Objective: Drawing on the conservation of resources theory (COR), the research aims to reveal the influence of artificial intelligence (AI) awareness on employees’ mental health and behaviors, particularly examining whether and how employees’ AI awareness impacts their counterproductive work behaviors (CWB) in human-intelligence collaborations.Methods: Data was collected from 327 Chinese employees who collaborated with AI in sales, manufacturing, logistics, and other industries. The measurement instruments included scales for AI awareness, psychological contract (PC), emotional exhaustion (EE), and counterproductive work behavior (CWB). We used Hayes’s PROCESS macro to analyze the data.Findings: AI awareness had a significant positive impact on CWB (β = 0.448, p < 0.01). PC and EE play a role as partial mediators in the relationship between AI awareness and CWB. The mediating pathways consist of three sequences: “AI awareness → PC → CWB,” “AI awareness → EE → CWB” and “AI awareness → PC → EE → CWB,” with the respective contributions to the overall effect amounting to 8.04, 18.53, and 4.91%.Discussion: Our research contributes to the study of AI in the management field by elucidating the relationship between AI awareness and CWB, as well as the mediating mechanisms of this relationship, which enriches the literature on CWB and expands the understanding of the associations between AI and CWB.

The emotional expressions in live streaming e-commerce possess a strong contagious effect, enabling viewers to easily resonate with the specific emotions conveyed by the streamers and consciously build an empathy transmission chain. This study constructs a regression model based on the emotional contagion theory and explores the impact of empathy between streamers and viewers on sales performance. Using data from 30 live streams, totaling 22,707 min, from one of China’s most popular live streaming rooms, “East Buy”, between February and April 2024, we demonstrate the significant positive impact of empathy between streamers and viewers on sales. Additionally, product type positively moderates this relationship. The unexpected thing is that live streaming time does not significantly affect the relationship between empathy and sales. This study employs text sentiment analysis methods to extract emotional features from the streamers’ speech and real-time comments from viewers. Our research extends the application of emotional contagion theory to the context of live-streaming e-commerce, enriches the literature on emotional interaction in service marketing, and provides practical insights for live-streaming platforms and streamers. Streamers can optimize marketing strategies and achieve sales goals by creating a more engaging and empathetic live-streaming experience.

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.

Based on the quality level that consumers can discover at various stages, the literature summary divides organic food attributes into three categories: trust, search, and experience. This paper deeply analyzes the internal relationship among the search attribute, trust attribute, and perceived quality and the mechanism of effect on customer trust. After distributing and collecting 310 consumers’ valid questionnaires, the research hypotheses were empirically tested utilizing a structural equation model and mediation effect test. The research results indicate that: (1) The food safety attribute and nutritional content attribute in the organic food trust attribute have positive effects on the perceived quality and customer trust. (2) The price and label in the organic food search attribute positively affect the perceived quality, i.e., the price harms customer trust, while the label has no significant effect on customer trust. Perceived quality plays a mediating role between the trust attributes, search attribute, and customer trust, i.e., the price and label indirectly affect customer trust through perceived quality. (3) The perceived quality of organic food positively affects customer trust. The results provide an important theoretical basis for enterprises to implement effective strategies to enhance consumers’ trust in organic food.

This study aims to investigate the effect of hosts’ facial features of different genders on customers’ booking intentions on the Airbnb shared accommodation platform. A comprehensive model was built to analyze hosts’ facial features in the United States (n = 105,084) through big data combined with an artificial intelligence facial recognition system. Results show that beauty, smile, aging, and wearing glasses positively affect customers’ booking intentions. There is gender heterogeneity in the effect of hosts’ facial features on customers’ booking intentions. Compared to female hosts, smiling is significant for male hosts, and wearing glasses has a greater impact on female hosts. Host reputation somewhat weakens the positive effect of host facial features on customers’ booking intentions, and the shared housing type strengthens the positive effect of facial features on customers’ booking intentions. This study provides insights into customer decision-making that may be influenced by hosts’ facial features.