Job satisfaction and digital talent are topics of growing interest in the context of digital transformation. Digitalization is changing the way organizations operate and how employees perceive their work. The state and its administrative staff is no exception, as these capabilities are essential to perform operational tasks that underpin the public institution's documentary processes. This study investigates the influence of digital talent (independent variable) on job satisfaction (dependent variable), employing structural equation modeling (SEM) using WarpPLS software. Digital Talent is broken down into three sub-variables: Digital Competencies of Employees (DCE), Capacity for Digital Innovation and Creativity (CIDC) and Adaptability and Continuous Learning (ACL), while Job Satisfaction is measured through two sub-variables: Work Environment (WE) and Professional Development Opportunities (PDO). The analyses revealed that Capacity for Innovation and Digital Creativity (CIDC) has a significant impact on Work Environment, with a path coefficient (β) of 0.13 (p = 0.01). Similarly, adaptability and continuous learning (ACL) positively influence the work environment, with a path coefficient (β) of 0.10 (p = 0.04). In addition, a strong relationship was found between professional development opportunities (PDO) and work environment, with a path coefficient (β) of 0.68 (p < 0.001). For the relationship between digital competencies (DCE) and career development opportunities, the path coefficient was 0.10 (p = 0.04). Digital talent is a key predictor of job satisfaction in administrative staff. The results suggest that investing in the development of digital capabilities, especially innovation and creativity, as well as adaptability, is essential to improve the work environment and career development opportunities.

Social cybernetics, as an interdisciplinary field, has gained increasing interest in the last decade due to the influence of information technologies in society through connectivity, Internet of things, process automation, artificial intelligence among others. This research focuses on exploring the relationship between social cybernetics (communication and control mechanisms) based on information technologies and local development, using structural equation modeling as an analytical tool. The design was non-probabilistic, with a sample of 482 people. The independent variables under study were Use of ICT for communication between local authorities and the population (CAP), Use of ICT for collaboration between public and private institutions (CPPC), Use of ICT for shared decision making (SDM), Use of ICT for local development planning (LDP) and Use of ICT for knowledge management (KM); and the dependent variable was Local Development (LD). It was determined that there is a relationship between all of them except with CAP. The direction and magnitude of the other ratios were: + 0.1390; - 0.3661; + 0.4472 and + 0.8432 respectively. The coefficient of determination (R2) was 93.69% facilitating the prediction of future results. The model has an adequate fit.

The business context has been affected by the action of Covid 19, and companies must react to this new reality. The research analyzes people, processes and technology and their effect on the development of the respective business strategy in small companies in Huancayo, Peru. The study design was non-probabilistic, cross-sectional, qualitative-quantitative and explanatory in a sample of 99 business units. The analytical technique used was structural equation modeling based on PLS. The results indicate that there is a positive relationship between the processes and technology used and the implementation of actions to achieve their objectives. No significant relationship was found with people, given that in the face of the health emergency, economic paralysis, closure of markets and sectors, the companies had to make decisions and postpone concern for human resources. The coefficient of determination (R2) was 79.3%, which shows that the model presented allows predicting future results.