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When emergencies occur, the attention of the public towards emergency information on social media in a specific time period forms the emergency information popularity evolution patterns. The purpose of this study is to discover the popularity evolution patterns of social media emergency information and make early predictions.

We collected the data related to the COVID-19 epidemic on the Sina Weibo platform and applied the K-Shape clustering algorithm to identify five distinct patterns of emergency information popularity evolution patterns. These patterns include strong twin peaks, weak twin peaks, short-lived single peak, slow-to-warm-up single peak and slow-to-decay single peak. Oriented toward early monitoring and warning, we developed a comprehensive characteristic system that incorporates publisher features, information features and early features. In the early features, data measurements are taken within a 1-h time window after the release of emergency information. Considering real-time response and analysis speed, we employed classical machine learning methods to predict the relevant patterns. Multiple classification models were trained and evaluated for this purpose.

The combined prediction results of the best prediction model and random forest (RF) demonstrate impressive performance, with precision, recall and F1-score reaching 88%. Moreover, the F1 value for each pattern prediction surpasses 87%. The results of the feature importance analysis show that the early features contribute the most to the pattern prediction, followed by the information features and publisher features. Among them, the release time in the information features exhibits the most substantial contribution to the prediction outcome.

This study reveals the phenomena and special patterns of growth and decline, appearance and disappearance of social media emergency information popularity from the time dimension and identifies the patterns of social media emergency information popularity evolution. Meanwhile, early prediction of related patterns is made to explore the role factors behind them. These findings contribute to the formulation of social media emergency information release strategies, online public opinion guidance and risk monitoring.

This study aims to investigate the influence of parent company personnel embedding on the stock price crash risk (SPCR) of listed companies, along with the moderating effect of disparate locations between parent and subsidiary companies and other major shareholders.

This research empirically tests hypotheses based on a sample of listed subsidiaries in China during the period between 2006 and 2021.

Our results demonstrate that personnel embeddedness in the parent company significantly alleviates SPCR in subsidiaries. This effect is even more substantial when the parent and subsidiary companies are in different places. However, other major shareholders in the subsidiary company weaken it. Our additional analysis indicates that, relative to executive embeddedness, director embeddedness exerts a stronger effect on the SPCR of the subsidiary. Mechanism examination reveals that the information asymmetry and the level of internal control (IC) within the subsidiary are significant channels through which the personnel embeddedness from the parent company influences the SPCR of the subsidiary.

This study expands the literature on how personnel arrangements in corporate groups within emerging countries influence SPCR. We have extended the traditional concept of interlocking directorates to corporate groups, thereby broadening the understanding of the governance effects of interlocking directors and executives from a group perspective.

We seek to contribute to the literature by comparing and analyzing the relationship between Australian and Taiwanese students regarding environmental value attitude, action intention and green marketing intention. Specifically, by comparing the green marketing intention of hospitality and tourism (H&T) students in the East with that in the West.

A well-designed curriculum examines student thinking and behavior (learn). This study compares and analyzes the value and attitude and the application of big data to green marketing among Taiwanese and Australian university students using the stimulus-organism-response (S-O-R) model. Structural equation modeling (SEM) was used to test the hypotheses in a sample of 633 H&T students in Taiwan (389) and Australia (244).

This study also shows how the national differences between Australia and Taiwan have interference effects on the relationship between value attitudes and action intentions and between action intentions and the green marketing intention. We also combined the application of big data and related variables and estimated the mediating effect of related variables to evaluate the impact on action intentions and green marketing of big data applications.

There are significant differences in the sustainable behavior and intentions of H&T higher education students that reflect the educational differences between the East and the West. These different results may be due to a lack of natural resources and the relatively smaller size of Taiwan. With the strengthening of environmental action intention (AI) and green marketing intention (GMI), Taiwanese hospitality and management (H&M) students' sense of crisis increases, and their performance in GMI is slightly higher than that in Australia.

These findings indicate that it is necessary to consider students' ecological concepts, environmental knowledge, environmental value attitude and environmental action intention to improve their intentions to engage in green marketing under the stimulus-organism-response (S-O-R) framework. We also found that environmental knowledge has a mediating effect on the relationship between ecological concepts and environmental value attitude.