https://hdl.handle.net/20.500.12724/20631 2026-05-13T05:10:44Z 2026-05-13T05:10:44Z Pimentel Bernal, Paul Marcelo Dávila Calle, Guillermo Antonio Cuenca Jiménez, María Teresa Durst, Sussane https://hdl.handle.net/20.500.12724/19932 2026-04-21T13:28:12Z 2023-01-01T00:00:00Z

Knowledge Absorptive Capacity in Fintechs: Evidence From Latin America Pimentel Bernal, Paul Marcelo; Dávila Calle, Guillermo Antonio; Cuenca Jiménez, María Teresa; Durst, Sussane Fintechs use technologies to offer financial services in a different way than traditional ones. The Fintech sector has seen rapid growth in the global economy and has expanded access to financial services to a wide variety of users; consequently, it has drawn the attention of academics around the world. Being knowledge-intensive organisations, Fintechs can absorb knowledge as a key competence. Absorptive capacity – which includes practices to acquire, assimilate, transform, and apply knowledge - has been analysed in previous studies as a factor that can influence organisational performance. However, most studies have used data from developed countries. The objective of this ongoing study is to analyse the knowledge management (KM) practices used by a Latin American Fintech to absorb knowledge. For this, a case study with a qualitative approach will be presented, using data from an international company of Peruvian origin, specializing in leasing. Data was collected through interviews. The information collected was transcribed and categorized for analysis. The analysis will include content analysis and narrative analysis techniques, supported by Atlas.ti software. The results contribute to the KM literature in two ways: First, by describing how knowledge absorption occurs in Fintechs. Second, by systematizing evidence on how KM practices act in organisations in emerging contexts, to support absorption capacity, and consequently, contribute to organisational results. On the empirical side, this study provides specific insights to managers of companies in the financial and technological sectors in emerging contexts, on how and which practices implementing to improve KM in their organisations.

2023-01-01T00:00:00Z Ortiz Esaine, Nicolas Martin Dominguez Gutierrez, Diana https://hdl.handle.net/20.500.12724/17585 2024-10-25T18:31:46Z 2022-01-01T00:00:00Z

Collective Rural Brands and Consumption of Agroecological Products Ortiz Esaine, Nicolas Martin; Dominguez Gutierrez, Diana This work focuses on the development of rural communities through their productive and commercial orientation. This is done through collective rural brands as a vehicle to achieve identity, representation, and commercial value. Concepts and scopes of ecological consumption and commercialization of artisanal, ecological, and organic products are analyzed. At the field level, shared intelligence is built through interviews with marketing, sustainability, and ecology experts. And interviews with organic food consumers. It is complemented with the analysis of real cases of implementation of rural collective brands. Finally, an integrative analysis of schemes and models is proposed for the participatory development of collective and scalable brands.

2022-01-01T00:00:00Z Méndez Lazarte, Christiam Ismael Bohorquez Lopez, V.W. https://hdl.handle.net/20.500.12724/12389 2025-03-07T00:45:04Z 2020-01-01T00:00:00Z

Identification of main factors to characterize young people with greatest intention of buying footwear online Méndez Lazarte, Christiam Ismael; Bohorquez Lopez, V.W. The study seeks to identify the main factors that contribute to characterize young people in an emerging city like Lima, one of the cities with the lowest online channel penetration in Latin America, intending to buy a physical product online. Surveys were applied and then analyzed using a logistic regression model that resulted in men with a greater experience of use, with a favorable perception of return, and with the possibility of being influenced by other people, have a greater probability of having high intention to buy online a physical product. From all the perceived risks that were originally proposed, the logistical risk (delivery and/or return) and the social risk were those that showed a better behavior to characterize young people with greater probability of buying footwear online.

2020-01-01T00:00:00Z Bohorquez Lopez, Victor Wilfredo Méndez Lazarte, Christiam Ismael Altube, Lucas Santana, Enmanuel https://hdl.handle.net/20.500.12724/9315 2026-04-30T15:45:34Z 2019-01-01T00:00:00Z

Identificación del sentimiento expresado usando redes sociales en un contexto político Bohorquez Lopez, Victor Wilfredo; Méndez Lazarte, Christiam Ismael; Altube, Lucas; Santana, Enmanuel Este estudio pretende proponer una solución al problema de identificar el sentimiento de comentarios en español, debido a las variaciones idiomáticas existentes en los diversos países Latinoamericanos, plasmados en redes sociales usando como ejemplo el contexto político de una provincia de Argentina. Para lograrlo, se utilizó una combinación de un algoritmo de aprendizaje no supervisado, para hacer la pseudo clasificación, con un algoritmo de aprendizaje supervisado, para el modelo de clasificación. Los resultados muestran que el nivel de precisión obtenido es 93%, lo cual es mayor que los niveles de precisión encontrados en estudios previos. Entre las contribuciones del estudio podemos resaltar la necesidad de incluir una capa de pre-procesamiento, para corregir faltas ortográficas y reducir la vectorización al generar un clasificador con mayor precisión; y un proceso de pseudo-clasificación, como alternativa de clasificar de forma manual miles de comentarios para lograr un dataset para entrenamiento de un clasificador.; This study aims to propose a solution to the problem of identifying the feeling of comments in Spanish, due to the linguistic variations existing in the different Latin American countries, expressed in social networks using as an example a political context of an Argentinian Province. To achieve this, a combination of an unsupervised machine-learning algorithm was used to do the pseudo classification, with a supervised machine-learning algorithm, for the classification model. The results show that the level of accuracy obtained is 93%, which is higher than the levels of accuracy found in previous studies. Among the contributions of the study, we can highlight the need to include a layer of pre-processing, to correct spelling errors and reduce vectorization by generating a classifier with greater precision; and a pseudo-classification process, as an alternative to manually classifying thousands of comments to achieve a dataset for training a classifier.

2019-01-01T00:00:00Z