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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.

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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.

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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.

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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.

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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.

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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.

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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.

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In this study, we set out to investigate the transforming power of social media for agricultural extension delivery services in Ghana. We employed a quantitative research approach and drew insights from 374 farmers. We used descriptive and inferential statistics to analyse the data. Cocoa farmers have some level of awareness of agricultural information on social media (Overall Mean = 1.88). Farmers regard social media platforms as potential sources of agricultural information (Perception Index = 3.38). Majority of farmers own smartphones (53.74 %) and have internet access (53.74 %). About 31.86 % of farmers spend 30 min to 1 h daily time browsing social media for agricultural information. About 57.65 % use social media for accessing agricultural information and implementing farming practices. According to 89.38 % of farmers, social media information helps to improve crop yield and pest management. The main constraint facing farmers in the use of social media is high data costs (Mean = 7.30). We recommend that the government in collaboration with telecommunication companies should explore innovative pricing models to reduce the cost barrier for farmers accessing agricultural content online.

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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.

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This pilot study addresses the pervasive issue of burnout among nurses and health disciplines, often exacerbated by the use of electronic health record (EHR) systems. Recognizing the potential of dictation to alleviate documentation burden, the study focuses on the adoption of speech recognition technology (SRT) in a large Canadian urban mental health and addiction teaching hospital. Clinicians who participated in the pilot provided feedback on their experiences via a survey, and analytics data were examined to measure usage and adoption patterns. Preliminary feedback reveals a subset of participants rapidly embracing the technology, reporting decreased documentation times and increased efficiency. However, some clinicians experienced challenges related to initial setup time and the effort of adjusting to a novel documentation approach.

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Background: Modern telemedicine (TM) technologies play a crucial role in enhancing access to Assistive Technology in healthcare services. However, the full benefits of this technology will not be realized unless it is widely accepted among service users (patients). This study aimed to investigate the impact of patient trust and perceived risk on the acceptance of TM for Assistive Technology in healthcare. Materials and Methods: A comprehensive survey instrument was developed and refined through expert feedback and a pilot study, leading to data collection from 917 participants. The theoretical framework guiding this research was based on the Trust factors in TM, which helped in conceptualizing the factors influencing patient acceptance of TM. Results: The study revealed a significant gap in patient trust in TM and highlighted the multifaceted nature of perceived risk, emphasizing the need to consider individual risk factors separately. Results also indicated that trust in technological reliability and the perceived effectiveness of TM were critical factors influencing its adoption. The findings underscore the importance of building trust among service users and promoting the reliability of TM for achieving desirable medical outcomes. Conclusion: In conclusion, to facilitate widespread acceptance of TM for Assistive Technology, a multi-faceted approach involving healthcare providers, organizations, and governments is essential to address patient concerns, enhance trust, and promote the benefits of this technology.

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AI-powered chatbots hold great promise for enhancing learning experiences and outcomes in today's rapidly evolving education system. However, despite the increasing demand for such technologies, there remains a significant research gap regarding the factors influencing users' acceptance and adoption of AI-powered chatbots in educational contexts. This study aims to address this gap by investigating the factors that shape users' attitudes, intentions, and behaviors towards adopting ChatGPT for smart education systems. This research employed a quantitative research approach, data were collected from 458 of participants through a structured questionnaire designed to measure various constructs related to technology acceptance, including perceived ease of use, perceived usefulness, feedback quality, assessment quality, subject norms, attitude towards use, and behavioral intention to use ChatGPT. Structural model analysis (SEM) Statistical techniques were then utilized to examine the relationships between these constructs. The findings of the study revealed that Perceived ease of use and perceived usefulness emerged as significant predictors of users' attitudes towards ChatGPT for smart education. Additionally, feedback quality, assessment quality, and subject norms were found to positively influence users' behavioral intentions to use ChatGPT for smart educational purposes. Moreover, users' attitudes towards use and behavioral intentions were significantly proved for the actual adoption of ChatGPT. However, a few hypotheses, such as the relationship between trust in ChatGPT and perceived usefulness, were not supported by the data. This study contributes to the existing body information systems applications for the determining factor of technology acceptance in smart education context.

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In 2023, European governments submitted a proposal to comprehensively ban per- and polyfluoroalkyl substances (PFAS), prompting a shift toward PFAS-free alternatives. However, recent research has emphasized the need for an integrated approach to chemical assessment and environmental management rather than abrupt PFAS substitution, as alternatives may pose similar or greater risks. The PFAS Guide was developed to aid corporations in this critical transition. Its goal is to simplify PFAS identification, safe substitution, and gradual elimination. This study applies the Technology Acceptance Model (TAM) to assess how perceived usefulness (PU) and ease of use of the PFAS Guide impact corporate intentions to adopt it. Our analysis, based on responses from 104 European companies, demonstrates a positive link between PU, ease of use, and a company's intent to adopt the PFAS Guide. This underscores the importance of corporations perceiving the PFAS Guide as a valuable and user-friendly resource, given its substantial impact on PFAS phase-out. Descriptive statistics revealed an interesting finding: 51.9% of the participants fell into the "other" group, as outlined in the PFAS Guide. This raises questions regarding the grouping of companies into various sectors. Based on our results, we propose improvements to the PFAS Guide by broadening sector representation to encompass a more diverse range of industries with sector-specific guidance, ensuring content relevance, and accentuating user experience using interactive resources. Future research should focus on the actual adoption and use of the guide to gain deeper insights into adoption rates and long-term PFAS Guide utilization. Furthermore, additional investigations should incorporate subgroup analyses, data triangulation, and a longitudinal approach to enhance our understanding of the factors that support and hinder integrated chemical assessment and environmental management. These research efforts are pivotal in guiding chemical policy and management practices, contributing to a PFAS-free future. Integr Environ Assess Manag 2024;20:1-14. © 2024 The Author(s). Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

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Instrumented mouthguards (iMGs) are a novel technology being used within rugby to quantify head acceleration events. Understanding practitioners' perceptions of the barriers and facilitators to their use is important to support implementation and adoption. This study assessed men's and women's rugby union and league iMG managers' perceptions of staff and player interest in the technology, data and barriers to use. Forty-six iMG managers (men's rugby union and league n = 20 and n = 9 and women's rugby union and league n = 7 and n = 10) completed an 18-question survey. Perceived interest in data varied across staff roles with medical staff being reported as having the most interest. The iMG devices were perceived as easy to use but uncomfortable. Several uses of data were identified, including medical applications, player monitoring and player welfare. The comfort, size and fit of the iMG were reported as the major barriers to player use. Time constraints and a lack of understanding of data were barriers to engagement with the data. Continued education on how iMG data can be used is required to increase player and staff buy-in, alongside improving comfort of the devices. Studies undertaken with iMGs investigating player performance and welfare outcomes will make data more useful and increase engagement.

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INTRODUCTION: Smart healthcare technologies (SHCTs) exhibit the great potential to support older Hong Kong adults with their health problems. Although there are various SHCTs in the Hong Kong market, and some adoption predictors have been proposed and investigated, little is known about older users' views on and real-life experiences with these technologies. This exploratory study examined the experiences, functional needs, and barriers of three kinds of SHCT (i.e., smart wearable devices, smart health monitors, and healthcare applications) with older adults in real life. METHODS: A convenience sampling method was applied to recruit twenty-two older adults from the Hong Kong community. The interview was designed in semi-structured and conducted in a face-to-face setting. The content analysis was used to summarize the older adults' functional needs and barriers in real life. RESULTS: We found older adults mainly applied SHCTs to address physical health, but there are few technological solutions for mental health in practice. There are four types of barriers in using SHCT. However, social support in Hong Kong community greatly helps reduce the barriers in technology use. Based on the findings, we discussed the possible solutions based on the social and technology perspective. CONCLUSION: Current technologies still could not fully address older adults' needs for healthy aging, and various barriers still hinder the actual adoption. By deeply understanding and considering the social context, technology innovation can facilitate the adoption of SHCT and promote a healthy aging society.

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The dynamics in intra-household decision-making are often neglected in literature on the adoption of agricultural innovations. However, households' farm management decisions are often made following negotiations between female and male farmers. These may differ in terms of individual bargaining power and personal preferences. A better understanding of the links between gender roles in household decision-making and the adoption of technologies is postulated to enhance the uptake of innovations in smallholder farming systems. In this study, we use survey data from 1,088 wheat-producing households in Ethiopia to analyze the links between women's role in household decisions concerning crop production and the adoption and turnover rates of rust-resistant wheat varieties. We interviewed female and male respondents from the same households, but separately, which facilitated capturing individual perceptions and the intra-household dynamics in decision-making. To account for observed heterogeneity that may simultaneously determine the level of women's agency and varietal adoption by households, we employed Inverse Probability-Weighted Regression Adjustment (IPWRA). A positive association was found between women's role in decision-making concerning choice of wheat seed and household adoption of rust-resistant wheat varieties and wheat varietal turnover. Spouses may be in agreement or have different opinions regarding their decision-making roles. The disagreement scenario in which the wife claims to have a role in decision-making is associated with lower adoption rates of rust-resistant wheat varieties and less frequent testing of new varties in recent growing seasons, compared to a scenario where both spouses agree that wives do not have a role. We conclude that gender-disaggregated data and the examination of intra-household decision-making can offer novel and valuable insights for designing and implementing strategies to enhance the uptake of agricultural technologies among smallholders. The results emphasize the need to include complementary perspectives on the intra-household decision-making process.

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This study aims to explore the scopes and challenges, rank the challenges, and provide strategic solutions for adopting Industry Revolution 4.0 (IR 4.0) in Bangladesh's textile and apparel industry. A random survey was administered to a total of 142 factories in Bangladesh. Both quantitative and qualitative methods of analysis were used in this study. The survey includes questions on important study variables, such as big data, smart factories, cyber-physical systems (CPS), the Internet of Things (IoT), interoperability, textile production, and industry performance. The Variable Destination Multiple Access (VDMA) Model has been adopted to design the questionnaire, focusing on qualitative and quantitative questions. The survey dataset was investigated through SmartPLS 4.0 by normality and confirmatory tests. Likert scale data have been analyzed through IBM SPSS software version 26.0 by the exploratory factor analysis method to rank the IR 4.0 adoption variables. Analysis of the survey data indicates the level of adoption of Industry 4.0 in terms of organizational strategy, investment, infrastructure, IT (Information Technology), Ready Made Garments (RMG) skilled workers, smart operations, and smart factories. The study shows that the variable "Review of the strategy using indicators" got the highest ranking in the external factor, 0.791. This clearly indicates that strategy formulation is the topmost priority among other IR 4.0 adoption variables. Consequently, "digital integration" got the lowest loading at 0.620, as IR 4.0 digital technology adoption is very low. The overall maturity level for IR 4.0 adoption in the Bangladesh textile and apparel industry is 1.91 on a 5-point scale, indicating a low adoption level. This study can help concerned policymakers and industrialists who want to implement Industry 4.0 in the textile and RMG sectors to stay competitive in the global market. Alongside this study, it also summarizes the IR 4.0 adoption level in 9 broad categories.

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Introduction: Drawing on the Unified Theory of Acceptance and Use of Technology 2 and the Diffusion of Innovation Theory, this article investigates the adoption of telemedicine services from a patient perspective in Germany, Spain, and the United States using a mixed-methods approach. Digital health technologies have the potential to improve access to care and to alleviate the burden on traditional health care systems and are becoming more integrated into everyday medicine. Therefore, understanding the factors that impact patients' intentions to use telemedicine is crucial to ensure successful development. Methods: Based on 1,200 surveys collected in Germany, Spain, and the United States, structural equation modeling (IBM SPSS Amos 24) is employed to test the hypotheses. The article also explores how age and gender moderate the proposed relationships. Results: Seven out of the 10 hypotheses (performance expectancy, hedonic motivation, habit, relative advantage, and perceived security) are found to be positive, direct, and statistically significant. Furthermore, findings suggest stronger effects for telemedicine usage intention for younger female users than their male counterparts. Discussion: With digital health technologies becoming more prevalent, the outcomes of this study can endorse the development of effective strategies to promote the adoption of telemedicine, ultimately improving access to care and contributing to the advancement of and modern health care.

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The rapid integration of google workspace tools in higher education has the potential to transform education. To fully realize this potential, it is crucial to understand the factors that influence educators' attitudes and intentions toward adopting these tools. However, current research has mainly focused on specific contexts, highlighting the need for a comprehensive examination in different educational settings. This study delves into the complexities of the Technology Acceptance Model and expands its scope by considering additional external variables. Data was collected through an online survey, with 396 educators sharing their perspectives and intentions regarding google workspace tools. We used composite-based structural equation modeling, implemented by the SEMinR package in the R programming language, to rigorously assess the measurement and structural models of the constructs. The study's findings reveal significant relationships among the factors that shape educators' perceptions and behaviors in relation to google workspace tools. Notably, all paths show significant influence, except those connecting social influence to perceived usefulness and ease of use to attitude. Additionally, the research identifies the moderating impacts of gender, which do not significantly contribute to the observed relationships. This study contributes substantially to the growing knowledge of technology adoption in higher education. Furthermore, it offers valuable insights that can benefit educators, institutions, and policymakers who want to leverage the potential of google workspace tools for teaching and assessment. Lastly, the study provides clear directions for future research in this area.

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While phosphorus fertilizers contribute to food security, part of the introduced phosphorus dissipates into water bodies leading to eutrophication. At the same time, conventional mineral phosphorus sources are increasingly scarce. Therefore, closing phosphorus cycles reduces pollution while decreasing trade dependence and increasing food security. A major part of the phosphorus loss occurs during food processing. In this article, we combine a systematic literature review with investment and efficiency analysis to investigate the financial feasibility of recovering phosphorus from dairy processing wastewater. This wastewater is particularly rich in phosphorus, but while recovery technologies are readily available, they are rarely adopted. We calculate the Net Present Value (NPV) of investing in phosphorus recycling technology for a representative European dairy processing company producing 100,000 tonnes of milk per year. We develop sensitivity scenarios and adjust the parameters accordingly. Applying struvite precipitation, the NPV can be positive in two scenarios. First, if the phosphorus price is high (1.51 million EUR) or second if phosphorus recovery is a substitute for mandatory waste disposal (1.48 million EUR). However, for a variety of methodological specifications, the NPV is negative, mainly because of high input costs for chemicals and energy. These trade-offs between off-setting pollution and reducing energy consumption imply, that policy makers and investors should consider the energy source for phosphorus recovery carefully.

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