Online scams continue to be a pervasive issue, and designing personalized scam warnings can effectively reduce the number of victims. This study explores how individual personality traits influence compliance with different persuasion strategies in China. We designed various scam warnings based on six key persuasion strategies and collected data from 1,186 Chinese participants through an online experiment. The results reveal that warnings using authority, social proof, and scarcity strategies significantly enhance effectiveness. Network analysis further uncovers the complex relationships between the Big Five personality traits and persuasion strategies. Individuals with higher conscientiousness respond more positively to consistency strategies, while those with high neuroticism are more influenced by social proof and authority strategies. In contrast, individuals with high openness show a greater responsiveness to reciprocity strategies and are less sensitive to authority and scarcity tactics. The findings highlight the importance of tailoring scam warnings based on personality traits to improve their effectiveness in China.

The perceived similarity between human and robot personalities significantly impacts human–robot interaction (HRI), carrying ethical and psychological implications. This study examines the longitudinal evolution of personality similarity by analyzing Big Five personality trait descriptions in the Google Books corpus from 1960 to 2019. Our research revealed two primary findings: (a) Personality similarity demonstrated an overall increasing trend with a notable deceleration around 1980, potentially reflecting technological advancements and societal apprehensions; (b) A distinct hierarchy of trait similarities emerged. Openness exhibited the highest similarity to human personalities, followed by agreeableness, extraversion, neuroticism, and conscientiousness. This nuanced hierarchy suggests that human expectations of robotic personalities are not uniform across traits. By tracing historical perceptions of human-robot personality similarities, this study provides a comprehensive foundation for future HRI research and ethical considerations in robotics and artificial intelligence.

Smartwatch comfort is a fundamental factor that significantly influences the user experience and provides crucial guidance for the evolution of wearable technology. However, there is currently a lack of a comprehensive theoretical model to describe the dimensions of smartwatch comfort and their corresponding influencing factors. Therefore, the present study employed a bottom-up grounded theory approach to construct a user-centered model for smartwatch comfort. Through the coding of in-depth interviews with 64 smartwatch users, we discovered that smartwatch comfort encompasses both physiological dimensions (e.g., pressure and foreign body sensation) and psychological dimensions (e.g., perceived intelligence and satisfaction of needs). Furthermore, the features of smartwatches, including physical attributes (e.g., size and material) and functionalities (e.g., interoperability and automation capabilities), directly impact the comfort experience. Additionally, individual and contextual factors can explain variations in the comfort experience of smartwatches. Users with different physiological characteristics (e.g., wrist size and body sensitivity) and psychological needs (e.g., utilitarian or hedonic needs) are influenced differently by the factors of smartwatches that affect their comfort experience. The adaptability of smartwatches across different contexts (including task context, social context, and temporal context) is also a significant influencing factor on comfort. This substantive grounded theory provides crucial guidance for the selection of core variables in future quantitative research and contributes to the development of smarter, more comfortable, and user-centric smartwatches.

Human–robot interactions (HRIs) are significantly influenced by the personality of robots. However, research on robot personality from the perspective of big data text mining remains scarce. To address this gap, our study delves into the portrayal of Big Five personality traits in robots across millions of Google Books, spanning from 1920 to 2019. In this study, we identify intriguing trends in how robot personalities have been described over the years. Notably, we observe that the trait of openness has consistently been the most frequently cited Big Five personality factor throughout the twentieth century. Following closely are conscientiousness, agreeableness, extraversion, and neuroticism. However, a noteworthy shift occurs in the late twentieth century, where extraversion garners increasing attention, ultimately becoming the most prominent Big Five personality factor after 2010. Furthermore, our analysis uncovers a fascinating positivity bias in the portrayal of robot personality. Robots are more commonly depicted as extroverted rather than introverted and open rather than reserved. These trends also correlate with the evolution of core personality words. For instance, the term intellectual robot gradually yields to intelligent robot over the course of the twentieth century. Additionally, in the twenty-first century, social robot emerges as the most prevailing topic. Understanding the interplay between human records and their perception of robot personalities provides valuable insights into both real descriptions and ideal expectations of robots. This research serves as a critical reference for further advancements in robot personality studies, shedding light on the dynamic nature of HRIs.