Martínez Velasco, Antonieta Teodora
Main Affiliation
Preferred name
Martínez Velasco, Antonieta Teodora
Official Name
Martínez Velasco, Antonieta Teodora
ORCID
0000-0001-6535-1440 
Researcher ID
DWK-4326-2022
Scopus Author ID
57192978286
38 results
Now showing 1 - 10 of 38
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Branding interno en una empresa de servicios en MéxicoEl objetivo de la presente investigación es analizar los procesos de branding interno de una empresa de servicios del conocimiento en México, de manera que estos permitan fortalecer sus ventajas competitivas a través del liderazgo de su capital humano. Lo anterior se logra a través de dilucidar si la capacitación y motivación hacia la marca influyen positivamente en el desempeño de esta. La estrategia metodológica es de tipo cuantitativo a través de herramientas estadísticas y de machine learning. La evidencia muestra que la alineación por parte de los empleados con los valores de la marca muestra un impacto positivo y significativo con su motivación a favor de la marca y el desempeño de esta. Los factores más significativos dentro de la dimensión de compromiso es el binomio capacitación y liderazgo de los jefes.1 14 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Knowledge Management and Innovation in the Furniture Industry in Mexico(Springer, 2023); Knowledge is an asset for all companies. Consequently, learning and accumulating new knowledge over time constitute the essence of the innovation process. Thus, knowledge management is of the utmost importance. This research aims to analyze and propose a knowledge management model for innovation through Bayesian networks with machine learning techniques in the furniture industry. We develop a model where we identify and quantify the impact of critical factors on the generation of innovative value. The results show that the most relevant factors for optimal knowledge management in the furniture industry are suppliers, distributors, the enterprises’ business model structure, quality and risk management, strategic planning, value system model, national and international markets, e-commerce, and human capital. ©Springer ©The authors.5 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Metodología para determinar los factores de riesgo asociados con enfermedades complejas: Degeneración Macular Relacionada con la Edad y Preeclampsia(2021); 7 21 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Innovation and technological management model in the tequila sector in Mexico(2022); ; Scopus© Citations 2 2 8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Physicochemical, Microbiological, Proximate, and Consumer Characterization of Traditional Tenate Cheese in Two Mexican Regions(MDPI AG, 2026-07-08); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Transformación digital para la competitividad de las empresas(2024); <jats:p>La transformación digital es un proceso tanto tecnológico como sociocultural que involucra la adopción de tecnologías digitales y modificaciones en los modelos de negocio y la estrategia de las firmas. La literatura que estudia relaciones entre la transformación digital y la competitividad de las empresas se incrementó en el periodo de postpandemia, sin embargo, las investigaciones antes de la pandemia son escasas, por lo cual el objetivo de esta investigación es analizar el efecto de la transformación digital en la competitividad de las empresas antes de esta crisis. Los datos que se utilizaron para el análisis son de las Encuestas Nacionales sobre Productividad y Competitividad de las Pymes en Sectores Estratégicos en México. El análisis se realizó en dos partes: I. geoestadístico para buscar clústeres geográficos de corte natural y II. estadístico mediante la regresión Ridge. Los resultados evidenciaron que tanto la transformación digital como la competitividad se distribuyen de manera desigual en los territorios; sin embargo, se encontró un efecto positivo de la transformación digital en la competitividad de estas. De acuerdo con lo anterior, se concluyó que las empresas que quieran aumentar su competitividad deben incrementar su transformación digital, por lo que los gobiernos deben continuar estableciendo políticas y programas de transformación digital en todos los sectores y en forma más equitativa.</jats:p>40 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Machine learning method to establish the connection between age related macular degeneration and some genetic variations(2016); ; 2 16Scopus© Citations 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Management of scientific and ancestral knowledge: a decision-making model in mezcal industry in Mexico(Frontiers Media SA, 2025); ; Leyva-Hernández, Sandra NellyIntroduction: Knowledge management is essential to ensure the sustainability of rural communities and small producers since it generates value for innovation, productivity, and competitiveness. The aim of this study is to identify relevant factors for adequate decision-making in managing knowledge in the Mexican mezcal industry and its impact on developing rural communities and small producers - mezcaleros. For this purpose, a decision-making model for managing scientific and ancestral knowledge is created to support links with universities, research centers, and rural communities to accelerate innovation and competitiveness in this sector. Methods: The analysis methods were carried out through decision-making, machine-learning techniques, and fuzzy logic. Results: The Bayesian Network model suggests that the preceding variables to optimize the Mezcaleros Knowledge Management are the Mezcaleros Indigenous community, the Denomination of Origin, Scientific and Ancestral Knowledge, Waste Management and Use, and Jima. Discussion: This knowledge management model aims to guide small producers to be more productive and competitive through the support of a facilitator. ©The authors ©Frontiers in Artificial Intelligence ©Frontiers Media SA.16 16 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Entrepreneurship: Analysis by Country Through Machine Learning TechniquesThis research aims to analyze entrepreneurship worldwide through the dimensions and pillars of the entrepreneurship ecosystem of each country, identifying the contribution and patterns of behavior and correlation within the entrepreneurship ecosystem. This analysis intends to show the main actions that countries have carried out in support of entrepreneurship and entrepreneurs. The tool used to analyze is machine learning, where various algorithms are applied. The evidence shows that the most relevant pillars in the entrepreneurial ecosystem are I. Opportunity Startup, II. Technology Absorption, III. Risk Acceptance, IV. Risk Capital and V. Process Innovation. The pillars that best correlate are I. Competition and Opportunity Startup, II. Opportunity Startup, and Risk Acceptance, III. Opportunity Startup and Technology Absorption, IV. Cultural Support and Opportunity Startup, and V. Opportunity Startup and Risk Capital. The present work aims to provide knowledge to decision-makers in both the public and private sectors to channel public policies that support entrepreneurs in this time of crisis and promote the generation and strengthening of entrepreneurial activity. Although there are still no reliable GEI data for the years 2020 to 2022, the economic crisis generated by the stagnation in the development of the countries has reduced support for entrepreneurs, which in many cases can be a key factor for the rescue of the most disadvantaged countries.Scopus© Citations 1 2 14 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, University–Industry Collaboration: A Sustainable Technology Transfer ModelFaced with the pandemic caused by COVID-19, universities worldwide are giving a powerful response to support their communities. One way to provide support is via the collaboration between universities and industries, allowing the co-creation of knowledge that leads to innovation. Historically, universities, as knowledge-intensive organizations (KIOs), have produced knowledge through research. At present, its important contribution to countries’ economy is widely recognized through the development of new knowledge and technical know-how. Universities are a source of innovation for firms, which ultimately translates into social welfare improvements. The objective of this research is to analyze the university–firm linkage. The methodological strategy is carried out using Bayesian networks through a model where the main elements of university–industry linking, which impact competitiveness and innovation, are identified and quantified. The technology transfer model shows that the most crucial processes are Technology Strategy, Value Proposal, Knowledge Management, Control and Monitoring, Innovation Management, Needs Detection, Knowledge Creation, New Products and Services, and Absorption Capacity. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.Scopus© Citations 14 1 18