The increasing number of studies that underline the relationship between industry 4.0 and sustainability shows that sustainability is one of the pillars of smart factories. Through a bibliometric performance and network analysis (BPNA), this research describes the existing relationship between industry 4.0 and sustainability, the strategic themes from 2010 to March 2019, as well as the research gaps for proposing future work. With this goal in mind, 894 documents and 5621 keywords were included for bibliometric analysis, which were treated with the support of Science Mapping Analysis Software Tool (SciMAT). The bibliometric performance analysis presented the number of publications over time and the most productive journals. The strategic diagram shown 12 main research clusters, which were measured according to bibliometric indicators. Moreover, the network structure of each cluster was depicted, and the patterns found were discussed based on the documents associated to the network. Our findings show the scientific efforts are focused to enhance economic and environmental aspects and highlights a lack of effort relating the social sphere. Finally, the paper concludes the challenges, perspectives, and suggestions for the potential future work in the field of study relating to industry 4.0 and sustainability.

Precision Agriculture (PA) and Agriculture 4.0 (A4.0) have been widely discussed as a medium to address the challenges related to agricultural production. In this research, we present a Systematic Literature Review (SLR) supported by a Bibliometric Performance and Network Analysis (BPNA) of the use of A4.0 technologies and PA techniques in the coffee sector. To perform the SLR, 87 documents published since 2011 were extracted from the Scopus and Web of Science databases and processed through the Preferred Reporting Items for Systematic reviews and Meta-Analyzes (PRISMA) protocol. The BPNA was carried out to identify the strategic themes in the field of study. The results present 23 clusters with different levels of development and maturity. We also discovered and presented the thematic network structure of the most used A4.0 technologies in the coffee sector. Our findings shows that Internet of Things, Machine Learning and geostatistics are the most used technologies in the coffee sector, we also present the main challenges and trends related to technological adoption in coffee systems. We believe that the demonstrated results have the potential to be considered by researchers in future works and decision making related to the field of study.

Covid-19 represents the greatest challenge facing mankind today. In December 2019, several cases of pneumonia of unknown etiology were reported from China. This coronavirus infection subsequently identified as Covid-19 aroused worldwide concern. As a result, the scientific community has focused attention on Covid-19, as revealed by recent research reported in literature based on a holistic approach. In this regard, this study conducts a bibliometric analysis of coronavirus research in the literature with an emphasis on Covid-19 disease, using as a reference the publications in the Web of Science Core Collection from 1970 to 2020. This research analyzes 12,571 publications from 1970 to (April 18) 2020 by applying advanced bibliometric techniques in SciMAT bibliometric analysis software. The current research therefore provides a complete conceptual analysis of the main coronavirus types and strains in the literature by quantifying the main bibliometric performance indicators, identifying the main authors, organizations, countries, sources, and research areas, and evaluating the development of this field. Furthermore, a science map is constructed to understand the corresponding intellectual structure and main research lines (themes). SciMAT thereby offers a complete approach to the field and evaluates the main performance indicators related to coronavirus, with a focus on Covid-19. Finally, this research serves as a framework to strengthen existing research lines and develop new ones, establishing synergistic relationships that were not visible without the maps generated herein. Covid-19 represents the greatest challenge facing mankind today. In December 2019, several cases of pneumonia of unknown etiology were reported from China. This coronavirus infection subsequently identified as Covid-19 aroused worldwide concern. As a result, the scientific community has focused attention on Covid-19, as revealed by recent research reported in literature based on a holistic approach. In this regard, this study conducts a bibliometric analysis of coronavirus research in the literature with an emphasis on Covid-19 disease, using as a reference the publications in the Web of Science Core Collection from 1970 to 2020. This research analyzes 12,571 publications from 1970 to (April 18) 2020 by applying advanced bibliometric techniques in SciMAT bibliometric analysis software. The current research therefore provides a complete conceptual analysis of the main coronavirus types and strains in the literature by quantifying the main bibliometric performance indicators, identifying the main authors, organizations, countries, sources, and research areas, and evaluating the development of this field. Furthermore, a science map is constructed to understand the corresponding intellectual structure and main research lines (themes). SciMAT thereby offers a complete approach to the field and evaluates the main performance indicators related to coronavirus, with a focus on Covid-19. Finally, this research serves as a framework to strengthen existing research lines and develop new ones, establishing synergistic relationships that were not visible without the maps generated herein.

El profesional de la información (EPI): Bibliometric and thematic analysis (2006-2017)

Competitive Intelligence (CI) is a relatively novel discipline that is generating a growing interest in the field of strategic management. Its novelty, approach and origin have meant that the definition of the CI entails different interpretations and utilities depending on the audience. This research develops a conceptual analysis of CI in literature by quantifying the bibliometric performance indicators, identifying the main authors, countries, journals and research areas and evaluating the intellectual structure and evolution of the discipline using SciMAT as bibliometric analysis software. The bibliometric performance analysis is focused on the citation-based impact of the scientific output, while the science mapping illustrates the evolution of the research themes that build the discipline through the use of bibliometric network analysis techniques. To this purpose, the publications related to CI from 1984 to 2017 available at Scopus have been retrieved (5,275 publications). Finally, it offers a framework to support future researches.