Online learning is gaining popularity, but users can easily find alternatives and switch between learning platforms. Reducing users switching behavior is a critical condition for the sustainable development of an online learning platform; therefore, it is necessary to investigate the influence factors of users switching behavior between different platforms to retain users and enhance the competitiveness of enterprises. Push-Pull-Mooring (PPM) theory is adopted to construct a structural equation model of customer switching behavior on online learning platforms and to explore the mechanism of user switching behavior between learning platforms. The model is tested with data collected from 313 online learning users. The results show that information overload and dissatisfaction, as push factors, significantly affect user switching behavior. Functional value and network externality as pull factors positively affect user switching behavior, switching cost, and affective commitment as mooring factors negatively correlate with switching behavior. Further, this study also revealed that there are obvious different influencing factors for different online learning platforms. Overall, this study provides some practical strategies for the online learning platform and can help them to gain a competitive advantage.

The mobile internet has resulted in intimate partner violence (IPV) events not being viewed as interpersonal and private issues. Such events become public events in the social network environment. IPV has become a public health issue of widespread concern. It is a challenge to obtain systematic and detailed data using questionnaires and interviews in traditional Chinese culture, because of face-saving and the victim’s shame factors. However, online comments about specific IPV events on social media provide rich data in understanding the public’s attitudes and emotions towards IPV. By applying text mining and sentiment analysis to the field of IPV, this study involved construction of a Chinese IPV sentiment dictionary and a complete research framework. We analyzed the trends of the Chinese public’s emotional evolution concerning IPV events from the perspectives of a time series as well as geographic space and social media. The results show that the anonymity of social networks and the guiding role of opinion leaders result in traditional cultural factors such as face-saving and family shame for IPV events being no longer applicable, leading to the spiral of an anti-silence effect. Meanwhile, in the process of public emotional communication, anger often overwhelms reason, and the spiral of silence remains in effect in social media. In addition, there are offensive words used in the IPV event texts that indicate misogyny in emotional, sexual, economic and psychological abuse. Fortunately, mainstream media, as crucial opinion leaders in the social network, can have a positive role in guiding public opinion, improving people’s ability to judge the validity of network information, and formulating people’s rational behaviour.

The COVID-19 pandemic increased public health awareness, changing consumers’ sensitivity and beliefs about food health. Food anxiety and health scares turn consumers toward safe and healthy foods to strengthen their immunity, which makes green food more popular. However, it remains unclear how to understand the gap between consumer intention to purchase green food and their actual purchasing behaviour. Taking rice as an object of study, comparing differences in consumer perceptions and emotions towards green-labelled rice and conventional rice is beneficial for understanding the components and psychological characteristics of consumer perceptions of green food. Therefore, we used topic modelling and sentiment analysis to explore consumers’ focus of attention, attitudinal preferences, and sentiment tendencies based on the review (n = 77,429) from JD.com. The findings revealed that (1) consumers’ concerns about green-labelled rice are increasing rapidly, and most have a positive attitude; (2) consumers of green-labelled rice are more concerned about origin, aroma, and taste than conventional rice; (3) consumers of conventional rice are more concerned about the cost-performance ratio, while consumers of green-labelled rice are also price-sensitive; (4) green label mistrust and packaging breakage during logistics are the leading causes of negative emotions among consumers of green-labelled rice. This study provides a comparative analysis of consumer perceptions and emotions between the two types of rice, thus revealing the main influencing factors of the intention-behaviour gap and providing valuable consumer insights for the promotion of green consumption and the sustainable development of the green food industry.

Nanoindentation is an effective nondestructive method for small scale determination of mechanical properties of materials. However, indentation response of metallic materials is very sensitive to indenter tip roundness, size effects, loading rate, and so forth. This study will analyze the effect of indenter shape imperfections on hardness determination. For this purpose, experimental investigations and finite element simulations are carried out. At first, it is found that hardness values determined with Oliver and Pharr’s method are affected by errors caused by imperfect indenter tip: errors increase for imperfect indenters with larger tip radii. Afterwards, several commonly used methods accounting at different extents for tip radius variations are compared. However, most of those methods are found not to be accurate for shallow indentation. For this reason, a novel hardness determination method based on geometrical relations of the imperfect indenter tip is developed. Results show that the new approach is very effective even in the case of shallow indentation.

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Analyzing the perception differences and influencing factors of cross-cultural groups in heritage tourism can help heritage sites to formulate differentiated service and improve tourist satisfaction. This research adopted the BERT model to undertake sentiment analysis of 17,555 Chinese online reviews for nine scenic spots in Melaka. Using vocabulary filtering, co-occurrence analysis, and semantic clustering technology, the emotional characteristics of Chinese outbound tourists when they visited heritage sites in Melaka were analyzed, which revealed the factors influencing their positive and negative emotions. Results showed that: 1. The BERT-based deep learning approach can obtain improved sentiment predictive performance. 2. Chinese tourists’ general emotional perceptions of Melaka were positive and they were very interested in heritage sites. 3. The most important reason for the negative emotions of Chinese tourists was a lack of cultural experience in Melaka. This research expands the application of deep learning in the field of tourism, and it helps heritage tourism destinations to improve their marketing plans for Chinese tourists and achieve long-term sustainable development of the destination.

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