Mapping artificial intelligence journals and job stress: A bibliometric and citation network analysis
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Abstract
This research allows the development of a mapping of journals focused on artificial intelligence and work stress where a bibliometric analysis of the study variables is performed, it aims to identify the most relevant information as far as scientific production worldwide is concerned, taking into consideration relevant aspects such as co-citations, cluster management and countries with the largest amount of production in this field has been written; the amount of retrospective literature in this field is quite representative throughout this time, for this research, quite relevant indicators were selected so as not to hinder the vision that we want to obtain with regard to the results, therefore the methodology used is the technique of mapping and grouping of indicators that help the visualization of information and therefore the structure of the literature, The results of this study is the grouping and systematic exploration of the research and thus provide a taxonomic scheme that serves as a basis for future research, the data analyzed were extracted from the Scopus database and the lens. A total of 745 contributions were identified as potential to reinforce the understanding of the structured taxonomy that will benefit the scientific community.
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