Abstract

Abstract
With the rapid expansion of Information and Communication Technologies (ICT) throughout almost all aspects of tourism and hospitality industries, the task of integrating technology into higher education curriculum is a need. This paper analyzes the relevance given by the various Portuguese and Spanish institutions of higher education to ICT in their degrees. The analysis carried out was done in degree courses operating in academic year 2018/2019, in Portuguese and Spanish universities and polytechnics. A comparison was also made with the reality of 2012/2013.

Abstract

Abstract
Purpose The purpose of this study is twofold, being the first to grasp a broad picture of entrepreneurship determinants in Qatar, and second, to explore the intermediate effect of gender upon other factors affecting the propensity to become an entrepreneur and highlight gender heterogeneity. Combining theories on entrepreneurship determinants and gender, the study analyses the role of education (general and specific to enterprise), skill perception, social context and fear to fail as determinants of new venture creation in Qatar. The objective of the study is to appraise the determinants of the entrepreneurial activity in Qatar and understand if they hold across genders in terms of significance and magnitude. If so, policy actions can be adjusted to overcome gender gaps. This study aims to design policy recommendations to reinforce the Qatari entrepreneurial ecosystem and promote positive discrimination towards women initiatives in the Gulf region. Design/methodology/approach To understand male and female propensity to entrepreneurial activity in Qatar, a database from Global Entrepreneurship Monitor (GEM) was used, considering data from 2014, with a sample that includes 4,272 individuals. To explore how the explanatory variables affect entrepreneurial propensity and if they hold significance across genders, three logistic regressions were run, the first including the entire sample, and the second and third separating individuals according to the gender. Then, to statistically appraise the differences among groups, a Kruskal-Wallis test was run to evidence group heterogeneity. Marginal effects of the model reinforce gender differences. The analysis was performed using Stata. Findings Different patterns of entrepreneurial propensity can be found among genders, allowing the exploration male and female determinants. The analysis shows that Qatari women are less prone to start a business when compared to men in equal conditions. For women, age is a deterring factor, contrarily to men. Both genders seem to be unconstrained by the fear to fail, still the self-perception of skills has a stronger effect on women. Originality/value The study identifies gender differences in entrepreneurial propensity. The potential differences are firstly put in theoretical terms and followed by an exploratory analysis comprising statistical analysis and econometric estimations. The results allow examining the profile of male and female entrepreneurs and non-entrepreneurs, the determinants of entrepreneurial initiatives and gender gaps. The study helps policy makers to elaborate adequate strategies to foster gender equality on entrepreneurship, aiming to increase overall entrepreneurial activity and consequently socio-economic development.

Abstract

Abstract
Social networks have provided the means for constant connectivity and fast information dissemination. In addition, real-time posting allows a new form of citizen journalism, where users can report events from a witness perspective. Therefore, information propagates through the network at a faster pace than traditional media reports it. However, relevant information is a small percentage of all the content shared. Our goal is to develop and evaluate models that can automatically detect journalistic relevance. To do it, we need solid and reliable ground truth data with a significantly large quantity of annotated posts, so that the models can learn to detect relevance over all the spectrum. In this article, we present and confront two different methodologies: an automatic and a human approach. Results on a test data set labelled by experts' show that the models trained with automatic methodology tend to perform better in contrast to the ones trained using human annotated data.