Drawing on innovation chain analytical framework and institutional logic theory, this research provides a theoretical analysis of the mechanisms underlying innovation-chain fragmentation in key core technology development and, on this basis, explores the mechanisms through which the new system for mobilizing resources nationwide supports end-to-end breakthroughs in key core technologies. The study finds that key core technology development is embedded in institutional complexity, where state logic, scientific logic, and market logic coexist and interact, and innovation-chain fragmentation is a concentrated manifestation of the coordination difficulties generated by such institutional complexity across different stages of innovation. It further finds that an enabling government, through institutional coupling, field construction, and actor orchestration, establishes the mechanisms that support innovation-chain integration, promotes effective linkages among basic research, applied research, and market diffusion, and ultimately provides end-to-end support for breakthroughs in key core technologies.

This paper takes Henan Tianshan as a case study and applies the conservation of resources theory to reveal how can enterprises create organizational resilience through value co-creation under the background of state-owned capital optimization and reorganization. The findings are as follows: In the “pillar and beam” stage, the enterprise establishes bilateral partnerships between internal capital entities, stabilizing operations through the foundation of key elements and the construction of a collaborative order. In the “building momentum”, the enterprise shifts to multilateral collaboration involving “internal capital entities + external stakeholders,” driving sustainable growth through resource aggregation and development and resource upgrading and diffusion. Enterprises deepen social capital and achieve transitions between stages through bidirectional symmetrical communication, mutual trust optimization, and cross-institutional regulatory collaboration.

Drawing on organizational imprinting theory, this study adopts a longitudinal single-case research design and examines Wanhua Chemical to explore the process and underlying mechanisms through which entrepreneurial innovation imprints evolve into organizational innovation imprints. The results show that the innovative imprints of entrepreneurs, such as results orientation, exploratory spirit, and long-termism, were translated through institutional, strategic and cultural mechanisms, forming three imprint transformation paths: agile execution, strategic breakthrough, and systemic support, continuously improving the innovation performance of the organization; and entrepreneurial innovation imprints can be institutionalized at the organizational level and exert a sustained influence on firms’ innovation processes.

From the perspectives of institutional theory and resource-based view，this study examines the link between the breadth and depth of internationalization and the survival risk of foreign subsidiaries, using a sample of Chinese multinationals listed on the A-share market. It employs the Cox proportional hazard model to analyze these relationships and explores how digital transformation moderates them. Findings indicate that there exists an inverted U-shaped relationship between international breadth and survival risk, and a U-shaped one between international depth and risk, with digital transformation diminishing both. Further, international breadth has a stronger impact on foreign subsidiaries’ survival risk in low-equity firms than in high-equity ones. International depth significantly affects survival risk in high-equity firms and non-“Belt and Road” countries, but not in low-equity firms and “Belt and Road” countries.

Taking the low-altitude economy as the research context, this study draws on signaling theory and constructs a dataset of 414 announcements issued by Chinese listed companies related to the low-altitude economy from 2021 to 2025. Using event study and multiple linear regression, this paper examines firms’ symbolic management behaviors and market recognition. The results show that substantive investment and conceptual positioning are two commonly used symbolic management approaches. On the announcement day, firms with substantive investment receive a valuation premium of 2.1%. Moreover, when the asset specificity of substantive investment is stronger, the degree of market recognition is higher. Specifically, the signaling effect of substantive investment is weakened among upstream firms but strengthened among midstream firms. In terms of information disclosure, compared with disclosures triggered by investor inquiries, firms’ voluntary disclosure of business information strengthens the signaling effect of substantive investment. Further analysis shows that the market responds more positively to substantive investment by non-state-owned enterprises. In addition, in regions with a higher concentration of low-altitude economy concept firms, substantive investment is more likely to gain market recognition. Compared with eVTOL pilot cities, the signaling effect of substantive investment is more significant in non-eVTOL pilot cities.

Based on the sensemaking theory, by analyzing data from two-stage questionnaire survey of 87 team leaders and 434 team members, this study focuses on the impact of paradoxical leadership on employees’creativity, and examines the mediating effects of job autonomy and risk-taking willingness, as well as the moderating effects of risk-taking climate. Results show that paradoxical leadership can stimulate employees’ creativity, and can also enhance employees’ creativity by separately enhancing their job autonomy and risk-taking willingness, and it can also improve employees’ creativity through the serial mediating effect of these two factors. The risk-taking climate serves as a boundary condition for the influence of paradoxical leadership on employees’ creativity.

Based on affective event theory, the research constructs a model of the mechanism by which negative customer feedback, through work rumination, affects the career resilience of gig workers. Data from 123 food delivery riders was collected over five days using a diary study method, and these 615 valid empirical data points were analyzed and tested. The results are as follows: First, negative customer feedback can stimulate problem-solving rumination, thus positively promoting career resilience. Second, negative customer feedback can simultaneously induce affective rumination in gig workers, but the negative depletion and mediating effect of this rumination on career resilience were not supported. Finally, occupation type (full-time/part-time) significantly moderates the relationship between negative customer feedback and affective rumination, and this relationship is stronger when the occupation type is full-time.

Based on the ecological situation theory and the knowledge-based view, this study uses panel data from 1459 listed manufacturing enterprises from 2015 to 2023 to explore the mechanism of the impact of enterprise niche on innovation spillover effects. The findings reveal that the two-dimensional characteristics of enterprise niche-niche state and niche potential-exert a positive impact on innovation spillover effects. Furthermore, knowledge coupling acts as a mediator between these two niche dimensions and innovation spillover effects. Additionally, resource dependence is found to negatively moderate the relationship between niche potential and innovation spillover effects.

Using panel data of Chinese listed firms from 2011 to 2023, this study examines the effects of digital technology innovation and its spillovers on enterprise new quality productivity and the underlying mechanisms. The findings reveal that both digital technology innovation and its spillovers significantly increase enterprise new quality productivity. Mechanism tests show digital technology innovation drives short-term leaps by enhancing absorptive capacity, reconfiguring factors of production and promoting green development, while technology spillover sustains long-term growth via data element scale and data right multiplier. Technology spillover positively moderates the incentive effect of digital technology innovation, especially for R&D-alliance and high-user-interaction firms, low-data-monopoly and digital-tech sectors, and open-data or strong-institution regions.

Drawing on creativity association theory, a field experiment and three laboratory experiments were conducted to explore the mechanism and boundary conditions underlying the effect of task attributes on consumers’AI type preferences (generic vs. domain-specific AI). The findings showed that compared to regular tasks, creative tasks heighten consumers ‘need for inspiration, which in turn increases their preference for generic AI which is characterized by broader data sources and wider applicability. Subjective knowledge moderates the effect of task attributes on AI type preferences, and the need for inspiration plays a moderating mediating role. When consumers’ subjective knowledge is high, the effect of task attributes on AI type preferences through the need for inspiration is no longer significant.

Based on dramaturgy theory and parasocial interaction theory, this study adopts a grounded theory approach and codes multi-source qualitative data, including interviews with consumers and marketing practitioners, media reports, industry reports, and online community articles, to compare the advantages of self-created versus collaborative virtual influencers in brand endorsement. At the corporate level, the self-created approach offers advantages in such aspects as virtual character design and autonomy in marketing output, whereas the collaborative approach has advantages in such aspects as character design cost and marketing output efficiency. At the consumer cognitive and affective level, the two types have different emphases: the self-created approach helps highlight brand-character relevance and fit, whereas the collaborative approach is more effective at capturing consumer attention and increasing the emotional value of products. At the endorsement effectiveness level, the self-created approach focuses on long-term outcomes such as brand memory, identification, and loyalty, whereas the collaborative approach focuses on short-term outcomes such as immediate interaction and impulsive consumption.

To address the real-world problem of the decline of small and medium-sized merchants and platform ecosystem imbalance caused by rising traffic costs, this study draws on platform ecosystem theory to develop a multi-agent coopetition simulation model involving the platform, leading merchants, and small and medium-sized merchants. It examines how traffic allocation strategies affect platform profits and ecosystem health, as well as the underlying mechanisms, and validates the findings through real-world business cases. The results indicate that increasing the profit orientation of traffic allocation strategies may enhance platform profits; however, it deteriorates ecosystem health, inflates average product prices, and causes user attrition, indirectly undermining profitability. Furthermore, platform traffic monetization exerts a dual-edged effect on small and medium-sized merchants. Maintaining an appropriate proportion of organic traffic can safeguard the participation of small and medium-sized merchants while intensifying paid-traffic competition can increase platform revenue, thereby achieving synergistic optimization of profitability, ecosystem health, and social responsibility.

This study addresses the conflict between data utilization and consumer data protection in firms’ AI-driven pricing. A game-theoretic framework incorporating consumer data manipulation behavior is proposed. The monopolist’s optimal pricing strategy and consumers’ optimal data manipulation efforts, as well as the impact of data manipulation, are investigated. The results show that when the algorithm development cost is relatively low, the AI-driven pricing strategy yields the highest profit. When the algorithm development cost is relatively high and the proportion of high-value consumers is high, a uniform high price is optimal. When the algorithm development cost is relatively high and the proportion of high-value consumers is low, a uniform low price is optimal. Consumer data manipulation significantly reduces the decision space of AI-driven pricing, reduced the firm’s willingness to invest in algorithms, and increases consumer surplus, whereas the cost of consumer data manipulation has an expanding effect on the strategic space of AI pricing.

Utilizing the data of China’s A-share listed enterprises from 2010 to 2022, this study constructs a cross-linked connection network based on supplier-customer relationships and examines the peer effect of enterprise ambidextrous innovation in the supply chain networks. It is found that both exploitative and exploratory innovation decisions exhibit peer effects within supply chain networks. Further research shows that the peer effect of enterprise ambidextrous innovation in supply chain networks exhibits different behavioral characteristics. From the perspective of enterprises’ own network characteristics, supply chain network relationship embedding positively moderates the peer effect of exploitative innovation and negatively moderates the peer effect of exploratory innovation. Supply chain network structure embedding positively moderates the peer effect of exploratory innovation. From the macro-environmental perspective, the peer effect of exploitative innovation is more significant when the macro environmental uncertainty is high, the peer effect of exploratory innovation is more significant when the macro environmental uncertainty is low. Both the peer effect of exploitative innovation and the peer effect of exploratory innovation are more significant in the samples of non-state-owned enterprises and in industries with sluggish development prospects.

Based on the quasi-natural experiment of the pilot policy of industrial-finance cooperation, and using the data of Chinese A-share listed companies from 2011 to 2023, this study empirically examines the impact of industrial-finance cooperation on corporate leverage manipulation. The research finds that industrial-finance cooperation effectively restrains corporate leverage manipulation by expanding external financing channels, improving the internal information environment of the enterprises, and strengthening the main business operations. Heterogeneity analysis indicates that the inhibitory effect of industrial-finance cooperation on leverage manipulation is more significant in enterprises with higher bank-enterprise asymmetry, lower analyst attention, higher economic policy uncertainty, and lower financial development levels in the region. Economic consequence tests show that after suppressing corporate leverage manipulation, industrial-finance cooperation will further reduce the default risk of enterprises and improve the main business performance.

To examine the impact of environmental regulation on corporate carbon activities from a capital market perspective, this study integrates illegal emission penalty announcement information and corporate carbon emission data with micro-level data of listed enterprises. Using a sample of Chinese listed companies from 2009 to 2022 and employing a fixed-effects model regression analysis, it investigates how investors adjust corporate carbon risk premium following the disclosure of environmental penalty and the underlying mechanisms. The findings reveal that corporate carbon risk premium significantly increases after emission-related penalties, with investor sentiment serving as the primary transmission channel- such penalties reshape investors’ carbon emission projections by altering investors’ sentiment. Furthermore, the impact of penalties exhibits significant heterogeneity depending on enterprise ownership type and monitoring intensity, and is moderated by firms’ environmental information disclosure levels and ESG performance.

Based on a manually compiled directory of regulated firms in pilot carbon markets, this study empirically examines the impact of the carbon emission trading scheme on green transformation through its “bottom-up” transmission along the supply chain. The findings reveal that when customers are subject to the constraints and incentives of the carbon emission trading scheme, they can drive their suppliers’ green transformation, with persistent effects observed in the long term. Moreover, pressure transmission effects and benefit-driven effects are identified as key drivers of suppliers’ green transformation. The pathway for suppliers involves moving away from “superficial and end-of-pipe” transformations toward achieving “substantive and cleaner production” transformations. Further analysis shows that the promoting effect of customers’ inclusion in carbon emission trading pilots on suppliers’ green transformation is more pronounced when suppliers operate in regions with stronger government environmental governance or possess lower cost-pass-through capabilities. Additionally, the application of artificial intelligence technology by suppliers can provide essential support for their green transformation. Finally, when customers are included in carbon emission trading pilots and drive their suppliers’ green transformation, a win-win effect of green supply chain collaboration is generated, which becomes more effective over the long term.

To clarify the roles and application pathways of large language models in intelligent digital services, this study reviews relevant research through the lens of information processing theory and summarizes application patterns across users, service providers, platforms, and multi-actor interactions. The findings reveal a progressive intelligence paradigm comprising perceptual, cognitive, reasoning, and execution intelligence. Major approaches include prompt engineering, fine-tuning, retrieval-augmented generation, and reinforcement learning, while key challenges involve intent understanding, knowledge retrieval, reasoning hallucinations, and multi-agent coordination. These challenges hinder the translation of model capabilities into service effectiveness, requiring coordinated support in information perception, knowledge cognition, logical reasoning, and action execution.

Based on sociotechnical systems theory, this study conducts a systematic literature review of 310 Chinese and English core journal articles published between 2015 and 2026. An integrative analytical framework centered on “bidirectional human-machine adaptation” is developed to systematically elucidate the conceptual structure, influencing factors, mechanisms, and outcomes of human-machine collaboration. The findings reveal that human-machine collaboration presents a continuum from instrumental use to value co-creation within the “interaction depth-task structure” two-dimensional framework; collaboration effectiveness is systematically shaped by four categories of factors: individual characteristics, technological boundaries, psychological bonds, and situational contexts; human-machine collaboration generates pronounced double-edged sword effects at the individual, team, and organizational levels through three pathways: cognitive appraisal, resource flow, and identity matching.