Small scale African farmers are facing increasing challenges due to climate change. One of the most important strategies identified in the literature to improve farmers’ resilience is the enhancement of information sharing between all the actors involved, as a key step of the capacity building process. To provide insights into this objective, we analyse Mozambique farmers’ perception of climate change, agricultural adaptation options, the information sources most used currently by farmers, and which are preferred to be strengthened in the future. We also analyse the main barriers for improving the exchange of information, as well as geographical and gender differences. We collected quantitative and qualitative data from a survey to 100 farmers (52 women and 48 men) and 9 focus groups with farmers, plus 9 interviews to local and provincial government officials and NGOs technicians. We studied four case study areas that showed different vulnerability to climate change and socioeconomic characteristics: two case studies close to the capital city of Mozambique (Maputo) and two case studies far away from the capital city. With the data from the survey we performed descriptive and comparative statistics and a generalized linear model regression analysis. With the data from the focus groups we built Fuzzy cognitive maps that were used for assessing the impacts of information enhancement policies on the communities’ adaptation capacity. We demonstrate the need for adopting a contextualized approach for defining the most suitable climate change adaptation option in each study area. Oral communications and extension agents are the key short term resources to improve the exchange of information with small scale farmers, with smartphones showing a high potential only in the long future, due to the existing barriers to their massive use (high prices for buying and using them, low quality of mobile network, high proportion of illiterate farmers).

The prevalence of diabetes is greater in older, low-income, urban South African populations. Exploring the potential of mobile applications to aid in this challenge, the research focuses on 514 diabetic patients in Cape Town, applying the Theory of Planned Behaviour. Utilizing Partial Least Squares Structural Equation Modelling, the study identifies age-specific variations in factors influencing mobile app use. While overall predictability of intention to use mobile health apps for exercise is moderate (R 2 = 11.6%), specific relationships differ across age groups. Notably, relationships involving Social Norm, Intention, and Perceived Behavioural Control show insignificance in certain age brackets. Although the study retains significance for the oldest age group at a 95% confidence interval, compared to the original 99%, the overall model exhibits low predictive power. The findings underscore the necessity for designers to tailor mobile applications to diverse age requirements. This research contributes to theoretical understanding by highlighting age-related nuances in the Theory of Planned Behaviour, offering practical insights for intervention design and public health policies to enhance diabetes management among various age groups in South Africa.

Information sources (ISs) are crucial for enhancing green behaviour among farmers. However, few in-depth studies have been conducted on the differential effects of different ISs. This poses a challenge for the design of efficient information intervention strategies. This study designed an extended theoretical framework of planned behaviour to examine the differential effects of different ISs on farmers’ intentions to adopt organic fertilisers. Survey data from 361 Funing watermelon farmers in China were analysed using partial least squares structural equation modelling. The results showed that the utilisation of personal ISs (both formal and informal) had significant positive effects on intentions, whereas impersonal ISs did not play a significant role. Among these, personal informal ISs were the strongest informational determinant. Moreover, psychological factors played a key mediating role between ISs and intentions. Attitude was the strongest direct psychological factor, followed by perceived behavioural control and subjective norms. These findings provide theoretical and practical insights for policymakers to design targeted information interventions to promote the adoption of organic fertilisers by farmers. When relying on formal ISs, the Chinese government should focus on the role of informal ISs.

Africa has a wealth of knowledge that is currently facing extinction unless drastic measures are taken to disseminate it. This study investigates the dissemination of indigenous knowledge by traditional leaders in the Mashonaland East Province in Zimbabwe. A qualitative methodology that used hermeneutic phenomenology and knowledge conversion theory was used to explore the dissemination of indigenous knowledge by traditional leaders in the Mashonaland East Province, in Zimbabwe. Purpose sampling was used to determine the study population. Data collected from interviews with traditional leaders and other members of the community were augmented with reviewed documents from the ministry responsible for the appointment of traditional leaders. Data was analysed thematically according to the study objectives. The findings of this study revealed that traditional leaders are among the custodians of indigenous knowledge who should ensure that indigenous knowledge is preserved for posterity. It was established that the constitution of Zimbabwe empowers traditional leaders to guard jealously the indigenous knowledge of their communities through the development of strategies for spreading the knowledge to future generations. Various indigenous knowledge scholars concurred that the lack of proper dissemination of indigenous knowledge culminates in its extinction. Dissemination strategies that include the use of information communication technologies were proposed. Furthermore, the study recommends that indigenous knowledge should first be captured and documented to enable greater dissemination.

The unfamiliar and unusual stressful conditions due to COVID-19 have motivated employees to excessively use smartphones for non-work-related activities to alleviate the negative feelings associated with these stressful conditions. To conceptualize our research model, we employed the Compensatory Internet Use Theory (CIUT), which is associated with problematic technology usage. The proposed model investigates the influence of the following negative factors that emerged because of COVID-19 as the determinants of problematic smartphone use (PSU): remote work arrangement (RWA), intolerance of uncertainty (IU), COVID-19 anxiety (CA), and social isolation (ISO). In addition, we suggest that, although employees tend to use smartphones excessively to alleviate stressful life conditions, these conditions (RWA, IU, CA, and ISO) will lead to emotional exhaustion (EE), which will consequently result in the excessive use of smartphones. Furthermore, we are investigating the influence of PSU on employee work productivity. Finally, we are also interested in exploring the moderating role of gender in the relationships within our model. The results have shown that all hypotheses are supported, except for the influence of RWA on EE and PSU, and the influence of CA on PSU. We believe that our findings will provide researchers and businesses with a deeper understanding of this critical issue, especially in the context of smartphone usage at work.

This study investigates the effectiveness of machine learning and deep learning models for early prediction of student performance in higher education institutions. Using the Open University Learning Analytics (OULA) dataset, various models, including Decision Tree, Support Vector Machine, Neural Network, and Ensemble Model, were employed to predict student performance in three categories: Pass/Fail, Close to Fail, and Close to Pass. The Ensemble Model (EM) consistently outperformed other models, achieving the highest overall F1 measure, precision, recall, and accuracy. These results highlight the potential of data-driven techniques in informing educational stakeholders’ decision-making processes, enabling targeted interventions, and facilitating personalized learning strategies tailored to students’ needs. By identifying at-risk students early in the academic year, institutions can provide additional support to improve academic outcomes and retention rates. The study also discusses practical implications, including the development of pedagogical policies and guidelines based on early predictions, which can help educational institutions maintain strong academic outcomes and enhance their reputation for academic excellence. Future research aims to investigate the impact of individual activities on student performance and explore day-to-day student behaviors, enabling the creation of tailored pedagogical policies and guidelines.

The purpose of this paper is the comparative analysis of traditional education and e-learning by the application of key performance indicators. The general objective of the study is to indicate the advantages and disadvantages of traditional education and e-learning through the analysis and comparison of these two forms of education by applying defined key performance indicators. The research was carried out in 15 gymnasiums and vocational high schools in the Republic of Serbia, with a sample of 11.204 students (the school year 2018–19) and 11.251 students (the school year 2019–20). The results of the research and conducted comparative analysis presented in this paper indicate that e-learning during the COVID-19 pandemic has significant advantages over traditional education, such as a reduction in the percentage of students who were sent to the make-up exam and who did not complete the school year, a reduction of the number of imposed disciplinary measures and absences from classes per student at the end of the school year and an increasing percentage of excellent students at the school level at the end of the school year. Based on the results, recommendations for improving the education process by implementing e-learning in developing countries are proposed. This paper presents a valuable resource for educators and scholars in defining the elements that can improve student learning outcomes, motivation and satisfaction levels.

Due to globalization and ongoing technological advancements, businesses are confronted with unprecedented competition. To preserve their competitive edge, businesses are increasingly turning to business intelligence (BI) systems. While BI has been acknowledged to enhance decision-making, efficiency, innovation, performance and provide organizational value, the literature offers a limited understanding on its dark sides, especially in less affluent and developing nations. In this investigation, we diagnose the nexus between BI system usage, technostress, and work withdrawal behaviors (WWBs) using transactional theory of stress. We applied Partial Least Squares Modeling approach on cross-sectional data collected from travel agencies in Jordan to examine the model. The findings back the idea that BI system usage is associated with increased technostress, and technostress is associated with increased WWBs. Technostress mediated the path between BI system usage and WWBs. The findings offer implications that extend our understanding of the scholarship of BI system usage and present building blocks for practical reconsiderations.

Through social media and digital platforms, fake news and both professional as well as non-professional news sources are increasingly circulated, thus having a greater impact on people's lives and society. As a result, evaluating news has emerged as a crucial skill. However, it is yet unclear what variables contribute to this increase. To offer more insights, this study attempts to investigate through political news accessibility the factors influencing political news assessment of university students in Northeast Thailand. This study was a quantitative research, and the theory underlying it was news literacy. 1200 samples from undergraduate students from state, private, and autonomous universities were obtained through a multi-stage sampling. 400 questionnaires, utilized as a research tool, were distributed to each type of university for data collection. In order to examine the reliability of the research instrument, Cronbach's alpha was also used in a pre-test stage along with a content validity analysis of the questionnaire. For all elements, the alpha coefficient was 0.93. The findings demonstrated that political news assessment was most influenced by political news accessibility, which is the intervening variable (Beta = 0.265); followed by political news exposure from social media (Beta = 0.217); inner drives for the follow-up of political news (Beta = 0.190); influence of groups and social networks (Beta = 0.170); specific characteristics of the Internet (Beta = 0.151); importance of political news (Beta = 0.147); political news exposure from websites (Beta = 0.099); benefits of political news (Beta = 0.075); and political news exposure from print media (Beta = 0.042). The student characteristic factor, motivation for political news exposure factor, and political news exposure factor accounted for 67.45 percent of the variance in explaining political news evaluation among Thai students, through political news accessibility (R2 = 0.6745). Consequently, improving these elements will lead to better political news evaluation.

The increasing ubiquity of Artificial Intelligence (AI) chatbots across a variety of sectors has sparked a burgeoning interest in deciphering the determinants that govern their adoption and usage. This study aims to examine the pivotal factors that influence the practical usage of the AI chatbot, ChatGPT, among a sample of university students. Leveraging the theoretical framework of planned behavior, the research model scrutinizes the interplay between knowledge application, perceived intelligence, usability, attitude, subjective norms, perceived behavioral control, trust, behavioral intention, and actual usage. Data procured from a survey of university students were examined through the lens of structural equation modeling. The outcomes reveal that knowledge application, perceived intelligence, and usability have a positive impact on attitudes towards ChatGPT. Perceived intelligence also influences knowledge application, usability, and trust. Concurrently, attitude and subjective norms notably affect behavioral intention. Contrary to expectations, perceived behavioral control did not significantly influence behavioral intention. Trust emerged as a crucial factor steering behavioral intention, which in turn, positively correlates with the actual usage of ChatGPT. These insights enrich the academic discourse on AI chatbot adoption and provide practical implications for AI developers, educators, and policy makers, striving to enhance user engagement with AI systems in educational contexts.