VIDEO GAMES CONTRIBUTION TO STUDENTS’ ENTREPRENEURIAL TRAITS AND INTENT

Given the popularity of video games and the influences they may pose on individuals’ psychology and behavior, the present study analyses whether video game playing among university students can be correlated with traits associated with an entrepreneur’s profile, which may, in turn, lead to an entrepreneurial intent. The results of the study reveal that students who do play video games show a higher entrepreneurial intent, this relationship being mediated by several psychological and cognitive characteristics. With regards to the psychological and cognitive factors studied, the results also suggest that a favorable attitude towards playing videogames fosters students’ entrepreneurial potential and has a positive effect on the entrepreneurial intent

MODELLING THE INFLUENCE OF ONLINE MARKETING COMMUNICATION ON BEHAVIOURAL INTENTIONS

The present study addresses the manners in which potential consumers react to and examine online marketing communication efforts, and how their perceptions influence various decisions. By drawing from theories of consumer behaviour, several variables are taken into consideration, a model designed to integrate existing theories and a three-way study of online user behaviour in response to online marketing messages is defined and tested. The results of the study demonstrate that there are direct and positive links between the manner in which users perceive online marketing communication efforts, and direct and positive links between users’ attitudes towards online communication and their intention to either further inform themselves, forward the information obtained, or even become loyal to the company

E-Commerce Sales Revenues Forecasting by Means of Dynamically Designing, Developing and Validating a Directed Acyclic Graph (DAG) Network for Deep Learning

As the digitalization process has become more and more important in our daily lives, during recent decades e-commerce has greatly increased in popularity, becoming increasingly used, therefore representing an extremely convenient alternative to traditional stores. In order to develop and maintain profitable businesses, traders need accurate forecasts concerning their future sales, a very difficult task considering that these are influenced by a wide variety of factors. This paper proposes a novel e-commerce sales forecasting method that dynamically builds a Directed Acyclic Graph Neural Network (DAGNN) for Deep Learning architecture. This will allow for long-term, fine-grained forecasts of daily sales revenue, refined up to the level of product categories. The developed forecasting method provides the e-commerce store owner an accurate forecasting tool for predicting the sales of each category of products for up to three months ahead. The method offers a high degree of scalability and generalization capability due to the dynamically incremental way in which the constituent elements of the DAGNN’s architecture are obtained. In addition, the proposed method achieves an efficient use of data by combining the numerous advantages of its constituent layers, registering very good performance metrics and processing times. The proposed method can be generalized and applied to forecast the sales for up to three months ahead in the case of other e-commerce stores, including large e-commerce businesses