open access
메뉴Background: With the rapid advancement of AI and robotics, Human-Robot Interaction (HRI) has been increasingly adopted in medical care, educational counselling, service reception and other scenarios. User trust directly impacts robots’ acceptance and operational effectiveness, and the lack of such trust has become a key barrier to the development of human-robot collaboration. Purpose: This paper focuses on the construction mechanism of user trust in HRI, aiming to explore its key influencing factors and action paths to provide theoretical support and design insights for trust-oriented interaction design. Methods: The study combines literature analysis, typical case analysis, and cross-case comparison, summarizing core design features and user feedback of representative human-robot products at home and abroad. Results: This study proposes a multi-factor collaborative trust construction framework. It emphasizes that the design of future human-robot interaction should focus on establishing a dynamic trust relationship. This approach aims to improve the system's transparency, predictability and emotional connection to enhance the user acceptance and reliance on robots. The findings of this study are significant for optimizing user experience and increasing the social acceptance of intelligent interactive products Conclusion: The findings are of great significance for optimizing user experience and enhancing the social acceptance of intelligent interactive products, with practical implications for HRI design practice.
Background: The rapid advancement of artificial intelligence (AI) has accelerated the growth of AI-generated content (AIGC), with China's AIGC industry projected to surpass 70 billion yuan by 2026. AI-generated advertising is transforming the advertising landscape, however, research on its characteristics and their impact on consumer behavior remains limited compared to traditional advertising. Purpose: This study examines the entertainment value, informativeness, and innovativeness of AI-generated advertisements and their influence on consumers’ purchase intentions. Through a comparative analysis of multiple brand cases and empirical research, this study aims to develop a framework for analyzing the characteristics of AI-generated advertising and provide theoretical insights that can enhance intelligent advertising practices Methods: A literature review was conducted to synthesize prior research on advertising characteristics. Case studies of three distinct brands were analyzed to explore the features of AI-generated advertisements. Empirical research was employed to test theoretical hypotheses and assess how these characteristics affect consumers’ purchase intentions. Conclusion: Unlike prior research, which predominantly focuses on traditional advertising, this study highlights the role of entertainment, informativeness, and innovativeness in AI-generated advertisements. The results of the multi-brand case analysis and empirical research demonstrate that all three characteristics significantly enhance consumers’ purchase intentions, with informativeness exerting the strongest influence, followed by entertainment and innovativeness.
Background: As consumer demand shifts from functional utility to emotional value, China's cultural and creative industries have emerged as a critical pillar of economic and cultural development. However, these industries currently face challenges of product homogenization and superficial cultural expression. The successful localization of the British brand Jellycat in China, which leverages emotional design strategies, offers insights for Chinese cultural and creative enterprises seeking to combat homogenization. Guided by Norman’s three-level model of emotional design, this study uses Jellycat’s localization case to analyze how emotionally-driven cultural and creative features influence consumer purchase intention, and explores the transition from "simple imitation" to "cultural adaptation" in product development. Methods: This research uses the "Tianshui Malatang" series from the Gansu Provincial Museum as a case study and employs a quantitative approach. Data from 300 valid questionnaires were analyzed using SPSS 29.0 regression models to examine the effects of four key dimensions—cultural connotation, entertainment value, artistic aesthetics, and innovative design—on purchase intention. Results: The regression analysis shows that all four dimensions significantly and positively impact purchase intention, with the model accounting for 36.9% of the variance (p < 0.01). Among these, entertainment value demonstrates the strongest influence. Additionally, diagnostic tests confirm no significant collinearity issues among the independent variables. Conclusion: Cultural and creative products should enhance interactive elements, deepen cultural interpretation, and optimize artistic and innovative design. Emotional design proves to be an effective strategy for enhancing market competitiveness. Future research may incorporate cross-cultural comparative studies and qualitative methodologies to enrich the understanding of emotional design in various contexts.
Background: China’s aging population worsens the imbalance between the supply and demand for elderly care resources. Applications that provide elderly care service integrating medical, caregiving, and daily life functions are critical for promoting digital inclusion. However, the factors that influence seniors’ adoption of these technologies have not been thoroughly examined. Purpose: This study explores how three core attributes—perceived ease of use, perceived usefulness, and perceived safety—affect elderly users’ behavioral intentions, aiming to inform app optimization and advance smart elderly care practices. Methods: A mixed-methods approach was adopted, combining literature review, functional analysis of 3 mainstream apps, and quantitative analysis of 302 valid questionnaires via SPSS regression modeling. Results: The findings indicate the following key points: 1) Perceived ease of use is the strongest predictor of behavioral intention, as a simplified interface and smooth operation directly enhance user engagement; 2) Perceived usefulness ranks second, with practical functions significantly improving user retention; and 3) Perceived safety primarily fosters trust but has relatively limited direct impact on engagement. Conclusion: Three optimization strategies are proposed: 1) Develop age-appropriate interaction frameworks with simplified operations; 2) Enhance the adaptability of core services (e.g., health monitoring, emergency calls); 3) Integrate transparent data encryption and privacy protection measures. This study provides empirical support efforts in bridging the digital divide and promoting sustainable innovation in smart elderly care.
Background: The rapid development of generative artificial intelligence (AI) is fundamentally changing the creative practices of design students in higher education. This major shift needs a thorough examination of how AI influences their creative development. Purpose: This study aims to investigate the specific impact mechanisms of generative AI on the creative process of design students and to propose effective, evidence-based optimization strategies. Methods: We used a rigorous mixed-methods approach, integrating a comprehensive literature review, in-depth case studies, and controlled educational experiments. This allowed for a systematic exploration of how technological intervention facilitates the multidimensional reconstruction of creative thinking. Results: Our findings highlight generative AI's multifaceted impact on design students. Cognitively, AI broadens conceptual boundaries by enabling vast data retrieval, yet risks over-reliance on existing patterns, which can hinder originality. Behaviorally, AI tools boost prototype iteration efficiency but may inadvertently lessen deep critical thinking. Emotionally, human-machine collaboration stimulates innovation, but can also lead to increased anxiety. Based on these insights, we propose a "three-dimensional collaborative" educational strategy. This includes a curriculum based on an "AI Toolchain Design Methodology," a "Process-Based Creativity Graph" evaluation model, and the establishment of school-enterprise AI design laboratories with ethical guidance. Conclusion: This research provides a critical theoretical foundation and actionable practical pathways for the ongoing reform of design education in the age of artificial intelligence. It also lays essential groundwork for future longitudinal studies exploring the advance applications of generative AI in educational contexts.
