Hao Zhang

Generative artificial intelligence now drafts travel itineraries that travelers cannot easily verify. Integrating privacy calculus theory with a dual-pathway trust model, this study examines how system attributes shape tourist trust and continued usage, and how technology readiness conditions those links. A two-wave panel (433 Chinese and European travelers), a validation wave (372), and 42 interviews were analyzed using structural equation modeling, importance–performance map analysis, fuzzy-set qualitative comparative analysis, and thematic analysis. Data transparency and content authenticity raised both cognitive and affective trust, and affective trust sustained continued usage most strongly. Technology readiness reversed the expected pattern: readier travelers scrutinized fluent output rather than relying on it, weakening the behavioral effect of trust. Providers should replace generic privacy notices with in-context, verifiable data-use disclosures and checkable citations, and segment interfaces so that ready travelers receive verification tools and less-ready travelers receive reassurance. Regulators can make verifiability, not fluency, the disclosure standard.

Purpose: This study investigates how mandatory artificial intelligence (AI) agent implementation, conceptualized as structural-capital deployment, paradoxically reconfigures human capital in cross-border e-commerce SMEs. Drawing on the intellectual capital (IC) literature and Job Demands–Resources (JD-R) theory as the micro-foundational lens, we examine the dual psychological pathways through which structural-capital investments translate into divergent human-capital outcomes. Design/methodology/approach: A sequential multi-method design integrates a two-wave survey of 461 employees across China and Europe – analyzed via partial least squares structural equation modeling (PLS-SEM), the measurement invariance of composite models and multi-group analysis (MICOM/MGA), importance–performance map analysis (IPMA), and fuzzy-set qualitative comparative analysis (fsQCA) – with 45 semi-structured interviews (30 Chinese and 15 European) coded using Gioia et al.'s three-stage procedure. Mandatory AI use was verified through HR confirmation, policy-document audit, and self-report triangulation. Findings: Mandatory AI simultaneously augments human capital via perceived task enablement and erodes it via perceived job insecurity, while exerting no direct effect on employee experience. Full measurement invariance and non-significant MGA results confirm cross-regional structural consistency; interviews revealed institutional boundary conditions affecting how the mandate is interpreted. Trustworthiness uncertainty operates predominantly configurationally rather than through simple linear moderation. Practical implications: IPMA prioritizes managerial action: insecurity mitigation > trustworthiness calibration > enablement maintenance. Three profiles – Enablement-Dominant Adopters, Trust-Tested Power Users, and Mandate-Tolerant Stable Performers – translate fsQCA configurations into intervention-ready employee types. Originality/value: The study supplies the micro-foundational mechanism missing from prior IC accounts, introduces perceived human capital development as a complementary IC-specific outcome and robustness check, and uncovers equifinal configurations through which compelled structural-capital deployment yields positive human-capital outcomes across institutional contexts.

As artificial intelligence transforms corporate operations globally, organizations face mounting pressure to integrate ethical AI practices within their corporate social responsibility (CSR) frameworks. This study investigates how corporate artificial intelligence ethics influences sustainable development outcomes, examining the critical role of international innovation as a moderator. Drawing on digital responsibility theory and technological ethical risk management frameworks, we analyze survey data from 449 corporations across China and Europe—two regions with distinct CSR regulatory environments and sustainability approaches. Our findings reveal that corporate AI ethics significantly enhances sustainable development performance, with this relationship strengthened by international innovation capabilities. While direct effects on innovation remain non‐significant, international innovation amplifies the sustainability benefits of ethical AI practices, particularly in European firms where stringent ESG regulations prevail. Regional analysis uncovers that Chinese firms leverage innovation more effectively for sustainability outcomes, whereas European firms demonstrate stronger moderation effects. These results provide actionable insights for organizations seeking to align AI deployment with ESG objectives and offer practical guidance for developing accountability mechanisms in digital transformation. The study contributes to CSR literature by demonstrating how ethical technology governance creates value for multiple stakeholders while advancing environmental and social sustainability goals.

The rapid integration of generative artificial intelligence (GenAI) into higher education has precipitated a critical tension between the pursuit of efficiency and the imperative for deep, transformative learning. While GenAI offers unprecedented speed in knowledge processing, it raises concerns that efficiency-driven motivations may erode the cognitive struggle essential for intellectual growth. This study investigates how Human-GenAI pedagogical partnerships influence transformative learning through the dual cognitive pathways of vigilance and offloading, moderated by efficiency orientation. Employing a rigorous mixed-methods design across three cultural contexts (China, Europe, and the United States, N = 912), we combine structural equation modeling, importance–performance map analysis (IPMA), fuzzy-set qualitative comparative analysis (fsQCA), and semi-structured interviews to unpack these complex dynamics. The findings reveal a counterintuitive paradox: pedagogical partnerships simultaneously activate cognitive vigilance (critical evaluation) and cognitive offloading (strategic delegation). Contrary to traditional assumptions that delegation diminishes learning depth, our results indicate that strategic cognitive offloading—when exceeding specific thresholds—liberates mental resources for higher-order reflection, thereby enhancing transformative learning experiences. Furthermore, efficiency orientation acts not as a barrier but as an amplifier, strengthening both vigilance and offloading behaviors through mechanisms of pragmatic risk management. These findings reconceptualize student-AI dynamics from a zero-sum resource competition to a synergistic collaboration, offering a nuanced framework for educators to integrate GenAI in ways that preserve human agency and foster quality education in the digital age.