In the 21st century, brand communication has been significantly transformed through the interaction of users and artificial intelligence (AI), who co-create and recreate texts in digital environments. This evolution challenges traditional disciplines and roles, opening new perspectives for textual production on multiple platforms. The study examines the current state and application of the textual component in brand communication, exploring its disciplinary foundations, rhetorical traces, and research methodologies. To this end, a content analysis of 97 relevant publications from 2000 to 2024 was conducted, selected for their impact on the field of brand communication and following the guidelines established in the PRISMA statement. The results identified three sources of textual creation: Organization, users and algorithms. In addition, persuasion and sentiment take precedence at the rhetorical level, while data mining stands out in message analysis. In conclusion, the advertising text, which previously prevailed in brand communication with corporate authorship, formal prefiguration and a closed entity, now expands in a media and networked context. This text originates from a multiplicity of human and automated sources, overlapping rhetorical phases and fluid textualities. The shift implies a transition from unidirectional communication, characterized by repeated impacts, to multidirectional communication with spiraling trajectories and iterative adjustments. This challenges the boundaries of genres and formats, merging the persuasiveness of rhetoric and the imagination of storytelling. This situation demands commercial policies that integrate new professionals and roles, in partnership with the educational sector, and that address copyright with AI and users.

The growth of mobile Internet has facilitated access to information by minimizing geographical barriers. For this reason, this paper forecasts the number of users, incomes, and traffic for operators with the most significant penetration in the mobile internet market in Colombia to analyze their market growth. For the forecast, the convolutional neural network (CNN) technique is used, combined with the recurrent neural network (RNN), long short-term memory network (LSTM), and gated recurrent unit (GRU) techniques. The CNN training data corresponds to the last twelve years. The results currently show a high concentration in the market since a company has a large part of the market; however, the forecasts show a decrease in its users and revenues and the growth of part of the competition. It is also concluded that the technique with the most precision in the forecasts is CNN-GRU.

Background: Various studies have demonstrated the usefulness of Google search data for public health-monitoring systems. The aim of this study is to be estimated interest of public in infectious diseases in infectious diseases in South Korea, the five other countries. Methods: We conducted cross-country comparisons for queries related to the H1N1 virus and Middle East respiratory syndrome coronavirus (MERS-CoV). We analyzed queries related to the novel coronavirus disease (COVID-19) from 20 January to 13 April 2020, and performed time-descriptive and correlation analyses on trend patterns. Results: Trends in H1N1, MERS-CoV, and COVID-19 queries in South Korea matched those in the five other countries and worldwide. The relative search volume (RSV) for the MERS-CoV virus increased as the cumulative number of confirmed cases in South Korea increased and decreased significantly as the number of confirmed cases decreased. The volume of COVID-19 queries dramatically increased as South Korea’s confirmed COVID-19 cases grew significantly at the community level. However, RSV remained stable over time. Conclusions: Google Trends provides real-time data based on search patterns related to infectious diseases, allowing for continuous monitoring of public reactions, disease spread, and changes in perceptions or concerns. We can use this information to adjust their strategies of the prevention of epidemics or provide timely updates to the public.

This research aimed to investigate the role of humanizing leadership in enhancing the effectiveness of change management strategies within organizations. Specifically, it focused on how humanizing leadership influences change outcomes and the extent to which organizational culture moderates this relationship. The study addressed critical questions regarding the impact of leadership behaviors, such as model vulnerability, emotional intelligence, open communication, and psychological safety on effective change management and employee performance. A quantitative approach was employed to provide a comprehensive analysis of the phenomena. Quantitative data were collected from a sample of 325 employees through surveys that measured perceptions of Humanizing leadership behaviors, organizational culture, and change outcomes. Data was analyzed by IBM SPSS 26.0. The findings revealed that humanizing leadership behaviors significantly enhances the success of change initiatives, primarily through improved employee engagement and reduced resistance. Organizational culture was found to play a moderating role, amplifying the positive effects of empathetic and inclusive leadership practices. The study provides actionable recommendations for organizational leaders and managers to foster a culture that supports humanizing leadership. By adopting leadership strategies that emphasize vulnerability, empathy, and inclusivity, organizations can enhance their adaptability and resilience against the backdrop of continuous change. These findings are particularly valuable for enhancing managerial practices and informing policy within corporate settings.