Dávila-Aragón, Griselda
Preferred name
Dávila-Aragón, Griselda
Official Name
Dávila-Aragón, Griselda
Alternative Name
gdavila
Main Affiliation
ORCID
0000-0001-5268-5636
Scopus Author ID
56957235600
Researcher ID
ELY-9463-2022
13 results
Now showing 1 - 10 of 13
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Reconstructing household financial well-being; The case of Mexican households(Universidad Nacional Autonoma de Mexico01861042, 2025-12-29); ; Mopya Ponce, Claudine - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Knowledge Management for Open Innovation: Bayesian Networks through Machine Learning(2021); ; 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.Scopus© Citations 24 52 5 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Financial prudential behavior and economic growth(2020); 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.15 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Measuring familiness in private family firms : a bayesian network model(2018); ; 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.Scopus© Citations 2 22 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Cálculo del Valor en Riesgo Operacional de una Empresa Aseguradora Mediante Redes Bayesianas(2019); It was in the 1990’s when the concept of Operational Risk was defined, since then the institutions, especially those in the financial sector, are worried about this type of risk since their exposure could have fatal consequences. In case of the insurance sector its study originates due to the new European regulatory framework of Solvency II. The purpose of this research is the development of a methodology based on Bayesian networks to identify and measure operational risk in order to determine the solvency capital requirement in the online policy quotation process of an insurance company that recently entered into this way of operating. For this, a Bayesian network model was designed with a priori and a posteriori distributions that allowed estimating the frequency and severity of the losses, with the posteriori distributions, an estimate of the expected loss for a period of one year was made using Monte Carlo simulation.28 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Caracterización de la productividad de una empresa mexicana desarrolladora de tecnología mediante control difuso(2022)24 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Cálculo del valor en riesgo operacional mediante redes bayesianas para una empresa financiera(2016); ; The aim of this paper is to outline the methodology based on the use of Bayesian networks (BN) to identify and quantify operational risk (OR) factors associated with processing financial transactions through electronic means in a financial company. BN model developed is exemplified with data from simulated events equivalent to six years period, from information provided by experts in this type of process. This represents one of the main advantages of using BR, they allow modeling the cause-effect relationships between different OR factors. Finally operational value at risk (OpVaR) for the example is calculated, where interacting factors that are not considered in the traditional model are incorporated, providing better conditions of credibility to this value.Scopus© Citations 8 20 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Las finanzas de los hogares mexicanos: análisis con Redes Bayesianas(2021); ; Cabrera Llanos, Agustín IgnacioEl 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.Scopus© Citations 2 14 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, La popularidad de las marcas y su valor económico en el marco de las finanzas corporativas: un análisis de aprendizaje máquina(2022); Over time, the brand has played a significant role in the business sphere, the perception of commercial image, and added value. This study is focused on exploring the components of brand value from a diagnosis and machine learning techniques to develop a series of models associated with the dimensions of perceived brand value from a more current concept of popularity. The machine learning methodology prioritizes prediction over inference. Unlike classical statistics, it does not impose a specification or a theory, where a model is required to be specified; this represents an alternative dynamic way to understand how one of the most critical resources of companies is present in the market, which undoubtedly has repercussions on the financial and risk management of the company. The results obtained through three different machine learning techniques show that the eleven variables proposed in the study positively influence brand popularity with different intensities. © 2023 Universidad Nacional Autonoma de Mexico. All rights reserved.Scopus© Citations 1 43 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Survival Likelihood of Micro and Small Businesses Facing a Catastrophe(2021); ; 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.Scopus© Citations 1 36 1