This study delves into investors’ perceptions of the polluting label attached to listed manufacturing firms, emphasizing the interplay between external political ties and internal green innovative capability in influencing these perceptions. Drawing on a longitudinal analysis of listed manufacturing firms in China from 2010 to 2020 and employing a difference-in-differences (DID) approach, we treat firms identified under the National Specially Monitored (NSM) program as the treated group, while non-NSM firms form the control group. The time variable captures the period post the introduction of the NSM program. Our findings highlight that the polluting label created a loss prospect for investors, signifying diminishing returns over time. Interestingly, firms with closer connections to local governments experienced amplified negative investor perceptions. In contrast, strong affiliations with the central government and robust green innovative capabilities cushioned these adverse reactions. Notably, central ties proved even more beneficial when complemented by green innovative capability. By melding signal theory with the literature on sense-making, this research adds nuance to the discourse on the role of resources in determining firm success amidst environmental controversies.

Grounded in the universal human predisposition to seek social connection, this study uniquely integrates social media data with mobility patterns to provide a holistic view of public emotional dynamics during the pandemic. Drawing on 45 million geocoded tweets from SINA.com, 57 billion trip records across 335 cities from Tencent Location Big Data, and 296 million mutual following records from social media users, we construct a large-scale, daily city-level emotional metric across different stages of pandemic. Our findings reveal significant emotional autocorrelation across different ties and identify four distinct feedback mechanisms shaping emotional interactions during the pandemic: amplified positive feedback in social media ties during the warning phase; shrinking positive feedback in geographic ties during the isolation phase; and negative and asymmetric feedback in social media and mobility ties, respectively, during the normalization phase. Notably, the easing of pandemic restrictions corresponded with a surge in positive emotional interactions in geographic and mobility ties. These findings underscore the role of physical and digital social networks, spanning geographic, mobility, and social media ties, in shaping public emotional interactions throughout different pandemic stages. Understanding these dynamics can inform crisis communication strategies, mitigate public distress, and enhance social resilience, offering valuable insights for policymakers managing future large-scale disruptions.

This study investigates how emotionally structured online interaction networks shape consumer perception of new energy vehicle (NEV) technology. Drawing on discussion forum data from two leading NEV brands, Brand_T and Brand_B, we focus on how users respond to brand technological narratives and how these responses translate into distinct patterns of peer-to-peer interaction. Using a behavioral network analysis framework, we integrate sentiment analysis, topic modeling, and Exponential Random Graph Modeling (ERGM) to uncover the psychological and structural mechanisms underlying consumer engagement. Three main findings emerge. First, users display brand-specific emotional-cognitive profiles: Brand_T communities show broader technological engagement but more heterogeneous emotional responses, whereas Brand_B communities exhibit more emotionally aligned discussions. Second, emotional homophily is a robust driver of interaction ties, particularly in Brand_B forums, where positive sentiment clusters into dense and supportive discussion subnetworks. Third, perceived technological benefits, rather than risk sensitivity, are consistently associated with higher interaction intensity, underscoring the motivational salience of anticipated gains over cautionary concerns in shaping engagement behavior. The study contributes to behavioral science and transportation behavior research by linking consumer sentiment, cognition, and social interaction dynamics in digital environments, offering an integrated theoretical account that bridges the Elaboration Likelihood Model, social identity processes, and behavioral network formation. This advances the understanding of technology perception from static individual evaluations to dynamic, group-structured outcomes. It highlights how emotionally patterned interaction networks can reinforce or recalibrate technology-related perceptions, offering practical implications for NEV manufacturers and policymakers seeking to design psychologically informed communication strategies that support sustainable technology adoption.

Generative artificial intelligence (AI) is increasingly used to produce multimedia content, raising important questions about how users evaluate and respond to such content. While prior research has examined AI transparency and technology adoption, the cognitive mechanisms linking external cues to user trust and behavioral outcomes remain insufficiently understood. Grounded in the heuristic–systematic model (HSM), this study investigates how transparency labels and presentation quality influence cognitive processing, perceived authenticity, trust, and perceived risk in AI-generated video content. A 2 × 2 between-subjects experiment was conducted with 617 participants. The results show that AI transparency labels reduce perceived authenticity but do not directly affect trust or adoption intention. Higher presentation quality reduces reliance on heuristic processing. Perceived authenticity is associated with lower cognitive load and greater systematic processing, systematic processing is positively associated with trust, whereas heuristic processing is negatively associated with trust. Trust is positively associated with both adoption intention and sharing intention, whereas perceived risk is negatively associated with these outcomes. These findings highlight the central roles of cognitive processing and trust–risk dynamics in shaping user responses to AI-generated content and provide implications for transparency design and trust development in AI-mediated environments.