Purpose: The purpose of this paper is to provide insights on the improvement of academic impact and social attention of Chinese collaboration articles from the perspective of altmetrics.Design/methodology/approach: The authors retrieved articles which are from the Chinese Academy of Sciences (CAS) and indexed by Nature Index as sampled articles. With the methods of distribution analysis, comparative analysis and correlation analysis, authors compare the coverage differences of altmetric sources for CAS Chinese articles and CAS international articles, and analyze the correlation between the collaborative information and the altmetric indicators.Findings: Results show that the coverage of altmetric sources for CAS international articles is greater than that for CAS Chinese articles. Mendeley and Twitter cover a higher percentage of collaborative articles than other sources studied. Collaborative information, such as number of collaborating countries, number of collaborating institutions, and number of collaborating authors, show moderate or low correlation with altmetric indicator counts. Mendeley readership has a moderate correlation with altmetric indicators like tweets, news outlets and blog posts.Practical implications: International scientific collaboration at different levels improves attention, academic impact and social impact of articles. International collaboration and altmetrics indicators supplement each other. The results of this study can help us better understand the relationship between altmetrics indicators of articles and collaborative information of articles. It is of great significance to evaluate the influence of Chinese articles, as well as help to improve the academic impact and social attention of Chinese collaboration articles.Originality/value: To the best of authors’ knowledge, few studies focus on the use of altmetrics to assess publications produced through Chinese academic collaboration. This study is one of a few attempts that include the number of collaborating countries, number of collaborating institutions, and number of collaborating authors of scientific collaboration into the discussion of altmetric indicators and figured out the relationship among them.

Purpose: The authors hope to filter valuable information from online reviews, obtain objective and accurate information about the demands of auto consumers and help auto companies develop more reasonable production and marketing strategies for healthy and sustainable development. This paper aims to discuss the aforementioned objectives.Design/methodology/approach: The authors collected review data from online automotive forums and generated a corpus after pre-processing. Then, the authors extracted consumer demands and topics using the LDA model. Finally, the authors used a trained Word2vec tool to extend the consumer demand topics.Findings: Different types of vehicle consumers have the same demands, such as “Space,” “Power Performance,” and “Brand Comparison,” and distinct demands, such as “Appearance,” “Safety,” “Service,” and “New Energy Features”; consumers who buy new energy vehicles are still accustomed to comparing with the brands or models of fuel vehicles; new energy vehicles consumers pay more attention to services and service quality during the purchasing and using process.Research limitations/implications: The development time of new energy vehicles is relatively short, with some models being available for only one year or even six months. The smaller amount of available data may impact the applicability of topic models. The sample size, especially for new energy vehicles, needs to be increased to improve the general applicability of topic models further.Practical implications: First, this measure helps online review websites improve their existing review publication mechanisms, enhance the overall quality of online review content, increase user traffic and promote the healthy development of online review websites. Second, this allows for timely adjustments in future product production and sales plans and further enhances automotive companies' ability to leverage online reviews for Internet marketing.Originality/value: The authors have improved the accuracy and stability of the fused topic model, providing a scientific and efficient research tool for multi-dimensional topic mining of online reviews. With the help of research results, consumers can more easily understand the discussion topics and thus filter out valuable reference information. As a result, automotive companies may gain information about consumer demands and product quality feedback and thus quickly adjust production and marketing strategies to increase sales and market share.

This study extracted the demand preference topic words of new energy vehicle consumers with the help of the topic model, calculated the similarity between the word vectors and the topic keywords and expanded the topic keywords, analyzed and compared the demand topics and feature expansion words of different car models, and summarized the demand differences of other consumer groups. The analysis results show that consumers’ demands of different groups have the exact demand dimensions such as new energy features and brand features, and different demand dimensions such as application, services, and professional performance. The research findings help consumers filter valuable information from online review data and help car companies objectively and accurately obtain consumer demands, develop more reasonable marketing strategies, and achieve healthy and sustainable corporate development.

In China, new-energy vehicles are viewed as the ultimate goal for the automobile industry, given the current focus on the “dual-carbon” target. Therefore, it is important to promote the sustainable development of this new-energy market and ensure a smooth transition from fuel-driven vehicles to new-energy vehicles. This study constructs a tripartite evolutionary game model involving vehicle enterprises, consumers, and the government. It improves the tripartite evolutionary game through the mechanisms of dynamic and static rewards and punishments, respectively, using real-world data. The results show the following. (1) A fluctuation is present in the sales of new-energy vehicles by enterprises and the active promotional behavior of the government. This fluctuation leads to instability, and the behavior is difficult to accurately predict, which is not conducive new-energy vehicles’ promotion and sales. (2) A static reward and punishment mechanism can change the fluctuation threshold or peak value. Nevertheless, the stability of the system’s strategy is not the main reason that the government has been actively promoting it for a long time. However, enterprises are still wavering between new-energy and fuel vehicles. (3) The linear dynamic reward and punishment mechanism also has its defects. Although they are considered the stability control strategy of the system, they are still not conducive to stability. (4) The nonlinear dynamic reward and punishment mechanism can help the system to achieve the ideal stabilization strategy.

The development of China's new energy vehicle industry has been gradually transformed from past policy support to the survival of enterprises determined by the market, which forces the new energy vehicle industry to accelerate towards the market-oriented direction. We collected review data from online automotive forums and generated a corpus after pre-processing. Next, we extracted the statistical and topic semantic features of online reviews for new energy vehicles and clarified the quality evaluation indicators for online reviews. Then, the entropy weight-TOPSIS method was used to determine the indicators’ weights and assign values to the quality of online reviews. Finally, the quality scoring rules of online reviews were sorted out from multidimensional perspectives. The research results have shown that the statistical features and topic semantic features of online reviews on new energy vehicles can be used as evaluation indicators to construct a quality evaluation system. It was also found that the quality scores of lower-priced vehicle reviews tend to be consistent in different price groups, while the quality scores of mid-priced vehicle reviews are the most dispersed. The quality of online reviews varies significantly among other groups or models. The study results provide a simple criterion for consumers’ cognition of the value of online reviews. The results will enhance the ability of new energy vehicle companies to carry out online marketing with the help of online reviews and transform the purchase intention of potential consumers into actual purchase behavior.

Purpose: Microblogs are communication platforms for companies and consumers that challenge companies' brand marketing strategies. This paper provides a theoretical basis for expanding microblog applications and a practical basis for improving the effectiveness of brand marketing.Design/methodology/approach: The authors use factor analysis to extract the factors of microblog user influence and construct a structural equation model to reveal the interaction mechanism of the influencing factors. Additionally, the authors clarify the promotion and enhancement effects of these factors.Findings: Microblog user influence can be converted into richness, interaction and value factors. The richness factor significantly affects the latter two, whereas the interaction factor does not affect the value factor.Research limitations/implications: First, the sample used is limited to media industry practitioners. To increase generalizability, diverse groups should be included in future studies. Second, this model's theoretical explanatory ability can be further developed by adding other meaningful factors beyond the existing ones.Originality/value: This study analyzes the factors of microblog user influence in China and validates the relevant elements. As a result, it improves the influence research on social media users and benefits the practice of information recommendation and microblog marketing.