This research aims to empirically examine the role of learning organization practices in enhancing sustainable organizational performance, utilizing knowledge management and innovation capability as mediating variables. The study was conducted in public IT companies across China, which is a vital sector for driving innovation and economic growth. A mixed-methods approach was employed, with quantitative methods accounting for 70% and qualitative methods for 30% of the research. Purposive sampling was utilized to distribute questionnaires to 546 employees from 10 public IT companies. Statistical analysis was conducted using Structural Equation Modeling (SEM). The findings indicate that learning organization practices significantly influence knowledge management practices (β = 0.785, p < 0.001) and innovation capability (β = 0.405, p < 0.001). Furthermore, knowledge management practices positively contribute to sustainable organizational performance (β = 0.541, p < 0.001), while innovation capability also has a positive effect (β = 0.143, p < 0.001). Moreover, knowledge management practices partially mediate the relationship between learning organization practices and sustainable performance, with a total effect of 0.788 (p < 0.001). The mediating role of innovation capability is also significant, with a total effect of 0.422 (p = 0.045). The study further includes qualitative in-depth interviews with 20 managers from 10 IT companies across five regions in China: East, South, West, North, and Central. Senior managers were selected through a stratified sampling method to ensure comprehensive representation by including both the largest and smallest companies in each region. These findings underscore the critical role of learning organizations in promoting sustainability through effective knowledge management and innovation capabilities within the IT sector.

The study’s goal was to investigate the impact of e-learning determinants on student satisfaction and intention to use e-learning tools. The dependent and independent variables in this study were based on the technological acceptance model. The study examines three determinants, including usefulness, ease of use, and facilitating conditions, as independent variables, while student satisfaction and intention to use were used as dependent variables. Additionally, this study is unique by adding student satisfaction as a dependent variable and a mediator to examine the relationship between e-learning determinants and intention to use. A questionnaire was prepared and distributed to 324 undergraduate students from Jordan’s private universities on the basis of a convenience sample. The proposed hypotheses were investigated using the quantitative techniques of regression in SPSS and SEM in AMOS. The findings of this study revealed that student satisfaction and intention to use e-learning were positively impacted by e-learning determinants. It found that intention to use was positively impacted by student satisfaction. Furthermore, e-learning intention to use was found to be positively impacted by e-learning determinants via student satisfaction. Universities and other educational institutions are advised to identify the appropriate e-learning determinants that satisfy students’ demands and motivate them to use e-learning tools in light of the study’s findings. Private universities can accomplish their goals, stay ahead of the competition, and obtain a competitive advantage by properly understanding e-learning determinants, student satisfaction, and the application of successful e-learning solutions.

Lighting conditions in learning spaces can affect students’ emotions and influence their performance. This research seeks to verify the influence of classroom lighting on students’ academic performance under different conditions and measurement forms. The research method is based on the systematic review of research articles establishing case analyses characterizing lighting intensity and color temperature to determine ranges favorable to a higher level of attention and long-term memory. Also, this study shows relevant aspects of the cases representative of a sustainable solution and proposes a research model. The study found light intensity values between 350 and 1000 lux and color temperatures between 4000 and 5250 Kelvin that favor attention. Long-term memory reached the highest levels of measurement by analyzing different parameters sensitive to lighting conditions and questionnaires. In conclusion, it was demonstrated that an adequate light intensity and color temperature based on the greatest possible amount of natural light complemented with Light Emitting Diode (LED) light generates optimal lighting for the classroom, achieving energy efficiency in a sustainable solution and promoting student well-being and performance.

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.