The emergence of the COVID-19 pandemic led to the need to move educational processes to virtual environments and increase the use of digital tools for different teaching uses. This led to a change in the habits of using information and communication technologies (ICT), especially in higher education. This work analyzes the impact of the COVID-19 pandemic on the frequency of use of different ICT tools in a sample of 950 Latin American university professors while focusing on the area of knowledge of the participating professors. To this end, a validated questionnaire has been used, the responses of which have been statistically analyzed. As a result, it has been proven that participants give high ratings to ICT but show insufficient digital competences for its use. The use of ICT tools has increased in all areas after the pandemic but in a diverse way. Differences have been identified in the areas of knowledge regarding the use of ICT for different uses before the pandemic. In this sense, the results suggest that Humanities professors are the ones who least use ICT for didactic purposes. On the other hand, after the pandemic, the use of ICT for communication purposes has been homogenized among the different knowledge areas.

This study evaluated the performance of several machine learning classifiers—Decision Tree, Random Forest, Logistic Regression, Gradient Boosting, SVM, KNN, and Naive Bayes—for adaptability classification in online and onsite learning environments. Decision Tree and Random Forest models achieved the highest accuracy of 0.833, with balanced precision, recall, and F1-scores, indicating strong, overall performance. In contrast, Naive Bayes, while having the lowest accuracy (0.625), exhibited high recall, making it potentially useful for identifying adaptable students despite lower precision. SHAP (SHapley Additive exPlanations) analysis further identified the most influential features on adaptability classification. IT Resources at the University emerged as the primary factor affecting adaptability, followed by Digital Tools Exposure and Class Scheduling Flexibility. Additionally, Psychological Readiness for Change and Technical Support Availability were impactful, underscoring their importance in engaging students in online learning. These findings illustrate the significance of IT infrastructure and flexible scheduling in fostering adaptability, with implications for enhancing online learning experiences.

The hospital is a complex system, which evolving practices, knowledge, tools, and risks. This study aims to assess the level of knowledge about risks at Hassan II Hospital among healthcare workers (HCWs) working in three COVID-19 units. The action-research method was adopted to address occupational risks associated with the pandemic. The study involved 82 healthcare professionals in the three COVID-19 units mentioned above. All participants stated they were familiar with hospital risks. Seventy-four HCPs reported no knowledge of how to calculate risk criticality, while eight mentioned the Occurrence rating, Severity rating, and Detection rating (OSD) method, considering Occurrence rating, Severity rating, and Detection rating as key elements for risk classification. Staff indicated that managing COVID-19 patients differs from other pathologies due to the pandemic’s evolving protocols. There is a significant lack of information among healthcare professionals about risks associated with COVID-19, highlighting the need for a hospital risk management plan at a subsequent stage.

Nowadays, customer service in telecommunications companies is often characterized by long waiting times and impersonal responses, leading to customer dissatisfaction, increased complaints, and higher operational costs. This study aims to optimize the customer service process through the implementation of a Generative AI Voicebot, developed using the SCRUMBAN methodology, which comprises seven phases: Objectives, To-Do Tasks, Analysis, Development, Testing, Deployment, and Completion. An experimental design was used with an experimental group and a control group, selecting a representative sample of 30 customer service processes for each evaluated indicator. The results showed a 34.72% reduction in the average time to resolve issues, a 33.12% decrease in service cancellation rates, and a 97% increase in customer satisfaction. The implications of this research suggest that the use of Generative AI In Voicebots can transform support strategies in service companies. In conclusion, the implementation of the Generative AI Voicebot has proven effective in significantly reducing resolution time and markedly increasing customer satisfaction. Future research is recommended to further explore the SCRUMBAN methodology and extend the use of Generative AI Voicebots in various business contexts.

Social and environmental issues gain more importance for society that stimulates companies to adopt and integrate more sustainability practices into their business activities. This study is embedded in almost uncovered in the literature context of Russian business that undergoes its ESG transformation in conditions of unprecedented sanctions and hostile institutional environment. The study aims to reveal the role of internal stakeholders (top managers, line managers, and employees) in successful implementation of a company’s ESG practices along various dimensions. Using the primary data from 29 large Russian companies the fsQCA method is applied to identify various configurations of contingencies that stimulate their ESG performance. The analysis results in identification of two alternative core conditions for high ESG performance in Russian companies: high top management commitment to sustainability and low employees’ commitment to sustainability or the employees’ awareness about sustainability. At the end, the study results in two generic profiles composed of top management commitment, line management support, and employees’ awareness, behavior, and commitment towards ESG performance. The results show two different approaches towards ESG transformation that may bring a company to the comparably similar desired outcome. The study has a potential for generalization on a wider scope of emerging market contexts.

The present study attempted to assess the impact of fundamental ratios on the share prices of selected telecommunication companies in India. India has dramatically expanded over the past ten years to become the second-biggest telecoms market worldwide, with 1.17 billion users. The Indian telecom industry has proliferated thanks in part to the government of India’s liberal and reformist policies and strong customer demand. It has become a lucrative investment sector for investors due to its recent and prospective growth. Data on 13 telecom firms indexed in the S&P BSE telecommunication index from 2013 to 2022 were taken from companies’ annual reports, the BSE website (Bombay Stock Exchange), and other secondary sources. Six firm-specific fundamental factors viz. Debt to Equity ratio (D/E), Current ratio (CR), Total Assets Turnover ratio (ATR), Earnings per share (EPS), Price to earnings ratio (P/E), Return on equity (ROE), and three country-specific fundamental factors viz. Gross Domestic Product, Inflation rate, and S&P BSE Sensex return were considered. Fixed effect panel regression through Generalized Least Square (GLS) model was performed to find inferences. Debt Equity ratio and Inflation rate were found to impact share price negatively. Conversely, the Total Assets Turnover ratio (ATR), Earnings per share (EPS), Price to Earnings ratio (P/E), and Return on Equity (ROE) positively impacted selected companies’ share prices. The study results will benefit individual & institutional investors in formulating their investment and portfolio diversification strategies for gaining a high effective rate of return on their investments.