The aim of this research is to explore the relationship between remuneration, job satisfaction, and employee performance. Remuneration, in this context, refer to a system synchronization that is based on performance appraisal result. In this, regard, the research employed a descriptive quantitative method, with a population comprising all University of Padjadjaran lecturers which were a total of 2,090. Furthermore, in order to gather the research sample, a probability sampling technique was employed. This technique was selected because of its reputation as the most general strategic sampling technique in quantitative research to achieve representativeness (1). The obtained result showed that there was a positive and significant relationship between the remuneration and job satisfaction of lecturers in University of Padjadjaran. Accordingly, a significant value of 0.000 < 0.05 and a t-count value of 19.330 > 1.95 was observed, meaning the H1 hypothesis in this research was accepted. It is also expedient to acknowledge that a positive and significant relationship was found between job satisfaction and the performance of the lecturers in study area. For this relationship, a significant value of 0.010 < 0.05 and a t-count value of 5.676 > 1.95 was found. These findings led to the acceptance of the H2 hypothesis proposed in this research. Similarly, the relationship between remuneration and the performance of the observed lecturers was found to be positive and significant. The observed significant value in this regard was 0.000 < 0.05 and the t-count value was 4.057 > 1.95, indicating that H3 hypothesis was also accepted. Lastly, the relationship between remuneration and employee performance mediated by job satisfaction of lecturer in University of Padjadjaran was explored, and it was found to also be positive and significant, with a significant value of 0.000 < 0.05 and a t-count value of 5.429 > 1.95. This indicated that the H4 hypothesis proposed in the research was accepted.

Introduction: Chatbots are increasingly utilized in education, offering real-time, personalized communication. While research has explored technical aspects of chatbots, user experience remains under-investigated. This study examines a model for evaluating user experience and satisfaction with chatbots in higher education. Methodology: A four-factor model (information quality, system quality, chatbot experience, user satisfaction) was proposed based on prior research. An alternative two-factor model emerged through exploratory factor analysis, focusing on “Chatbot Response Quality” and “User Experience and Satisfaction with the Chatbot.” Surveys were distributed to students and faculty at a university in Ecuador to collect data. Confirmatory factor analysis validated both models. Results: The two-factor model explained a significantly greater proportion of the data’s variance (55.2%) compared to the four-factor model (46.4%). Conclusion: This study suggests that a simpler model focusing on chatbot response quality and user experience is more effective for evaluating chatbots in education. Future research can explore methods to optimize these factors and improve the learning experience for students.

This work presents a review of Mexican Higher Education during the decade of education for sustainable development and how today it faces the commitments made for the Sustainable Development Agenda 2030. By portraying the agreements that support the UN’s Development Program in advising higher education institutions, the SDGs which can be served through universities and their by-products, the success stories of some universities are shown. This case study addresses the theoretical value of quality of life and harmony of the environment, remarking on how different universities in Mexico have approached this matter as a key part of their curricula, policy, and research. Showcasing a special emphasis given to the work carried out by the University of Sonora, specifically for their institutional practices for sustainability and the study of sustainability from the perspective of Environmental Psychology.

This study aims to predict whether university students will make efficient use of Artificial Intelligence (AI) in the coming years, using a statistical analysis that predicts the outcome of a binary dependent variable (in this case, the efficient use of AI). Several independent variables, such as digital skills management or the use of Chat GPT, are considered.The results obtained allow us to know that inefficient use is linked to the lack of digital skills or age, among other factors, whereas Social Sciences students have the least probability of using Chat GPT efficiently, and the youngest students are the ones who make the worst use of AI.