The media’s influence on public attitudes and decisions exhibits a dual nature, serving as a catalyst for both positive and negative perceptions, especially regarding autonomous vehicles (AVs). Although extensive research has addressed the role of social and mass media in this domain, the differential impacts of mass media and electronic word-of-mouth (eWOM) on the adoption of these technologies remain underexplored. To bridge this gap, this study explores how various media channels shape public perception and adoption intentions toward AVs. A key innovation of this study lies in its integration of the stimulus-organism-response (SOR) model and social cognitive theory (SCT) to examine how external environmental stimuli and internal belief systems jointly shape consumer behavior in autonomous vehicles. Methodologically, the study employed a structured questionnaire to survey Chinese consumers, gathering data on their media-influenced perceptions. This study’s theoretical implications were grounded in the TAM framework. Two types of external environmental stimuli: mass media and electronic word-of-mouth, were introduced, enriching the environmental variable system of the technology adoption model. Secondly, by using the integrated perspective of SOR-SCT, it revealed the differentiated influence mechanisms of different information sources on consumers’ adoption intentions through three psychological paths: self-efficacy, subjective norms, and trust. Practically, the research offers insights into how service providers can strategically use media channels to enhance public acceptance of new technologies. The results indicate that mass media has a positive impact on the public’s willingness to adopt autonomous vehicles. EWOM facilitated the creation of subjective norms by providing authentic, transparent, and diverse information, as well as enabling interaction with other users.

This study examines volatility spillovers between Chinese and U.S. equity markets by developing a comprehensive framework that captures asymmetric volatility, extreme co-movements, and dynamic correlations. We propose an integrated methodology combining EGARCH models with Student-t innovations, a Student-t copula, and a Dynamic Conditional Correlation (DCC) structure. Using daily returns of the Hang Seng Index (HSI) and the S&P 500, our analysis reveals three principal findings. First, the EGARCH model effectively captures the pronounced leverage effect and fat-tailed distributions characteristic of both markets. Second, the Student-t copula demonstrates the best fit among competing specifications, indicating significant symmetric tail dependence between the two markets. Third, time-varying correlations exhibit high persistence, rising during crises yet remaining within a moderate range. Crucially, out-of-sample forecasting shows that our unified framework achieves superior predictive accuracy relative to standard benchmarks. These findings provide valuable insights for investors designing hedging strategies, exchanges determining margin requirements, and policymakers monitoring financial contagion. Our approach offers a robust tool for analyzing volatility transmission between developed and emerging markets.

1. Crude oil, as a pivotal strategic commodity, profoundly influences global economic stability, geopolitical dynamics, industrial production costs, and inflation trajectories (Hamilton 2003; Kilia...