Journal of Knowledge Management, Economics and Information Technology

In this research, the quantile regression is applied to investigate the affecting factors associated with the application of e-learning. The findings provide a comprehensive picture about the relationships between the application of e-learning and its determinants. It sheds light on these complicated relationships that, at the different quantiles of the conditional distribution of e-learning adopting levels, the influence of the determinants on the application of e-learning is different. Moreover, this research also offers statistical evidence on the moderating role of students’ gender on the relationships between the application of e-learning and its determinants. The findings are useful to educational managers by offering them with a better understanding of the complex links associated with the application of e-learning. As a result, they can deliver better decisions on the choice and adoption of e-learning in their institutions.

  Utilizing the Quantile Regression to Explore the Determinants on the Application of E-Learning (237.3 KiB, 2,055 hits)

In this paper the effects of schooling on wage inequality across a group of European countries are examined. Using the latest available EU-SILC dataset, a positive relation between these two variables is found across the whole distribution by means of a quantile regression (QR) approach. Such effect is stronger effect at the highest quantiles than at the lowest. This result corroborates the possibility of an increase in the wage dispersion when higher educational attainments are achieved and it is found to be rather robust as showed by linear hypothesis tests. Finally, the country-specific results highlight that the OLS technique misleads relevant information over cross-countries heterogeneity in the impact of education on within group inequality at different points of the wage distribution compared to the QR approach.

  Returns to Schooling in Europe: Evidence From Quantile Regression on EU-SILC Data (405.1 KiB, 2,232 hits)