Bibliometric Dynamics of Innovation in Emerging Economies: Evolution, Correlations, and Strategic Patterns
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Abstract
To analyze the bibliometric evolution of innovation during the 2016–2019 period based on scientific output and citation records obtained from sources indexed in Scopus, taking into account publication trends, levels of academic impact, and thematic relationships related to the field of study. Methodology: The study was conducted using a quantitative bibliometric approach, utilizing information from indexed publications and citation databases corresponding to the selected period. To assess the association between scientific output and academic impact, linear regression models and Pearson correlation tests were applied. Additionally, titles and keywords were reviewed to identify recurring thematic lines and predominant trends within the analyzed document corpus. Results: The statistical results showed a positive association between the number of publications and the impact measured by academic citations (R²=0.71; p<0.01). Likewise, a significant relationship was observed between innovation and artificial intelligence (r=0.76; p<0.01), although some categories exhibited moderate differences in their thematic patterns. Among the most frequently published topics were digital transformation, data analytics, and sustainability. Conclusions: During the period analyzed, research related to innovation and emerging technologies showed a progressive increase, especially in areas associated with artificial intelligence and digital transformation. The findings also allow for the identification of a thematic concentration in certain fields of research, an aspect that may contribute to the development of future analyses on scientific policies, academic evolution, and research projections.
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