Examinando por Autor "Ballestar, Maria Teresa"

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A novel machine learning approach for evaluation of public policies: An application in relation to the performance of university researchers
(North Holland, 2019-12) Ballestar, Maria Teresa; Doncel, Luis MIguel; Ortigosa, Arturo; Sainz, Jorge
La investigación se ha convertido en el principal punto de referencia de la vida académica en las universidades modernas. Los incentivos a la investigación han sido un tema controvertido, debido a la dificultad de identificar quiénes son los principales beneficiarios y cuáles son los efectos a largo plazo. Aun así, se han adoptado nuevas políticas que incluyen incentivos financieros para aumentar la producción investigadora a todos los niveles posibles. Se ha dedicado poca literatura a la respuesta a esos incentivos. Para colmar esta laguna, realizamos nuestro análisis con datos de un programa de seis años desarrollado en Madrid (España). En lugar de utilizar un enfoque econométrico tradicional, diseñamos un modelo multinivel de aprendizaje automático para descubrir sobre quién, cuándo y durante cuánto tiempo tienen efecto esas políticas. El modelo empírico consiste en una agrupación longitudinal anidada automatizada (ANLC) realizada en dos etapas. En primer lugar, realiza una estratificación de los académicos y, en segundo lugar, realiza una segmentación longitudinal para cada grupo. La segunda parte considera la información sociodemográfica y académica de los investigadores y la evolución de su rendimiento a lo largo del tiempo en forma de variación porcentual anual de sus notas durante el periodo. La nueva metodología, cuya robustez se comprueba con una red neuronal artificial perceptrón multicapa con un algoritmo de aprendizaje de retropropagación, muestra que los investigadores titulares presentan una mejor respuesta a los incentivos que los titulares, y también que el género desempeña un papel importante en el mundo académico.
Predicting customer quality in e-commerce social networks: a machine learning approach
(Springer, 2019-12-14) Ballestar, Maria Teresa; Grau-Carles, Pilar; Sainz, Jorge
The digital transformation of companies is having a major impact on all business areas, especially marketing, where audiences are most volatile and loyalty is at its scarcest. Many large retail brands try to keep their client base interested by becoming partners in cashback websites. These websites are based on a specific type of affiliate marketing whereby customers access a wide range of merchants and obtain financial rewards based on their activities. Besides using this mix of traditional marketing strategies, cashback websites attract new target customers and increase existing customers’ loyalty through recommendations, using a word-of-mouth marketing strategy built on economic incentives for users who refer others to these sites. The literature shows that this strategy is one of the major areas of success of this business model because customers who join following recommendation are more active and are therefore more profitable and loyal to the brand. Nevertheless, the new users who are referred to these sites vary considerably in terms of the number of transactions they make on the site. This study advances research on the design of recommendation-based digital marketing strategies by providing companies with a predictive model. This model uses data science, including machine learning methods and big data, to personalize financial incentives for users based on the quality of the new customers they refer to the cashback website. Companies can thus optimize and maximize the return on their marketing investment.
Productivity and employment effects of digital complementarities
(Elsevier, 2021-07-03) Ballestar, Maria Teresa; Camiña, Esther; Díaz-Chao, Angel; Torrent-Sellens, Torrent
Modern economic growth is no longer found in total factor productivity (TFP) because there are gains from technological change that are never recorded in the returns from innovation or in the National Accounts. The existence of complementarities among technologies derived from the use of robotics, electronic commerce, or innovation is difficult to assess through country-level records. Because the literature has mainly focused on robotisation at an aggregate or industry level, research focusing on a firm level and complementarities analysis have been limited. To fill the gap, in this paper, we intend to provide new evidence regarding the effects of robotisation, digitisation, and innovation on productivity and employment in firms, by using a large sample of 5511 Spanish manufacturing firms for the period 1991–2016. This data captures the payoff to the high rates of investment necessary to upgrade the production technology for firms in a new globally competitive framework.