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The tourist industry has to adopt a big data-driven foresight approach to enhance decision-making in a post-COVID international landscape still marked by significant uncertainty and in which some megatrends have the potential to reshape society in the next decades. This paper, considering the opportunity offered by the application of the quantitative analysis on internet new data sources, proposes a prediction method using Google Trends data based on an estimated transfer function model.

The paper uses the time-series methods to model and predict Google Trends data. A transfer function model is used to transform the prediction of Google Trends data into predictions of tourist arrivals. It predicts the United States tourism demand in Italy.

The results highlight the potential expressed by the use of big data-driven foresight approach. Applying a transfer function model on internet search data, timely forecasts of tourism flows are obtained. The two scenarios emerged can be used in tourism stakeholders’ decision-making process. In a future perspective, the methodological path could be applied to other tourism origin markets, to other internet search engine or other socioeconomic and environmental contexts.

The study raises awareness of foresight literacy in the tourism sector. Secondly, it complements the research on tourism demand forecasting by evaluating the performance of quantitative forecasting techniques on new data sources. Thirdly, it is the first paper that makes the United States arrival predictions in Italy. Finally, the findings provide immediate valuable information to tourism stakeholders that could be used to make decisions.

Turnover in high-tech companies has long been a concern for managers and executives. Recent meta-analyses from the general turnover literature consistently show that job satisfaction is a major attitudinal antecedent to turnover intention and turnover behavior. Additionally, the available research on information technology (IT) employees focuses primarily on turnover intentions and not on a risky decision-making perspective and actual turnover (turnover behavior). The paper aim is to focus on that.

This study uses hierarchical ordinary least squares, process (Preacher and Hayes, 2004) and logistic regression.

The main predictor of actual turnover is risky decision-making, whereas job satisfaction is the main predictor of turnover intention.

The joint effects of risk and job satisfaction on turnover intention and behavior have not been studied in the IT domain. Hence, this study extends our understanding of turnover in general and particularly among IT employees by studying the combined effect of risk and job satisfaction on turnover intentions and turnover behavior. The study’s theoretical and practical implications are likewise discussed.