Doctorado
Permanent URI for this communityhttps://hdl.handle.net/11285/551013
Colección de Tesis presentadas por alumnos para obtener un Doctorado del Tecnológico de Monterrey.
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- El potencial formativo del cine, estrategias para cultivar las capacidades de crecimiento humano contenidas en la narrativa audiovisual(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-10-25) Echeverria Navarro, Gustavo; Domínguez Cáceres, Roberto; emimmayorquin; Carrillo Cal y Mayor, Juan Carlos; Escuela de Humanidades y Educación; Campus Ciudad de México; Camargo Castillo, Javier AlejandroEsta tesis explora el uso del cine como una herramienta pedagógica en la educación media superior en México. A medida que los adolescentes tienen acceso constante a películas y series, surge la pregunta central de la investigación: ¿Qué beneficios específicos pueden obtenerse de estos productos audiovisuales en el estudio de las humanidades? El objetivo principal de la investigación es proporcionar a los docentes de humanidades una metodología didáctica de análisis cinematográfico que permita enriquecer el proceso de enseñanza-aprendizaje y contribuir al crecimiento personal de los estudiantes. Para ello se considera que la narrativa audiovisual no solo es un medio de entretenimiento, sino que, al ser un objetvo cultural, también desempeña un papel relevante en la formación de la identidad y la agencia de los espectadores. A partir de lo anterior se desarrolla un modelo de análisis cinematográfico que integra categorías teóricas y técnicas para ayudar a los docentes a guiar a sus estudiantes a una comprensión más profunda de los contenidos del curso. Este modelo se basa en la premisa de que el cine puede ser un vehículo eficaz para explorar temáticas relevantes, fomentando la empatía y el pensamiento crítico. Además, se presentan estrategias didácticas concretas que facilitan la vinculación entre las narrativas cinematográficas y los temas del currículo de humanidades durante la sesión de clase. Al hacerlo, esta investigación busca promover un diálogo enriquecedor entre los estudiantes y el contenido académico, permitiendo que el cine no solo sea un recurso de aprendizaje, sino también una herramienta para el desarrollo integral del estudiante.
- Impact of ESG Scores on Stock Returns: studies looking into the COVID-19 Pandemic, ESG Momentum, Region & Firm Size(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11-27) Escobar Saldívar, Luis Jacob; Santillán Salgado, Roberto Joaquín; emimmayorquin; Saucedo de la Fuente, Eduardo; Amorós Espinosa, José Ernesto; EGADE Business School; Campus Monterrey; Villarreal Samaniego, DacioThe question of whether a socially responsible company is inherently more profitable, or if being a responsible corporate citizen negatively affects financial performance, has been a matter of enduring interest. The literature contains arguments both in favor and against the impact of Environmental, Social, and Governance (ESG) scores on a firm’s stock returns, as a measure of financial performance. Empirical reports attempting to disentangle these effects show mixed results. All the studies in this dissertation tackle this question and are consistent with the segment of the literature that finds a negative relationship between ESG and stock returns. However, each study digs deeper into this relationship contributing with a contrast between ESG and ESG Momentum, a comprehensive analysis of ESG and its components across regions and firm sizes, and an analysis of ESG’s impact during the COVID-19 pandemic. Return and its related volatility are the central elements that define an investment, and the suitable balance between these two variables is contingent upon each investor profile. Chapter 2 aims to explore the relationship between ESG ratings and the change in ESG scores, or ESG Momentum, concerning both returns and risk of a sample of 3,856 stocks traded on U.S. exchanges. The analysis employs a dataset spanning 20 years of quarterly information, from December 2002 to December 2022. We applied multi-factor models and tested them through pooled ordinary, fixed effects, and random effects panel regression methods. The main implication of our findings is that while high ESG scores are associated with lower stock returns in the long run, an improvement in a company’s ESG score tends to yield immediate positive returns. So, the primary contribution of this research lies in the revelation that ESG Momentum has a significant positive impact on stock returns. This might explain why the literature has some mixed results, since some of them could confound the effects of ESG scores and ESG Momentum. A longstanding debate in finance centers on whether social responsibility has an influence on a firm’s long-term profitability. The study in Chapter 3 aims to provide a broad viewpoint of the relationship between ESG, its individual components, and stock returns. It examines time-entity observations from December 2014 to December 2023 for European and US companies, applying panel regression models to analyze the data collectively, by region, and by firm size. The findings consistently reveal a negative relationship between ESG ratings, their individual components, and stock returns, which we attribute to both risk reduction from social responsibility and decreased profitability due to the associated costs of ESG policies implementation. ESG and its individual pillars’ coefficients used as explanatory variables of stock returns are significant in most cases, with some exceptions for the governance and environmental pillars. In the case of the environmental pillar, the results reveal it has a stronger influence in Europe, across firm sizes while, in the US that influence is observed among larger companies only. In the case of governance, the observed variations are consistent with the argument of different ownership structures across regions, and evolving investor concerns as firms grow, with the influence being stronger in Midcaps of both regions and in US Large Caps. The study in Chapter 4 analyzes the relationship of firm-level Environmental, Social and Governance (ESG) scores and stock returns from a worldwide database of the automotive industry. It measures the significance of the ESG and Corporate Financial Performance (CFP) relationship during the last decade and includes a comparison of those firms with different levels of ESG scores, as well as between firms with ESG scores and firms that lack such scores. A quasi-experimental difference-in-differences (DID) design, and panel data regressions are estimated to examine the impact of ESG scores and ESG Combined (ESGC1) scores on firms’ stock returns before and during the COVID-19 pandemic period. The results suggest that sustainable policies during the pandemic lessened stock returns, as evidenced by the negative coefficients of the ESGC and ESG scores. The interaction terms of ESGC and ESG with firm size had a positive relationship with stock returns during the pandemic. Thus, larger firms’ returns benefited from higher ESG scores during the COVID-19 crisis. This research in the context of the COVID-19 sanitary emergency is an original contribution to the literature on the ESG-CFP relationship.
- Market reactions to North American economic indicators: comparison of the financial crisis versus the COVID 19 crisis(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11-13) Agatón Lombera, Dante Iván; Núñez Mora, José Antonio; López Cabrera, Jesús Antonio; Amorós Espinosa, José Ernesto; EGADE Business School; Campus Ciudad de México; Cerecedo Hernández, DanielThis paper presents a comparative analysis of the impact of economic crises on the development of financial markets, focusing on the United States and specifically on the 2008 global financial crisis and the COVID-19 pandemic. It is divided into four chapters that examine the nature, causes, consequences, and responses to such crises. The first chapter establishes a replicable methodology for analyzing the impact of crises at the micro- and macro-temporal levels, focusing on the subprime and COVID-19 crises. The second chapter examines the nature and causes of economic crises, highlighting the importance of identifying patterns and trends to prevent future crises. The need for supervision and regulation to address vulnerabilities in the financial system is emphasized. The third chapter defines relevant financial indicators and reviews previous studies to better understand the impact of crises on these indicators, providing guidance for investors, regulators, and policymakers. A comparative analysis of the subprime and COVID-19 financial and health crises in the United States reveals notable differences in their impact on financial indicators and the broader economy. The subprime crisis triggered a climate of financial panic, with high volatility and a marked decline in the valuation of financial indices, affecting both aggregate supply and demand. In contrast, the COVID-19 crisis generated turbulence in the markets, albeit to a lesser extent, focusing mainly on aggregate supply due to supply chain disruptions and production constraints. These results highlight the importance of understanding the specificities of each crisis in order to adopt effective public policies that promote financial and economic stability. In terms of policies, it recommends the implementation of flexible and adaptive strategies that address the specific needs of each crisis, including measures to promote economic growth and improve consumer confidence. It also emphasizes the need for stabilization policies that counteract adverse effects on aggregate supply and demand, maintain macroeconomic stability, and promote an environment conducive to long-term investment and development. These approaches can help mitigate the adverse effects of future crises by strengthening the economy's ability to withstand and recover from economic and financial shocks. Finally, the fourth chapter proposes a methodology for analyzing financial returns during crises, facilitating a quantitative assessment of the impact of specific market events. The paper concludes that economic crises, such as the 2008 and COVID-19 crises, pose significant challenges for firms and workers, exacerbate financial insecurity, and highlight the need for inclusive and equitable policies. It also underlines the importance of flexible and adaptive economic policies, as well as a detailed assessment of the impact of crises on financial markets, to promote long-term economic and financial stability. The findings of this analysis offer valuable insights for decision-making in both investment and economic policy. It is evident that the effective management of the risks associated with crises is of paramount importance in order to mitigate their adverse impacts and to facilitate the sustainable economic and social recovery that is so crucial for the future of any nation.
- Estudio clínico fase I/II-A para evaluar la tolerabilidad, seguridad y eficacia de una formulación de polifenoles contenidos en el extracto de arándano azul en conjunto con omega 3 en paciente con ojo seco y moderado(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-05-29) Ng Alemán, Dalia Denisse; Altamirano Vallejo, Juan Carlos; emimmayorquin; Navarro Partida, Jose; Gonzalez de la Rosa, Alejandro; Anaya Prado, Roberto; Escuela de Medicina y Ciencias de la Salud; Campus Monterrey; Santos Garcia, ArturoLa distribución mundial de la enfermedad de ojo seco (EOS), su prevalencia y potencial daño al epitelio corneal la han convertido en un problema de salud pública; a razón de nuestra exposición a la luz de monitores esta problemática ha aumentado, y la investigación sobre terapias eficaces se ha convertido en una necesidad. Debido al potencial terapéutico sobre el estrés oxidativo del medio ocular, así como para la regulación de la cascada inflamatoria los polifenoles son la propuesta en la investigación enfocada en prevenir o controlar el padecimiento. Desarrollamos un ensayo clínico aleatorizado donde los participantes fueron asignados a uno de cuatro grupos para estudiar el efecto de la formulación oral con polifenoles contra placebo, en ausencia o conjunto con lubricante; realizamos evaluaciones clínicas al inicio y al primer y tercer meses de seguimiento en los que verificamos la seguridad y tolerancia del tratamiento e implementamos pruebas específicas para medir su eficacia. Observamos mejora con significancia estadística en todos los tiempos de evaluación en la prueba de OSDI para ambos grupos de tratamiento (grupo formulación y grupo formulación y lubricante), de igual manera en el tercer mes para la puntuación del tiempo de ruptura del desgarro de la película no invasiva (NIF-BUT) y el tiempo de ruptura promedio no invasivo (NIAvg-BUT), así como en la prueba de Schirmer y tinción de verde lisamina y cálculo de osmolaridad lagrimal. Los grupos placebo no mostraron impacto en la mejora. Concluimos que la formulación oral con polifenoles es eficaz como tratamiento primario, mejora los síntomas de estrés visual y el estado de la película lacrimógena, y en tratamiento conjunto con lubricante es superior a la combinación del placebo y las lágrimas artificiales.
- Artificial Intelligence Systems in Retail: Examining Customer Behavior and Adoption(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11-20) Calvo Castro, Ana Valeria; Franco Valdez, Ana Dolores; emimmayorquin; Frasquet Deltoro, Marta; Amorós Espinosa, José Ernesto; EGADE Business School; Campus Ciudad de México; Valdez Cervantes, AlfonsoArtificial Intelligence (AI) is a disruptive innovation that has driven digital transformation in the retail industry. Technologies such as robots, chatbots, conversational agents, and generative AI are reshaping customer interactions. Although AI origins date back to the 1950s, when Alan Turing posed the question, “Can machines think?”, the use of this technology has exponentially evolved in recent years across various contexts and functions. These advancements increasingly simulate the capabilities of the human mind, enabling companies to achieve what once was considered impossible. This dissertation explores the antecedents and possible outcomes of AI technology within the retail industry, focusing specifically on AI acceptance and customers’ behavior regarding AI usage. The research undertakes an exploration of the role that AI systems could play in configuring and enhancing customer experiences through three interrelated articles, each offering unique insights into the role of AI in retail strategies. The first study provides a comprehensive analysis of insights on the impact of AI on omnichannel customer experience (OCE), incorporating perspectives from top-retail managers, consultants, and customers. The second study presents a conceptualization and validation of a measurement model for customers’ acceptance of artificial intelligence (CAAI), providing a robust framework for measuring AI acceptance among customers. The third, and final study, investigates and analyze the influence of CAAI on word-of-mouth (WOM), reuse intention, and the moderating effect of trust in technology. Together, these studies present a comprehensive exploration of how AI can be utilized to transform customer acceptance and optimize retail strategies. This dissertation aims to contribute to a deeper understanding of how AI technologies can be leveraged to enhance retail strategies and customer interactions, offering insights for both academic researchers and industry practitioners.
- Machine learning analysis of antiretroviral procurement strategies in the Mexican government(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11-27) Blanca Iveth Mayorga Basurto; Nuñez Mora, José Antonio; emimmayorquin; Moncada Freire, Galo José; Fuentes Castro, Hugo Javier; Carrasco Acevedo, Guillermo; Amorós Espinosa, José Ernesto; EGADE Business School; Campus Ciudad de México; León Alvarado, Martha AngélicaThis dissertation investigates trends in antiretroviral medication (ARV) prices and their impact on public health in Mexico during 2019. The study leverages a dataset comprising 15,220 procurement records collected between 2016 and 2019 to analyze price fluctuations and predict their implications for healthcare systems. Using machine learning models developed in Python-Logistic Regression, Ramdom Forest, and K-Nearest Neighbors (KNN)-this research identifies patterns of increasing and decreasing prices and the factors influencing these trends. The data preprocessing phase involved extensive cleaning, imputation of missing values, feature scaling, and one-hot encoding to handle categorical variables. The dataset was partitioned into training and testing sets using an 80/20 split, ensuring robust validation. Hyperparameter optimization techniques, including grid search and cross-validation, were applied to enhance model performance. The integration of ensemble methods, as exemplified by Ramdom Forest, enabled the capture of complex, non-linear relationships between variables, a critical advantage over simpler models. KNN provided complementary insights into local price clusters, while Logistic Regression offered interpretable coefficients for key predictors. In addition to predictive modeling, the study incorporates a financial evaluation of ARV price fluctuations, estimating the budgetary impact on public health systems. Consolidated purchasing schemes were found to yield significant cost reductions, enhancing access to ARVs for individuals living with HIV/AIDS. A unified ARV pricing database was developed, integrating fragmented data from government procurement systems, ensuring transparency and facilitating reproducibility in future research. This research underscores the transformative potential of data-driven approaches in optimizing pharmaceutical procurement. It highlights the necessity of leveraging machine learning techniques not only for predictive analytics but also for informed decision-making in public health policy.
- Synthesis and Characterization of FAPbI3 Perovskite and its Incorporation into a Photovoltaic Heterostructure(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-10) Miró Zárate, Jorge Luis; Elias Espinosa, Miilton Carlos; emimmayorquin; Rosas Meléndez, Samuel Antonio; Melo Máximo, Dulce Viridiana; Flores Ruíz, Francisco Javier; School of Engineering and Sciences; Campus Ciudad de México; Diliegros Godines, Carolina JananiConsidering the importance of having the α-FAPbI3 as it is the photoactive and functional phase for the use of this perovskite in a solar cell and understanding the growth process by incorporating an additive. In this work, it is presented a methodology that combine a method for deposition called sequential deposition with the incorporation of a pseudo halogen additive NH4SCN at various concentration of moles into the PbI2 solution, in order to have α-FAPbI3 perovskite deposited at open atmosphere. This research focuses on the mechanisms of growth of the FAPbI3 perovskite films over glass with the NH4SCN additive. Subsequently, the incorporation of the FAPbI3 perovskite into a heterostructure is presented. The architecture FAPbI3/ETL/ITO/Glass is presented, where the ETLs used are TiO2 and SnO2. The incorporation of FAPbI3 into a heterostructure allows us to evaluate the perovskite's properties for its photovoltaic application. Based on the outstanding electrical properties, WS2 was incorporated into the heterostructure through interface engineering, forming the heterostructure FAPbI3/WS2/ETL/ITO/Glass. Both architectures are compared in terms of their optoelectronic and morphological properties to determine the best FAPbI3-based heterostructure for improved photovoltaic application.
- Instant deliveries in Mexico City: a socio-economic analysis and profit maximization framework for couriers(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11-27) Galindo Muro, Ana Bricia; Mora Vargas, Jaime; emipsanchez; Dablanc, Laetitia; Ugalde Monzalvo, Marisol; De Unanue Tiscareno, Adolfo Javier; School of Engineering and Sciences; Campus Ciudad de México; Cedillo Campos, Miguel GastónThis thesis introduces an engineering approach to understanding instant delivery operations within the platform economy. During the first step, through two surveys, the study highlighted couriers’ significant risks and challenges, shedding light on their precarious working conditions and financial pressures. The results emphasize the glaring disparity between the platform economy’s promise of flexibility and independence and the harsh reality experienced by most couriers. Furthermore, the study presents an assignment model to support technological advancements, which can lead to more effective decision-making, benefiting all actors involved in the urban instant delivery platform. By incorporating a fee algorithm and operational cost calculations, the quantitative model developed in this study demonstrates that a 20% increase in couriers’ income compared to traditional assignment models is advantageous for all parties. This approach seeks to raise awareness about the socioeconomic implications of emerging technologies such as Instant Deliveries and their regulation, particularly in rapidly developing urban areas. It offers valuable insights to build a more socially responsible and environmentally sustainable optimization approach in engineering.
- From classical to quantum machine learning for analyzing and predicting alumni outcomes(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-12) Ramos Pulido, Sofía; Hernández Gress, Neil; Torres Delgado, Gabriela; Hervert Escobar, Laura; Garza Villarreal, Sara Elena; Méndez Hinojosa, Luz Marina; School of Engineering and Sciences; Campus Monterrey; Ceballos Cancino, Héctor G.This thesis is submitted to the graduate program at the School of Engineering and Sciences as part of the requirements for obtaining the degree of Doctor of Philosophy in Computer Science. This study aims to generate models using both classical and quantum machine learning (ML) methodologies to accurately predict three key outcomes for alumni: job level, career satisfaction, and first employment. The data analyzed comes from Tec de Monterrey university alumni surveys. The study’s objectives also include the identification of important and actionable features for alumni outcome predictions. Among the challenges in finding models to predict and explain alumni outcomes, we encountered issues such as handling imbalanced classification, hyperparameter tuning, model prediction interpretation, and long training times. To address the latter, we proposed a method that reduces execution time when working with large datasets, particularly in methodologies like support vector machines. This proposal effectively resolves time and memory limitations in high-dimensional classification problems without compromising performance accuracy. The results show that classical machine learning models accurately predicted alumni outcomes. For instance, gradient boosting was most accurate in predicting job level and career satisfaction, while support vector machines outperformed in employment prediction. Significant features identified included current salary and number of people supervised for job level, with higher salaries and more supervisory responsibilities correlating with higher job positions. For career satisfaction, life and income satisfaction were important indicators, as higher satisfaction levels in these areas predicted greater career satisfaction. In the case of employment, networking support resulted as the most important feature, with stronger professional connections significantly increasing the likelihood of securing employment shortly after graduation. Additionally, the research identified actionable features that can impact both educational institutions and students. For job level, soft skills, particularly communication and teamwork, were found to be crucial in advancing to higher positions. Institutions can focus on enhancing these skills through their programs, while students are encouraged to develop them actively. For career satisfaction, the effective use of skills and technological tools acquired during education was a strong predictor, indicating the importance of aligning academic training with the demands of the job market. Facilitating robust professional networks proved essential for employment, emphasizing the need for institutions to create networking opportunities and for students to build social connections proactively. Many more interesting trends and findings related to alumni outcomes are highlighted in the thesis. Regarding quantum machine learning (QML) models, this research demonstrates the v feasibility of predicting alumni outcomes. A hybrid quantum-classical approach was particularly effective in predicting the three alumni outcomes in reduced datasets without substantially affecting accuracy. For example, quantum support vector classifiers (QSVC) showed comparable performance to classical support vector classifiers (SVC) while utilizing a reduced dataset versus SVC with complete datasets. Although QML is still in its early stages, this research suggests that QML could become a viable alternative in educational data mining as the field expands.
- Influence of human error and situational awareness in decision-making in complex tasks. Case of study: forklifts operators(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2024-11-19) Arias Portela, Claudia Yohana; Mora Vargas, Jaime; emipsanchez; Castillo Martínez, Juan Alberto; González Mendoza, Miguel; Thierry Aguilera, Ricardo; School of Engineering and Sciences; Campus Ciudad de México; Caro Gutiérrez, Martha PatriciaThis dissertation investigates situational awareness (SA) and human errors in logistics operations, using a multiphase and multifactorial approach as an innovative approach. The research responds the question of how SA errors can be assessed, along with their influence on decision-making in complex tasks, by considering a comprehensive HFE approach to various triggering factors. Characterization of the process with ethnography and process mapping, analysis of visual attention with Eye-tracking and retrospective think-aloud (RTA), an Error taxonomy and the bases of a data science approach were used to study the diverse cognitive, behavioral, and operational aspects affecting SA. Analyzing 566 events across 18 tasks, the research highlights eye-tracking's potential by offering real-time insights into operator behavior, and RTA as a method for cross-checking the causal factors underlying errors. Critical tasks, like positioning forklifts and lowering pallets, significantly impact incident occurrence, while high cognitive demand tasks such as hoisting and identifying pedestrians/obstacles, reduce SA and increase errors. Driving tasks are particularly vulnerable and are the most affected by operator risk generators (ORG), representing 42% of events with a risk of incident. The study identifies driving, hoisting and lowering loads as the tasks most influenced by system factors. Limitations include the task difficulty levels, managing physical risk, and training. Future research is suggested in autonomous industrial vehicles and advanced driver assistance systems (ADAS). This study provides valuable insights for improving safety in logistics operations by proposing a multiphase and multifactorial approach to uncover patterns of attention, perception and cognitive errors, and their impact on decision-making in the logistic field
