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.

In this paper, we will provide an extensive analysis of how Generative Artificial Intelligence (GenAI) could be applied when handling Supply Chain Management (SCM). The paper focuses on how GenAI is more relevant in industries, and for instance, SCM where it is employed in tasks such as predicting when machines are due for a check-up, man-robot collaboration, and responsiveness. The study aims to answer two main questions: (1) What prospects can be identified when the tools of GenAI are applied in SCM? Secondly, it aims to examine the following question: (2) what difficulties may be encountered when implementing GenAI in SCM? This paper assesses studies published in academic databases and applies a structured analytical framework to explore GenAI technology in SCM. It looks at how GenAI is deployed within SCM and the challenges that have been encountered, in addition to the ethics. Moreover, this paper also discusses the problems that AI can pose once used in SCM, for instance, the quality of data used, and the ethical concerns that come with, the use of AI in SCM. A grasp of the specifics of how GenAI operates as well as how to implement it successfully in the supply chain is essential in assessing the performance of this relatively new technology as well as prognosticating the future of generation AI in supply chain planning.

Universities play a key role in university-industry-government interactions and are important in innovation ecosystem studies. Universities are also expected to engage with industries and governments and contribute to economic development. In the age of artificial intelligence (AI), governments have introduced relevant policies regarding the AI-enabled innovation ecosystem in universities. Previous studies have not focused on the provision of a dynamic capabilities perspective on such an ecosystem based on policy analysis. This research work takes China as a case and provides a framework of AI-enabled dynamic capabilities to guide how universities should manage this based on China’s AI policy analysis. Drawing on two main concepts, which are the innovation ecosystem and dynamic capabilities, we analyzed the importance of the AI-enabled innovation ecosystem in universities with governance regulations, shedding light on the theoretical framework that is simultaneously analytical and normative, practical, and policy-relevant. We conducted a text analysis of policy instruments to illustrate the specificities of the AI innovation ecosystem in China’s universities. This allowed us to address the complexity of emerging environments of innovation and draw meaningful conclusions. The results show the broad adoption of AI in a favorable context, where talents and governance are boosting the advance of such an ecosystem in China’s universities.

Artificial intelligence (AI) has rapidly evolved, transforming industries and addressing societal challenges across sectors such as healthcare and education. This study provides a state-of-the-art overview of AI research up to 2023 through a bibliometric analysis of the 50 most influential papers, identified using Scopus citation metrics. The selected works, averaging 74 citations each, encompass original research, reviews, and editorials, demonstrating a diversity of impactful contributions. Over 300 contributing authors and significant international collaboration highlight AI’s global and multidisciplinary nature. Our analysis reveals that research is concentrated in core journals, as described by Bradford’s Law, with leading contributions from institutions in the United States, China, Canada, the United Kingdom, and Australia. Trends in authorship underscore the growing role of generative AI systems in advancing knowledge dissemination. The findings illustrate AI’s transformative potential in practical applications, such as enabling early disease detection and precision medicine in healthcare and fostering adaptive learning systems and accessibility in education. By examining the dynamics of collaboration, geographic productivity, and institutional influence, this study sheds light on the innovation drivers shaping the AI field. The results emphasize the need for responsible AI development to maximize societal benefits and mitigate risks. This research provides an evidence-based understanding of AI’s progress and sets the stage for future advancements. It aims to inform stakeholders and contribute to the ongoing scientific discourse, offering insights into AI’s impact at a time of unprecedented global interest and investment.

The introduction of artificial intelligence (AI) marks the beginning of a revolutionary period for the global economic environments, particularly in the developing economies of Africa. This concept paper explores the various ways in which AI can stimulate economic growth and innovation in developing markets, despite the challenges they face. By examining examples like VetAfrica, we investigate how AI-powered applications are transforming conventional business models and improving access to financial resources. This highlights the potential of AI in overcoming obstacles such as inefficient procedures and restricted availability of capital. Although AI shows potential, its implementation in these areas faces obstacles such as insufficient digital infrastructure, limited data availability, and a lack of necessary skills. There is a strong focus on the need for a balanced integration of AI, which involves aligning technological progress with ethical considerations and economic inclusivity. This paper focuses on clarifying the capabilities of AI in addressing economic disparities, improving productivity, and promoting sustainable development. It also aims to address the challenges associated with digital infrastructure, regulatory frameworks, and workforce transformation. The methodology involves a comprehensive review of relevant theories, literature, and policy documents, complemented by comparative analysis across South Africa, Nigeria, and Mauritius to illustrate transformative strategies in AI adoption. We propose strategic recommendations to effectively and ethically utilize the potential of AI, by advocating for substantial investments in digital infrastructure, education, and legal frameworks. This will enable Africa to fully benefit from the transformative impact of AI on its economic landscape. This discourse seeks to offer valuable insights for policymakers, entrepreneurs, and investors, emphasizing innovative AI applications for business growth and financing, thereby promoting economic empowerment in developing economies.

This study aims to predict whether university students will make efficient use of Artificial Intelligence (AI) in the coming years, using a statistical analysis that predicts the outcome of a binary dependent variable (in this case, the efficient use of AI). Several independent variables, such as digital skills management or the use of Chat GPT, are considered.The results obtained allow us to know that inefficient use is linked to the lack of digital skills or age, among other factors, whereas Social Sciences students have the least probability of using Chat GPT efficiently, and the youngest students are the ones who make the worst use of AI.