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As there are different interpretations of the object of study in the preface to the first edition of Capital (Volume I) by Karl Marx, disagreements arise over the object of study on political economy, which becomes a “difficult problem.” The purpose of the paper is to bring a new solution to the “difficult problem.”

Based on the analysis of the logic of the original text, the authors attempted to give a new interpretation of the “difficult problem” by analyzing the structure of Capital. The object of study of political economy is “the relations of production in the broad sense” of the capitalist mode of production.

It comprises relations of production in the narrow sense and exchange relations in the broad sense, and the latter can be divided into exchange relations in the narrow sense and distribution relations. The three of them correspond to Volume I, II and III of Capital, respectively. Consumption in “the four-section theory” is not studied by the political economy.

And the four-section theory is not a part of the theory of Marxist economics but a part of the classical economics criticized by Marx. Therefore, the object of study of socialist political economy with Chinese characteristics is “the relations of production in the broad sense” regarding the socialist mode of production with Chinese characteristics, which is different from the capitalist relations of production in the broad sense.

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.