Extensive research on pro-environmental behaviour (PEB) reveals a significant knowledge gap in understanding the influence of social class, perceived status and the middling tendency on pro-environmental behaviour. Using the International Social Survey Programme Environment dataset, and conducting multilevel mixed-effects linear regressions, we find that the middling tendency and biased status perceptions significantly influences pro-environmental behaviour. Those who deflate their social position have higher pro-environmental behavior and this reinforces the idea that pro-environmental behaviour is driven by a post-materialist effect rather than a status enhancement effect. Moreover, the objective middle class is still a stronger contributor to higher PEB levels compared to subjective middle class. We also find the relation between class, status and PEB vary by country. These findings provide vital insights into the intricate and heterogenous dynamics between class, status and pro-environmental behaviour among different countries and shed light on class and status as driving forces behind pro-environmental behaviour.

A The meaning of life is the purpose that defines a person’s existence based on a set of fundamental objectives that give meaning to life or not. Furthermore, not all individuals have a meaning in life, and it may be absent at some point or stage of life. Objective: To analyze Peruvian older adults’ socioeconomic factors and the meaning of life. Method: A descriptive, comparative, quantitative cross-sectional study was conducted. One thousand older adults were intentionally selected through quotas of 100 older adults in 10 localities in Arequipa, Peru. They were administered a survey validated with high levels of reliability on the meaning of life and socioeconomic factors. Results: A moderate level of meaning in life was found. Most older adults believe that increasing age decreases the purpose of living, and existential emptiness grows. Conclusions: Statistically significant differences (p < 0.05) were found between the meaning of life and the following socioeconomic factors: retirement, religion, educational level, cohabitation, marital status, income, and occupation. It is understood that older adults who scored higher on these factors indicate having meaning in life because they still fulfill the role of providers for the family economy, being util to their families compared to the majority who scored low, which indicates an absence of meaning of life leading to an increase existential void.

The main goal of this study is to assess the moderating role of digital leadership capabilities (DLC) in improving the overall performance of telecom companies through their organisational knowledge capabilities. The author builds a conceptual model with six hypotheses and tests them with data collected through an electronic questionnaire. The data is analysed using WarpPLS 8.0 software as an application of the structural equation modelling technique. The sample size included 528 participants. The study revealed that individual knowledge capability (IKC) does not significantly affect organisational performance (PR). Also, the results reveal that managerial knowledge capability (MKC) and organisational collaborative capability (OCC) have a positive but weak impact on the performance of telecom companies (PR). However, it was clear that individual knowledge capability (IKC) and organisational collaborative capability (OCC) do not affect organisational performance (PR) through the moderator, digital leadership capabilities (DLC). On the other hand, it was also evident that managerial knowledge capabilities (MKC) significantly negatively affect the performance of telecom companies (PR) through the moderator role of digital leadership capabilities (DLC). The author recommends that telecom companies adopt knowledge-based practices to ensure enduring high performance. He also suggests creating a knowledge management department to foster a culture of creativity and cooperation across departments, which is essential to establishing a work environment that promotes continuous learning and development. Findings may help telecom sector CEOs boost the company’s performance value. The research highlights the importance of fostering appropriate knowledge pillars and building digital leaders to shift telecom companies to a new successful stage. These findings offer tangible benefits that can be directly applied in the telecom industry, making the research highly relevant and valuable.

In this paper, we assess the results of experiment with different machine learning algorithms for the data classification on the basis of accuracy, precision, recall and F1-Score metrics. We collected metrics like Accuracy, F1-Score, Precision, and Recall: From the Neural Network model, it produced the highest Accuracy of 0.129526 also highest F1-Score of 0.118785, showing that it has the correct balance of precision and recall ratio that can pick up important patterns from the dataset. Random Forest was not much behind with an accuracy of 0.128119 and highest precision score of 0.118553 knit a great ability for handling relations in large dataset but with slightly lower recall in comparison with Neural Network. This ranked the Decision Tree model at number three with a 0.111792, Accuracy Score while its Recall score showed it can predict true positives better than Support Vector Machine (SVM), although it predicts more of the positives than it actually is a majority of the times. SVM ranked fourth, with accuracy of 0.095465 and F1-Score of 0.067861, the figure showing difficulty in classification of associated classes. Finally, the K-Neighbors model took the 6th place, with the predetermined accuracy of 0.065531 and the unsatisfactory results with the precision and recall indicating the problems of this algorithm in classification. We found out that Neural Networks and Random Forests are the best algorithms for this classification task, while K-Neighbors is far much inferior than the other classifiers.

The objective is to determine the impact of economic growth on the externalities of infrastructure investments for the Peruvian case for the periods from 2000 to 2022. The methodologies used are descriptive, explanatory and correlational, analyzing qualitative and mainly quantitative methods. Econometric software was used, and correlations of variables were created for each proposed hypothesis. The estimated model shows that all the independent variables have a significant t-statistic greater than 2 and a probability of less than 5%, which indicates that they are significant and explains the model. The R² is 98.02% which indicates that there is a high level of explanation by the independent variables to the LOG(RGDP). The results of the estimated models demonstrate the existence of a positive and significant relationship of investments in infrastructure and externalities on the growth of the non-deterministic component of real GDP, therefore, in a practical way, private and public investment has a positive effect on the non-deterministic growth of real GDP.

This study analyzes the perception of university students regarding the use of virtual reality (VR) in higher education, focusing on their level of knowledge, usage, perceived advantages and disadvantages, as well as their willingness to use this technology in the future. Using a mixed-methods approach that combines questionnaires and semi-structured interviews, both quantitative and qualitative data were collected to provide a comprehensive view of the subject. The results indicate that while students have a basic understanding of VR, its use in the educational context is limited. A considerable number of students recognize VR’s potential to enhance the learning experience, particularly in terms of immersion and engagement. However, significant barriers to adoption were identified, such as technical issues, the high cost of equipment, and inadequate access to technological infrastructure. Additionally, there is a need for broader training for both students and faculty to ensure the effective use of this technology in academic environments. The semi-structured interviews confirmed that perceptions of VR vary depending on prior exposure to the technology and access to resources. Despite the challenges, most students appreciate VR’s potential to enrich learning, although its effective adoption will depend on overcoming the identified barriers. The study concludes that strategies must be implemented to facilitate the integration of VR into higher education, thus optimizing its impact on the teaching-learning process.

This research presents an in-depth examination of the emotional effects of synchronous hybrid education on undergraduate university students at a pioneering private institution in educational innovation. The study had encompassed all courses that were delivered in a synchronous hybrid format, covering 16 courses and involving 241 students. Each student had been observed and recorded on two separate class sessions, with each recording lasting approximately 30 min. This comprehensive data collection had resulted in 409 recordings, each approximately 30 min in duration, translating to nearly an hour of observation per student across the classes, totaling close to 205 h of recordings. These recordings were subsequently processed using neuroscience software tools for advanced statistical analysis, effectively serving as a comprehensive survey of courses within this modality. The primary focus of the research was on the emotions experienced during both face-to-face and online classes and their subsequent influence on student behavior and well-being. The findings reveal higher emotional time ratios for positive emotions such as joy and surprise in face-to-face students. Notably, both groups exhibited comparable ratios for negative emotions like anger and sadness. The research underscores the emotional advantages of face-to-face interactions, which elicit stronger emotions, in contrast to online students who often feel detached and isolated.

This study investigates the evolution of monetary policy in Ghana and explores the potential of Central Bank Digital Currencies (CBDCs), specifically the e-Cedi, as a tool to enhance financial inclusion and modernize the country’s financial system. Ghana’s monetary policy framework has undergone significant transformations since the establishment of the Bank of Ghana in 1957, with notable achievements in stabilizing the economy and managing inflation. However, large segments of the population, particularly in rural areas, remain unbanked or underbanked, highlighting the limitations of traditional monetary tools. The introduction of the e-Cedi presents an opportunity to bridge these gaps by providing secure, efficient, and accessible financial services to underserved communities. The study employs a qualitative research design, integrating historical analysis, case studies, and thematic analysis to assess the potential benefits and challenges of CBDCs in Ghana. Key findings indicate that while the e-Cedi could significantly enhance financial inclusion, challenges related to technological infrastructure, cybersecurity, and public trust must be addressed. The study concludes that a balanced approach, which prioritizes digital infrastructure development, strong cybersecurity measures, and collaboration with financial institutions, is essential for maximizing the potential of CBDCs in Ghana. Recommendations for future research include a deeper exploration of the impact of CBDCs on financial stability and further analysis of rural adoption barriers.