Huimin Wang

With the ubiquitous connectivity and exposure of social network service (SNS), the stressors it causes have received extensive attention in the academic community. Unlike previous studies, this research focuses on the cross-cultural dimension and explores the different effects of multiple SNS-generated stressors on user behavior outcomes. Based on the “Stressors-Strain-Outcome” (SSO) theoretical framework, we constructed a “technical stressors—exhaustion—reduced SNS usage intention” pathway to systematically investigate five types of technical stressors. These were perceived information overload, perceived social overload, perceived compulsive use, perceived privacy concern, and perceived role conflict. We introduce “fear of missing out” (FOMO) as a moderating variable to explore its moderating role in SNS exhaustion and reduced SNS usage intention. In this study, we took SNS users from China and the United States as the research subjects (338 samples from China and 346 samples from the United States), and conducted empirical tests using structural equation models and multiple comparative analyses. The results show that there are significant cultural differences between Chinese and American users in terms of the perceived intensity of technostress, the path of stress transmission, and the moderating effect of FOMO. Against the background of collectivist culture in China, perceived information overload, privacy concerns, and role conflicts have a significant positive impact on SNS exhaustion, and SNS exhaustion further positively drives the intention to reduce usage of SNS. However, the direct impacts of perceived social overload and perceived compulsive usage are not significant, and FOMO does not play a significant moderating role. In the context of the individualistic culture found in the United States, only perceived information overload and perceived social overload have a significant positive impact on SNS exhaustion, and FOMO significantly negatively moderates the relationship between exhaustion and reduced SNS usage intention, as high FOMO levels will strengthen the driving effect of exhaustion on reduced usage intention. The innovation this study exhibits lies in verifying the applicability of the SSO model in social media behavior research from a cross-cultural perspective, revealing the cultural boundaries of the FOMO moderating effect, and enriching the cross-cultural research system of reduced usage intention of SNS. The research results not only provide empirical support for a deep understanding of the psychological mechanisms of users’ SNS usage behaviors in different cultural backgrounds, but also offer important references that SNS enterprises can use to formulate differentiated operation strategies and optimize cross-cultural user experiences.

Driven by digital technology, live streaming business is becoming increasingly common worldwide. Unlike traditional online shopping, live streaming commerce integrates real-time interaction, social communication, and e-commerce. It also eliminates the limitations of one-way information transmission and promotes purchasing behavior by conveying signals such as products, brands, and the personal charm of live streamers to consumers through real-time communication. The core issues explored in this study are whether the cues between the signal transmitters and receivers are consistent and how they affect consumers’ purchasing behavior. In this study, the consistency of the signal is measured by five dimensions, namely self–product fit, live streamer–product fit, live content–product fit, danmaku content–product fit, and self–live streamer fit. In order to study this problem, we constructed a structural equation modeling (SEM) model. In the causal relationship between signal consistency and purchase intention, performance responses have also been discussed as mediating variables. Accordingly, signal consistency enables consumers to perceive performance expectancy, whereby consumers believe that products that perform well positively influence satisfaction. To verify these hypotheses, 443 randomly collected valid questionnaires were used in empirical analyses. The results showed that most of our hypotheses were validated, aside from the relationship between self–live streamer fit and perceived performance expectancy. The findings suggest that signal consistency cues such as live streamer–product fit, live content–product fit, danmaku content–product fit, and self–product fit positively influence consumers’ perceived performance expectancy and satisfaction, which in turn promote purchase intention. These findings not only enrich the application of signaling theory in the field of live streaming commerce but also expand consumer behavior theory. Moreover, they also inspire practitioners in live streaming commerce by helping them to improve live streaming sales and bolster their market competitiveness through signal display optimization, content planning, and precision marketing.

AI virtual anchors are an emerging innovation that are gaining significant attention, as they hold promising applications across various fields. This study examines how users perceive live product selling by AI virtual anchors and its impact on brand image and brand loyalty. A two-stage PLS-SEM and ANN approach was employed to analyze data from a sample of 336 individuals in China who had experienced and utilized AI virtual anchors for purchases during branded live streaming sessions. The findings indicate that perceived usefulness, perceived enjoyment, and novelty positively impact brand image, with artificial neural network (ANN) analysis identifying brand image as the primary predictor. Furthermore, brand image acts as a mediator between these user perceptions and brand loyalty. These insights offer brand managers a strategic approach to utilize AI virtual anchors for fostering a positive brand image and building loyal customer bases. The study also contributes to the academic understanding of consumer behavior and brand management in the context of AI.

This paper adopts the fuzzy set qualitative comparative analysis method to carry out an empirical analysis on Chinese latecomer enterprises and examines the key factors and realization paths that affect the technology path transformation of latecomer enterprises. This paper finds that there are configuration characteristics among the influencing factors of the technological path transformation of latecomer enterprises. The paper reveals the causal complexity mechanism of multiple factors that jointly affect technology path transformation. The key factors that affect the technological path transformation of latecomer enterprises include organizational vigilance, strategic transformation capability, network connections breadth, resource governance depth, dynamics of the technological environment, and the scale of social demand for technology. The above factors not only emphasize that latecomer enterprises should focus on new technologies rather than the market but also reveal that strategic transformation capability and resource governance depth are the keys for enterprises to achieve technology path transformation. In addition, it is pointed out that latecomer enterprises should pay attention to the “window of opportunity” brought about by the scale of social demand for technology. The transition from technology path dependence to technology path transformation of latecomer enterprises is a process in which the key factor becomes stronger.

Vegetation plays an important role in water and energy cycles on land surfaces. Nevertheless, the physical effects of vegetation are not explicitly considered in many hydrologic-modeling works. In this study, a coupled land-surface-hydrologic model was used to investigate the vegetation effects on hydrologic processes over the years 1980–1987 in the Huaihe River basin, China. The vegetation coverage of the basin was assessed by the International Satellite Land Surface Climatology Project (ISLSCP) Initiative II historic and potential land cover data set. Farmland was declared the dominant vegetation type of the basin in 1970 by the historic land cover scenario, and the potential vegetation cover is mixed forest. Firstly, the coupled model was calibrated by observed streamflow at Bengbu station. The correlation coefficient and Nash-Sutcliffe coefficient of efficiency of multiannual daily series were 0.987 and 0.968, respectively, which indicate that the capability of the coupled model system is acce...

The evolution of logistics industrial cluster reflects the self-organization mechanism and function of the complex system, which provides an analytical angle for the logistics industrial cluster. The multi-agent simulation method of the complex system theory is adopted to explore the general evolutional pattern of logistics industrial cluster and a basic idea for logistics industrial cluster based on multi-agent systems is proposed. This paper also inquires into the structure of each agent in this system and the corporation among all the agents, poses the simplified simulated model and studies the method to implement this model, offering a theoretical guide and a methodological basis to the study of logistics industrial cluster based on multi-agent systems. According to this theory, the simulated experimental system is constructed based on SWARM, and simulated results confirm the rationality of the system.

Water inventory control plays an important role in the research of supply chains of water resources of South-to-North water transfer. Based on the practical conditions of water resources allocation in China, the article chooses the node lake of water resources supply chains of South-to-North water transfer as the research object and uses water ordering quantity raised by the downstream district as the decision-making variable. It also establishes a central control decision-making model of water resources supply chains under the conditions of asymmetrical information. Finally, thorough the positive analysis of Dongping Lake, it proves the validity of the model