Despite the rapid development of China’s organic food industry in recent years, the market size of this industry remains relatively small. Since the organic food market started late in China, consumer groups are mainly concentrated in large cities at present. It is, therefore, urgent to take effective measures to promote the development of China’s organic food market. The current study focuses on the direct and indirect relationships between food safety incidents and organic food purchases by considering the Chinese context, using stimulus-organism-response model and information use environment theory, and introducing a hyper attention cognitive model. The results show that external stimulus (food safety incidents) and internal stimulus (consumer environment orientation) can significantly affect consumers’ response (namely, consumer organic cognition), and the enhancement of consumer organic cognition can promote consumer organic purchase. In addition, consumers’ information environment (the information relating to food safety incidents and environment) can significantly affect their organic food purchase. Moreover, food safety incidents can attract consumers’ hyper attention and thus has a positive impact on consumers’ cognition of organic food. These findings have important implications for research and practice.

In the distributed Cognitive Industrial Internet of Things (CIIoT), since industrial devices may self-organize to determine their connection and dispersion and the network may be distributed with no infrastructure, network management with self-organizing characteristics is still an unsolved and a difficult problem. To overcome this challenge, this article proposes a hierarchical network management strategy for distributed CIIoT with imperfect channel state information (CSI) considering services with different transmission delay requirements. First, we build a two-layer architecture that supports collaborative spectrum sensing and multinode spectrum sharing in a distributed CIIoT. Then, to improve the network management efficiency and reduce the number of backbone nodes (BNs) while achieving spectrum sharing for massively distributed nodes, we establish a minimum BN set mathematical model and design the simplest backbone network optimization algorithm (SBNOA). For the problem of spectrum sharing with imperfect CSI while considering the delay-sensitive industrial services, we establish a time delay model that incorporates delay-sensitive and delay-tolerant services. Based on the model, we establish a robust optimization model with multiple conditional aim-listed probability constraints that consider imperfect CSI and transform the stochastic optimization problem into a convex optimization by using the quadratic transformation method and Gaussian $Q$ function to solve it. Simulation results show that the proposed algorithm has good performance in a distributed CIIoT.

Purpose: This study aimed to analyze existing problems in the dissemination and management of emergency information on social media platforms, improve social media users' experience regarding such information, increase the efficiency of emergency information dissemination and curb the spread of misinformation.Design/methodology/approach: In this study, the emergency information quality on social media platforms was examined. Based on the evaluation principles of the quality of mature information, social media information characteristics and the rules of emergency information dissemination, combined with relevant academic research results, an index to evaluate the quality of emergency information on social media was constructed. In addition, the authors have introduced cloud theory as an information quality evaluation method and used social media users' emotional characteristics to assess information quality evaluation results. A comprehensive system for evaluating emergency information quality, including indexes, methods and detection strategies was established. Based on a comprehensive system, a case study was conducted on the forest fires in Sichuan Province and the African swine fever events as reported on the Zhihu platform. In accordance with the results of the case study, the authors expanded the research and introduced the emotional characteristics of social media users as an independent evaluation dimension to evaluate the quality of emergency information on social media.Findings: The comprehensive system's effectiveness was verified through the case study. Further, it was found that users' emotional characteristics (reflected in their information behavior) are inconsistent with their evaluation of websites' information quality regarding major emergencies. Integrating users' emotional characteristics into the information evaluation system can enhance its effectiveness following major emergencies.Originality/value: First, an evaluation index system of emergency information quality on social media about major emergencies was offered. Unlike the commonly available index system for information quality evaluation, this proposed evaluation index system not only accounted for the characteristics of social media, such as massive disordered information, multiple information sources and rapid dissemination, but also for the characteristics of emergency events, such as variability and the absence of precursors. This proposed evaluation index system enhances the pertinence of the information quality evaluation and compensates for the shortcoming that the current research only focuses on evaluating social media information quality in a broad context, but pays insufficient attention to major emergencies. Second, cloud theory was introduced as a method to evaluate the emergency information quality found on social media. Existing research has primarily included the use of traditional statistical methods, which cannot transform numerical values into qualitative concepts effectively. Various indeterminate factors inevitably affect the quality of emergency information on social media platforms, and the traditional methods cannot eliminate this uncertainty in the evaluation process. The method to assess emergency information quality based on cloud theory can effectively compensate for the gaps in the research and improve the accuracy of information quality assessment. Third, the inspection and the dynamic adjustment of assessment results are absent in the research on information quality assessment, and the research has relied principally on the information users' evaluation and has paid insufficient attention to their attitudes and behaviors toward information. Therefore, the authors incorporated users' emotional characteristics into the evaluation of emergency information quality on social media and used them to test the evaluation results so that the results of the information quality assessment not only include the users' explicit attitudes but also their implicit attitudes. This enhances the effectiveness of the information quality assessment system. Finally, through this case study, it was found that an inconsistency exists between user evaluation and user emotional characteristics after major emergencies. The reasons for this phenomenon were explained, and the necessity of integrating user emotional characteristics into information quality assessment was demonstrated. Based on this, the users' emotional characteristics were used as a separate evaluation dimension for assessing the quality of emergency information on social media. Compared with assessing the quality of general information, integrating the user's emotional characteristics into the evaluation index system can lead the evaluation results to include not only the users' cognitive evaluation but also their emotional experience, further enhancing their adaptability.

Interaction is the main feature of social media, while in recent years, frequent privacy disclosure events of the social media user affect users’ privacy disclosure behavior. In this paper, we explore the mechanism of interaction characteristics by social media apps on users’ privacy disclosure behavior. Using SOR theoretical models and the privacy calculus theory, the effects of privacy disclosures on TikTok are examined. Structural equation modeling is used to analyze the data from 326 questionnaires. We concluded that human–computer interaction (perceived personalization, perceived control) and interpersonal interaction (perceived similarity) positively and negatively affected perceived benefits and perceived risks, respectively, and had positive effects on intention to disclose privacy through perceived benefits and perceived risks, respectively, except that perceived personalization had no effect on perceived risk and intention to disclose privacy. In addition, perceived benefits and perceived risks played an intermediary role in interactivity and privacy disclosure intention. Finally, we provided countermeasures and suggestions for social media operators and policy makers.

This paper develops a theoretical model of determinants influencing multimodal fake review generation using the theories of signaling, actor-network, motivation, and human–environment interaction hypothesis. Applying survey data from users of China’s three leading E-commerce platforms (Taobao, Jingdong, and Pinduoduo), we adopt structural equation modeling, machine learning technique, and Bayesian complex networks analysis to perform factor identification, path analysis, feature factor importance ranking, regime division, and network centrality analysis of full sample, male sample, and female sample to reach the following conclusions: (1) platforms’ multimodal recognition and governance capabilities exert significant negative moderating effects on merchants’ information behavior, while it shows no apparent moderating effect on users’ information behavior; users’ emotional venting, perceived value, reward mechanisms, and subjective norms positively influence multimodal fake review generation through perceptual behavior control; (2) feature factors of multimodal fake review generation can be divided into four regimes, i.e., regime 1 includes reward mechanisms and perceived social costs, indicating they are key feature factors of multimodal fake review generation; merchant perception impact is positioned in regime 2, signifying its pivotal role in multimodal fake review generation; regime 3 includes multimodal recognition and governance capabilities, supporting/disparaging merchants, and emotional venting; whereas user perception impact is positioned in regime 4, indicating its weaker influence on multimodal fake review generation; (3) both in full sample, male sample, and female sample, reward mechanisms play a crucial role in multimodal fake review generation; perceived value, hiring review control agency, multimodal recognition and governance capabilities exhibit a high degree of correlation; however, results of network centrality analysis also exhibit heterogeneity between male and female samples, i.e., male sample has different trends in closeness centrality values and betweenness centrality values than female sample. This indicates that determinants influencing multimodal fake review generation are complex and interconnected.

Purpose: The purpose of this paper is to explore the factors influencing the encountering information adoption of virtual live streaming from the perspective of the immersion experience. In addition, the paper aims to provide new theoretical perspectives and analytical frameworks for virtual live information behavior.Design/methodology/approach: Based on a review of relevant literature and theories, a model of the encountering information adoption of virtual live streaming users is constructed. In order to complete the empirical study, two experiments and questionnaires have been designed to investigate the relationship between high and low immersion experiences. A total of 1,332 valid survey samples were collected and analyzed, utilizing the structural equation model. In order to delineate the regimes, Gradient Boosted Regression Tree (GBRT) and Lasso regression were further utilized.Findings: The research findings indicate that users' immersion experience in virtual live streaming has a positive effect on perceived usefulness, trust, and commitment. Furthermore, perceived usefulness and trust have a positive effect on users' emotional arousal and enhance the content experience, while commitment has a negative effect on the content experience. The emotional arousal and content experience of users contribute to their encountering information adoption. The effect of immersion experience on encountering information adoption is partially mediated by perceived usefulness, trust, commitment, emotional arousal, and content experience. The relationship between content experience and encountering information adoption is moderated by digital literacy to a significant extent. In the context of virtual live streaming, the factors influencing users' encountering information adoption can be divided into three distinct regimes. The most significant factors affecting encounter information adoption are trust and commitment, which are located in the first regime. Emotional arousal and digital literacy are situated in the third regime, with the least significant influence on encountering information adoption.Originality/value: This study constructs a model of virtual live streaming users' encountering information adoption and explores the formation mechanism of encountering information adoption from the perspective of immersion experience, which provides a new perspective for further understanding the influence of virtual live-streaming users' encountering information adoption.