This paper associates the speculative culture with the corporate total factor productivity (TFP), aiming to explore the influence of speculative culture on corporate high-quality development. The results reveal that the presence of a speculative atmosphere varies across different regions in China, leading to uneven development among companies located in these regions. Specifically, companies situated in regions with a weaker speculative culture demonstrate higher TFP. The finding suggests a causal relationship between speculative culture and corporate high-quality development by adopting an instrumental variable test using natural calamities. Moreover, the research indicates that speculative culture can impact corporate high-quality development by influencing their innovation activities. Additionally, the study identifies heterogeneous effects of speculative culture on companies of different sizes and levels of analyst attention. The implications of this research shed light on the influence of speculative culture on the real economy, providing insights into the informal institutional factors contributing to the corporate high-quality development in different geographical areas. These findings contribute to the promotion of economic high-quality development at the geographical level and inform related policy formulation.

With the rapid advancement and widespread adoption of artificial intelligence (AI), AI-powered personalized product recommendation (AI-PPR) has become a core tool for enhancing user experience and driving monetization on short-form video platforms, fundamentally reshaping consumer behavior. While prior research has largely focused on impulse buying intention (I-BI)—purchases triggered by emotional and sensory stimuli—there remains a lack of systematic exploration of spontaneous buying intention (S-BI), which emphasizes rational and cognitively driven decisions formed in unplanned contexts. Addressing this gap, this study integrates the Technology Acceptance Model (TAM) with a perceived evaluation perspective to propose and validate a dual-mediation framework: “AI-PPR → Perceived Usefulness/Perceived Trust → S-BI”. Using a large-scale survey of Generation Z users in mainland China (N = 754), data were analyzed via SPSS 26.0, including reliability and validity tests, regression analysis, and Bootstrap-based mediation analysis. The results indicate that AI-PPR not only has a significant positive direct effect on S-BI but also exerts strong indirect effects through perceived usefulness and perceived trust. Specifically, perceived usefulness accounts for 35.17% and perceived trust for 31.18% of the mediation, jointly constituting 66.35% of the total effect. The findings contribute theoretically by extending the boundary of purchase intention research, differentiating rational S-BI from emotion-driven impulse buying, and enriching the application of TAM in consumption contexts. Practically, the study highlights the importance for short-form video platforms and brand managers to enhance recommendation transparency, interpretability, and trust-building while pursuing algorithmic precision, thereby fostering rational spontaneous buying and achieving a balance between short-term conversions and long-term user value.

With the rise of live-streaming services on e-commerce platforms, AI-powered virtual idols have demonstrated tremendous application potential and thus possess high commercial value. From the perspective of psychological distance, this study adopts the Stimulus–Organism–Response (S–O–R) theoretical framework to construct a research model of “AI-powered virtual idols–psychological distance–impulsive buying intention”. The model aims to explore how AI-powered virtual idols promote digital natives’ impulsive buying intention in the context of e-commerce live streaming. Furthermore, this study examines the moderating effect of technology readiness on the relationship between AI-powered virtual idols and psychological distance. The findings reveal that the level of intelligence of AI-powered virtual idols—including interactivity, anthropomorphism, homogeneity, and reputation—enhances digital natives’ impulsive buying intention by reducing psychological distance. For digital natives with lower technology readiness, the effect of AI-powered virtual idols in narrowing psychological distance is more pronounced. These findings enrich AI-driven consumer behavior models from a theoretical perspective and offer theoretical support and practical insights for developing AI-empowered digital marketing strategies tailored to the psychological traits and technological adaptability of digital natives.

Purpose: This paper aims to examine, from a knowledge management perspective, how industrial foundation models enable interconnected innovation among large, medium and small enterprises and illustrates these mechanisms through Chinese business cases. Design/methodology/approach: Guided by the knowledge-based view, this paper adopts a theory-building design with case illustration. It identifies four major knowledge bottlenecks constraining interconnected innovation among large, medium and small enterprises, develops an explanatory framework drawing on key knowledge management theories, and uses four Chinese industrial foundation model cases to illustrate the proposed mechanisms and their managerial relevance. Findings: The study finds that industrial foundation models function as cross-organizational knowledge infrastructures that enable interconnected innovation across firms of different sizes. Specifically, they support this process through four interrelated pathways, namely platform-based knowledge integration, tacit knowledge externalization, scenario-based knowledge orchestration and trustworthy knowledge governance. Through these pathways, firms can move from data interconnection to knowledge co-creation. Research limitations/implications: This paper extends knowledge management research from intra-firm knowledge processes to knowledge collaboration across organizations of different sizes. It also integrates cross-organizational knowledge integration, tacit knowledge externalization, knowledge absorption and orchestration and knowledge governance into a unified analytical framework for understanding how industrial foundation models support interconnected innovation. Practical implications: Large enterprises should act as knowledge organizers and scenario openers, medium-sized enterprises should strengthen specialized scenario capabilities and network embeddedness, and small and medium-sized enterprises should accelerate capability upgrading through modular knowledge tools and intelligent agents. Such role differentiation can support value co-creation and improve interconnected innovation performance. Originality/value: From a knowledge-based view, this paper explains how industrial foundation models reshape knowledge connection, transformation, orchestration and governance across firms of different sizes, and offers practice-based insights from China into how large, medium and small enterprises can advance interconnected innovation.

Purpose: This study aims to examine how artificial intelligence (AI)-driven knowledge management addresses interfirm collaboration dilemmas in manufacturing supply chains that digitalization alone cannot resolve. It focuses on cross-organizational knowledge understanding, trust governance and tacit knowledge transfer. Design/methodology/approach: Based on three typical Chinese cases, this study analyzes how AI-driven knowledge management responds to inconsistent data understanding, lack of relational trust and barriers to knowledge absorption in manufacturing supply chains. Findings: The study identifies three knowledge-based collaboration dilemmas in digitalized manufacturing supply chains: interpretive misalignment, trust deficits and absorptive barriers. These appear as shared data without shared understanding, formal contracts without relational trust and tacit knowledge transfer without internalization. AI-driven knowledge management addresses these dilemmas through three mutually reinforcing mechanisms. Semantic alignment transforms dispersed data into shared meanings, causal relationships and negotiable decision rationales. Evidence-based trust governance converts collaborative behaviors into verifiable and jointly interpretable process evidence. Digital apprenticeship embeds AI-enabled guidance, feedback and contextualized learning into workflows, enabling tacit knowledge absorption and capability codevelopment. Research limitations/implications: This study emphasizes mechanism identification and case-based insight. The generalizability, causal effects and boundary conditions of the three mechanisms require further empirical examination. Practical implications: Manufacturing supply chain firms should move beyond data interface integration and information visualization by using AI to enhance semantic alignment, build evidence-based cogovernance and transform individual tacit experience into reusable cross-organizational capabilities. Originality/value: This study reframes digitalized manufacturing supply chain collaboration as a knowledge management problem. It advances research from data visualization to meaning construction, from contractual or relational governance to evidence-based trust governance and from tacit knowledge transmission to workflow-embedded digital apprenticeship.