Dávila-Aragón, Griselda
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Preferred name
Dávila-Aragón, Griselda
Official Name
Dávila-Aragón, Griselda
Alternative Name
gdavila
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ORCID
Scopus Author ID
56957235600
Researcher ID
ELY-9463-2022

Research Output
Now showing 1 - 10 of 12
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La popularidad de las marcas y su valor económico en el marco de las finanzas corporativas: un análisis de aprendizaje máquina

2022 , Morales González, Víctor Miguel , Dávila-Aragón, Griselda , Ortiz Arango, Francisco

A lo largo del tiempo, la marca ha tomado un papel significativo en el ámbito empresarial, la percepción de la imagen comercial y el valor agregado. Este estudio está enfocado en explorar los componentes del concepto del valor de marca a partir de un diagnóstico y técnicas de aprendizaje máquina, para desarrollar una serie de modelos asociados a las dimensiones del valor de marca percibido desde un concepto más actual de la popularidad. La metodología de aprendizaje máquina, prioriza la predicción frente a la inferencia. No impone una especificación ni una teoría, a diferencia de la estadística clásica, donde se requiere especificar un modelo; esto representa una forma dinámica alternativa para entender cómo uno de los recursos más importantes de las empresas en el mercado está presente, lo que sin duda repercute en la gestión financiera y de riesgos de la empresa. Los resultados obtenidos mediante tres técnicas diferentes de aprendizaje máquina, muestran que las once variables propuestas en el estudio influyen positivamente con diferente intensidad en la popularidad de la marca.

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Las finanzas de los hogares mexicanos: análisis con Redes Bayesianas

2021 , Dávila-Aragón, Griselda , Ortiz Arango, Francisco , Cabrera Llanos, Agustín Ignacio

El bienestar de los hogares está ligado en gran parte al desarrollo de los mercados financieros. El estudio de las finanzas de los hogares analiza las formas en que estos utilizan instrumentos financieros para satisfacer sus necesidades y objetivos; este análisis representa un gran desafío debido a la escasa información estadística y la interrelación entre las variables consideradas. En este trabajo, pionero en el uso de las redes bayesianas en este campo, utilizamos de manera conjunta las finanzas tradicionales y las conductuales. Medimos la probabilidad de prevalencia de estabilidad financiera de los hogares en México; obtenemos un resultado base y posteriormente, al generar distintos escenarios, descubrimos que las variables más determinantes son el manejo del crédito y la conformación de los hogares. Estos resultados subrayan la importancia de promover iniciativas de educación financiera en los distintos niveles, modalidades y subsistemas educativos. © 2021 Universidad Nacional Autónoma de México. In today's economy, the well-being of households is considered to be linked mainly to the development of financial markets. The field of household finance analyzes how households use financial instruments to satisfy their needs and achieve objectives. This analysis represents a significant challenge due to scarce statistical information and the interrelation among the variables involved. We follow two aspects: Traditional and behavioral finance. This paper pioneers the use of Bayesian networks in the field. A model measuring the probability of prevalence of financial stability of households in Mexico is used; a baseline result is obtained and then, while generating different scenarios, we discover that credit management and household composition are the most determining variables. These results underscore the importance of promoting different financial education initiatives at different educational levels, modalities, and subsystems. © 2021 Universidad Nacional Autónoma de México.

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Cálculo del valor en riesgo operacional mediante redes bayesianas para una empresa financiera

2016 , Dávila-Aragón, Griselda , Ortiz Arango, Francisco , Cruz Aranda, Fernando

El objetivo del presente trabajo es plantear la metodología basada en el uso de redes bayesianas (RB) para identificar y cuantificar los factores de riesgo operacional (RO) asociados al proceso de transacciones financieras a través de medios electrónicos en una empresa financiera. El modelo de RB desarrollado se ejemplifica con datos de eventos simulados en un periodo equivalente a seis años a partir de información proporcionada por expertos en este tipo de procesos. Lo anterior representa una de las principales ventajas del uso de RB, pues permiten modelar las relaciones causa-efecto entre los diferentes factores de riesgo operacional. Finalmente se realiza el cálculo del Valor en Riesgo Operacional (OpVaR) para el ejemplo, en el que se incorporan factores de interacción que no son considerados en el modelo tradicional, proporcionando mejores condiciones de credibilidad a este valor.

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Digital consumer behavior and medical tourism: A regional analysis in Mexico

2023 , Arrioja Castrejón, Edmundo , López-Fernández, Andreé Marie , Ramírez Pérez, Héctor Xavier , Dávila-Aragón, Griselda

Medical tourism has increasingly become an important alternative to receive healthcare services given medical systems’ limitations such as: treatment availability, access, and price. The industry has significantly grown with the availability of internet services and digital platforms which enable consumers to connect with service providers as well as other stakeholders around the world. And, considering medical tourism profiles related to travel frequency, expenditure, place, and degree of digital platform use, the question is how does digital platform use impact medical tourism consumer behavior related to the type of destination? Cluster analysis and georeferencing analytics were utilized to study the correlation between digital platform use and the preferred type of destination for medical tourism. The study shows a clear positive correlation between the variables compared.

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Knowledge Management for Open Innovation: Bayesian Networks through Machine Learning

2021 , Terán-Bustamante, Antonia , Martínez Velasco, Antonieta Teodora , Dávila-Aragón, Griselda

Knowledge management within organizations allows to support a global business strategy and represents a systemic and organized attempt to use knowledge within an organization to improve its performance. The objective of this research is to study and analyze knowledge management through Bayesian networks with machine learning techniques, for which a model is made to identify and quantify the various factors that affect the correct management of knowledge in an organization, allowing you to generate value. As a case study, a technology-based services company in Mexico City is analyzed. The evidence found shows the optimal and non-optimal management of knowledge management, and its various factors, through the causality of the variables, allowing us to more adequately capture the interrelationship to manage it. The results show that the most relevant factors for having adequate knowledge management are information management, relational capital, intellectual capital, quality and risk management, and technology assimilation. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

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Survival Likelihood of Micro and Small Businesses Facing a Catastrophe

2021 , Dávila-Aragón, Griselda , Rivas Aceves, Salvador , Ramírez Pérez, Héctor Xavier

This chapter proposes a measurement methodology throughout a Bayesian Network to quantify the survival probability of micro and small enterprises (MSEs) facing a catastrophic event, and to assess if a Business Continuity Plan (BCP) is a unique alternative to prevent companies from bankruptcy. Empirical evidence for a developing country shows the majority of companies are MSEs and without enough knowledge about a BCP; therefore, the likelihood of businesses’ survival will depend on BCP and several other elements that should be taken into account for owners when making decisions towards negative effects of catastrophic events. Results showed that for MSEs businesses with high face-to-face customer interaction, a BCP might be useful as well as the experience in crisis of the management team, but not as the only variable.

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Measuring familiness in private family firms : a bayesian network model

2018 , Dávila-Aragón, Griselda , Ramírez Pérez, Héctor Xavier , Rivas Aceves, Salvador

The objective of this analysis was to identify the causality among variables that originate the highest level of familiness in private family firms. The Bayesian Networks (BN) theory was applied to measure the effectiveness of resources and capabilities provided by the family members within a family business to understand causal relations among variables by using probabilistic reasoning throughout a graphic. Re­sults showed that if salary of family members was higher than salary of employees in the same position, if family members shared information among themselves, and if family firms presented family-employee bonds, there was an 83%, 70%, and 79% of probability of having a high level familiness, respectively. The limitation of the study is that any modification in the BN might show different outcomes. These findings expand the knowledge on family business discipline and suggest a path for family business’ leaders to increase familiness. If family firms want to strengthen their competitive advantage, the main variables they should focus, among all the resources and capabilities that represent familiness, are salaries of family members, sharing information, and family-employee bonds.

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The Future of Companies in the Face of a New Reality : Impact and Development in Latin America

2021 , Dávila-Aragón, Griselda , Rivas Aceves, Salvador

This book analyzes the changes brought on to economic and business activities in Latin America due to the new scenarios, environments and social dynamics the world is facing as a result of the COVID-19 pandemic, at both micro- and macroeconomic levels. Recent changes to working environments has brought discussions on work-life balance to the forefront, and creating support mechanisms to attract and retain the next generation of workers has become a primary focus for talent managers. At an industry level, there are expectations that once the crisis passes, there will be massive capital inflows toward ESG investments in emerging markets driving the transformation of companies. Consequently, ESG business models will have a cascading effect in the whole supply chain (upstream, midstream and downstream) and will generate greater value for all stakeholders. At the same time, technologies of the fourth industrial revolution, such as Blockchain and Artificial Intelligence, have gradually been adopted by companies leading the charge in ESG business models. The financial sector has taken the lead in these two technologies, but the challenge generated by the COVID-19 pandemic forced other sectors to innovate rapidly in order to remain afloat. Using empirical and theoretical frameworks, the contributors in this book identify the most attractive alternatives to benefit consumers in an adverse environment like the one the world is facing as a result of the COVID-19 pandemic, which while posing a significant challenge for most industries, has also created new opportunities for innovation and ingenuity, analyzing case studies from the coffee and medical tourism sectors in particular. © Springer Nature

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Medical Tourism in Mexico. Analysis of the Economic and Technological Model in the COVID-19 Pandemic Era

2021 , Dávila-Aragón, Griselda , Arrioja Castrejón, Edmundo

In the last decades, organizations have generated and exploited the data product of their operational activity using technological tools to support executives in decision-making, seeking to incorporate economic and social benefits. A key factor today to increase the competitiveness of service providers is taking advantage of the exponential increase in Internet purchases that has been further enhanced by the COVID-19 pandemic. The use of social networks as a means of reference and knowledge of recommendations based on the experience of other users, as well as the use of mobile applications, have contributed to exponentially exploding e-commerce and making it increasingly profitable for companies. This document analyzes the data obtained from various sources, in order to determine the behaviors and preferences related to medical tourism. The study seeks to determine which are the main factors that allow predicting consumption habits and leading the various options for socially responsible medical tourism through the use of advanced analytical and artificial intelligence tools, in order to identify the most attractive alternatives to benefit consumers in an adverse environment like the one the world is facing because of the global pandemic caused by COVID-19, which represents a significant challenge for most industries, but also generates new opportunities with significant benefits for those who know how to take advantage of them.

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Financial prudential behavior and economic growth

2020 , Rivas Aceves, Salvador , Dávila-Aragón, Griselda

The 2008 global financial crisis showed not only that there is a link between real economy and financial markets, but also that financial stability is necessary for investment, innovation and of course economic growth. Regarding the link between real and financial sectors, several studies long before the 2008 financial crisis revealed positive impacts from financial sector on real economy, basically because a solid financial system promote physic and human capital accumulation, see Banerjee and Newman (1993) Galor and Zeira (1993), Aghion and Bolton (1997), Piketty (1997), Levine (1997), Levine and Zervos (1998), Rajan and Zingales (1998). When considering well-developed financial markets as economic growth promoters the researches of Levine (2005), Aghion et al. (2005) and Acemoglu et al. (2006) proved that financial develop indeed accelerates economic growth.

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