Purpose: The purpose of the research is to assess the risk of the financial market in the digital economy through the quantitative analysis model in the big data era. It is a big challenge for the government to carry out financial market risk management in the big data era.Design/methodology/approach: In this study, a generalized autoregressive conditional heteroskedasticity-vector autoregression (GARCH-VaR) model is constructed to analyze the big data financial market in the digital economy. Additionally, the correlation test and stationarity test are carried out to construct the best fit model and get the corresponding VaR value.Findings: Owing to the conditional heteroscedasticity, the index return series shows the leptokurtic and fat tail phenomenon. According to the AIC (Akaike information criterion), the fitting degree of the GARCH model is measured. The AIC value difference of the models under the three distributions is not obvious, and the differences between them can be ignored.Originality/value: Using the GARCH-VaR model can better measure and predict the risk of the big data finance market and provide a reliable and quantitative basis for the current technology-driven regulation in the digital economy.

Objective: With consumers’ concerns about food safety and the environment growing, the interest in organic food has increased. However, due to the late start of the organic food market in China, the market size of the Chinese organic food industry is still relatively small. This study aims to examine whether organic food credence attributes have an impact on consumers’ attitudes and willingness to pay a premium (WTPP), in order to provide valuable information to facilitate the development of the organic food market in China.Methods: A questionnaire survey was conducted with 647 respondents in China. Structural equation modeling (SEM) was utilized to verify the model and test the relationships among the constructs.Results: SEM analyses showed that credence attributes stimulate consumers’ attitudes and increase consumers’ WTPP. Utilitarian attitudes and hedonistic attitudes play a partially mediating role in the relationship between credence attributes and WTPP. Uncertainty negatively moderates the role between utilitarian attitudes and WTPP, while it positively moderates the role between hedonistic attitudes and WTPP.Discussion: The findings reveal the motivations and barriers for Chinese consumers to purchase organic food at a premium, providing a theoretical basis for companies to gain a deeper understanding of consumer groups and develop organic food marketing strategies.

The intelligent strategy of the new energy vehicle (NEV) industry has triggered the rapid prevalence of in-vehicle anthropomorphic artificial intelligence (AI) assistants. There is still a lack of clarity regarding NEV users' attitudes toward this cutting-edge technology and whether they receive a satisfactory intelligent service experience. To circumvent potential emerging technology resistance, in this article, we utilize text analysis techniques for the identification of AI interaction emotions, love and disgust (enablers and inhibitors) with significant influence on user satisfaction, and validates the improving role of multimodality on the effectiveness of anthropomorphic interaction. In addition, this study innovatively constructs a multidimensional corpus of modality × emotion, using structural topic modeling to uncover the constituent elements and real-time changes of love and disgust emotions in different modalities, from which development opportunities and improvement directions for AI anthropomorphic interaction technologies are identified. The findings provide new insights into the application of emotion analysis methods to improve users' intelligent service experience and provide a realistic reference for mitigating emerging technology resistance in the NEV industry.

To effectively address challenges that stem from e-commerce, it is crucial to harness diverse review data from e-commerce platforms. These data support consumers in making informed purchase decisions and aid manufacturers in optimizing product attributes. Incorporating sentiment data from heterogeneous reviews across different time periods into a decision-making framework is a pivotal consideration in purchase decisions and product design. The goal of the study is to establish an online product decision support method grounded in consumer irrational behavior and segmented reviews over time. It aims to offer users reliable and consistent outcomes when making personalized purchase decisions. The probabilistic linguistic term set is employed to represent consumer sentiments with varying degrees of granularity across different time periods. Subsequently, stochastic sampling is utilized to simulate the decision-making process of individual consumers. Regret theory is then applied to analyze consumers' irrational psychological behavior. Building upon heterogeneous data gathered from e-commerce platforms, including review ratings, likes, and follow-up reviews, a multiperiod group decision approach based on maximum similarity and review helpfulness is proposed. This decision-making method is advanced through a decomposition-aggregation process, safeguarding against information distortion and ensuring result reliability. This method provides consumers with product selection solutions across the temporal dimension and serves as a theoretical compass for manufacturers and sellers seeking product enhancement and sales optimization.