 %, and 73.8 %, respectively, all with a p-value of 0.000. In addition, the meta-regression analysis results indicate that household experience, distance to the market, and income are significant moderators that affect the technology adoption decisions of farmers in rural Ethiopia. These findings suggest that policymakers should focus on improving the financial facilities and extension systems for rural farmers to enhance the adoption of agricultural technologies to increase production efficiency.

German sugar beet farmers' intention to use autonomous field robots for seeding and weeding.

Robotic weed control is not yet widely adopted, despite its technological availability and proven economics and sustainability in crop cultivation by replacing seasonal labor and synthetic pesticides. This impedes technologically enabled changes toward more sustainable agricultural systems. Given that adopting robotics for the weeding process requires changing existing systems, farmers' appraisals for the new and the current weeding technology may constitute barriers. However, this dualism has been largely ignored by previous studies. Based on a duality approach, we investigate farmers' beliefs, and adaptive and maladaptive appraisals of current and new robotic weeding in sugar beets. The main variable of interest is their behavioral intention to adopt weeding robots. For our sample of German farmers, we identify the main enablers perceived efficacy of the robots and social norms. The main barrier are maladaptive rewards from traditional weeding. We recommend policy incentives to promote large-scale uptake of new and more sustainable robotic technologies. To improve efficacy perceptions of such robotic systems public demonstrations/talks are mostly relevant. Maladaptive rewards can be reduced, for instance, by notifying about the dependency of the current practices on future availability of synthetic inputs or seasonal workers.

Investigating the motivating factors that influence the adoption of blended learning for teachers' professional development.

Blended learning (BL), a teaching method merging online and face-to-face learning, is lauded for its potential to enrich educational outcomes and tackle challenges entrenched in conventional teaching practices. In countries like Pakistan, where equitable access to quality professional development remains an obstacle, BL is a promising avenue to surmount training barriers. While BL adoption has evolved swiftly, research into its integration within teacher training remains limited. Notably, no comprehensive model exists describing the motivational factors influencing teachers' perceptions and intentions regarding the blended mode of teacher training. This study aims to identify the motivational elements that motivate schoolteachers in teacher training institutions in Pakistan to incorporate blended learning into their programs. The motivational factors identified in BL literature have been employed to craft a motivation model grounded in their causal relationship. This quantitative study examines the interplay between multiple motivational factors and their impact on BL adoption within teacher training and the BL environment. Surveying 350 schoolteachers (participants) from teacher training institutions, we employed Structural Equation Modeling (SEM) techniques with Smart PLS 4.0 for data analysis. Results reveal that extrinsic and intrinsic motivational factors significantly influence teachers' motivation to adopt BL for training. Notably, "overall training quality" and "educational environment" were non-influential. Overall, the findings underscore that considering a blend of extrinsic and intrinsic factors can wield a 65 % influence on BL adoption. The study's results provide practical guidance for educational leaders, curriculum designers, and faculty members aiming to cultivate a unified blended learning environment for teacher professional development. These insights also underscore the importance of incorporating essential motivational factors into forthcoming blended learning training programs.

Advancing radiology with GPT-4: Innovations in clinical applications, patient engagement, research, and learning.

The rapid evolution of artificial intelligence (AI) in healthcare, particularly in radiology, underscores a transformative era marked by a potential for enhanced diagnostic precision, increased patient engagement, and streamlined clinical workflows. Amongst the key developments at the heart of this transformation are Large Language Models like the Generative Pre-trained Transformer 4 (GPT-4), whose integration into radiological practices could potentially herald a significant leap by assisting in the generation and summarization of radiology reports, aiding in differential diagnoses, and recommending evidence-based treatments. This review delves into the multifaceted potential applications of Large Language Models within radiology, using GPT-4 as an example, from improving diagnostic accuracy and reporting efficiency to translating complex medical findings into patient-friendly summaries. The review acknowledges the ethical, privacy, and technical challenges inherent in deploying AI technologies, emphasizing the importance of careful oversight, validation, and adherence to regulatory standards. Through a balanced discourse on the potential and pitfalls of GPT-4 in radiology, the article aims to provide a comprehensive overview of how these models have the potential to reshape the future of radiological services, fostering improvements in patient care, educational methodologies, and clinical research.

The Influence of Self-Efficacy on Older Adults' Use of Mobile Health Applications.

mHealth apps can especially benefit older adults with chronic conditions, but their usage rates remain often low. This study examines how older adults' self-perceived technical skills and confidence affect their use of a mHealth app. It was conducted in southern Germany and included older adults (65 years and older) with and without age-related chronic conditions. Results indicate that perceived self-efficacy does not always match actual capability. This discrepancy raises concerns about how it might impact the use and prescription of these apps.

Agricultural extension service, technology adoption, and production risk nexus: Evidence from Bangladesh.

Rice production is inherently risky and volatile, and farmers in Bangladesh face a wide range of risks, including weather, pest and disease attacks, interruptions to input supply, and market-associated risks. Moreover, poor farm households often perceive risks in adopting new technology, even though it could improve productivity and food security. Such households are thus caught in a "risk-induced trap" that precludes them from realizing the benefits of technological innovation. Extension service is one way to help farmers improve risk management skills and escape risk-induced traps, but there is limited empirical analysis of its impact in Bangladesh. The objective of the study is to measure the nexus between agricultural extension services, technology adoption, and production risks as well as women empowerment in agriculture index. IFPRI utilized stratified random sampling to determine the 5603 households in 2018 (which is nationally called the BIHS-2018 dataset) from rural and pre-urban areas of Bangladesh. Out of these 5603 households, 2663 households were specifically selected for the study related to rice farming to achieve the main objective of the study. Focusing on rice farming, a moment-based Poisson regression model is estimated with 2SLS and identifies risks associated with key technologies and potential productivity and risk-reducing effects. The results revealed that wealthier households are more likely to adopt technology for minimizing production risk and women's empowerment which can positively affect productivity by mitigating risk. The result revealed a positive and significant difference in WEAI between the AES participant and non-participant group. We find that engagement in agricultural extension services was associated with technology adoption and production risk reduction. The agricultural extension services increased, technology adoption by 4.2 % and decreased production risk by 2.4 %. Based on the findings, it is concluded that more comprehensive extension services can enhance rice production and ameliorate farmers' risk in rice production to some extent.

Technophobia Mediates the Associations Between Age, Education Level, and Readiness to Adopt New (Health) Technology Among Aging Adults.

Current research on technophobia and readiness to adopt new technology in the aging population is often limited to the context of specific technologies and treats technophobia as a unidimensional construct. In this study, we investigate the role of demographic variables and various aspects of technophobia in determining Slovenian aging adults' readiness to adopt new technology. Partial least squares structural equation modeling revealed that age and educational level generally significantly predicted technophobia and indirectly contributed to readiness to adopt new technology via the human versus machine ambiguity dimension of technophobia. Moreover, age and human versus machine ambiguity were significant direct negative predictors of readiness to adopt new technology. Findings obtained specifically in the health sub-domain were similar. Our results have important implications for addressing the low adoption of new technology among aging adults as they provide guidance on whom should be targeted with interventions and which aspects need to be addressed.

Barriers and Enablers of Nurses' Adoption of Digital Health Technology to Facilitate Healthcare Delivery in Resource-Limited Settings.

The study investigated barriers and enablers of nurse's adoption of digital health technology to facilitate the delivery of healthcare in resource-limited settings. Using a self-administered questionnaire, data were collected from ninety-three nurses. Descriptive statistics were conducted to analyse and summarise the data. The study found that barriers to digital technology use included workload, time constraints, limited access to computers and a lack of skills in searching for information, while positive attitudes and confidence were enabling factors. Providing access to technology and skills training will improve the adoption of technology in healthcare delivery by nurses.