With the emergence of artificial intelligence (AI), supply chains are transitioning from traditional operating models to intelligent and data-driven systems. An AI-based intelligent supply chain is an advanced integrated technology and management system built on AI and machine learning, which achieves intelligence, networking, collaboration, integration, and automation. By combining human expertise with machine capabilities, these supply chains are evolving into autonomous systems that are capable of self-awareness, self-direction, and self-optimization, with resilience and sustainability as their objectives. AI-based intelligent supply chains increasingly depend on AI and machine learning to automate decision-making processes. By integrating advanced technologies such as AI, the Internet of Things, and big data, these systems are able to monitor and analyze each link of the supply chain in real time and promptly detect emerging issues and risks. The effectiveness of these systems hinges on development on both the supply and demand sides. On the supply side, organizational structure, process integration, and the coordination of key elements form the foundation for effective use of AI. On the demand side, users’ cognitive, emotional, and functional recognition regarding AI are central determinants of its actual effectiveness. Ultimately, it is the alignment between supply and demand that shapes the performance of AI-based intelligent supply chains. The governance of these systems requires a hybrid approach that seamlessly integrates contractual, relational, and algorithmic mechanisms, with the extent of each varying by context.
Artificial intelligence (AI) has emerged as a pivotal force in empowering entrepreneurial development, which plays an increasingly important role in its opportunity discovery and creation, resource integration, and value creation. Although attention and research interest in AI within entrepreneurship research have grown substantially, existing studies remain dispersed and lack systematic integration. This study adopts an unsupervised machine-learning approach, Structural Topic Model (STM), which combines automated coding with manual coding, to analyze 122 articles retrieved from the Web of Science. This study maps the current landscape of research on AI-enabled entrepreneurship across its antecedents, contextual settings, processes, outcomes, theoretical lenses, and methodological tools. The analysis reveals six areas requiring further inquiry: (1) deepening the study of motivational mechanisms, (2) enriching research on contextual fields, (3) exploring pathway mechanisms more thoroughly, (4) examining the positive and negative effects of AI empowerment, (5) breaking new ground in underlying theoretical logic, and (6) integrating diverse technological approaches. Building on these insights, it proposes a future research framework and agenda that (1) investigates the motivation of AI-enabled entrepreneurship in the macro, meso, micro, and cross-level scales; (2) conducts multi-contextual studies spanning regions, industries, and domains; (3) analyzes process pathways from cross-level and dynamic perspectives; (4) attends comprehensively to both positive outcomes and potential negatives of AI-enabled entrepreneurship; and (5) develops a distinctive theoretical system grounded in emerging practices, supported by robust data, tools, and methods. By synthesizing the extant literature and outlining this framework, this study offers a reference for enriching and deepening research on AI-enabled entrepreneurship.
Excellent corporate culture embodies an enterprise’s values and represents a crucial informal institutional factor driving its high-quality development. Artificial intelligence (AI) is deeply integrated into various sectors of the Chinese economy, and corporate culture inevitably evolves alongside changes in organizational production modes. From the perspective of team spirit, this study empirically examines whether and how the adoption of AI influences corporate culture. The findings indicate that the adoption of AI substantially enhances team spirit within enterprises, and this effect remains robust after a battery of endogeneity and robustness checks. Mechanism analyses reveal that AI promotes team spirit through income creation, human capital upgrading, and increased market attention. Moreover, this positive effect is more pronounced in regions where Confucian cultural influence is weaker, and elevated team spirit boosts enterprises’ market valuation. In the era of the digital economy, therefore, the deployment of AI actively shapes team spirit, thereby providing a scientific basis for cultivating an exemplary corporate culture aligned with socialist core values.
Artificial intelligence (AI) has reshaped the subject of product innovation and triggered transformations in product innovation strategies and processes. This study proposes a subject-strategy-process (SSP) framework for business intelligence (BI) for big data-driven product innovation through logical deduction, drawing on the theory of big data cooperative assets and an adaptive innovation perspective on enterprise-user interaction. The aim is to explore new mechanisms through which AI influences product innovation in manufacturing. This study indicates three aspects. Firstly, the two-way involvement of humans and AI forms a dual feedback-enhancement mechanism of factor combination and knowledge accumulation. This mechanism drives structural changes in innovation subjects and forms a new foundation for strategic and process transformations in product innovation. Secondly, the alignment between an enterprise’s cognitive strategy about AI, competitive strategy, organizational culture, business model, and ecosystem jointly shapes the integrated application of AI in innovation processes. Thirdly, the new features of the big data-driven product innovation process include full-process diffusion from the fuzzy front end, nonlinear iteration of demand-solution pairs, and generative self-testing in intelligent manufacturing. Taken together, the study demonstrates that the SSP framework is well- suited to analyzing the new mechanisms of BI for big data-driven product innovation, which offers a fresh lens for examining the relationship between AI and product innovation.
Driven by the initiatives of data-driven intelligence empowerment, robots and artificial intelligence (AI) technology profoundly influence economic and social development. China is the world’s largest market for robot adoption. This makes the impact of its industrial robot use on supply chains an important and urgent issue to explore. This paper uses Chinese A-share manufacturing listed firms from 2011 to 2019 to investigate the impact of industrial robot adoption at the enterprise level on customer stability. The empirical results indicate that an increase in the scale of industrial robot adoption will significantly improve the stability of enterprise customers. Mechanism testing shows that industrial robot adoption mainly enhances customer stability through the “technological progress effect” and “risk governance effect.” Additionally, the heterogeneity analysis finds that the positive effect of industrial robot adoption on customer stability becomes more potent for firms that are capital-intensive, less digitally transformed, non-state-owned, and trading with younger customers. Finally, applying industrial robots is conducive to adding firm value by strengthening customer stability. This paper enriches the relevant literature on the empowerment of the digital economy and customer governance to some extent. The research also provides a reference for popularizing the adoption of AI technology in enterprises and promoting the high-quality development of supply chains.