Producción Científica

This study addresses the significant impact of technology on the growth and prominence of contemporary organizations, examining the relationship between digital transformation and business sustainability. The research highlights the importance of digital adaptations to maintain relevance in the global market. The main focus is on conducting a bibliometric analysis of digital transformation, identifying emerging themes that contribute to the valorization of organizations. The adopted methodology consists of a literature review, using the Scopus database and tools such as VOSviewer and Gephi for network analysis of keyword co- occurrence, co-citation of cited references, bibliographic coupling of documents, and identification of emerging trends. Additionally, content analysis is employed for a more detailed examination of the identified themes. The results indicate that the effectiveness of digital transformation in an organization is intrinsically linked to its level of maturity in electronic governance. Furthermore, it is evident that the adoption of an IT governance framework is crucial for a successful implementation of digital transformation.

Abstract: Over recent years, disruptive technologies have shown considerable potential to improve supply chain efficiency. In this regard, numerous papers have explored the link between machine learning techniques and supply chain performance. However, research works still need more systematization. To fill this gap, this paper aims to systematize published papers highlighting the impact of advanced technologies, such as machine learning, on supply chain performance. A structured content analysis was conducted on 91 selected journal articles from the Scopus and Web of Science databases. Bibliometric analysis has identified nine distinct groupings of research papers that explore the relationship between the machine learning and supply chain performance. These clusters cover topics such as big data and supply chain management, knowledge management, decision-making processes, business process management, and the applications of big data analytics within this domain. Each cluster’s content was clarified through a rigorous systematic literature review. The proposed study can be seen as a kind of comprehensive initiative to systematically map and consolidate this rapidly evolving body of literature. By identifying the key research themes and their interrelationships, this analysis seeks to elucidate the current state-of-the-art and to highlight potential directions for future research in this critical field.

This study conducts a comprehensive exploration of expert retrieval using a dual approach of bibliometric analysis and systematic review, guided by the Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) methodology. From 2000 to 2023, our investigation reveals a notable upward trajectory in expert locating study, focusing on 494 articles identified from Scopus using specific keywords related to Expert Finding (EF) and Expert Finding Systems (EFSs). Through bibliometric analysis, utilizing VOSviewer, we identify prominent co-author groups, highly-cited documents, and global participation, shedding light on the collaborative and internationally expansive nature of EF investigations. Keyword co-occurrence and text analysis reveal thematic clusters, signaling the evolving emphases in the field from foundational expert search tasks to considerations of platform interactions. Simultaneously, our systematic review, conducted on a subset of 51 articles using NVivo, explores domains seeking expert solutions, prevalent datasets, and common evaluation methods. This research not only synthesizes the current state of EF and EFS literature but also charts a course for future exploration, contributing to a deeper understanding of the field and guiding the trajectory of forthcoming research endeavors.