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

A conceptual model of consumer intention to continue buying eco-labeled products

The study of eco-labeled product purchasing behavior usually starts from a multidisciplinary set of fundamental theories and models depending on the approach towards the ecological behavior of the consumer. The proposed conceptual model is based on quantitative research on the intention of consumers to continue to buy eco-labeled goods for personal use. The model is analyzed using the structural equation modeling (SEM) technique, based on variance analysis, on a sample of 587 respondents. The analysis process consisted of two distinct stages, evaluating the accuracy of measurements, and testing the hypotheses. The conformity of the model was validated by studying its specific indicators. The 17 study hypotheses were tested by path coefficient analysis within the structural model, as well as the postulated moderating effect. Based on the conformity indices obtained, the model has good predictive and explanatory capacity. The only hypothesis refuted is the relationship between consumers' perception of personal effectiveness and their intention to continue buying eco-labeled products. This relationship was not verified at the chosen significance threshold of .01. By analyzing the indirect and total effects, we demonstrated the existence of certain beliefs that directly influence consumers' intention to continue buying eco-labeled products. The influence is also indirect through other variables such as perceived trust and perceived usefulness. The existence of a statistically significant effect from variables on an indirect path (expectations confirmation, ease of use of the ecolabel in purchasing eco-products) was also demonstrated. The explanatory and predictive capacity of the intention to continue buying eco-labeled products was increased by validating the proposed model

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