With the rise of gamification in virtual corporate social responsibility (CSR) campaigns, its impact on user engagement has drawn increasing attention. However, the definition and impact of gamification affordances remain unclear, especially in virtual CSR co-creation. This study examines how gamification affordances affect users’ continued intention to participate, using the “Affordances-Psychological Outcomes-Behavioral Outcomes” framework. Based on a survey of 581 Ant Forest users, structural equation modeling (SEM) shows that both human-computer (autonomy support, achievement, telepresence) and human-human (competition, interactivity) affordances positively influence flow experience and continued intention to participate. Moreover, flow experience fosters self-expansion, mediating the relationship between gamification affordances and continued intention to participate. The Fuzzy-set qualitative comparative analysis (FsQCA) results provided insights into the multiple causal solutions and configurations of continued intention to participate. This study advances gamification research, refines affordance classifications, and provides practical insights for designing CSR initiatives that promote user engagement and co-creation.

As a new artificial intelligence product, Companion robots have gradually appeared in the elderly market. However, in practice, older people have low satisfaction and low intention to use companion robots, which makes it challenging to realize the intrinsic value of companion robots effectively. Therefore, this study examined the effect of companion robot anthropomorphism on the satisfaction of older adults through a laboratory experimental study (N = 87) and a questionnaire study (N = 305). The results show that older people have higher satisfaction with companion robots with higher levels of anthropomorphism, and anthropomorphism has a significant positive effect on satisfaction. Psychological distance partially mediated the relationship between anthropomorphism and satisfaction. Perceived control positively moderates the relationship between anthropomorphism and psychological distance and satisfaction. This study aims to expand the existing literature on anthropomorphism and provide practical guidance for designers of companion robots.

More and more product designers are adopting anthropomorphic design strategies to facilitate the widespread use of AI assistants. However, existing studies show that the ideal boundary of the degree of anthropomorphism is still unclear. Therefore, we design two scenario experimental studies to explore the influence of different degrees of anthropomorphism on human–AI interaction quality. We also consider the difference in impact between two usage contexts: the hedonic and utilitarian contexts. The results show that different degrees of anthropomorphism significantly affect human–AI interaction quality in different ways; when AI assistants have a medium-level degree of anthropomorphism, the positive effect is pronounced. Furthermore, the positive relationship between anthropomorphism and human–AI interaction quality is more robust in the hedonic usage context; this positive effect disappears in the utilitarian usage context. We expect this study to provide practical guidance for AI product designers to achieve their products’ long-term feasibility and sustainability.

In recent years, smart home assistants have been used by a large number of people due to their simple, hands-free, voice-based operation. To ensure the long-term success and widespread dissemination of a product, it is important to evaluate its continued use. This study is mainly based on uses and gratifications theory to explore the relationship between the initial use of, gratification provided by, and continued use intention of smart home assistants, and to analyze differences in use by different age groups. The results confirm that different types of SHAs use lead to different levels of gratification in different categories. And gratification of different categories of users has a significant positive impact on the continued use intention. In addition, significant differences exist in the impact path of using smart home assistants to alleviate loneliness, among different age groups.

In this paper, we propose a novel calibration-free cross-camera target association algorithm that aims to relate local visual data of the same object across cameras with overlapping FOVs. Unlike other methods using object's own characteristics, our approach makes full use of the interactions between objects and explores their spatiotemporal consistency in projection transformation to associate cameras. It has wider applicability in deployed overlapping multi-camera systems with unknown or rarely available calibration data, especially if there is a large perspective gap between cameras. Specifically, we first extract trajectory intersection which is one of the typical object-object interactive behaviors from each camera for feature vector construction. Then, based on the consistency of object-object interactions, we propose a multi-camera spatiotemporal alignment method via wide-domain cross-correlation analysis. It realizes time synchronization and spatial calibration of the multi-camera system simultaneously. After that, we introduce a cross-camera target association approach using aligned object-object interactions. The local data of the same target are successfully associated across cameras without any additional calibration. Extensive experimental evaluations on different databases verify the effectiveness and robustness of our proposed method.