A new technological revolution is reshaping the global innovation landscape, making it increasingly important for firms to increase their research and development (R&D) investments. These investments are pivotal for accelerating technological upgrades and maintaining competitiveness in a globalized economy. It is widely accepted that firms increase R&D investment because of their ample resources and positive performance feedback. However, despite a significant rise in R&D investment by listed firms in recent years, their profitability has not demonstrated a corresponding increase. This discrepancy raises an important question: Has the continuous increase in R&D investment by firms surpassed the positive effects of performance feedback?

Building upon the resource-based view and performance feedback theory, a growing body of literature has focused on how external stakeholders, such as the government and venture capitalists, influence firm's R&D investment decisions. In particular, the actions of other firms within the same field can reshape the business environment and influence the allocation of resources among stakeholders. From the perspective of institutional isomorphism theory, it has been found that, in uncertain environments, firms tend to imitate the behavior of other firms operating in similar contexts, particularly when those firms are embedded within the same organizational field. This imitation helps firms reduce decision-making risks and gain legitimacy, especially in a competitive and resource-constrained environment.

Most existing research has examined the role of imitation pressure in promoting R&D investment within a single organizational field. However, focal firms are not only embedded in one field but often operate within both industry and regional fields simultaneously. The overlap between these two fields generates a new organizational space known as the regional-industry dual field. This dual-field environment creates unique dynamics for firms that are embedded in both fields, and raises several important questions. For example, how do new "same-group" firms, those that are both in the same region and industry, influence one another's R&D investment behavior? Furthermore, what happens when imitation pressures from the two fields conflict? consider the situation where firms in the same region have relatively high levels of R&D investment, but firms within the same industry have lower investment levels. How will the focal firm respond in such a conflicting environment? Existing organizational innovation research has not provided clear answers to these questions, especially when it comes to the simultaneous pressures from industry and regional fields.

To address these gaps, this study applies institutional isomorphism theory to explore the impact of dual-field imitation pressure on firm's R&D investment. Additionally, it examines the moderating effects of imitation pressure conflicts. The analysis investigates the relationship between imitation pressure and R&D investment in firms of different market positions, while also assessing whether dual-field imitation pressure helps firms overcome the manipulation of R&D investment levels. Using a two-way fixed effects model, this study conducts an empirical analysis of A-share manufacturing companies listed on the Shanghai and Shenzhen stock exchanges between 2008 and 2022.

The results of the study indicate that dual-field imitation pressure positively influences firms to increase their R&D investment. Furthermore, when imitation pressures from the two fields conflict, the driving effect on R&D investment is strengthened. Firms with varying market positions align with the imitation pressures from the different fields they are embedded in, and, when facing dual-field pressures, they tend to increase their substantive R&D investment. This study not only enriches the literature on the role of imitation pressure in corporate R&D investment but also contributes to the understanding of how social innovation demands can be translated into corporate behavior. By analyzing the interplay between different organizational fields, this study helps provide new insights into the dynamics of R&D investment and the role of external pressures in shaping corporate innovation strategies.

Small and medium-sized enterprises (SMEs) are the driving force of innovation, entrepreneurship, and economic development in China. In the era of digital economy, with the deep integration of digital technology and the real economy, data resources have become the key for SMEs to gain competitive advantages. In practice, some SMEs have seized the opportunities of the times and achieved rapid development through digital transformation, but more SMEs still face significant challenges in the process of digitization, with phenomena such as reluctance, inability, and fear of digital transformation being widespread. At the same time, in the face of complex and volatile market, technology, and institutional environment, the “specialized, refined, differentiated, and innovative (SRDI)” development path is considered an important strategic pathway for high-quality growth of SMEs in China. Existing studies have attempted to explore the impact and boundary conditions of the digital economy on the growth of “SRDI” SMEs, confirming that the business environment has a synergistic effect on the digital economy to enhance the cultivation effectiveness of “SRDI” SMEs. However, most existing studies focus on the macro-level influence of the digital economy on the development of “SRDI” SMEs, without delving into the micro-level analysis of the role of digital technology application, nor examining the internal mechanisms and boundary conditions by which the digital technology application drives the “SRDI” development of SMEs. Therefore, based on the theory of firm endogenous growth mechanism, this paper constructs a theoretical framework of the process by which the application of digital technology empowers the growth of SMEs, and uses questionnaire survey data for empirical analysis to reveal the internal mechanisms and key contexts of the aforementioned process, namely how the application of digital technology promotes enterprise growth by enhancing the “SRDI” strategic orientation of SMEs, as well as the moderating effect of external market conditions (intensity of market competition) and SMEs’ ability to acquire ecosystem resources (acquisition of ecosystem resource). Empirical analysis reveals that the digital technology application can promote the growth of SMEs, and this promotion is achieved by strengthening their “SRDI” strategic orientation. However, the impact of digital technology application on SMEs’ “SRDI” strategic orientation is moderated by external market conditions and the SMEs’ ability to acquire ecosystem resources. Specifically, the more intense the market competition or the easier the access to ecosystem resources, the weaker the impact of digital technology application on the “SRDI” strategic orientation. The theoretical contributions of this paper mainly include: First, by focusing on the impact of digital technology application on the growth of SMEs, this paper enriches the research on the driving factors of SME growth. Second, from a strategic perspective, this paper reveals the internal mechanism by which digital technology application empowers the growth of SMEs. Lastly, this paper identifies the important situational factors for the promotion of “SRDI” development of SMEs through the application of digital technology. This paper also makes some practical contributions: First, SMEs in the era of digital economy can embark on the path of “SRDI” development by actively applying digital technologies. Second, SMEs that adhere to the “SRDI” strategic orientation should not blindly pursue digital transformation but should consider it strategically.

The 14th Five Year Plan points out to “accelerate the development of services such as research and development design, industrial design, inspection and testing certification”, and “to build an innovation and service complex to form an emerging industry growth engine with complementary advantages and reasonable structure”. It can be seen that building a service chain that matches the industrial innovation chain and driving the fusion of innovation chain and service chain (dual-chain) in emerging industries is crucial for facilitating efficient matching between innovation demands and service supplies and enhancing industrial innovation capability. Digitalization propels changes in dual-chain entities and diversifies the dual-chain fusion models, accelerating the dual-chain fusion process and presenting new evolutionary characteristics and structures. Drawing on the 6C theoretical framework and using the new energy vehicle industry as a vertical case study, this study explores the dual-chain fusion mechanism in emerging industries driven by digitalization. The findings show that the process of dual chain fusion in emerging industries follows a causal logic of external context (driven by digitalization) - fusion behavior (structural adaptability adjustment, supply-demand relationship connection, flexible functional configuration) - fusion result (improvement of industrial innovation capability). The dual-chain fusion has gone through three evolution stages of “fusion exploration→fusion interaction→fusion upgrading” (Change). The dominant elements and combination modes of digital driven context in the three stages of fusion are different, presenting an evolutionary feature of “guided→interactive→pioneering” (Context). Under the drive of digitalization, the dual chain entities undergo strategic updates and sequentially carry out structural adjustments of “digital shaping chain→digital strengthening chain→digital expansion chain” (Construct). As an important support and promotion condition for fusion behavior, it guides the formation and reshaping of the “complementary efficiency cross-border” relationship connection mode (Cooperation). With the evolution of linkage modes, the types and goals of cooperation between dual chain entities have dynamically changed, resulting in the formation of a “single point→modular→ecological” functional configuration for dual chain fusion (Configuration). This not only supports the strategic progression of dual chain entities, but also provides timely feedback for relationship linkage, promoting the optimization of chain network structure. In the continuous deepening of dual chain fusion, emerging industries have successively formed digital driven distributed, fused, and cross-border innovation capabilities (capability), promoting the upgrading of the dual chain fusion network to an ecosystem of “innovation service” cross-border docking and intelligent collaboration, and accelerating the development of new quality productivity. This study uncovers the black box of digital-driving mechanisms and process of dual-chain fusion in emerging industries, to make up for the shortcomings of existing research in exploring the mechanism of dual chain fusion from a static perspective, and the 6C theory is combined with the multi-agent, multi interaction, and multi-stage evolution characteristics of complex systems, providing a suitable framework and inspiration for the evolution research of more types of complex systems in the future. and also provides insights for the strategic planning of dual-chain fusion in industries and the cooperative practices of dual-chain entities.

The evaluation of public policies has been a long-standing concern for both the government and academia. However, most studies either focus on the effectiveness of the text or the outcomes rarely examining the complete chain from policy effectiveness to policy outcomes. This paper, based on the "dual effectiveness" mixed method, takes China's science popularization as an example, and uses panel data from 31 provinces from 2012 to 2022 to conduct empirical analysis, systematically investigating impact of policy effectiveness on policy outcomes and its mechanism. The study finds that policy effectiveness has a significant impact on policy outcomes; the level of policy effectiveness and outcomes significantly depending on the target audience, with the highest performance seen in incentive policies, followed by protective policies, and the weakest in environmental policies; among the four dimensions policy, policy measures have the greatest impact on the effectiveness of policy implementation, followed by policy feedback and policy goals. Therefore, the government should further strengthen the driving of incentive policies, improve the efficiency of incentive policy supply through revision of incentive measures. And improve the overall effectiveness and outcome of the policy by enhancing policy measures, goals, policy strength, and policy feedback. The marginal contributions of this paper are as follows:(1) By adopting a "dual-effect" mixed method, it first quantitatively measures the effectiveness of popular science policies, then analyzes the impact of policy effectiveness on policy outcomes, providing a more objective measurement of the validity of policy texts and their influence on actual implementation outcomes;(2) Starting from multiple factors influencing policy implementation outcomes, it comprehensively considers the impact of policy effectiveness, different categories of policy effectiveness, and various policy dimensions on policy execution results, in order to avoid one-sidedness and subjectivity in policy performance evaluation;(3) Using the established policy effectiveness evaluation model as a tool, a more accurate assessment of the implementation effects of different policy documents and policy instruments can be conducted.

The rapid advancement of Artificial Intelligence (AI) is profoundly changing the operational logic and organizational models of scientific research, fundamentally reshaping the theoretical foundation and methodological system of the Science of Science (SciSci).Based on the rise of AI-driven scientific research, this paper systematically analyzes the holistic transformation of SciSci research across its objects, core problems, and methodological tools under the empowerment of AI.The study indicates that AI acts simultaneously as a revolutionary force within the scientific system, generating new SciSci research objects and questions, and as a powerful analytical tool, breaking through the technical bottlenecks of traditional SciSci methodologies. This series of transformations will enable SciSci to achieve a fundamental expansion: from external representation to intrinsic mechanisms, from phenomenological description to mechanistic explanation, and from passive observation to active design. The new research paradigm exhibits core characteristics such as prospectiveness, systematicness, synergy, and designability.At the methodological level, AI promotes the objectification and multi-modality of SciSci's observation and measurement, the predictive and networked nature of its analysis and modeling, and the systemic and explainable nature of its explanation and prediction. This significantly enhances SciSci's practical capabilities in research evaluation, knowledge discovery, and scientific ecosystem design.Nevertheless, risks and challenges in technical, ethical, and institutional aspects urgently need to be addressed. This paper proposes that, while maintaining the unique identity of the SciSci discipline, we should drive paradigm evolution through methodological innovation, and construct an open, transparent, explainable, and prospectively guiding methodological system to achieve the continuous leap forward of SciSci empowered by AI.

Artificial Intelligence(AI) technology has experienced rapid development in recent decades, driving a profound paradigm shift in management science research and reshaping the theoretical framework, research methodologies, and practical application scenarios of the discipline. To systematically sort out the path, current situation, and challenges of the integration of AI and management science, this study conducted a large-scale quantitative analysis of high-quality journal literature indexed in the Web of Science database, with a focus on publications included in the Financial Management Society (FMS) journal list, and constructed and clarified the systematic framework of its fundamental transformation. This method covers 25,719 literatures in the fields of management science and economic science from 1996 to 2025, and uses professional analytical tools such as VOSviewer and RStudio to conduct visual analysis of keyword co-occurrence relationships, national cooperation networks, and research trend evolution. The results show that the number of publications in this field presents an exponential growth trend over the past three decades, with China and the United States as the core contributing countries; there are significant differences in the research focus between management science and economic science; research hotspots are concentrated in key clusters such as the application of AI technology in management practice, the identification and response to algorithmic risks, the construction of trust mechanisms in service science under the intelligent environment, and the AI-driven digital transformation of enterprises, while notably, significant differences exist in the research focus of management science and economic science during the integration process—management science tends to prioritize the optimization of practical management scenarios and efficiency improvement, while economic science focuses more on exploring the impact of AI on economic operation rules and resource allocation mechanisms. The conclusion indicates that AI has evolved from an auxiliary tool to a core driving force for promoting the reconstruction of management science theories and the transition of research paradigms towards the "human-object-information" three-dimensional space. Future research should focus on cutting-edge issues such as the internal mechanism of human-machine collaboration, the ethical boundaries of algorithm governance, and institutional adaptation in major decision-making, so as to promote the construction of a new management science theoretical system adapting to the intelligent era.

In an era characterized by intensifying globalization, rapid technological iteration, and geopolitical volatility, traditional organizational decision-making paradigms grounded in the theory of “bounded rationality” are facing unprecedented challenges. Decision-makers are increasingly constrained by inherent cognitive limits when processing real-time, high-dimensional information, leading to satisficing behaviors that are frequently insufficient for today’s high-risk, complex, and feedback-delayed environments. While Artificial Intelligence (AI) has emerged as a transformative force capable of reshaping the epistemological foundations of strategy and risk management, the theoretical mechanisms and boundary conditions governing “human-AI symbiosis” remain underexplored. Existing literature often treats AI as a monolithic variable or categorizes collaboration solely based on automation levels, failing to elucidate the underlying power dynamics—specifically, the allocation of decision rights and the locus of agency—that define the collaborative interface. Consequently, there is a critical need for a systematic framework that deconstructs how AI reconfigures the structural and procedural dimensions of organizational decision-making beyond mere technical functionalism. To address this significant theoretical gap, this study conducts a systematic literature review of 85 core articles published in UTD24 premier management journals between 2020 and 2025. Utilizing bibliometric analysis and systematic coding, the research maps the trajectory of AI application in organizational decision-making, identifying key thematic clusters such as information processing, machine learning, and trust dynamics. The central contribution of this work is the construction of the novel “Interaction-Support-Influence” classification framework, anchored in the power relations between humans and machines. This typology transcends prior functional descriptions to reveal distinct logics of agency and collaboration. The “Interaction Mode” represents a form of shared agency characterized by a recursive loop of output, feedback, and modification; here, decision power is dynamically shared, and AI acts as a reflexive partner that challenges human assumptions and co-creates solutions through bidirectional engagement. The “Support Mode” characterizes scenarios where AI functions as an augmentative tool for information processing or option generation, yet the final fiat remains exclusively with the human agent, focusing on enhancing efficiency and accuracy while retaining human cognitive sovereignty. The “Influence Mode” delineates scenarios where AI operates as an environmental variable or architect, shaping the decision-making context through information filtering or algorithmic choice architecture without explicit participation in the final choice, thereby structurally constraining human agency through latent algorithmic antecedents. The synthesis reveals that the efficacy of these modes is contingent upon a complex interplay of internal and external factors. Internally, individual traits such as algorithm aversion or appreciation, alongside organizational capabilities like absorptive capacity and strategic inertia, dictate adoption success. Externally, macro-institutional factors, including regulatory frameworks and cultural contexts, significantly bound the scope and legitimacy of collaboration. Regarding outcomes, the study distinguishes between performance metrics and behavioral evolution, highlighting a “double-edged sword” effect: while AI integration generally enhances financial efficiency and decision quality, it simultaneously introduces risks such as cognitive atrophy, algorithmic bias, and the erosion of unique human tacit knowledge. Despite these theoretical advances, the review identifies significant lacunae in current scholarship, particularly regarding the lack of integration between AI implementation and core organizational strategy. Existing research remains fixated on a “human-in-the-loop” paradigm that underestimates AI’s potential agency and neglects the profound impact of macro-environmental variables. To advance the field, this paper proposes a comprehensive future research agenda structured around two critical dimensions: the deep integration of AI with organizational strategy and the expansion of novel AI strategic horizons. First, regarding the deep integration of AI and strategy, the study calls for moving beyond static adoption to investigate dynamic human-machine authority allocation, the impact of macro-institutional variables—such as geopolitical shifts and cultural contexts—and the profound reconfiguration of organizational structures and relational networks. Second, regarding the expansion of new AI horizons, the paper emphasizes the urgent need to examine AI’s disruptive role across four emerging frontiers: facilitating scalable innovation and application, redefining power dynamics within platform ecosystem governance, managing systemic risks and compliance, and addressing the paradoxes of AI-driven ESG sustainability. Ultimately, this research extends the theoretical boundaries of sociotechnical systems in management, offering practitioners a diagnostic logic for designing differentiated human-AI mechanisms that align with specific strategic imperatives, thereby facilitating a shift from simple tool adoption to deep structural integration and sustainable competitive advantage.

The widespread application of the "AI-human" system in management decision-making has sparked discussions on enhancing the synergy of the "AI-human" system to improve management decision-making performance. Although existing research has proposed some methods to improve the synergy of the "AI-human" system in management decision-making, there is still a lack of validation in specific business scenarios. This article chooses the initial screening of journal articles as a scenario and delve into the specific process of sequential AI-Human collaboration to exploring the path to improving the synergy of the "AI-human" system in management decision-making.

This article proposes that we can use the scoring results of AI to eliminate some low-quality submitted papers before the editor starts the initial screening work. Then, the editor screens the remaining papers with higher scores. In this process, AI’s filtering effect will reduce the number of papers reviewed by editors. Therefore, we can use the sequential AI-Human collaborative mode to improve the overall efficiency of paper review work. That is to say, the key to this type decision-making lies in designing AI screening thresholds based on an evaluation of AI's screening effectiveness.

Firstly, this article introduces Spearman correlation and innovatively designs priority comparison coefficient to evaluate the overall decision-making efficiency of AI. Secondly, by defining the negative and positive states of the screening results, sensitivity, specificity, and accuracy indicators were introduced to evaluate the AI’s decision screening effectiveness; Then, the design of evaluating benchmarks is carried out by aggregating human collective judgments, and it is proposed that the judgments of multiple human experts can be combined, enhanced, and jointly determined to form different evaluation benchmarks. These benchmarks can avoid the limitation of using a single evaluation benchmark that cannot accurately reflect AI’s decision-making ability; Finally, by designing the minimum retention rate as an indicator, the threshold setting method for AI in screening decision was clarified. This indicator not only reflects AI’s screening effectiveness, but also enables the rapid implementation of AI applications in the specific scenario. The analysis of three groups of papers in a journal demonstrates that by retaining 75% of the papers during the initial screening phase using existing large language models, high-quality papers identified in relevant evaluation benchmarks can be effectively covered. This reduces editorial workload by approximately 25%, increases overall editorial efficiency by about 15%, and improves the efficiency of the entire editorial process by roughly 8%.

The research is not limited to the level of AI (or LLM) capabilities, but focuses on how to effectively introduce AI into management decision-making on the basis of existing AI capabilities, so as to accelerate the pace of AI implementation and application. The relevant evaluation indicators of AI decision-making capability and their usage logic proposed in this article can not only be applied to decision screening in other scenarios, but also adapt to the dynamic evolution of LLM capability. After significant changes in AI capability, the threshold for AI decision screening can be reanalyzed and calculated, achieving continuous improvement in the synergy level of the "AI-human" systems.

This study constructs a theoretical model of the relationship between AI capability, innovation agility, and innovation resilience. The positive significance of AI applications has been widely recognized, but the research results of this article indicate that AI capabilities have an inverted U-shaped impact on the innovation resilience of enterprises, which first promotes and then inhibits; There is also an inverted U-shaped relationship between AI capability and innovation agility, and innovation agility plays a mediating role between AI capability and innovation resilience. In addition, environmental turbulence positively moderates the impact of innovation agility on innovation resilience. The research results confirm the "double-edged sword" effect of AI capabilities, opening up new research perspectives for enterprise innovation management in the AI era, and providing management insights for enterprises to effectively utilize AI technology to achieve sustainable innovation development and avoid falling into the "digital trap".

The findings of this study offer three major contributions: Firstly, this paper combines AI with innovation research from a research perspective, treating AI capabilities as an important organizational capability in the digital age. It constructs an analytical framework of "AI capabilities - innovation agility - innovation resilience" and confirms the role path of AI and agility as driving forces in enhancing enterprise innovation resilience. That is, a set of key organizational strategic agility processes enabled by "digital intelligence" creates strategic resilience in innovation activities with a proactive and opportunity-centered attitude. Furthermore, this paper explores the non-linear interaction between AI capabilities and innovation agility and resilience, revealing the "double-edged sword" effect and "dark side" of AI capabilities, thereby breaking through the limitation of existing research that mainly focuses on the positive significance of AI applications.

Secondly, this paper contributes to the research on the antecedents of enterprise innovation resilience. Although the concept of innovation resilience has attracted attention in recent years, the research is rather scattered and lacks in-depth exploration of the role played by AI. Therefore, the concept of AI capability is introduced in this paper, and by revealing the role of AI capability in promoting enterprise innovation resilience and its internal mechanism, a new research perspective is opened for the discussion on how to improve enterprise resilience, and at the same time, it also enriches the existing research on improving innovation resilience.

Finally, based on the context of digital development and market transformation of Chinese enterprises, this paper takes environmental turbulence as a moderating variable and effectively reveals the contingency effect of enterprises implementing agile strategies to resist the challenges of high-turbulence environments. By closely integrating the dynamic capability factors at the organizational level with the characteristics of the external environment, it emphasizes that in highly turbulent environments, enterprises should take enhancing agility as the main development strategy. From this perspective, this study not only highlights the significant role of AI capabilities and organizational agility in enhancing enterprise resilience, but also recognizes that the effectiveness of these dynamic capabilities may vary depending on the environment and context in which they are applied. This response addresses the call from scholars in the past for future research to explore how digitalization and organizational agility interact to influence organizational resilience in different organizational environments and market scenarios.

To understand the dynamics of artificial intelligence (AI) regulation in the United States and explore policy arrangements in the context of Sino-U.S. AI competition is crucial for China's participation in global AI governance and its pursuit of global technological leadership. However, existing research has predominantly focused on macro-level comparative analyses of national AI policies, with limited exploration into state-level AI policy comparisons. The U.S. regulatory policy framework, shaped by its federal political system, exhibits significant complexity and fragmentation. This unique structure offers valuable insights for policymakers in other countries navigating the complex and rapidly changing artificial intelligence regulatory policy development settings. Using Latent Dirichlet Allocation (LDA), Dynamic Topic Modeling (DTM), and content analysis, this study conducts an in-depth examination of 397 policy texts from 36 U.S. states, illustrating state-level regulatory preferences, policy impact areas, and shifts in policy focus over time. Along with the aforementioned, the study also compares the regulatory proximity of China's artificial intelligence policy to that of the United States at the state level. The results show that the U.S. has established a foundational AI regulatory policy system, characterized by seven policy topics and four core policy instruments. The seven policy topics include consumer privacy protection, public opinion and election, fintech regulation, public-private data governance, advisory committee building, automated decision-making fairness and system security and effectiveness. Among these, system security and effectiveness emerge as the most aligned topic between Chinese and U.S. AI regulatory frameworks. Beyond the seven policy topics, four key policy tools were adopted by states in the U.S., including checklist documentation, mandatory disclosure requirements, prohibition and risk classification. Artificial intelligence regulatory committees in U.S. states fall into two categories: comprehensive committees, like those in Oregon, Maryland, and Indiana, addressing broad AI issues; and specialized committees, such as New Mexico’s group on automated decision-making and New York’s panel on AI, robotics, and automation. The study also identifies that states such as California, Illinois, and New York exhibit the most diverse distribution of policy topics. In terms of regulatory proximity to China, Hawaii, Oklahoma, and New York rank highest, with technologically advanced states like California and Massachusetts also featuring prominently. Finally, drawing on the complexity of the artificial intelligence regulatory policy system in the state of the U.S. and the need for artificial intelligence development and regulation, it is recommended that China adopt policy innovation approaches that are tailored to regulatory scenarios and enrich the scenario hierarchy of the regulatory system, deconstruct the entire system into components and improve the details of the subject of regulatory policy, prioritize compatibility and symbiosis to align the value of policy tools, and emphasize collaborative efforts to enhance agile effectiveness in artificial intelligence regulatory decision-making. The study presents the overall landscape of artificial intelligence governance arrangements in the United States and measures the proximity of Sino-US regulation policies. This could serve as a vital reference for Chinese policymakers, providing evidence-based recommendations to guide the innovation and refinement of AI regulatory policies. It is critical for aligning China's regulatory strategies with global best practices, so increasing its ability to reduce risks, encourage ethical AI development, and boost its leadership in the global AI governance environment.