ResumenCuando los ciudadanos votan en elecciones municipales, ¿tienen en cuenta solo factores a nivel nacional? O, por el contrario, ¿prestan también atención a elementos de carácter local? En este artículo sostenemos que la evaluación que hacen los ciudadanos de los candidatos a la alcaldía es un factor particularmente importante para explicar el sentido del voto en las elecciones locales en España. Para verificar empíricamente la existencia de esta relación, analizamos las elecciones de 1999, 2007 y 2011. Examinando una muestra relativamente heterogénea de municipios y gracias a las encuestas del Centro de Investigaciones Sociológicas, demostramos el efecto de las evaluaciones de los candidatos a expensas de un papel menos pronunciado de la ideología. La evidencia aportada también apunta a que este patrón es más fuerte en municipios pequeños y para elecciones más recientes. PalabRas ClaveCandidatos; Elecciones de segundo orden; Elecciones locales; España; Personalización. abstRaCt When voters cast their ballot in local elections, are they only taking into account national factors? Or do they, to the contrary, also pay attention to local elements? In this article we argue that voters' evaluations of candidates for mayor are particularly important to explain party choice in this type of subnational elections in Spain. To empirically verify the existence of this relationship, we focus on the 1999, 2007, and 2011 elections. By dwell-ing on a relatively heterogeneous sample of municipali-ties and working with surveys undertaken by the Centro de Investigaciones Sociológicas, we show the effect of candidates' evaluations at the expense of a less pronounced role of ideology. The provided evidence also suggests that this pattern becomes stronger in small municipalities and more recent elections.
RESUMENTanto Internet como las nuevas tecnologías tienen cada vez más importancia en las investigaciones académicas o profesionales que se realizan. Con este trabajo tratamos de analizar si las estadísticas de búsqueda en un buscador como Google tienen capacidad de predicción respecto al número visitantes que van a acudir a un museo. Se trata de demostrar científica-mente a través de un modelo econométrico que las estadísticas de búsqueda en Google sirven para conocer la posibilidad de visitantes de un museo. Con este artículo se pretenden realizar nuevas aportaciones a la gestión de los museos y sugerir la utilización de datos existentes que permitan conocer con cierta antelación la afluencia de público que se tendrá y así poder desarrollar estrategias.Palabras clave: Visitantes de museos, expectativas, Google Trends, turismo cultural, Internet. Activity of searches in Internet like variable for deteminar the visitors to museums
The investment in training and the improvement in the professional's abilities should anticipate an evolution in the professional's performance, but how could we estimate the probability of performance improvement from a formative action? The present work tries to quantify this using a study on 447 surveys carried out by Overlap consultant firm to professionals of the sales force of one of its clients. With this information, an artificial intelligence model has been trained. This model quantifies the relationship between skills and training on the performance of the professional and allows to measure how the probability of a better performance would increase after a training course or improvement capacity. Thus, the artificial intelligence model can be used to define the optimal profile of the professional that should carry out this activity, a profile to seek in the selection processes and in the planning of the company's internal training.
Summary The attempt to measure investors’ mood to find an early indicator of financial markets has evolved and developed with the advancement of technology over the years. The first attempts were based on surveys, a long and expensive process. Nowadays, big data has made it possible to measure the investor’s mood accurately and almost entirely online. This paper analyzes the explanatory and predictive capacity of Wikipedia pageviews for the Nasdaq index. For this purpose, two econometric models have been developed. In both models, the explanatory variable is the number of Wikipedia visits, and the endogenous variable is Nasdaq index return. As an alternative to this approach, an algorithmic trading system has been developed. It uses Wikipedia visits as investment signals for long and short positions to check the predictability power of this indicator. It is determined that the volume of queries about Nasdaq companies is a statistically significant variable for expressing the evolution of this index. However, it has no predictive capacity. Keeping in mind the capacity of Wikipedia to exemplify Nasdaq trends, further studies should be conducted to determine how to make this indicator profitable.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.