With the growing presence of algorithms and their far-reaching effects, artificial intelligence (AI) will be mainstream trends any time soon. Despite this surging popularity, little is known about the processes through which people perceive and make a sense of trust through algorithmic characteristics in a personalized algorithm system. This study examines the extent to which trust can be linked to how perceptions of automated personalization by AI and the processes of such perceptions influence user heuristic and systematic processes. It examines how fair, accountable, transparent, and interpretable people perceive the use of algorithmic recommendations by digital platforms. When users perceive that the algorithm is fairer, more accountable, transparent, and explainable, they see it as more trustworthy and useful. This demonstrates that trust is of particular value to users and further implies the heuristic roles of algorithmic characteristics in terms of their underlying links to trust and subsequent attitudes toward algorithmic decisions. The processes offer a useful perspective on the conceptualization of AI experience and interaction. User cognitive processes identified provide solid foundations for algorithm design and development and a stronger basis for the design of sensemaking AI services.

Digital platforms have empowered individuals and communities to re-negotiate long-established notions of religion and authority. A new generation of social media influencers has recently emerged in the Muslim world. They are western-educated, unique storytellers, and savvy in digital media production. This raises new questions on the future of Islam in the context of emerging challenges, such as the openness of technology and the often-perceived closedness of religious and cultural systems within Muslim societies. This paper uses a multiple case research design to examine the roles of social media influencers in reimagining Islam and reshaping spiritual beliefs and religious practices among young people in the Gulf Region, the Arab world, and beyond. We used thematic analysis of the Instagram and YouTube content of four social media influencers in the Gulf Region: Salama Mohamed and Khalid Al Ameri from the United Arab Emirates, Ahmad Al-Shugairi from Saudi Arabia, and Omar Farooq from Bahrain. The study found that social media influencers are challenging traditional religious authorities as they reimagine Muslim identities based on a new global lifestyle.

This study examines the impact of artificial intelligence (AI) on the ways in which users process and respond to misinformation in generative artificial intelligence (GenAI) contexts. Drawing on the heuristic–systematic model and the concept of diagnosticity, our approach examines a cognitive model for processing misinformation in GenAI. The study’s findings revealed that users with a high-heuristic processing mechanism, which affects positive diagnostic perception, were more likely to proactively discern misinformation than users with low-heuristic processing and low-perceived diagnosticity. When exposed to misinformation from GenAI, users’ perceived diagnosticity of misinformation can be accurately predicted by the ways in which they perform heuristic systematic evaluations. With this focus on misinformation processing, this study provides theoretical insights and relevant recommendations for firms to be more resilient in protecting users from the detrimental impacts of misinformation.

How much do anthropomorphisms influence the perception of users about whether they are conversing with a human or an algorithm in a chatbot environment? We develop a cognitive model using the constructs of anthropomorphism and explainability to explain user experiences with conversational journalism (CJ) in the context of chatbot news. We examine how users perceive anthropomorphic and explanatory cues, and how these stimuli influence user perception of and attitudes toward CJ. Anthropomorphic explanations of why and how certain items are recommended afford users a sense of humanness, which then affects trust and emotional assurance. Perceived humanness triggers a two-step flow of interaction by defining the baseline to make a judgment about the qualities of CJ and by affording the capacity to interact with chatbots concerning their intention to interact with chatbots. We develop practical implications relevant to chatbots and ascertain the significance of humanness as a social cue in CJ. We offer a theoretical lens through which to characterize humanness as a key mechanism of human–artificial intelligence (AI) interaction, of which the eventual goal is humans perceive AI as human beings. Our results help to better understand human–chatbot interaction in CJ by illustrating how humans interact with chatbots and explaining why humans accept the way of CJ.

The recent proliferation of artificial intelligence (AI) gives rise to questions on how users interact with AI services and how algorithms embody the values of users. Despite the surging popularity of AI, how users evaluate algorithms, how people perceive algorithmic decisions, and how they relate to algorithmic functions remain largely unexplored. Invoking the idea of embodied cognition, we characterize core constructs of algorithms that drive the value of embodiment and conceptualizes these factors in reference to trust by examining how they influence the user experience of personalized recommendation algorithms. The findings elucidate the embodied cognitive processes involved in reasoning algorithmic characteristics – fairness, accountability, transparency, and explainability – with regard to their fundamental linkages with trust and ensuing behaviors. Users use a dual-process model, whereby a sense of trust built on a combination of normative values and performance-related qualities of algorithms. Embodied algorithmic characteristics are significantly linked to trust and performance expectancy. Heuristic and systematic processes through embodied cognition provide a concise guide to its conceptualization of AI experiences and interaction. The identified user cognitive processes provide information on a user’s cognitive functioning and patterns of behavior as well as a basis for subsequent metacognitive processes.