Contemporarily, almost all the global IT giants have aimed at the smart home industry and made an active strategic business layout. As the early-stage and entry-level product of the voice-enabled smart home industry, the smart speakers have been going through rapid development and rising fierce market competition globally in recent years. China, one of the most populous and largest markets in the world, has tremendous business potential in the smart home industry. The market sales of smart speakers in China have gone through rapid growth in the past three years. However, the market penetration rate of related smart home devices and equipment still stays extremely low and far from mass adoption. Moreover, the market sales of smart speakers have also entered a significant slowdown and adjustment period since 2020. Chinese consumers have moved from early impulsive consumption to a rational consumption phase about this early-stage smart home product. Trust in the marketing field is considered an indispensable component of all business transactions, which plays a crucial role in adopting new technologies. This study explores the influencing factors of Chinese users’ perceived trust in the voice-enabled smart home systems, uses structural equation modeling (SEM) to analyze the interaction mechanism between different variables, and establishes a perceived trust model through 475 valid samples. The model includes six variables: system quality, familiarity, subjective norm, technology optimism, perceived enjoyment, and perceived trust. The result shows that system quality is the essential influence factor that impacts all other variables and could significantly affect the perceived trust. Perceived enjoyment is the most direct influence variable affected by system quality, subjective norm, and technology optimism, and it positively affects the perceived trust in the end. The subjective norm is one of the most distinguishing variables for Chinese users, since China has a collectivist consumption culture. People always expect their behavior to meet social expectations and standards to avoid criticism and acquire social integration. Therefore, policy guidance, authoritative opinions, and people with important reference roles will significantly affect consumers’ perceived trust and purchase intention. Familiarity and technology optimism are important influential factors that will have an indirect impact on the perceived trust. The related results of this study can help designers, practitioners, and researchers of the smart home industry produce products and services with higher perceived trust to improve consumers’ adoption and acceptance so that the market penetration rate of related products and enterprises could be increased, and the maturity and development of the voice-enabled smart home industry could be promoted.

The unprecedented development of artificial intelligence (AI) makes it possible for computers to imitate and surpass human intelligence (HI). Hybrid intelligence is the result of the co-evolution of AI and HI and has huge application potential in promoting the sustainable development of human society. This study starts from the similarities and differences between biological neural networks and artificial neural networks, compares the cognitive foundations of human intelligence and artificial intelligence, highlights the difference and connection between AI and HI, and puts forward the necessity and inevitability of their co-evolution to achieve hybrid intelligence with complementary advantages. Hybrid intelligence stands to become the pivotal force driving purposeful and planned sustainable creative behavior in the artificial intelligence era. This study proposes a design cognitive creation model based on human–computer collaboration that considers computational design thinking as the central concept. Moreover, the paradigm shift of design under hybrid intelligence intervention are explored from five aspects: “tool evolution”, “response mode”, “output result”, “iterative optimization” and “system innovation”. Finally, this article constructs a creative intervention mechanism of design creation driven by hybrid intelligence and discusses its role playing in the design activities of sustainable multiverse construction in the future. The proposal of the multiverse model transcends the confines of the metaverse’s virtual worldview and embraces sustainable development for value guidance. It advocates a future trajectory for humanity that hinges on technological progress, fostering a prosperous, balanced, and harmonious coexistence between the natureverse, socialverse, and digitalverse. This approach is not only rational and scientific, but also inherently sustainable.

The 21st century has marked the dawn of an aging population. China’s aging process ranks first worldwide. The country has recognized the gravity of this demographic shift and implemented strategies to address it at the national level. A vast majority of elderly Chinese individuals (approximately 90%) aspire to age in their own homes. Smart homes, endowed with cutting-edge digital technologies, such as AI, the Internet of Things, and big data, hold vast potential for enabling this vision. However, acceptance of smart home products and services among elderly individuals in China remains low. The main reason is that the related products and services fail to effectively alleviate the perceived risk of this population in the R&D process of related products and services, and there is a lack of effective measurement methods. To holistically assess the potential obstacles faced by elderly individuals using smart home products and services, this study targeted individuals aged 45–60 years in China. This study aimed to develop a comprehensive perceived risk scale specific to smart homes for this demographic. Initially, this study identified key risk dimensions and corresponding measurement items through a rigorous literature review, user interviews, and expert consultations. Subsequently, it ensured the reliability and validity of each dimension and its corresponding observation variables through preliminary research, exploratory factor analysis, and confirmatory factor analysis. This approach allowed for a comprehensive understanding of the challenges faced by future elderly individuals when utilizing smart home products and services, thus enabling the development of more effective solutions. The scale encompassed ten factors and seventy measurement items, including Privacy and Security Risk (seven items), Physical Risk (seven items), Technological Risk (nine items), Performance Risk (seven items), Service Risk (nine items), Financial Risk (five items), Psychological Risk (seven items), Industry and Market Risk (six items), Social Support Risk (six items), and Policy and Legal risk (seven items). The measurement scale developed in this study represents a groundbreaking first attempt to create a systematic scale for assessing the perceived risks associated with smart homes for the elderly in China. It not only enables professionals, businesses, and manufacturers to avoid or reduce barriers in the R&D process of related products and services, facilitating smart home industry growth and enhancing user adoption, but also serves as a universal reference for the potential obstacles that digital technology may encounter in addressing aging-related issues, which has significant theoretical value and practical importance.

Preventive behavior, such as hand hygiene, facemask wearing, and social distancing, plays a vital role in containing the spread of viruses during pandemics. However, people in many parts of the world usually encounter difficulties adhering to this behavior due to various causes. Thus, this article aims to develop a research framework and propose design strategies to prompt individuals’ behavior change during pandemics. Initially, we integrated a literature review and a structured interview (n = 22) to ascertain the core factors impacting behavior change during pandemics. These factors were categorized into four aspects: perceptional factors (attitudes, subjective norms and perceived behavior control, risk perceptions); social factors (knowledge and information dissemination, governmental regulations); physical factors (tools and facilities and surveillance); and sociocultural factor (cultural contexts). Then, a theoretical framework with antecedents was developed to reveal behavior intention and formation process. After that, an empirical study was carried out to test the research framework through a questionnaire survey (n = 549). The research findings indicated that all derived factors could directly or indirectly affect individuals’ preventive behavior during pandemics. This article strives to provide valuable insights for different stakeholders when coping with pandemic situations.