Evolutionary Dynamics of Scientific Innovation: A Bibliometric and Inferential Analysis of Academic Production (2016–2020)
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Abstract
This paper analyzes the bibliometric performance of scientific innovation between 2016 and 2020 based on indexed academic output and citation records, taking into account trends in impact and thematic concentration within the study period. The research was conducted from a quantitative perspective using bibliometric data obtained from international indexed databases for the 2016–2020 period. To examine the relationship between scientific output and academic impact, correlation and linear regression analyses were applied. Additionally, keywords and titles were reviewed to identify recurring thematic trends within the analyzed document corpus. A thematic categorization analysis based on document keywords and titles was conducted. The results show an association between the volume of publications and the number of citations recorded during the study period (r=0.87; p<0.01). The regression model exhibited stable statistical performance (R²=0.76), although with moderate differences among some thematic categories. The most frequently occurring areas were related to artificial intelligence, data analytics, and digital transformation. The results reveal a consistent relationship between scientific output and academic impact within the study period, particularly in research linked to emerging technologies and digital transformation. Furthermore, some recent studies highlight that factors associated with organizational resilience and knowledge management continue to be relevant within post-COVID-19 institutional adaptation processes (Carreón Muñoz, 2024).
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