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.

This study investigates the key factors which influence design learners’ behavioral intention to collaborate with AI in the educational metaverse (EMH-AIc). Engaging design learners in EMH-AIc enhances learning efficiency, personalizes learning experiences, and supports equitable and sustainable design education. However, limited research has focused on these influencing factors, leading to a lack of theoretical grounding for user behavior in this context. Drawing on social cognitive theory (SCT), this study constructs a three-dimensional theoretical model comprising the external environment, individual cognition, and behavior, validated within an EMH-AIc setting. By using Spatial.io’s Apache Art Studio as the experimental platform and analyzing data from 533 design learners with SPSS 27.0, SmartPLS 4.0, and partial least squares structural equation modeling (PLS-SEM), this study identifies those rewards, teacher support, and facilitating conditions in the external environment, with self-efficacy, outcome expectation, and trust in cognition also significantly influencing behavioral intention. Additionally, individual cognition mediates the relationship between the external environment and behavioral intention. This study not only extends SCT application within the educational metaverse but also provides actionable insights for optimizing design learning experiences, contributing to the sustainable development of design education.

Customer loyalty is critical for organizations to gain market share and maintain a sustained competitive advantage. However, no study has yet explored customer loyalty in online dating platforms. Perceived value theory suggests that perceived value is a key predictor of customer loyalty. Accordingly, this research constructed a conceptual model drawing on a multidimensional perspective of perceived value to explore customer loyalty in online dating platforms and investigated the mediating role of satisfaction. By quantitatively analyzing 352 customers who had experienced online dating platforms and utilizing structural equation modeling (SEM) to examine the relationships among propositions, the research demonstrated a strong positive correlation between perceived value, satisfaction, and loyalty, and a notable indirect impact on loyalty through satisfaction. In addition, the experiential value dimension of perceived benefits had the most positive and substantial influence on perceived value, while the perceived risk dimension of perceived sacrifice had the most negative and notable impact on perceived value. The results of the study provide designers, managers, and vendors of online dating platforms with valuable insights into customer behavior and practical recommendations for improvement, helping them to develop more effective strategies to enhance market competitiveness and ensure the sustainability of their platforms.

With the rapid development of metaverse technology in the marketing field, it has become increasingly important to understand consumer purchase intentions for AI Brand Endorsers (AIBEs) within this digital environment. Based on cognitive–affective–behavioral (CAB) theory, this study constructs a new theoretical framework to explore the key factors influencing consumer purchase intentions for AIBE-recommended products in the context of the metaverse. We conducted an online survey with 302 Generation MZ consumers who have purchasing experience, employing Partial Least Squares Structural Equation Modeling (PLS-SEM) for in-depth data analysis and model evaluation. Additionally, we performed Multi-Group Analysis (MGA) to reveal differences among various occupations and generations. The findings indicate that attractiveness (ATT), anthropomorphism (ANT), and interactivity (INT) significantly influence hedonic motivation (HM) and social presence (SP). Furthermore, authenticity (AUT) positively affects both SP and trust in AIBEs (TAI). Consumer purchase intention (PI) is significantly impacted by SP but is not directly influenced by HM and TAI. Notably, technology readiness (optimism and innovativeness) positively and significantly influences consumer PI but does not alter the potential moderating effects of HM, SP, and TAI. This study not only broadens and deepens the application of CAB theory but also elucidates the potential development of AIBEs in future metaverse research, providing practical implications and guidance for marketers to enhance consumer purchase intentions and boost product sales.