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Purpose
The global prevalence of vaccine misinformation has underscored the crucial necessity to combat false information and explore innovative solutions like chatbots. These artificial intelligence (AI)-powered tools play a pivotal role in disseminating accurate information and mitigating the adverse effects of misinformation. This study aimed to...
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Context 1
... we employed the approach outlined by Fornell and Larcker (1981) to evaluate the discriminant validity of the measurement model. The square root of the AVE for each latent variable ranged from 0.81 to 0.89, surpassing the correlations among other latent variables (as detailed in Table 2). For instance, the square root of AVE of antimisinformation behavior is 0.84, larger than the correlations with other variables such as information seeking (0.60), forwarding (0.69) and forfending (0.59). ...Similar publications
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Citations
... The development of artificial intelligence (AI) has significantly impacted various sectors, with many organizations turning to AI-powered technologies to enhance their interactions with the public, such as through service robots or chatbots (Cheng 2025;Jiang et al. 2022;Shumanov and Johnson 2021). Notably, nonprofits have adopted AI at a slightly higher rate than the private sector. ...
With the growing utilization of artificial intelligence-powered chatbot services in the realm of nonprofit communication and the increasing significance of public engagement on social media platforms, researchers are facing a pivotal question: How can nonprofit organizations effectively harness chatbot technologies to influence the public's social media engagement and foster donation intention? In this study, we explore this question by integrating theoretical insights from social exchange theory, the service robot acceptance model, and pertinent prior literature. A survey involving 591 chatbot users located in the United States was conducted to explore the interplay between the functional and emotional values attributed to chatbots and their impacts on users' trust in the World Health Organization's chatbot services, subsequently influencing social media engagement and donation intention. The findings of this study have valuable theoretical and practical implications for nonprofit organizations seeking to optimize their use of chatbot technologies, with the goals of enhancing user engagement and encouraging donation behavior.
... Therefore, scholars have called for more research on the factors that lead to authentic dialogue (Wirtz & Zimbres, 2018). Despite the tremendous potential of AI-enabled chatbots in corporate communication, many people still prefer engaging with human agents or bots that mimic human conversation rather than machines lacking human-like characteristics (Araujo, 2018;Cheng, 2025;Lei et al., 2021). However, for meaningful dialogue to occur, at least two parties must be willing to participate in dialogic interactions (Heath, Pearce, et al., 2006). ...
The use of artificially intelligent (AI) chatbots in the Chinese market is increasing along with corporate social responsibility (CSR). We surveyed 606 Chinese consumers to examine how a company's chatbot can contribute to CSR dialogues by enhancing users' perceptions of chatbot listening and shared meaning creation. The findings revealed that Chinese consumers appreciate the use of AI-enabled chatbots in CSR conversations, particularly if the chatbot has a social-oriented communication style. This communication style helps shape shared meaning of CSR and enhances consumers' perceptions of a chatbot's ability to listen. Furthermore, the communication style facilitates consumers' willingness to engage in CSR dialogues, provide support, and recommend the business to others (supportive intent). In addition, we found that perceived chatbot listening and shared CSR meaning partially mediate the positive link between the social-oriented communication style and users' supportive intent. Trust in the chatbot played a moderating role in the indirect effect of chatbot communication style (social-oriented) on supportive intent through perceived shared meaning. This effect increased with consumers' trust in the chatbot's competence and communication integrity. The theoretical and practical implications of these findings are also discussed.
