Objective: This study aimed to examine the psychometric properties of the 21-item Depression, Anxiety, and Stress Scale (DASS-21) in a sample of Moroccan students. Method: A total of 208 Moroccan students participated in this study. The dimensionality of the DASS-21 scale was assessed using exploratory factor analysis. Construct validity was assessed using the Stress Perception (PSS-10), State Anxiety (SAI), and Depression (CESD-10) scales. Results: Correlation analyses between Depression, Anxiety, and Stress subscales showed significant results. The exploratory factor analysis results confirmed the DASS’s three-dimensional structure. Furthermore, correlation analyses revealed positive correlations between the DASS-18 sub-dimensions and the three scales for Stress (PSS-10), Anxiety (SAI), and Depression (CESD-10). Conclusion: In line with previous work, the results of this study suggest that the DASS-18 reflect adequate psychometric properties, making it an appropriate tool for use in the university context.

This study developed a specific scale to measure the impact of extrinsic motivations on students’ decisions to pursue online graduate programs at business schools in Latin America. Using a mixed-methods approach, the research proceeded in three stages. In the first stage, the construct was defined by identifying key extrinsic factors motivating students to enroll in online graduate programs, followed by the creation and initial validation of the scale in Colombia. The second stage involved testing the scale in Chile to determine its cross-cultural applicability. In the third stage, the scale’s predictive validity was confirmed, demonstrating its effectiveness in explaining how extrinsic motivations influence students’ intentions to enroll in online graduate programs. The findings indicate that the scale, composed of five dimensions—Cost Reduction, Ability to Study from Any Location, Control Over Learning Pace, Flexibility to Balance Study and Work, and Avoiding Commuting Time—is a reliable predictor of student preferences and intentions in online graduate education. The final scale includes 25 items across these dimensions, measuring extrinsic factors through items related to flexibility, time savings, and global accessibility. Validation in two Latin American countries confirms the scale’s relevance across diverse cultural contexts, enhancing its applicability within the region. This study provides empirical evidence that extrinsic motivation is a key determinant of students’ intentions to enroll in online programs in developing countries. It confirms that extrinsic motivations reflect a preference for flexible learning options compatible with students’ lifestyles and professional needs, linked to their beliefs about time management, professional advancement, and career opportunities associated with earning a graduate degree.

The economy, unemployment, and job creation of South Africa heavily depend on the growth of the agricultural sector. With a growing population of 60 million, there are approximately 4 million small-scale farmers (SSF) number, and about 36,000 commercial farmers which serve South Africa. The agricultural sector in South Africa faces challenges such as climate change, lack of access to infrastructure and training, high labour costs, limited access to modern technology, and resource constraints. Precision agriculture (PA) using AI can address many of these issues for small-scale farmers by improving access to technology, reducing production costs, enhancing skills and training, improving data management, and providing better irrigation infrastructure and transport access. However, there is a dearth of research on the application of precision agriculture using artificial intelligence (AI) by small scale farmers (SSF) in South Africa and Africa at large. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) and Bibliometric analysis guidelines were used to investigate the adoption of precision agriculture and its socio-economic implications for small-scale farmers in South Africa or the systematic literature review (SLR) compared various challenges and the use of PA and AI for small-scale farmers. The incorporation of AI-driven PA offers a significant increase in productivity and efficiency. Through a detailed systematic review of existing literature from inception to date, this study examines 182 articles synthesized from two major databases (Scopus and Web of Science). The systematic review was conducted using the machine learning tool R Studio. The study analyzed the literature review articled identified, challenges, and potential societal impact of AI-driven precision agriculture.

This study aims to examine the entrepreneurial activities of 240 women in the districts of Konaseema, East Godavari, and Kakinada during 2021–2022, focusing on the diverse range of 286 enterprises they managed across 69 business types. These enterprises were tailored to local resources and market demands, with coconut wholesale, cattle breeding, and provision shops being the most common. The study also analyzes income distribution, noting that one-third of the women earned between ₹50,000–1,00,000 annually, while only 0.70% earned over ₹5,00,000. More than half of the enterprises served as the primary income source for their families. The research highlights the significant role these women entrepreneurs play in their communities, their job satisfaction derived from financial independence and social empowerment, and the challenges they face, such as limited capital and market access. Finally, the study offers recommendations to empower these women to seize entrepreneurial opportunities and enhance their success.

Freshwater problems in coastal areas include the process of salt intrusion which occurs due to decreasing groundwater levels below sea level which can cause an increase in salt levels in groundwater so that the water cannot be used for water purposes, human consumption and agricultural needs. The main objective of this research is to implementation of RWH to fulfill clean water needs in tropical coastal area in Tanah Merah Village, Indragiri Hilir Regency, with the aim of providing clean water to coastal communities. The approach method used based on fuzzy logic (FL). The model input data includes the effective area of the house’s roof, annual rainfall, roof runoff coefficient, and water consumption based on the number of families. The BWS III Sumatera provided the rainfall data for this research, which was collected from the Keritang rainfall monitoring station during 2015 and 2021. The research findings show that FL based on household scale RWH technology is used to supply clean water in tropical coastal areas that the largest rainwater contribution for the 144 m² house type for the number of residents in a house of four people with a tank capacity of 29 m² is 99.45%.

The need for strategic alignment within HR management increased managers' concern about individual behavior and how this behavior was related to the achievement of goals. In public management, effectively managing employees' performance has been necessary since Weber's bureaucratic administration. The individual performance appraisal is the right tool to assess employees' competencies. Thus, we proposed the following research question: Which factors, as pointed out by theory, have the most significant influence on the individual performance appraisal process? The quantitative method was applied to answer this question, developing and testing a scale via EFA and a hypothetical model via SEM-CB. The results indicated a scale with 25 items able to access the main points of the IPA process and a hypothetical model with 7 constructs that indicate the influence on employee engagement. The main finding is the significant influence of feedback on the whole process. The main theoretical contribution was the construction of the MIPAS scale, and the practical contribution was to identify the points where managers should focus on improving the IPA process with their subordinates.