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Rapid globalization has continually promoted integration between different cultures. This study attempts to examine responses toward culture mixing based on spatial metaphor theory. Specifically, it focuses on power relationships to explore the impact of space image schemas on consumer attitudes. The boundary conditions of this effect, the significance of cultural symbols and the dominance trait are also analyzed.

Three experiments are conducted, and 567 participants are involved in this research. The data are analyzed using ANOVA and Process 213.

The results show that the relative vertical positions of cultural symbols significantly impact consumer attitudes toward culture mixing, and demonstrate that perceived cultural threat enhances ethnocentric tendencies. The data also indicate that individuals only express their distinct attitudes toward culture mixing when significant cultural symbols are presented, and only high-dominance individuals, who prefer to link vertical spatial metaphor with power relationships, are sensitive to vertical spatial metaphor.

This research highlights the underlying mechanism, encouraging ethnocentric tendencies among the young population in China. It extends work on the use of metaphorical concepts and contributes to the increasing literature on power in consumer behavior. Additionally, it generates useful suggestions for multinational entrepreneurs who want to facilitate symbolic localization.

This study aims to explore the opposite effects of single-category versus multi-category products information diversity on consumer decision making. Further, the authors investigate the moderating role of three categories of visitors – direct, hesitant and hedonic – in the relationship between product information diversity and consumer decision making.

The research utilizes a sample of 1,101,062 product click streams from 4,200 consumers. Visitors are clustered using the k-means algorithm. The diversity of information recommendations for single and multi-category products is characterized using granularity and dispersion, respectively. Empirical analysis is conducted to examine their influence on the two-stage decision-making process of heterogeneous online visitors.

The study reveals that the impact of recommended information diversity on consumer decision making differs significantly between single-category and multiple-category products. Specifically, information diversity in single-category products enhances consumers' click and purchase intention, while information diversity in multiple-category products reduces consumers' click and purchase intention. Moreover, based on the analysis of online visiting heterogeneity, hesitant, direct and hedonic features enhance the positive impact of granularity on consumer decision making; while direct features exacerbate the negative impact of dispersion on consumer decision making.

First, the article provides support for studies related to information cocoon. Second, the research contributes evidence to support the information overload theory. Third, the research enriches the field of precision marketing theory.

Since crude oil is crucial to the nation's economic growth, crude oil futures are closely related to many other markets. Accurate forecasting can offer investors trustworthy guidance. Numerous studies have begun to consider creating new metrics from social networks to improve forecasting models in light of their rapid development. To improve the forecasting of crude oil futures, the authors suggest an integrated model that combines investor sentiment and attention.

This study first creates investor attention variables using Baidu search indices and investor sentiment variables for medium sulfur crude oil (SC) futures by collecting comments from financial forums. The authors feed the price series into the NeuralProphet model to generate a new feature set using the output subsequences and predicted values. Next, the authors use the CatBoost model to extract additional features from the new feature set and perform multi-step predictions. Finally, the authors explain the model using Shapley additive explanations (SHAP) values and examine the direction and magnitude of each variable's influence.

The authors conduct forecasting experiments for SC futures one, two and three days in advance to evaluate the effectiveness of the proposed model. The empirical results show that the model is a reliable and effective tool for predicting, and including investor sentiment and attention variables in the model enhances its predictive power.

The data analyzed in this paper span from 2018 through 2022, and the forecast objectives only apply to futures prices for those years. If the authors alter the sample data, the experimental process must be repeated, and the outcomes will differ. Additionally, because crude oil has financial characteristics, its price is influenced by various external circumstances, including global epidemics and adjustments in political and economic policies. Future studies could consider these factors in models to forecast crude oil futures price volatility.

In conclusion, the proposed integrated model provides effective multistep forecasts for SC futures, and the findings will offer crucial practical guidance for policymakers and investors. This study also considers other relevant markets, such as stocks and exchange rates, to increase the forecast precision of the model. Furthermore, the model proposed in this paper, which combines investor factors, confirms the predictive ability of investor sentiment. Regulators can utilize these findings to improve their ability to predict market risks based on changes in investor sentiment. Future research can improve predictive effectiveness by considering the inclusion of macro events and further model optimization. Additionally, this model can be adapted to forecast other financial markets, such as stock markets and other futures products.

The authors propose a novel integrated model that considers investor factors to enhance the accuracy of crude oil futures forecasting. This method can also be applied to other financial markets to improve their forecasting efficiency.

The purpose of this paper is to present a numerically adaptive finite element (FE) method for accurate, efficient and reliable eigensolutions of regular second- and fourth-order Sturm–Liouville (SL) problems with variable coefficients.

After the conventional FE solution for an eigenpair (i.e. eigenvalue and eigenfunction) of a particular order has been obtained on a given mesh, a novel strategy is introduced, in which the FE solution of the eigenproblem is equivalently viewed as the FE solution of an associated linear problem. This strategy allows the element energy projection (EEP) technique for linear problems to calculate the super-convergent FE solutions for eigenfunctions anywhere on any element. These EEP super-convergent solutions are used to estimate the FE solution errors and to guide mesh refinements, until the accuracy matches user-preset error tolerance on both eigenvalues and eigenfunctions.

Numerical results for a number of representative and challenging SL problems are presented to demonstrate the effectiveness, efficiency, accuracy and reliability of the proposed method.

The method is limited to regular SL problems, but it can also solve some singular SL problems in an indirect way.

Comprehensive utilization of the EEP technique yields a simple, efficient and reliable adaptive FE procedure that finds sufficiently fine meshes for preset error tolerances on eigenvalues and eigenfunctions to be achieved, even on problems which proved troublesome to competing methods. The method can readily be extended to vector SL problems.

The purpose of this paper is to evaluate the ability of the Chinese geographical indications (GI) system to offer extra guarantees on food safety.

Based on a network approach, the research includes a literature review and takes a sample of GI products in Jiangxi Province as a means of exploring the ability of the GI system to enhance food safety in China.

The safety level of Chinese GI food products is not automatically ensured by GI schemes alone. The Chinese Government appears to be using GI labels more as a promotional tool to enhance farmers’ incomes rather than as a means of reducing safety concerns and is thus missing an opportunity to enhance food safety in tandem with adding value.

This research is of academic value and of value to policy makers. The results suggest an urgent need to encourage producers to attend GI management programmes, to generate appropriate GI standards, to be actively involved in GI issuing processes, and thus to provide “safe” GI products. Meanwhile, with weak government enforcement, there is also a need to build an effective food safety regulatory system to ensure food safety and protect consumers’ rights.

With its large population and increasingly open approach to foreign business dealings, China has been heralded as a land of opportunity for Western business. “What are the keys to business success?” Addresses this issue by Investigating key success factors for trade with China. Presents results from a survey of New Zealand organisations trading with China. Top‐ranking issues reveal a micro‐business focus (e.g. negotiation strategy, business etiquette). Low‐ranking issues include the need to understand advertising in China, and to have an intensive knowledge of the Chinese language. Correlations between importance and knowledge scores suggest that cultural issues are perceived as less important by those with a high degree of knowledge concerning trade relation intermediaries. Larger firms are also found to rate an understanding of negotiation strategy as more important than small firms.

Technological innovations have affected and modified practically every element of human life and work. Education is one of the areas that has been greatly influenced by its dynamism. Technology has created an ecosystem in which education can be delivered via e-modes. The objective of this chapter is to study the impact of personalized learning on the student’s concentration level. The research methodology is top-down and includes both primary and secondary data sources. The sample was drawn using a probability-random sampling procedure with a response distribution of 50%, a margin of error of 10%, and a confidence level of 90%. The inquiry was carried out in various Indian states throughout the academic year 2020–2023. A 419-student sample was used in the study, and they were evaluated in two rounds using a pre-tested questionnaire. The association was determined for the experiencing group utilizing SPSS Version 25 and the analytic methods of factor analysis. This chapter presents options for higher education institutions, governments, and regulators to embrace and adapt. We are convinced that the study’s various recommendations will contribute to the development of a long-term strategy and plan to achieve the aim of ‘education for all, work for all’. The study investigates how the existing education model might adapt in the near future to keep up with the shifting paradigm and the arrival of Education 5.0.