Jiaxuan Gong

This study examines how real photographs and AI-generated photographs shape sustainable engagement and travel intention in tropical island tourism. We used a one-factor between subjects survey experiment with two independent conditions, real images and AI images, with 357 participants in each group. Guided by the SOR framework, we measured perceived authenticity, cognitive destination image, emotional comfort, and perceived information diagnosticity, together with sustainable engagement and travel intention. Structural equation modeling shows that under both visual conditions the four perceptual factors are positively associated with travel intention. In the real photo condition, sustainable engagement partially mediates the effects of all four factors on travel intention. In the AI photo condition, sustainable engagement mediates the effects of cognitive destination image, emotional comfort, and perceived information diagnosticity on travel intention, while the indirect pathway from perceived authenticity to travel intention through sustainable engagement is not significant. These findings support an actionable dual-track visual strategy. Use AI images to expand reach at low ecological cost, then use real images with verifiable cues to strengthen credibility and encourage responsible choices.

Cultural heritage institutions face a critical challenge in engaging Generation Z, a demographic of digital natives with high expectations for interactive and immersive experiences. While Virtual Reality (VR) offers a promising solution, many implementations are dismissed as superficial “gimmicks,” lacking a theoretical foundation for effective design. To bridge this gap, we develop and validate an extended Technology Acceptance Model (TAM) to explain the psychological mechanisms influencing Generation Z’s adoption of museum VR. Employing an explanatory sequential mixed-methods design, we analyzed 566 survey responses using CB-SEM and conducted 20 in-depth interviews. Our findings establish Perceived Usefulness and Perceived Enjoyment as the primary direct drivers of intention to use, with Immersion and Content Quality acting as crucial antecedents. This research delivers a dual contribution: theoretically, we extend technology acceptance theory for the specific context of museum VR and young audiences; practically, we offer an empirically grounded framework with actionable principles that empowers curators and designers to create more meaningful and resonant cultural heritage experiences.

In contemporary e-commerce, enterprises coordinate transactions, supply chains, and customer interactions within platform-based, data-intensive ecosystems. Integrated office application (IOA) serves as the operational backbone of these ecosystems by unifying communication, content management, workflow automation, and analysis across procurement, fulfillment, and after-sales service processes. As e-commerce processes become fully digitized, employees’ daily interactions with IOA directly impact service quality, operational efficiency, and sustainability outcomes. However, the micro-mechanisms by which IOA attributes translate into sustainable work practices are under-explored in the e-commerce literature. This study aims to explore how system quality, information quality, and collaboration quality influence user perceptions (perceived ease of use and usefulness), social influences (subjective norms), and satisfaction, thus jointly driving user intention and IOA-enabled sustainable behaviors. By integrating the Technology Acceptance Model, the Theory of Planned Behavior, and the IS Success Model, this research elaborates in a human-centered way on how an e-commerce enterprise’s system support can promote corporate and individual sustainability through employees’ adoption and continuous effective use.

This study investigates how university students adopt large language models (LLMs) for online consumption of sustainable products, integrating perceived value theory with the technology acceptance model (TAM). Cross-sectional survey data were analyzed using structural equation modeling (SEM) and artificial neural networks (ANNs). SEM results reveal partial mediation. Performance expectancy value (PEV) and information quality value (IQV) directly shape continue using intention (CUI). They also influence CUI indirectly through perceived ease of use (PEU) and perceived usefulness (PU). Green self-identity value (GSV) influences CUI both directly and via PEU, while trust transfer value (TTV) and green perceived value (GPV) affect CUI only via PEU. ANN findings confirm this hierarchy, as PU (86.7%) and PEU (85.7%) are the strongest predictors of CUI, followed by GSV (73.7%). Convergent evidence from both methods indicates that instrumental utility, effortless interaction, and sustainability identity congruence drive sustained LLM use in the context of online consumption of green products, whereas credibility cues and sustainability incentives play secondary roles. This study extends TAM by incorporating multidimensional value constructs and offers design recommendations for engaging and high-utility AI shopping platforms.