Massimiliano Serati

Internet searches and query data have become favorable sources for better forecasts of tourist volumes (Pan et al., 2012; Yang et al., 2014; Yang et al., 2015): search engines are used to plan routes, search for hotels, attractions, travel guides and reviews (Fesenmaier et al., 2011). Thanks to "hybrid models", i.e. the fusion of classical econometric methodologies and machine learning (Makridakis et al., 2018), it is possible to exploit such "big data" to obtain a forecasting performance superior to that of standard models (Li et al., 2017). By proposing an innovative modelling framework, we develop a hybrid model for the Lombardy tourism market, to verify the reliability of the ambitious target of becoming one of the most visited regions in Europe. We find mixed results for the Lombardy provinces, calling for a more refined and micro-founded tourism planning strategy. Furthermore, our framework is easily extendable with other countries and/or research areas, thus providing a valuable tool for assessing such issues.

Despite scholars have given much attention to research on managerial behaviours, the results have not been always consistent till today, heading thus in multiple directions. Previous studies on this area have generally adopted the point of view of managers, thus partially neglecting the perspective of their employees, who are among their most important stakeholders. In the attempt to enrich this strand of literature, in this study we aim at identifying which are the managerial behaviours that bring employees to recommend their manager as someone they should work for in the future. This is done by collecting data gathered from an online survey answered by 461 Italian employees.

Our paper aims to introduce a new model that will be able to explain jointly migration flows linked to brain drain, brain gain and brain return. Our simple indicator (LISE) measures level of individual well-being linked to each country and it was perfectly comparable. With our indicator it is possible to understand what are the drivers of migration flows, there is the possibility to prevent phenomena related to brain drain or to incentivate phenomena related to brain gain or brain return simply connecting the relative differences between the indicators calculated in various countries of the world. We have empirically tested the validity of our indicator on a sample of 12 countries. We have opened new avenues for research because using our indicator it is possible to make forecasts or to understand how and when a skilled migration flow can occur. We are able to prevent damages of Brain Drain and cross border skill mismatching using our simple indicator.