The developments of AI provide new opportunities for government service delivery. During disasters, government agencies are obligated to act as caregivers and provide support to citizens and residents in danger. However, limited studies are grounded in how chatbots and human governmental agents should apply supportive communication strategies. To fill this gap, this study conducted a 2 (Government Agent type: AI chatbot agent vs. Human agent) x 2 (Verbal Person-centeredness: High vs. Low) x 3 (Emoji Person-centeredness: High vs Low vs No emoji) design to explore their effects on emotional distress and evaluations of government service quality (N = 363). This study finds that higher verbal person-centeredness can reduce citizens’ emotional distress and evaluations of government service quality. Additionally, when lower levels of verbal and emoji person-centeredness were used, citizens evaluated service quality more positively when a chatbot agent sent the message than when a human agent did. Both theoretical and practical implications were discussed.

Prior research has produced contradictory findings regarding online daters’ potential to navigate the algorithmic systems to find compatible matches. Drawing upon a structuration algorithm media effects model, we examine whether online daters with a higher level of algorithm awareness experience less online mate-searching difficulty and report more optimism and hope after using online dating services. Analysing data from a national representative sample of American online daters (N = 871), we found that, in general, algorithm awareness was negatively related to mate-searching difficulty, which was negatively related to optimism but not hope. In addition, the relationship between algorithm awareness and mate-searching difficulty was stronger among female users than male users in our sample. The findings suggest a potentially positive role of algorithm awareness in promoting immediate online mate-searching experience on current dating platforms used by American online daters. We further discuss the implications on the role of algorithm awareness and positive immediate mate-searching experience in relation to more long-term outcomes, which calls for a dialectic view of algorithm awareness and immediate online success.

Fear of artificial intelligence (AI) has become a predominant term in users’ perceptions of emerging AI technologies. Yet we have limited knowledge about how end users perceive different types of fear of AI (e.g., fear of artificial consciousness, fear of job replacement) and what affordances of AI technologies may induce such fears. We conducted a survey (N = 717) and found that while synchronicity generally helps reduce all types of fear of AI, perceived AI control increases all types of AI fear. We also found that perceived bandwidth was positively associated with fear of artificial consciousness, but negatively associated with fear of learning about AI, among other findings. Our study provides theoretical implications by adopting a multi-dimensional fear of AI framework and analyzing the unique effects of perceived affordances of AI applications on each type of fear. We also provide practical suggestions on how fear of AI might be reduced via user experience design.

Artificial intelligence (AI) applications have become an integral part of our society. However, studying AI as one entity or studying idiosyncratic applications separately both have limitations. Thus, this study used computational methods to categorize ten different AI roles prevalent in our everyday life and compared laypeople’s perceptions of them using online survey data (N = 727). Based on theoretical factors related to the fundamental nature of AI, the principal component analysis revealed two dimensions that categorize AI: human involvement and AI autonomy. K-means clustering identified four AI role clusters: tools (low in both dimensions), servants (high human involvement and low AI autonomy), assistants (low human involvement and high AI autonomy), and mediators (high in both dimensions). Multivariate analyses of covariances revealed that people assessed AI mediators the most and AI tools the least favorably. Demographics also influenced laypeople’s assessments of AI. The implications of these results are discussed.

Is the current state of fitness applications effective at motivating and satisfying the needs of Hispanic users? With most mHealth research conducted with a predominantly white population, the answer to this question is lacking. In this study, we address this question through a survey study with Hispanic users of fitness applications (N= 211) and use the Motivational Technology Model (MTM) and Self-Determination Theory (SDT) as theoretical frameworks. We found that using interactivity features is essential to inspire more autonomous forms of motivation to use fitness applications. This is because interactivity helps satisfy users’ needs for relatedness. However, interactivity also decreased autonomy and competence suggesting the need to design fitness applications that increase relatedness without compromising autonomy. Implications for the design of fitness applications for the population at large and Hispanics, in particular, are discussed.