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    Item type:Publication,
    SECI model of knowledge management: A thematic analysis with emphasis on agricultural organizations
    (Pro-Metrics, 2024)
    López-Meza, Evelia
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    Leyva-Hernández, Sandra Nelly
    Objective. This research aimed to identify the thematic trends in knowledge management through Nonaka and Tekeuchi's socialization, externalization, combination, and internalization (SECI) model. First, an analysis of the application of the model in general and then in the field of agriculture was conducted. Design/Methodology/Approach. A bibliometric analysis was performed, and 2201 indexed papers from the Scopus database between the years 1994 and 2024 were considered. The study used the authors' keywords to identify thematic trends through word co-occurrences. Results/Discussion. Thematic cores related to innovation and open innovation were identified. This model has experienced a notable boom in recent years. In the agricultural sector, knowledge creation and transfer represent a part of the model that has experienced increasing use. The importance of understanding and effectively using the model to drive innovation and sustainable development in agriculture was stressed. Therefore, it was proposed that knowledge be transformed into a source of knowledge. Conclusions. Despite the criticisms received, this paper highlights the lack of research on using the SECI model in agriculture and its relevance in advancing knowledge management research. Furthermore, the results point out that, for the agricultural sector, future research on knowledge management should focus on organizational learning mechanisms, social innovation, critical success factors, business processes, and job satisfaction. ©The authors ©Iberoamerican Journal of Science Measurement and Communication ©Pro-Metrics.
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    Item type:Publication,
    Machine learning models in health prevention and promotion and labor productivity: A co-word analysis
    (Pro-Metrics, 2024)
    Domínguez Miranda, Sergio Arturo
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    Objective: The objective of this article is to carry out a co-word study on the application of machine learning models in health prevention and promotion, and its effect on labor productivity. Methodology: The analysis of the relevant literature on the proposed topic, identified in the last 15 years in Scopus, is considered. Articles, books, book chapters, editorials, conference papers and reviews refereed publications were considered. A thematic mapping analysis was performed using factor analysis and strategy diagrams to derive primary research approaches and identify frequent themes as well as thematic evolution. Results: The results of this study show the selection of 87 relevant publications with an average annual growth rate of 23.25% in related production. The main machine learning algorithms used, the main research approaches and key authors, derived from the analysis of thematic maps, were identified. Conclusions: This study emphasizes the importance of using co-word analysis to understand trends in research on the impact of health prevention and promotion on labor productivity. The potential benefits of using machine learning models to address this issue are highlighted and anticipated to guide future research focused on improvements in labor productivity through prevention and promotion of health. Originality: The identification of the relationship between work productivity and health prevention and promotion through machine learning models is a relevant topic but little analyzed in recent literature. The analysis of co-words allows us to establish the reference point of the state of the art in this regard and future trends.
    Scopus© Citations 1  20
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    Item type:Publication,
    Transformación digital en ciencias administrativas y contabilidad: tendencias de investigación en Scopus
    (Pro-Metrics, 2024)
    Salgado-García, Jorge Arturo
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    González-Zelaya, Vladimiro
    Objective. Identify thematic trends in digital transformation in administrative sciences and accounting. Design/Methodology/Approach. A bibliometric analysis was performed considering 7,519 documents indexed in the Scopus database between 1970 and 2023. The analysis was performed using the authors' keywords to identify thematic trends. Results/Discussion. Thematic cores related to Covid-19, digital marketing, emerging technologies, innovation, industry 4.0, and Fintech were identified. Conclusions. Covid-19 promoted digital transformation and research in this field applied to administrative sciences and accounting. However, the advancement of digital technologies has influenced scientific production. Likewise, other trends, such as sustainability, converged in the generation of knowledge. © Iberoamerican Journal of Science Measurement and Communication.
    Scopus© Citations 6  42