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This study aims to explore the internationalization of multinational enterprises (MNEs) from China and aims to examine the relationship between Chinese MNEs’ duration of internationalization and export intensity, and the contingent roles of the home country government.

By extending the springboard theory with institutional and cost-benefit analyses, the authors elaborate a two-phase framework of internationalization to explain how Chinese MNEs develop their international business under the influences of the home country government. Furthermore, the authors apply the Heckman two-stage method based on a panel data set of 19,994 firm-year observations of Chinese listed firms in 2008–2018 to test the hypotheses.

The research findings demonstrate an inverted U-shape relationship between the duration of internationalization and the export intensity of MNEs from China. The export intensity of MNEs from China increases during the initial phase of internationalization, and decreases during the subsequent. A further study reveals that the inverted U-shape of Chinese non-SOEs is steeper than that of SOEs, and this moderating effect is more salient after the Belt and Road Initiative. These results highlight the influence of the home government through state ownership and policies on the inverted U-shaped relationship.

This study helps to refine the understanding of Chinese MNEs’ global expansion by addressing time as an explicit dimension and revealing the mechanism of state ownership and the home country governmental policy in the dynamic internationalization process.

Organizations in many domains generate a considerable amount of heterogeneous data every day. Such data can be processed to enhance these organizations’ decisions in real time. However, storing and processing large and varied datasets (known as big data) is challenging to do in real time. In machine learning, streaming feature selection has always been considered a superior technique for selecting the relevant subset features from highly dimensional data and thus reducing learning complexity. In the relevant literature, streaming feature selection refers to the features that arrive consecutively over time; despite a lack of exact figure on the number of features, numbers of instances are well-established. Many scholars in the field have proposed streaming-feature-selection algorithms in attempts to find the proper solution to this problem. This paper presents an exhaustive and methodological introduction of these techniques. This study provides a review of the traditional feature-selection algorithms and then scrutinizes the current algorithms that use streaming feature selection to determine their strengths and weaknesses. The survey also sheds light on the ongoing challenges in big-data research.

In human history, poverty for most and prosperity for few is the norm. Thus, no theory or practice of common prosperity has been developed. Marxism first formulated the theory of common prosperity, and the classical Marxist authors conducted theoretical exploration on the issue of common prosperity, forming a series of scientific conclusions.

The century-long practical history of the Communist Party of China (CPC) is the great practice of leading the Chinese people in getting rid of poverty, letting some people and regions get rich first and ultimately achieving the goal of common prosperity.

Common prosperity is the great practice of the CPC that leads all Chinese people in building a modern socialist country in an all-round way in the new era.

The path of common prosperity with Chinese characteristics will certainly arise in the process of the great practice of common prosperity with Chinese characteristics. Based on the anti-poverty theory and the “spirit of poverty alleviation” from the battle against poverty with Chinese characteristics, the theory of common prosperity and its spirit with Chinese characteristics will certainly be formed. The above conclusions constitute the basic principles of the theory of common prosperity with Chinese characteristics.