"In Flow"! Why Do Users Share Fake News about Environmentally Friendly Brands on Social Media?

Consumer perception towards electronic products from recycled components in the current geopolitical context: A structural equation modelling approach

Russia's invasion of Ukraine had a negative impact worldwide, causing a severe energy crisis that also affected EU countries, which are now involved in combating the growing energy prices while also speeding up the deployment of renewable energy sources. This armed conflict impacted the electronic components supply chain, causing high prices and disruption for different raw materials, resulting in material shortages for electronic components and affecting electronic product (EP) manufacturing. Since the start of the geopolitical crisis due to the Russo-Ukrainian War, (dis)information has been disseminated via social media, affecting users' cognition, attitudes, and behavioral intentions. Therefore, this paper aims to assess the impact of social media usage, Russo-Ukrainian war fear, consumers' green values, perceived quality, usage enjoyment, and product image on consumers' purchase intention toward recycled electronic products. Based on the Stimulus-Organism-Response (SOR) approach, the authors propose a conceptual model highlighting the factors that enhance consumers' purchase intentions towards recycled electronic products. The model is tested empirically via quantitative-based research, with data collected from Romania, a close neighbor of the armed conflict, and assessed employing with structural equations modeling via SmartPLS 3.0. Results confirm that social media usage, consumers' green values, and the Russo-Ukrainian war fear do enhance consumers' image of recycled electronic products, thus leading to their increased purchase intention. The novelty of this paper consists in extending the SOR-based research regarding consumers' behavioral intentions toward buying recycled electronic products in the context of the Russo-Ukrainian war. The study highlights important managerial implications for both the electronic industry and retailers selling such goods.

Adaptive cost-sensitive stance classification model for rumor detection in social networks

As online social networks are experiencing extreme popularity growth, determining the veracity of online statements denoted by rumors automatically as earliest as possible is essential to prevent the harmful effects of propagating misinformation. Early detection of rumors is facilitated by considering the wisdom of the crowd through analyzing different attitudes expressed toward a rumor (i.e., users’ stances). Stance detection is an imbalanced problem as the querying and denying stances against a given rumor are significantly less than supportive and commenting stances. However, the success of stance-based rumor detection significantly depends on the efficient detection of “query” and “deny” classes. The imbalance problem has led the previous stance classifier models to bias toward the majority classes and ignore the minority ones. Consequently, the stance and subsequently rumor classifiers have been faced with the problem of low performance. This paper proposes a novel adaptive cost-sensitive loss function for learning imbalanced stance data using deep neural networks, which improves the performance of stance classifiers in rare classes. The proposed loss function is a cost-sensitive form of cross-entropy loss. In contrast to most of the existing cost-sensitive deep neural network models, the utilized cost matrix is not manually set but adaptively tuned during the learning process. Hence, the contributions of the proposed method are both in the formulation of the loss function and the algorithm for calculating adaptive costs. The experimental results of applying the proposed algorithm to stance classification of real Twitter and Reddit data demonstrate its capability in detecting rare classes while improving the overall performance. The proposed method improves the mean F-score of rare classes by about 13% in RumorEval 2017 dataset and about 20% in RumorEval 2019 dataset.

Application of McGuire's Model to Weight Management Messages: Measuring Persuasion of Facebook Posts in the Healthy Body, Healthy U Trial for Young Adults Attending University in the United States

Digital communication is a common intervention channel for weight loss, yet little is known about the types of messages that are most effective. Using McGuire's Model of Communication and Persuasion as a framework, this study investigates the persuasiveness of Facebook messages posted as part of the weight loss intervention in the Healthy Body Healthy U (HBHU) study to determine what message characteristics prompt higher engagement on Facebook, and whether certain messages are more appealing to certain demographics. The first four weeks of HBHU Facebook posts (n = 32) were coded according to McGuire's Input Communication Factors. Facebook engagement scores [(Total Engaged Users/Total Reach) × 100] were calculated for each post to determine effectiveness. The most effective posts were diet-related discussions or interactive polls. Participants who engaged with the highest and lowest effect posts were in their mid-twenties and tended to be female. Those engaged with the highest effect posts had an average BMI of 32.34 kg/m², while those engaged with the lowest effect posts had an average BMI of 31.31 kg/m². The least effective posts were didactic lessons (i.e., diet- or reminder-based), or video-based informational posts (edutainment). Future interventions should balance didactic content and interactive engagement to achieve persuasive messaging.

Environmental Regulation, Greenwashing Behaviour, and Green Governance of High-Pollution Enterprises in China

This study analyses the relationship between greenwashing behaviour, a lack of government supervision, and imperfect green certification mechanisms in China. To improve green governance and greenwashing governance in light of rent-seeking behaviour between high-pollution enterprises and third-party green certification institutions (GCIs), we construct a tripartite game model for the green governance system using an evolutionary game and analyse the interaction and evolutionary trajectory between the three parties. Our results indicate that increasing local government incentives and penalties not only facilitate strengthened green governance by high-pollution enterprises that do not greenwash but also help third-party GCIs to decline to engage in rent-seeking. However, increased incentives lead to relaxed governmental supervision. In addition, the government's incentives and penalties only meet conditions that each agent's total incentives and penalties exceed its speculative gain, and green governance systems can be effectively prevented from resulting in unsatisfactory and unstable strategies. Moreover, the accountability that higher levels of government have to local governments effectively enables high-pollution enterprises' refusal to greenwash and third-party GCIs' refusal to engage in rent-seeking. Our results counsel further research on environmental regulation, green governance, and enterprises' greenwashing, with theoretical and practical applications for both policymakers and enterprises.

The Mediation Effects of Social Media Usage and Sharing Fake News about Companies

Trust in social media information is gaining in importance and relevance for both companies and individuals as nowadays contemporary society is confronted with a wave of fake news about daily life situations, brands, organizations, etc. As it becomes more difficult to accurately assess social media information and to determine its origin or source, as well as to be able to double-check information spread across different Social Networking Sites (SNS), businesses must understand how individuals' perceived control, concentration, and time distortion enhances the social media usage, thus allowing them to correctly assess online information. Therefore, the scope of the paper is to assess, based on a conceptual model, the antecedents of trust in online information about companies by considering users' perceived control, concentration, and time distortion, while browsing social media networks and sharing fake news about companies in SNS. With the help of an online survey, data was collected from social media users, later being analysed with SmartPLS. The findings suggest that social media usage and sharing of fake news mediate the relationship between users' perceived control, concentration, and time distortion (i.e., flow characteristics) and trust in online information about companies.

Research on Sustainable Development of Transport Infrastructure Based on Corporate Culture and Low-Carbon Perspective

Excellence in corporate culture is the key to achieving sustainable business development. Sustainability can be a source of success, innovation and profitability for a company, driving the achievement of low-carbon goals for transport infrastructure enterprises. The aim of this study is to examine the relationship between corporate culture and corporate sustainability from the perspective of transport infrastructure enterprises, and to identify which corporate culture factors may have an impact on the sustainable low carbon development of transport infrastructure enterprises. To achieve this, we constructed a structural equation model based on 351 cases in Hunan Province and examined the relationship between corporate culture and sustainable low-carbon development using partial least squares structural equation modeling. The findings suggest that corporate values and corporate culture management capabilities play an important role in promoting sustainable development of transport infrastructure enterprises at the economic and low-carbon levels.