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, 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, 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, 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, A Conceptual Framework for Digital Transformation of Business Models: Advancing Towards Industry 5.0(Springer Nature Switzerland, 2026); ; Hernández-Lara, Ana BeatrizDigital transformation is progressing unevenly across industries, with varying levels of success influenced by organizational and sector-specific factors. Understanding where to focus investments and what type of transformation to adopt has become a crucial challenge for companies seeking competitiveness and market relevance in the digital era. This paper aims to analyze companies’ strategic decision making to foster digital transformation, conducting a literature review, and proposing a conceptual framework for digital transformation of business models. The study identifies key drivers of successful digital transformation, including digital strategy, human capital, scalability, customer focus, security and risk management. Integrating these factors, the proposed model emphasizes the strategic alignment of digital initiatives with organizational goals, fostering a culture of continuous innovation and adaptability. The findings contribute to a deeper understanding of the mechanisms and prerequisites for effective digital transformation, offering insights for organizations navigating the shift toward Industry 5.0. ©The authors ©Springer.58 16 - 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); ; Tenate cheese is a traditional Mexican pressed semi-hard cheese made from raw cow’s milk and wrapped in palm fiber. The characterization of this cheese remains scarce. This study presents an exploratory characterization of a single production batch of traditional Tenate cheese obtained from one artisanal producer, providing preliminary information on its physicochemical, microbiological, and proximate analyses, combined with consumer evaluation. The latter was analyzed using hierarchical cluster analysis (HCA) as an exploratory segmentation tool. Tenate cheese was characterized as a semi-hard cheese with active lactic fermentation, a lactic aroma, acidic and umami flavors, and a firm, granular texture. Microbiological analyses showed the absence of coliforms, enterobacteria, and Staphylococcus aureus among the microorganisms evaluated, whereas yeast counts exceeded the regulatory limit. As major foodborne pathogens were not included in the microbiological assessment, the overall microbiological safety of the product could not be confirmed. A total of 318 consumers from Aguascalientes (AGS, n = 149) and the Guadalajara Metropolitan Area (GMA, n = 169) evaluated the product using hedonic and Just-About-Right scales. Consumers from AGS reported significantly higher liking scores than those from GMA. Penalty analysis identified insufficient softness as the main attribute associated with lower liking in AGS, whereas low flavor intensity and weak aftertaste reduced acceptance in GMA. Hierarchical cluster analysis (HCA) identified three consumer segments in each location, revealing distinct preference patterns linked to regional expectations. The main contributions of this study are threefold. First, it contributes to the limited scientific knowledge available on Tenate cheese by providing a comprehensive characterization of the analyzed sample. Second, it shows that consumer acceptance differed between two regional markets, comparing two university-affiliated consumer groups, highlighting the value of consumer segmentation for product positioning. Third, it proposes and applies an integrated framework combining physicochemical, microbiological, nutritional, sensory, and consumer segmentation analyses that can be applied to the study of other artisanal cheeses. © The authors © Applied Sciences © MDPI. - 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, 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, Addressing Class Imbalance in Healthcare Data: Machine Learning Solutions for Age-Related Macular Degeneration and Preeclampsia(IEEE, 2024); ; Miralles-Pechuán, LuisThe use of machine learning in healthcare has transformed the way diseases are diagnosed and treatments are optimized. However, medical databases often lack balanced data due to challenges in data collection caused by privacy regulations. Certain health conditions are underrepresented, which hampers machine learning performance. To address this problem, a hybrid approach has been proposed that combines the Synthetic Minority Oversampling Technique (SMOTE) with undersampling and uses two specific techniques tailored for imbalanced datasets. Comparative evaluations were conducted using various thresholds to reduce one class and employing Balanced Accuracy to mitigate bias toward the majority class, with popular machine learning methods. The results showed that Balanced Bagging and Balanced Random Forest consistently outperformed other methods, performing the best with an average ranking of 1.42 and 3.58 out of 32 configurations in the two datasets, respectively. Tree-based approaches such as Random Forest and Gradient Boosting demonstrated similar effectiveness, emphasizing the power of aggregating predictions from multiple trees to reduce bias. Notably, undersampling and SMOTE proved advantageous for non-tree-based models like KNN, SVM, and Logistic Regression showcasing their usefulness across different algorithms. This study provides a robust solution for handling imbalanced datasets in healthcare, which could potentially optimize healthcare interventions and improve patient outcomes and care©IEEE Latin America Transactions, The authorsScopus© Citations 1 12 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Agro‐Leadership Model in the Aquaculture Sector in MexicoThis research aims to analyze leadership for innovation in the Mexican aquaculture sector. The main characteristics of aquaculture producers in the Mezquital Valley of Hidalgo State have been identified and grouped according to an Agro-leadership profile. The methodological strategy uses machine learning techniques. The data were obtained from a questionnaire of 40 owners or representatives of aquaculture farms. The results show that most leaders with the characteristics of the Agro-leadership model directly influence innovative products, processes, managers, and marketing. According to the developed model, the most relevant factors are seeking solutions to problems, leadership grounded in personal values, establishing connections among leaders, and providing opportunities for others' personal and professional development. These results provide empirical evidence for decision-making, both for entrepreneurs in the sector seeking to strengthen their innovation capacity and for public policymakers designing targeted support programs, by highlighting the fundamental role of values-based leadership and collaborative networks in transforming aquaculture practices. ©The authors © Wiley.9 3