África Ruiz-Gándara’s scientific contributions

A new family of inequality indices based on the deviation between the expected maximum and the expected minimum of random samples, called the nth Gini index is presented. These indices generalize the Gini index. At the same time, this family of indices and the S-Gini index are generalized by proposing the uv-Gini index, which turns out to be a convex combination of the S-Gini index and the Lorenz family of inequality measures. This family of Gini indices provides a methodology for achieving perfect equality in a given distribution of incomes. This is achieved through a series of successive and equal increases in the incomes of each individual.

A new family of inequality indices based on the deviation between the expected maximum and the expected minimum of random samples, called the nth Gini index is presented. These indices generalize the Gini index. At the same time, this family of indices and the S-Gini index are generalised by proposing the uv−Gini index, which turns out to be a convex combination of the S-Gini index and the Lorenz family of inequality measures. This family of Gini indices provides a methodology for achieving perfect equality in a given distribution of incomes. This is achieved through a series of successive and equal increases in the incomes of each individual.

Generative adversarial networks (GANs) have become a recent and rapidly developing research topic in machine learning. Since their inception in 2014, a significant number of variants have been proposed to address various topics across many fields, and they have particularly excelled not only in image and language processing but also in the medical and data science domains. In this paper, we aim to highlight the significance of and advancements that these GAN models can introduce in the field of Business Economics, where they have yet to be fully developed. To this end, a review of the literature of GANs is presented in general together with a more specific review in the field of Business Economics, for which only a few papers can be found. Furthermore, the most relevant papers are analysed in order to provide approaches for the opportunity to research GANs in the field of Business Economics.

This article explores the determinants of the adoption of artificial intelligence (AI) in small and medium-sized enterprises (SMEs) with special attention to the impact of competencies and skills. The research was based on data from a representative sample of SMEs in Spain and used logistic regression econometric analysis. Additionally, the study applied an innovative AI technique, Generative Adversarial Networks (GANs), to balance the data set. The findings indicate that SMEs whose business owners / managers have university degrees or high levels of professional training, those with information technology (IT) experts among their staff, and those providing IT-related training for employees are all more likely to adopt AI. Furthermore, SMEs equipped with management skills in Enterprise Resource Planning(ERP) systems and marketing analytics tools, and SMEs engaged in collaboration with universities and research centres, demonstrate a greater propensity to integrate AI into their operations. The implications of these findings are significant for both business management and public policy. From a managerial perspective, the results underscore the importance of investing in training programmes and initiatives aimed at upgrading the skill set of employees and managers to effectively use AI in business operations. On the policy front, the conclusions suggest an active role for public administrations in promoting the adoption of AI among SMEs by designing initiatives focused on improving key digital competencies. Keywords: Artificial intelligence, SME, digital competence, digital skill, digitalisation, information technology, GAN

Generative adversarial networks (GANs) have become a recent and rapidly developing research topic in Machine Learning. Since their inception in 2014, a significant number of variants have been proposed to address various topics across many fields, and has particularly excelled not only in image and language processing, but also in the medical and data science domains. In this paper, we aim to highlight the significance and advance that these GAN models can introduce in the field of Business Economics, where they have yet to be fully developed. To this end, a review of the literature of GANs is presented in general together with a more specific review in the field of Business Economics wherein only a few papers can be found. Furthermore, the most relevant papers are analysed in order to provide an approach the opportunity to research into GANs in the field of Business Economics.