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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
    ;
    Terán-Bustamante, Antonia  
    ;
    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
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    Item type:Publication,
    Machine Learning Model of Digital Transformation Index for Mexican Households
    (2022)
    García, Alfredo
    ;
    Salazar, Vladimir
    ;
    Ponce, Hiram  
    Digital transformation refers to the change in all aspects of human society by the adoption of digital technologies. Different methodologies and measurements have been proposed to determine the level of digital transformation in regions or countries. In this work, we propose the creation of a digital transformation index for Mexican households using machine learning models for digital transformation measurement analysis and estimation. We include three dimensions in terms of the information and communication technologies infrastructure, availability of services, and usage. We also use a public dataset from the Mexican government to build and train three machine learning models. Experimental results validate that our methodology can deliver a digital transformation measurement using machine learning models consistently with 84% of accuracy and 84% of F1-score. We also prototype a simple web application using the best machine learning model found. We anticipate that measuring the digital transformation in companies, governments, and households allows better decisions in business intelligence and public policy. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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