Strategically managing production systems is crucial for creating value and enhancing the competitive capabilities of companies. However, research on organizational culture within these systems is scarce, particularly in the Colombian context. This research aims to evaluate cultural profiles and their impact on the performance of production systems in Colombian firms. The regional focus is vital as cultural and contextual factors can vary significantly between regions, influencing organizational behavior and performance outcomes. To achieve this, we make a study in a sample of Colombian companies, with participation from working students of the Universidad Nacional Abierta y a Distancia (UNAD). We used a data analytics approach to collected data. The results will be relevant to both the scientific community and business practitioners. This research seeks to determine whether the perception of the work environment within a company influences the perceived performance of the company. The findings will provide a deeper understanding of the relationship between organizational culture and production system performance, offering a foundation for business decision-making and enhancing competitiveness in Latin American context.

The study’s objectives are to investigate the relationships between earnings management, government ownership, and corporate performance in the Gulf Cooperation Council (GCC) region during the period 2017–2021, utilizing a dataset comprising 188 companies. It further explores the moderating role of government ownership in the association between earnings management and company performance. The study used the panel regression data analysis to investigate the relationship between the variables under the study. Employing linear regression and moderated linear regression, the research discerns notable patterns. The result shows a positive effect emerges between government ownership and corporate performance. Conversely, the result shows a negative association is observed between earnings management and corporate performance. Finally, the moderating role of government ownership in GCC countries is a good governance mechanism to mitigate the agency problem.

Hospitals belong to public places, and implementing refined management in hospitals is a need for patients. Under party building management, hospitals must manage hospitals in accordance with the party's governing philosophy. In the new era, China's party building management is facing enormous challenges. In order to implement party and government management in the new era, hospitals must strengthen their understanding of party building and further implement refined management at a deeper level.

This paper explores the role of the agile approach in managing interorganizational relationships in innovation networks. Design/methodology/approach. Relevant literature related to agile team management, network theory, innovation theory and knowledge management was studied. Based on collaboration between different approaches, a conceptual model for agile management of an innovation network was generated. Conceptual modeling was supplemented with graphical notation (diagram) of the main elements of the model. At the stage of testing the conceptual model, the action research method was applied, which provides an opportunity for organizational innovations to be carried out with the participation of researchers. The object of the pilot implementation of the conceptual model is the Bulgarian division of a global non-governmental organization (NGO) dedicated to community service. The organizational innovation applied in the testing of the model is related to improving the communication environment between individual teams (clubs), which are autonomous, but in the conditions of a network can generate projects for common, large-scale initiatives for community service. Findings. The pilot testing of the model shows its applicability, insofar as a traditionally managed structure switches to an agile communication model, in which horizontal connections become more frequent and knowledge between individual participants is transferred more efficiently. The possibility of decentralized decision-making creates the potential for generating numerous new and larger-scale initiatives for the benefit of the final beneficiaries. The participants in the network have also outlined some shortcomings, such as the need for better preliminary preparation when introducing organizational innovations in order to adequately explain and accept them.

The economy, unemployment, and job creation of South Africa heavily depend on the growth of the agricultural sector. With a growing population of 60 million, there are approximately 4 million small-scale farmers (SSF) number, and about 36,000 commercial farmers which serve South Africa. The agricultural sector in South Africa faces challenges such as climate change, lack of access to infrastructure and training, high labour costs, limited access to modern technology, and resource constraints. Precision agriculture (PA) using AI can address many of these issues for small-scale farmers by improving access to technology, reducing production costs, enhancing skills and training, improving data management, and providing better irrigation infrastructure and transport access. However, there is a dearth of research on the application of precision agriculture using artificial intelligence (AI) by small scale farmers (SSF) in South Africa and Africa at large. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) and Bibliometric analysis guidelines were used to investigate the adoption of precision agriculture and its socio-economic implications for small-scale farmers in South Africa or the systematic literature review (SLR) compared various challenges and the use of PA and AI for small-scale farmers. The incorporation of AI-driven PA offers a significant increase in productivity and efficiency. Through a detailed systematic review of existing literature from inception to date, this study examines 182 articles synthesized from two major databases (Scopus and Web of Science). The systematic review was conducted using the machine learning tool R Studio. The study analyzed the literature review articled identified, challenges, and potential societal impact of AI-driven precision agriculture.