China’s AI surge is remaking management — but the gains depend on human wisdom. Across 18 papers, AI boosts efficiency and innovation when embedded in culturally aligned leadership, trust and networked resource orchestration, yet risks psychological alienation, ethical tensions and governance shortfalls if deployed without context‑sensitive oversight.

In 2025, China’s digital economy marked a new milestone, contributing nearly 45% of the national GDP and positioning the country as a global center for disruptive innovation [1]. This rapid evolution coincides with a pivotal era in which Chinese management, historically rooted in relational harmony and strategic flexibility, must confront the challenges posed by the cold logic of algorithms and automation. Despite the ambiguity and volatility of digital data governance, generative artificial intelligence (AI) has increasingly been used to augment human intelligence (HI) in corporate decision-making and strategic orchestration (Duan et al., 2024; Zhou and Li, 2023). To a certain extent, digital transformation and AI represent an unorthodox paradigm shift in international business, aimed at optimizing efficiency and value creation through integrating diverse cultural, ethical and regulatory landscapes across the West and East (Chin et al., 2025a).However, with the proliferation of AI adoption, more and more puzzles appear at the intersection of AI and management practice. First, AI not only reshapes organizational knowledge creation (KC), but also employees’ responses to AI-driven KC changes according to the designed characteristics of AI agents (Gopal et al., 2025; Guo et al., 2025). Related to this, human workers’ KC processes are being altered by AI-related autonomy mechanisms (Zhang et al., 2026; Chin et al., 2025b). Nevertheless, these KC dynamics have not been clearly addressed within organizational contexts. Second, although quite a few studies have discussed how AI transforms employees’ decision-making and behaviors, its cultural and ethical interventions into human activities remain insufficiently understood. The existing literature still places major emphasis on the links between AI implementation and financial outcomes, but overlooks AI’s broader psychological and behavioral impacts on HI and human capital, ranging from individual perceptions to macro-level governance (Cheng et al., 2024; Scuotto et al., 2025).The above-mentioned gaps in praxis cannot be filled by management theories alone as they necessitate phronesis being added to rationalization and interpretation. This implies the urgent need for the use of a phronesis-based view that derives from Aristotle’s philosophy of practical wisdom as a cardinal intellectual meta-virtue (Peltonen, 2022) to elucidate the micro-foundation of digital age wisdom in the fields of management. Moreover, such phronesis, or so-called management wisdom, may differ according to various cultural philosophies that involve a set of beliefs, values and principles the people of a nation collectively adhere to. Taking together these considerations, it is vital to explore how traditional Chinese management philosophies can be harmonized with AI to avoid algorithmic alienation while fostering sustainable innovation.More specifically, the collective behaviors arising from the interaction between AI and Chinese management represents a complex new sociology of humans and machines. This synergy cannot be predicted by conventional management logic alone, as it embodies a dual nature of radical efficiency and deep-seated cultural path dependency. Questions remain regarding the efficacy of AI-HI integration in Chinese firms: can it effectively manage sophisticated knowledge across diverse institutional rationales, or will it be hindered by the cultural biases and black-box nature of generative algorithms (Lythreatis et al., 2026; Chin et al., 2026)? Ultimately, these dynamics present a broad array of challenges and opportunities that merit closer, more comprehensive exploration at the intersection of hybrid intelligence, cultural factors and strategic management. This Special Issue (SI) is structured to address these critical gaps by demonstrating digital age wisdom across micro-, meso- and macro-level dimensions.Drawing on the motivations outlined above, this SI aims to integrate diverse scholarly perspectives to better understand the evolving functions and mechanisms of digital transformation and AI-HI integration in management practices within the unique Chinese context. Launched in 2024, the call for papers (CFPs) encouraged submissions grounded in real-world settings, including empirical investigations, conceptual frameworks, theoretical advancements and case-based analyses examining the collaborative interplay between human and machine intelligence. Beyond merely adapting Western management theories, this SI encourages the development of context-specific models that reflect the wisdom required to navigate China’s complex cultural and regulatory digital environment.Despite the proliferation of digital tools, a closer examination of the literature suggests that implementing AI-HI integration in Chinese management may generate significant debates. On the one hand, Chinese firms often operate in a data-rich but insight-poor environment; consequently, AI adoption enables managers to more efficiently use vast data streams for innovation and strategic orientation (Luo and Wang, 2026). Moreover, recent advancements have enabled AI to better emulate cognitive functions that were once the exclusive domain of human experts (Huang and Rust, 2021). Consequently, integrating AI and HI into decision-making could optimize the processing of heterogeneous organizational knowledge, enabling firms to enhance efficiency and competitive advantage (Zhu et al., 2026).Conversely, significant cognitive and ethical discrepancies emerge in AI-HI collaboration due to the fundamental differences in how AI and HI perform cognitive functions within culturally embedded management systems. Unlike HI, which uses intuition, Guanxi-based judgment and the art of balancing social harmony without relying solely on quantifiable data, AI operates as an amalgamation of big data and can only respond to existing patterns (De Cremer and Kasparov, 2021). This limitation raises critical questions regarding AI’s ability to navigate the tacit knowledge and ethical nuances that define Chinese management wisdom (Del Giudice et al., 2023). As digital transformation must accommodate diverse cultural and institutional perspectives, allowing AI to take the lead in strategic decision-making without human wisdom may be inappropriate.Considering these contrasting perspectives, we proposed several potential thematic directions that intersect AI-HI interactions, management wisdom and the Chinese context for the CFPs, as follows:From 2024 to 2026, more than 130 articles were submitted to this SI. Following a rigorous peer-review process, only 18 papers were accepted. To illustrate the conceptual architecture of these contributions, we envision a Sankey diagram (Hadid et al., 2022) to map the flow of knowledge across three hierarchical layers, as shown in Figure 1. The first layer represents our three primary research themes: micro-level interaction, meso-level dynamics and macro-level governance. The second layer displays the specific manuscript IDs and titles of the accepted papers, while the third layer extracts the core keywords that define the current frontiers of Chinese digital management. The width of these links is proportional to thematic density, highlighting the SI’s primary areas of inquiry and the synergistic relationships among the human, organizational and institutional dimensions. We elaborate on these contributions hereinafter.The ultimate success of digital transformation and the sustainable adoption of emerging technologies fundamentally rest on human actors. While organizational strategies provide the framework, it is the individuals’ cognitive processing, emotional responses and perceived agency that determine the efficacy of AI-HI synergy. Despite the promise of enhanced productivity, significant uncertainty remains regarding how psychological barriers and relational dynamics shape the adoption curve of AI across different service sectors. In this context, the first category of our SI comprises five articles that explore the multifaceted psychological mechanisms and behavioral intentions governing modern human-technology dynamics.Chen et al. (2026) use the Elaboration Likelihood Model to investigate the persuasive mechanisms driving the adoption of GenAI tools among cross-border e-commerce operators. Their findings reveal a recursive interplay where argument quality and source credibility significantly enhance subscription intentions and a willingness to pay more for services. Interestingly, their study identifies internet celebrity and user endorsements as key drivers of credibility, providing actionable guidance for technology providers navigating digitally mediated adoption decisions. Also focusing on adoption intentions, Chou et al. (2026a, 2026b) apply a machine learning-assisted analytical approach to internet-only banking. By integrating the Stimulus-Organism-Response framework, they confirm that although social influence plays a context-sensitive role, perceived trust and service quality remain the fundamental bedrock of consumer intention, further validated through K-means clustering to ensure data robustness.Beyond functional adoption, the emotional and relational risks of AI interaction represent a critical dark side of the digital transformation. Liu et al. (2026a, 2026b) explore this through the lens of perceived betrayal during AI service failures in hotel contactless services. Their experimental results demonstrate that the severity of AI failure significantly decreases forgiveness willingness, although this negative effect can be mitigated by high levels of brand attachment. In the high-stakes healthcare sector, Chou et al. (2026a, 2026b) use a two-stage SEM-ANN approach grounded in social cognitive theory to identify AI anxiety as a multifaceted hurdle. Their model ranks emotional affect and outcome expectations as essential influences, suggesting that administrators must prioritize alleviating workers’ concerns regarding professional replacement and diagnostic accuracy to improve usage intentions. Xie et al. (2026) investigate how AI-based decision-making in recruitment influences candidates’ perceptions. Drawing on control theory, their experiments reveal that candidates feel less satisfied with firms using AI evaluators compared to human experts due to a perceived loss of control over the application process, an effect particularly pronounced in individuals with an internal locus of control.Collectively, these studies demonstrate that digital management wisdom lies not only in algorithmic advancement but in the harmonious alignment of technology with human psychology, trust and emotional values.At the meso-organizational level, digital wisdom is manifested as the strategic coordination capabilities required to transform technological potential into sustainable competitive advantage. While digitalization provides the tools for transformation, its efficacy is highly dependent on the strategic vision of top management, the inclusivity of corporate culture and the dynamic orchestration of resources. The second category of this SI comprises eight research articles that explore the internal logic of corporate digitalization through the dimensions of leadership orientation, peer effects, resource orchestration and team synergy.Xu et al. (2026), examining Chinese state-owned enterprises, identify a positive relationship between top management teams’ technological orientation and digital transformation investment. Their findings suggest that while a strong technical orientation in leadership fosters transformation, this effect is contingent on managerial cognition and organizational context: managerial myopia and excessive organizational slack may dampen commitment to digital investment, whereas heightened environmental uncertainty strengthens the impetus for transformation. Complementing this perspective, Xu and Li (2026) focus on technological core executives in the manufacturing sector and demonstrate that such leaders significantly accelerate industrial AI transformation. By leveraging parent-subsidiary executive connections and mobilizing abundant subsidiary resources, they improve supply chain efficiency and digital innovation, highlighting the importance of governance structures and leadership embeddedness in enabling technological upgrading.Beyond internal leadership dynamics, external networks and informal cultural forces also emerge as critical drivers of digitalization. Zhao et al. (2026), drawing on social network analysis, reveal robust peer effects within common ownership networks. Firms occupying central network positions are more susceptible to peer influence, while industry leaders generate powerful demonstration effects that accelerate digital transformation among follower firms through information diffusion and competitive pressure. In parallel, Ma et al. (2026) demonstrate that innovation culture constitutes a core informal institutional force underpinning enterprise digital transformation. Using machine learning and text analytics, they show that such a culture stimulates R&D investment and aligns strategic orientations toward digitalization, with its positive impact further amplified by government innovation attention, particularly in technology-intensive and non-state-owned enterprises.Liao et al. (2026), drawing on Optimal Distinctiveness Theory and fuzzy-set qualitative comparative analysis (fsQCA), identify multiple equifinal pathways to high performance in digit-oriented spin-offs. They distinguish parent-oriented, independent-oriented and ambidextrous-oriented configurations, suggesting that successful ventures must dynamically balance resource embeddedness within parent networks and strategic autonomy. Similarly, Li et al. (2026) explore cross-level interaction mechanisms for high growth among digital startups and identify three models: the resource network orchestration model, the innovation resource development model and the entrepreneurial spirit coherence model. Their findings highlight digital resource integration capability as a universal condition underpinning entrepreneurial growth, reinforcing the view that sustainable scaling in the digital era depends on orchestrating heterogeneous resources across individual, organizational and environmental levels.The transformation of traditional firms and the reshaping of team dynamics further enrich the meso-level narrative. Zhu et al. (2026), through a longitudinal case study of Century Innovation, propose a context-strategy-outcome framework to explain how traditional printing firms undergo staged digital transformation. They delineate four developmental phases: Digital Element Sedimentation, Digital-Intelligent Formation, Convergent Network Integration and Smart Engine Leap, driven by the reciprocal meshing of resource orchestration and dynamic capabilities. Extending this, Du et al. (2026) investigate how team faultlines influence proactive behavior in AI-enabled contexts. From an information-processing perspective, they find that information-based faultlines can enhance proactive behavior through deep information processing, while AI adoption moderates and mitigates the negative effects of social-based faultlines on team cooperation.These studies reveal that digital transformation in Chinese enterprises is neither linear nor purely technological; rather, it is a multilevel process shaped by leadership orientation, network embeddedness, cultural norms, configurational pathways and evolving team structures, all of which interact to determine the pace and sustainability of organizational change.The institutionalization of digital technologies within the Chinese management landscape necessitates a robust framework for macro-level governance to ensure ethical compliance, economic resilience and sustainable development. Macro-governance addresses the systemic interactions among state policies, global market dynamics and risk management strategies. To explore these broader environmental factors, the final category of this SI includes five articles that collectively examine the evolution of digital governance and the of diverse et al. (2026a, 2026b) use a model to a longitudinal analysis of digital governance in Chinese enterprises, a marked shift in research toward innovation, digital and which the of the digital a perspective, et al. (2026) examine the of AI startups across how national entrepreneurial and such as digital structures to AI growth in and The impact of is by and (2026), use a on the to demonstrate that digital transformation effectively digital innovation and by optimizing industrial structures and financial the concerns of et al. (2026) a staged risk and for GenAI providing governance strategies that balance the of risk with the severity of potential the AI service et al. 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