The dissemination and preservation of cultural heritage has attracted much attention globally, and serious games play an important role in cultural heritage dissemination and preservation; however, it is not clear which experiences of serious games are effective in increasing people’s attention to cultural heritage. This study combines the experience economy and stimulus–organism–response (SOR) theory to construct a theoretical model to explore which experiences of serious games influence users’ behavioral intention to care about cultural heritage. We surveyed 302 users who experienced serious games and analyzed the data and measurement model using SPSS 27 and partial least squares structural equation modeling (PLS-SEM). The results showed that the four experiences of serious games (educational, entertaining, escapist, and esthetic) had a significant effect on users’ perceived value and memory and that perceived value and memory significantly influenced users’ participation. In addition, this study found that the entertainment experience was not only a key factor influencing perceived value and memory, but also the most significant factor directly affecting user participation. This finding provides specific recommendations for game developers and cultural heritage organizations to enhance user engagement. Enhancing the four experience dimensions and strengthening users’ perceived value and memory can effectively encourage users to pay attention to cultural heritage through serious games and contribute to the preservation and sustainable development of cultural heritage.

As a pivotal carrier of emerging human–computer interaction technologies, artificial intelligence (AI) conversational agents (CAs) hold critical significance for research on the mechanisms of users’ continuance usage behaviour, which is essential for technological optimization and commercial transformation. However, the differential impact pathways of multidimensional perceived interactivity on continuance usage intention, particularly the synergistic mechanisms between technical and affective dual-path dimensions, remain unclear. This study investigates the personalized AI-based CAs project “Dialogue with Great Souls,” launched on a Chinese social platform, using survey data from 305 users. A hybrid approach combining partial least squares structural equation modelling (PLS-SEM) and artificial neural networks (ANN) was employed for empirical analysis. The results indicate that technical dimensions, such as control and responsiveness, are key factors influencing trust, while affective interactive dimensions, including communication, personalization, and playfulness, significantly affect social presence, thereby shaping users’ continuance usage intention. ANN results corroborated most PLS-SEM findings but revealed inconsistencies in the predictive importance of personalization and communication on social presence, highlighting the complementary nature of linear and nonlinear interaction mechanisms. By expanding the interactivity model and adopting a hybrid methodology, this study constructs a novel framework for AI CAs. The empirical findings suggest that developers should strengthen socio-emotional bonds in anthropomorphic interactions while ensuring technical credibility to enhance users’ continuance usage intention. This research not only advances theoretical perspectives on the integration of technical and affective dimensions in agent systems but also provides practical recommendations for optimizing the design and development of AI CAs.

With the acceleration of the aging process in China, chronic diseases have become one of the main health threats for older adults, creating significant pressure on society and the healthcare system. As information technology and artificial intelligence advance rapidly, smart health services have become readily accessible. However, utilization rates among the older adults, especially those with chronic illnesses, remain low, preventing them from fully benefiting from these advanced technologies. The value of mobile health (mHealth) services can only be realized through sustained use. Therefore, this study empirically investigates the continuous use intention of mHealth services from the perspective of older adults with chronic illnesses, integrating the Technology Acceptance Model (TAM) and Value-Based Adoption Model (VAM). A total of 372 questionnaires were collected from various cities in China, and data were analyzed using SPSS 24.0 and Partial Least Squares Structural Equation Modeling (PLS-SEM). Results indicate that perceived ease of use (β = 0.155, p = 0.004; β = 0.116, p = 0.027) and perceived usefulness (β = 0.175, p = 0.001; β = 0.151, p = 0.004) have a significant positive impact on attitude and perceived value. Perceived enjoyment significantly influences attitude (β = 0.147, p = 0.010), while perceived risk (β = −0.189, p < 0.001; β = −0.281, p < 0.001) and perceived cost (β = −0.155, p = 0.003; β = −0.130, p = 0.022) have a significant negative impact on attitude and perceived value. Both attitude (β = 0.357, p < 0.001) and perceived value (β = 0.314, p < 0.001) positively impact continuous intention. In total, only one of the twelve hypotheses was not supported. This study not only provides strong evidence for the effectiveness of the integrated TAM and VAM model in the mHealth field but also offers theoretical insights and practical recommendations for product optimization and promotion to mHealth service providers and designers.

The Metaverse Online Art Museum (MOAM) represents an innovative application of metaverse technology in the art field, with an increasing number of art galleries and museums transitioning to the metaverse. In the metaverse environment, audiences can overcome geographical and economic limitations, enjoying artworks anytime and anywhere, resulting in a more unique and immersive experience. However, there is still a lack of research on audience switching intention from traditional offline art museum (TOAM) to MOAM. To fill this academic gap, our study aims to explore and validate the key factors influencing Chinese audiences’ Switching Intentions (SWI) to MOAM. Specifically, we integrated Flow Theory (FT) and Self-Determination Theory (SDT) into the Push-Pull-Mooring framework to develop a new conceptual model, elucidating the internal mechanisms of push, pull, and mooring effects on switching intentions (SWI). Through an online questionnaire, we recovered 305 valid samples from the audience that experienced MOAM and systematically analyzed the data using Partial Least Squares Equation (PLS-SEM). The results of the study showed that eight of the ten hypotheses were valid and that two push factors (visiting inconvenience and dissatisfaction), five pull factors (concentrated attention, perceived enjoyment, presence, autonomy, and competence), and one mooring factor (visiting inertia) had a significant effect on SWI. But, Perceived risk and Relatedness did not significantly influence switching MOAM. The findings of this study not only contribute valuable theoretical insights for metaverse and MOAM, but also provide important insights for relevant service providers in optimizing audience experience and improving switching rate at the practical level. Additionally, it gives direction and reference to the government and enterprises in formulating specific policies and management measures. It also helps to promote the sustainable development of the metaverse art ecology.

The swift rise of ChatGPT and the growing integration of AI-generated content (AIGC) technologies are reshaping the digital marketing landscape in profound ways. These advancements are not only changing how social media marketing content is produced, but also altering its underlying characteristics. Despite this shift, there remains a noticeable gap in empirical research on how businesses and brands can effectively harness AIGC to drive consumer engagement. This study draws on the Stimulus–Organism–Response (SOR) theoretical framework to examine how AI-generated content influences consumer cognition, builds trust, and ultimately shapes decision-making behavior. Based on 348 valid survey responses, we employed a mixed-methods approach that integrates Partial Least Squares Structural Equation Modeling (PLS-SEM) with Artificial Neural Network (ANN) analysis. The findings indicate that entertainment, interactivity, trend relevance, electronic word-of-mouth, and visual appeal all positively influence both perceived value and trust. In contrast, customization was found to enhance perceived value only. Both perceived value and trust were shown to significantly increase consumers’ purchase intentions. The ANN model further supported the PLS-SEM results, confirming consistency across methods. Among the predictors, entertainment (β = 0.176, ni = 86.28%) was most influential for perceived value, EWOM (β = 0.192, ni = 85.94%) played a key role in shaping trust, and perceived value (β = 0.344, ni = 99.19%) had the strongest impact on purchase intention. These insights contribute to a deeper theoretical understanding of social media marketing and consumer behavior, while also offering actionable recommendations for businesses aiming to refine their content strategies in an AIGC-driven environment. Additionally, the study highlights promising directions for the evolution of digital consumption and the long-term sustainability of brand development.

The breakthrough in artificial intelligence technology and the development of smart healthcare models have significantly improved modern healthcare services. However, the elderly population still faces numerous challenges. Therefore, the aim of this study is to enhance the interactive experience of elderly users and to propose effective design strategies through optimized design methods. Based on the INPD research methodology, the design process is divided into four stages. First, in the SET phase, product opportunity gaps are identified, followed by in-depth interviews and surveys to gather user needs. Second, the AHP method is used to establish a hierarchical model and judgment matrix to determine the subjective weights of each need, while the EWM method, based on survey data, determines the objective weights of each need. To ensure the scientific nature of the overall weight, a combined weighting approach is used, followed by a final prioritization of needs. Third, after translating user needs into design requirements, three design schemes are produced, and the TOPSIS method is used to calculate the weights and evaluate the optimal scheme. Fourth, the product opportunities are implemented and tested. The research results indicate that the proposed optimization design method is effective and not only reduces the barriers and challenges elderly users face when interacting with intelligent products but also enhances their overall experience. Moreover, it provides a practical approach to the sustainable development of smart healthcare. As an essential component of future healthcare services, the sustainability of smart healthcare will depend on a deep understanding of user needs and continuous optimization. The design strategy proposed in this study offers practical application value, improving elderly users’ satisfaction while also providing insights that may be useful for other smart services.