The outbreak of a novel coronavirus (SARS-CoV-2) has caused a large number of residents in China to be infected with a highly contagious pneumonia recently. Despite active control measures taken by the Chinese government, the number of infected patients is still increasing day by day. At present, the changing trend of the epidemic is attracting the attention of everyone. Based on data from 21 January to 20 February 2020, six rolling grey Verhulst models were built using 7-, 8- and 9-day data sequences to predict the daily growth trend of the number of patients confirmed with COVID-19 infection in China. The results show that these six models consistently predict the S-shaped change characteristics of the cumulative number of confirmed patients, and the daily growth decreased day by day after 4 February. The predicted results obtained by different models are very approximate, with very high prediction accuracy. In the training stage, the maximum and minimum mean absolute percentage errors (MAPEs) are 4.74% and 1.80%, respectively; in the testing stage, the maximum and minimum MAPEs are 4.72% and 1.65%, respectively. This indicates that the predicted results show high robustness. If the number of clinically diagnosed cases in Wuhan City, Hubei Province, China, where COVID-19 was first detected, is not counted from 12 February, the cumulative number of confirmed COVID-19 cases in China will reach a maximum of 60,364–61,327 during 17–22 March; otherwise, the cumulative number of confirmed cases in China will be 78,817–79,780.

Purpose: On December 20, 2021, Viya, a social media influencer (SMI) with the largest number of followers in China, was exposed for having evaded RMB 643 million in taxes during 2019 and 2020. Consequently, she was fined a total of RMB 1.341 billion by the tax authorities. While the strict government regulations demonstrated in the Viya event may build confidence in the consumers for future purchases, the exposure of issues and problems through implementation of the stronger government regulations may warn consumers off. Thus, the main objective of this paper is to examine the effect of government regulations on consumers' usage intentions of live streaming e-commerce by taking the Viya event as an example.Design/methodology/approach: The authors consider both the positive effect of consumers' perceived benefits of the government regulations and the negative effect of their perceived risks of the Viya event on the usage intentions of live streaming e-commerce. After collecting 314 subjects with diverse gender, ages, education levels and income profiles, the data are processed by partial least squares-based structural equation modeling (PLS-SEM) and SmartPLS software.Findings: The results demonstrate that strict government regulations can build trust in consumers of live streaming e-commerce by increasing the perceived benefits of restricting the behavior of SMIs. Among the potential perceived risks (social risk, safety risk and psychological risk), the safety risk is supported to have a negative effect on consumers' trust in live streaming e-commerce platforms. Besides, the authors have also identified different types of usage intentions in live streaming e-commerce, i.e. watching intention and purchase intention, and have empirical support for the positive relationships between the consumers' trust in live streaming e-commerce platforms and different usage intentions.Originality/value: The authors' findings contribute to the application of commitment-trust theory, institutional theory and organizational control theory in the context of the live streaming e-commerce industry. Particularly, the authors use the Viya event as an example to quantitatively examine the effects of strict government regulations, which enriches the existing literature on this topic.

Promoting the intelligent upgrades of small and medium-sized enterprises is one of the important tasks of implementing “Made in China 2025” in China. As a front runner of nation-level reform, Zhejiang Province has provided much room for innovation and development, along with the emergence of a new type of ecology, accelerated formation of two ecosystems and international cooperation, and a supportive policy environment. Therefore, this paper uses 173 Small and Medium-sized Enterprises (SMEs) from Zhejiang Province as the research objects, builds a binary selection model, and analyzes the dynamic and constraining mechanism of intelligent upgrades of SMEs with regard to employee qualification, technology, capital, policy environment, and so on. The study finds that: First, among three main industries, manufacturing is the major industry for the intelligent upgrades of SMEs and there are significant demonstration effects and industry heterogeneity. Secondly, the willingness to upgrade intelligently for SMEs is relatively strong. More than half of SMEs that have not intelligently upgraded show willingness to implement intelligent upgrades. Thirdly, factors such as corporate profitability, human capital quality, and industry intelligence level have significantly promoted the intelligent upgrades of SMEs, while the impact of labor cost, capital structure, government subsidies, and other variables are not significant. This conclusion still works after a number of robustness tests. Last but not least, based on the above conclusions, this paper proposes corresponding policy recommendations which are practically beneficial to the development of SMEs in China.

Purpose: Online reviews are regarded as a source of information for decision-making because of the abundance and ready availability of information. Whereas, the sheer volume of online reviews makes it hard for consumers, especially the older adults who perceive more difficulties in reading reviews and obtaining information compared to younger adults, to locate the useful ones. The main objective of this study is to propose an effective method to locate valuable reviews of mobile phones for older adults. Besides, the authors also want to explore what characteristics of the technology older adults prefer. This will benefit both e-retailers and e-commerce platforms.Design/methodology/approach: After collecting online reviews related to mobile phones designed for older adults from a popular Chinese e-commerce platform (JD Mall), topic modeling, term frequency-inverse document frequency (TF-IDF), and linguistic inquiry and word count (LIWC) methods were applied to extract latent topics and uncover potential dimensions that consumers frequently referred to in their reviews. According to consumers' attitudes towards different popular topics, seven machine learning models were employed to predict the usefulness and popularity of online reviews due to their excellent performance in prediction. To improve the performance, a weighted model based on the two best-performing models was built and evaluated.Findings: Based on the TF-IDF, topic modeling, and LIWC methods, the authors find that older adults are more interested in the exterior, sound, and communication functions of mobile phones. Besides, the weighted model (Random Forest: Decision Tree = 2:1) is the best model for predicting the online review popularity, while random forest performs best in predicting the perceived usefulness of online reviews.Practical implications: This study’s findings can help e-commerce platforms and merchants identify the needs of the targeted consumers, predict reviews that will get more attention, and provide some early responses to some questions.Originality/value: The results propose that older adults pay more attention to the mobile phones' exterior, sound, and communication function, guiding future research. Besides, this paper also enriches the current studies related to making predictions based on the information contained in the online reviews.

The purpose of this study is to predict enterprises’ financial fraud. After collecting financial data and employing feature selection methods, totally eight features are selected. We select two best-performance machine learning models according to five indicators including accuracy, recall, specificity, AUC and the misclassification cost. Besides, an optimal weighted machine learning model, based on the two best-performance models, is proposed and the results confirm its good performance in forecasting enterprises’ financial fraud.