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The purpose of this paper is to construct a grey incidence model for panel data that can reflect the incidence direction and degree between indicators.

Firstly, this paper introduces the concept of a negative matrix and preprocesses the data of each indicator matrix to eliminate differences in dimensions and magnitudes between indicators. Then a model is constructed to measure the incidence direction and degree between indicators, and the properties of the model are studied. Finally, the model is applied to a practical problem.

The grey-directed incidence degree is 1 if and only if corresponding elements between the feature indicator matrix and the factor indicator matrix have a positive linear relationship. This degree is −1 if and only if corresponding elements between the feature indicator matrix and the factor indicator matrix have a negative linear relationship.

The example shows the number of days with good air quality is negatively correlated with the annual average concentration of each pollutant index. PM_2.5, PM₁₀ and O₃ are the main pollutants affecting air quality in northern Henan.

This paper introduces the negative matrix and constructs a model from the holistic perspective to measure the incidence direction and level between indicators. This model can effectively measure the incidence between the feature indicator and factor indicator by integrating information from the point, row, column and matrix.

This research aims to discover the relationship between social media usage (SMU) and the critical thinking ability (CTA) of university students, and to answer the question that whether social media dependence (SMD) affects the development of CTA, and thus providing a reference for the social media access strategy of academic libraries from the perspective of media information literacy.

The research data were collected via 300 valid questionnaires whose respondents are students from three universities in China. Multistage stratified cluster sampling method was used to select the respondents, which guarantees statistical representativeness. A pre-test was conducted to ensure the validity of the questionnaire.

It is shown that the total score of CTA and the six sub-dimensions are significantly positively correlated with SMU, but strongly negatively correlated with SMD. Based on the mediating effect testing, it is discovered that the degree of SMD can affect the promoting relations between the usage intensity of social media (UISM) and CTA. Clearly, SMU is a double-edged sword. While it narrows the digital gap in terms of accessibility, it widens the digital gap in terms of usage.

The differences in SMU have a significant impact on the development of CTA of university students. This inspires us to consider the ability of “using social media in a balanced way” as an important evaluation and training direction when inquiring media literacy. As social media is becoming a critical channel in cultivating individual's thinking skills, it is highly suggested that the amount of time spent on reading fragmented information on the internet should be controlled.

The primary objective of this research is to explore the impact of the repeated two-syllable communication strategy on the interaction effectiveness between AI and customers.

This study adopts an experimental research methodology to investigate the role of the repeated two-syllable communication strategy employed by AI customer service agents.

Study 1 shows that AI agents using the repeated two-syllable strategy enhance the interaction effectiveness between AI and customers. Study 2 identifies humanization perception as a key factor linking the strategy to better interaction effectiveness. Study 3 highlights how consumer materialism moderates this effect, while Study 4 examines how the type of agent (AI vs. human) influences the results.

This study extends the application of AI communication strategies in interactive marketing, specifically how AI agents enhance consumer interaction through repeated two-syllable communication. It pioneers the exploration of AI-human interaction, enriching the humanization theory by revealing how AI can evoke emotional responses. The study also integrates consumer materialism as a moderating factor, offering new theoretical and practical insights for brands to optimize AI-customer service interactions and improve engagement in real-world marketing contexts.