R&D investment is important for achieving innovative development and industrial upgrading. Based on the perspective of industry R&D competition, this paper explores the internal mechanism of dynamic evolution of the optimal interval for firms' R&D investment by constructing a dynamic optimization model including R&D barrier point and R&D saturation point, and using the time-varying threshold model for empirical tests. We find that firms have an optimal interval of R&D investment that depends on the R&D level of their peers. As the R&D level of the industry increases, the lower bound (R&D barrier point) of the optimal interval rises significantly, while the upper bound (R&D saturation point) decreases significantly. The mechanism analysis reveals that the dynamic evolution of firms' optimal R&D investment interval is influenced considerably by the "Red Queen effect." The risk consequence analysis further shows that achieving the optimal R&D interval enhances a firm's risk-bearing capacity and reduces the likelihood of financial distress. This paper expands the theoretical connotation of industry innovation ecology shaping enterprises' R&D decision-making, and has important implications for leading enterprises to implement an innovation-driven development strategy.

In the era of digital economy, the digital platform ecosystem has reshaped traditional value creation methods and market competition models. Based on the perspective of business model portfolio, this paper summarizes the formation mechanism and evolution path of digital platform ecosystem. Research finds that the evolution of digital platform ecosystem covers three stages, including business model construction in the initialization stage, business model superposition in the aggregation stage, and business model synergy in the diversification stage. During this process, each stage relies on different value chains, such as establishing basic value propositions, building a three-dimensional value network, promoting composite value co-creation, and achieving sustainable value sharing. On this basis, the study clarifies that the continuous evolution of the digital platform ecosystem is driven by underlying drivers of digital infrastructure such as data elements, digital technology, and modular platform architecture. Finally, this paper combines activity system view, business model portfolio, and digital platform ecosystem evolution into an organic whole connected by value flow to construct a theoretical model of digital platform ecosystem evolution driven by both platform capability and market demand, and provides theoretical basis and practical guidance for enterprises to successfully build a digital platform ecosystem.

Against the backdrop of the accelerated development of new quality productive forces, data elements have evolved into a strategic resource and are gradually becoming a core driving force for socio-economic transformation. Although the importance of data elements is increasingly recognized, relevant domestic research remains in the early stages, with a general lack of systematic and comprehensive analysis, which hinders the effective realization of their potential value. In response to this gap, this paper systematically reviews the current research progress in the field of data elements, summarizes key limitations in existing studies, and proposes a three-dimensional analytical framework of "value formation-market allocation-system governance". Based on this framework, and in consideration of China's practical context and future strategic needs, the paper further develops a theoretical framework for the coordinated development of data elements. The aim is to provide theoretical support and guidance for future academic research in this field, promote the construction of a more mature data elements market system, and facilitate the sustainable prosperity and high-quality development of the digital economy.

Driving green development through digitalization is the only way to promote the construction of ecological civilization in China. Based on the data of Chinese listed companies from 2010 to 2022, this paper uses the establishment of a comprehensive big data pilot zone as a quasi-natural experiment to empirically examine its impact on the green transformation of enterprises from the perspectives of environmental performance, economic performance, and green innovation. It is found that the comprehensive big data pilot zone has a positive impact on enterprises' environmental performance, economic performance and green innovation, thus leading to green transformation. Based on the external perspective, it can be seen that the comprehensive big data pilot zone promotes the green transformation of enterprises by enhancing external attention and promoting the agglomeration of regional information service industries; based on the internal perspective, it is found that the big data comprehensive pilot zone promotes the green transformation of enterprises by enhancing their management capabilities and optimizing their human capital structure. Heterogeneity analyses reveals that for enterprises with higher environmental investment and non-state-owned enterprises, the big data comprehensive pilot zone exhibits a stronger facilitating effect on enterprise green transformation. From an industry perspective, the promoting effect of environmental performance is mainly concentrated in heavily polluting industries and environmentally regulated industries, while the promoting effect for economic performance and green innovation is reflected in non-heavily polluting industries and non-environmentally regulated industries. This paper not only reveals the effect of digital economy policies in the green transformation of enterprises, but also provides policy insights for the government to grasp the opportunities of digital economy development and promote digitalization-driven green transformation.

The Yellow River Basin, as an important ecological and economic region in China, plays a crucial role in promoting the transformation and upgrading of regional economy. Given the challenges from both resource depletion and environmental pressure, building an innovative ecosystem has become a key path for achieving high-quality development of resource-based cities in the Yellow River Basin. Through fuzzy set qualitative comparative analysis, this paper deeply explores the complex causal relationships of the innovative ecosystem in resource-based cities in the Yellow River Basin. The research finds that the high-quality development of the innovative ecosystem in resource-based cities in the Yellow River Basin is influenced by the synergy of multiple factors. The specific paths include:"low optimization - light governance type", that is, an adaptable development path where both industrial optimization, digitalization level, and environmental governance are at a relatively low level; "strong control - weak innovation type", a development path that compensates for the lack of technological innovation and digitalization under high government regulation, industrial optimization, and environmental governance; "high control - high governance type", an innovation-driven path that mitigates the negative effects of excessive population concentration through high government regulation, industrial optimization, and environmental governance; and "high development - low ecological type", a balanced development path with high technological innovation, high government regulation, and high digitalization level, but with relatively weak population aggregation and environmental governance. The study analyzes the collaborative mechanism of resource allocation in the construction of the innovative ecosystem, providing scientific references and practical inspirations for the sustainable development of resource-based cities in the Yellow River Basin.

Agricultural entrepreneurship serves as a significant driving force in building China into an agriculturally powerful country of its own characteristics. Drawing upon 621 agri-entrepreneurship cases broadcast in the "Wealth Creation" programme of CCTV's Agricultural and Rural Channel, through text analysis and structured data processing, and based on the framework of "attitude-behavior-environment interaction⇒performance", this paper analyzes and examines the effects of multi-level entrepreneurial orientations on agri-entrepreneurship performance. The empirical evidence shows that attitude-level risk orientation, behavior-level innovation orientation, and social-environment-interaction-level social orientation all have positive value contributions to agri-entrepreneurship performance. Among them, the contribution of social orientation is the most significant and robust, while the contributions of risk orientation and innovation orientation are sensitive to survivorship bias. However, nature-environment-interaction-level green entrepreneurial orientation adversely affects agricultural entrepreneurial performance. This paper believes that this is the result of "bad money drives out good" in the green agricultural market with high information asymmetry. Further tests reveal that the rural government provides one-on-one support effects that not only strengthen the performance contributions of various entrepreneurial orientations, but also turn the negative influence of green orientation into a positive one. Meanwhile, local leading enterprises, functioning as an "invisible hand", improve entrepreneurial performance by guiding agricultural entrepreneurs to strengthen their entrepreneurial orientations. This study provides insights for "new generation farmers" in designing entrepreneurial orientations, recognizing their own shortcomings, and seeking external support.

This paper examines the impact of labor outsourcing on enterprise innovation. The research finds that labor outsourcing significantly promotes enterprise innovation. Mechanism tests reveal that labor outsourcing can enhance enterprise R&D innovation and core competitiveness through three channels:improving labor allocation efficiency, adjusting personnel structure, and mitigating risks. Heterogeneity analysis shows that the above effects are more pronounced for enterprises in high-tech and highly competitive industries, small-scale enterprises, and those with low levels of physical investment and market position. Further research indicates that the innovation promotion effect of labor outsourcing leads to an increase in enterprise total factor productivity and cost markups. This paper provides evidence on whether labor outsourcing can promote enterprise innovation and high-quality development, which is of great significance for promoting enterprise high-quality development and achieving the national innovation-driven development strategy.

Taking the science and technology talent policies of 9+2 cities in Guangdong-Hong Kong-Macao Great Bay Area from 2001 to 2020 as research object, this paper describes the characteristics and evolution patterns of the synergy among policy departments, objectives and tools through descriptive statistics and quantitative analysis of policy texts, and then constructs a production function model and applies ridge regression analysis to study the mechanism of how the synergy between policy objective and policy tool influences regional innovation performance. Based on the research, some findings are as follows. Firstly, with the promotion of the strategy on developing a quality workforce, the number of science and technology talent policies issued in Guangdong-Hong Kong-Macao Great Bay Area has increased overall, but there are regional differences. Secondly, policy objectives are not structurally balanced, as reflected in great importance attached to talent quality and efficiency but little attention paid to the flow and scale of talents; the use of policy tools is also unbalanced, with environmental policy tools used insufficiently. Thirdly, the cooperation among policy publishing departments strengthens, resulting in enhanced synergy of policy objectives and a trend towards comprehensive utilization of various policy tools. Fourthly, the impact of different policy objectives and tools on regional innovation performance has significant directional differences, and not all synergies are conducive to improving regional innovation performance. The research conclusions are meaningful for promoting the effective allocation of scientific and technological talent resources in the Guangdong-Hong Kong-Macao Greater Bay Area, for the government to formulate precise talent policies, and for promoting regional coordinated development and international competitiveness.

This study investigates the impact of digitalization on collaboration innovation efficiency between universities and firms. Using the matched data on provincal digitalization levels in China and the collaboration innovation efficiency of "Double First-class" universities from 2011 to 2017, we employ multi-dimensional econometric regression models to test our hypotheses. The results demonstrate that digitalization significantly improves the collaboration innovation efficiency between universities and firms. Digitalization can enhance the collaboration innovation efficiency by optimizing university-firm coupling coordination levels. Heterogeneity analysis indicates that, digitalization has a positive effect on the collaboration innovation efficiency between universities and firms in the eastern region in comparison with the western region. Digitalization plays a bigger role in enhancing the collaboration innovation efficiency for universities with weaker R&D capabilities than it does for those with stronger R&D capabilities. The research findings offer valuable insights for exploring effective strategies to enhance the collaboration innovation efficiency between universities and firms. These results also serve as a useful reference for government agencies to actively plan the strategic layout of digital development initiatives.

Against the challenging backdrop of global climate warming, consumer behavior plays a critical role in achieving carbon neutrality goals. Based on discrete choice experiments, this study uses Mixed Logit models and Latent Class Logit models to comprehensively analyze consumers' heterogeneous preferences and influencing factors regarding corporate carbon neutrality strategies (carbon reduction and carbon offsetting). The findings reveal that consumers generally show stronger preferences for carbon reduction strategies compared to carbon offsetting approaches, particularly among those with strong personal responsibility and high environmental awareness. When both strategies are available, environmentally conscious consumers, female respondents, and older demographics demonstrate marked preferences for combined carbon reduction-offsetting strategies over singular approaches. Similarly, consumers with higher levels of understanding of both strategies tend to avoid choosing carbon offsetting alone. The research provides a new perspective for understanding the behavioral mechanisms underlying consumers' responses to corporate carbon neutrality strategies, offering empirical evidence for enterprises to develop effective decarbonization strategies that align with consumer preferences and sustainable development requirements.

Eye contact is a key capability that robot develops in its evolution from a machine to a human-like agent. This study, through four experiments, demonstrates that service robots' direct gaze, as opposed to averted gaze, notably increases customer favor. This is because direct gaze is better able to impress customers with robot's warmth and competence. The research also indicates that in utilitarian scenarios, the perceived competence of the robot has a more pronounced influence on customer favor, whereas in hedonic scenarios, perceived warmth assumes greater significance. Additionally, this research examines the relationship between the degree of robots' anthropomorphic appearance and its effects, revealing that higher anthropomorphism may reduce the positive impact of direct gaze on customer favor. These insights not only advance the understanding of robot gaze behavior and its influence on customer interaction but also provide practical guidance for businesses in deploying service robots and designing service strategies.

When purchasing products in the live streaming room, consumers can not only gain a more intuitive understanding of product characteristics and performance through real-time video, but also consider the evaluation and attitude of the reference group as a crucial factor for decision-making. In the context of live streaming, the real-time reference group primarily influences users' purchase behavior by digitally displaying danmaku comments. Existing studies have predominantly examined reference groups within the context of reality, network communities, and online shopping platforms. However, there is still a need to explore how real-time reference groups during e-commerce live streaming influence consumers' purchasing behavior through danmaku. This study uses Python programming to extract 1,064,613 danmaku text data from 22 live broadcasts of the "Make a Friend" room on the TikTok platform, and collects relevant product data from the "Huitun" platform to explore the influence of real-time reference group on consumers' purchasing behaviors, while considering the moderating role of product types. The results show that the diversity, activeness, and sentiment of danmaku have an inverted U-shaped relationship with consumers' purchasing behavior. In terms of informational influence, search products strengthen this inverted U-shaped relationship compared to pseudo-experience products and experience products. In terms of normative influence, experience products strengthen this inverted U-shaped relationship compared to search products and pseudo-experience products. This study enhances the application scenarios of reference groups, extends the boundaries of research on e-commerce live streaming, and offers practical guidance for operating e-commerce live streaming.

In the context of the "dual carbon" goal, improving employees' capacity for green service innovation has become increasingly vital for the service industry to meet the growing demand for environmentally conscious consumption. Drawing on attention restoration theory, this study employs a laboratory experiment and a field survey to examine the mediating role of green mindfulness and the moderating effect of green performance appraisal in the relationship between biophilic work design and green service innovation behavior. The results indicate that:(1) Biophilic work design significantly promotes employees' green service innovation behavior; (2) Green mindfulness mediates the relationship between biophilic work design and green service innovation behavior; and (3) Green performance appraisal negatively moderates the effect of biophilic work design on green mindfulness, and further negatively moderates the indirect effect of green mindfulness in the relationship between biophilic work design and green service innovation behavior. This study not only advances the theoretical understanding of how biophilic work design drives employees' green service innovation behavior but also offers important practical insights for organizations to better implement biophilic work design.

The improvement of enterprise carbon performance hinges on the synergistic effects of executives' environmental cognition and organizational environmental strategy. Based on the upper echelons theory and cognitive psychology, this study uses a sample of Chinese A-share listed companies in energy-intensive industries from 2010 to 2022 to systematically examine the relationships between executives' environmental cognition (cognition), environmental strategy (behavior), and enterprise carbon performance, as well as the moderating effects and economic consequences of their complementarity. The findings reveal that both executives' environmental cognition and environmental strategy significantly improve the carbon performance of energy-intensive enterprises. Moreover, executives' opportunistic environmental cognition and proactive environmental strategy, as well as executives' responsible environmental cognition and reactive environmental strategy, have significant complementary effects, which can enhance the carbon performance of energy-intensive enterprises. The moderating effects suggest that political embeddedness and regulatory distance can strengthen the complementary effect between executives' opportunistic environmental cognition and proactive environmental strategy, but weaken that between executives' responsible environmental cognition and reactive environmental strategy. The economic consequence tests show that the improved carbon performance, driven by the complementary effect of executives' environmental cognition and organizational environmental strategy, can help enterprises enhance environmental reputation, secure environmental subsidies, and mitigate environmental penalties. This study broadens the research on enterprise carbon performance by introducing the "cognition and behavior unity" perspective, offering valuable empirical insights and practical guidance for promoting the green transformation of energy-intensive enterprises.

Based on emergence theory, through a research on the relationship between leader-subordinate creativity assessment matching and organizational routine updating, we construct a bottom-up organizational routine updating theoretical model, focusing on the influence of leader-subordinate employee creativity assessment matching on organization path mechanisms and boundary conditions for routine updating. Employing polynomial regression and response surface analysis, we conduct a multi-source field study with 262 leader-subordinate paired data to test our theoretical model. It is found that employee creativity is a key factor driving organizational routine updating; Meanwhile the results reveal that the lower congruence between employee creativity assessment by employees themselves and that by their leader, the more positive effect on organizational routine updating; In the case of aligned assessment of employee creativity, high assessment by both employees themselves and their leader is better able to promote organizational routine updating than low assessment by both employees themselves and their leader; In the case of non-aligned assessment of employee creativity, low assessment by employees themselves but high assessment by their leader is better able to promote organizational routine updating than high assessment by employees themselves but low assessment by their leader; Furthermore, employees' bootleg innovation acts as an indirect mechanism linking the (in) congruence in leader-subordinate creativity assessment to organizational routine updating. A higher level of entrepreneurial orientation amplifies this indirect effect by strengthening the positive influence of bootleg innovation on organizational routine updating. The research discusses the top-down organizational routine updating mechanism from the perspective of employees, and provides a theoretical explanation for the phenomenon that employee innovation emerges from the bottom up as organizational routines, which provides new ideas for the research on organizational routine updating and broadenes the perspective to the impact mechanism and situation of organizational routine updating.

The impact of algorithmic control on platform workers' behavior has attracted increasing attention from both industry and academia. However, in management practice, platforms often face the dilemma of the algorithmic control paradox. Drawing on rational choice theory, this study examines the double-edged sword effect of perceived algorithmic control on platform workers' compliance behavior from a cost perspective. Using three-wave time-lagged survey data from 465 platform workers, the analysis reveals that perceived algorithmic control, on the one hand, promotes compliance behavior by increasing the cost of violations (perceived behavioral monitoring and punishment expectancy); on the other hand, it inhibits compliance behavior by increasing the cost of compliance (work impediment and loss of control). Moreover, work impediment and loss of control respectively exert negative moderating effects on the relationships between perceived behavioral monitoring and compliance behavior, and between punishment expectancy and compliance behavior. This study enriches the theoretical understanding of the algorithmic control paradox and offers managerial implications for enhancing the positive effects of algorithmic control while mitigating its negative impacts.

The intensification of global trade tensions and protectionism has accelerated the restructuring of supply chains and industrial networks. Multinational manufacturing firms, under the pressure of tariff barriers and trade regulations, are faced with strategic choice regarding supply chain relocation. Relocating production to countries with lower tariffs has become a viable strategy to mitigate high tariff pressures, but this requires compliance with rules of origin and value-added constraints. This study constructs a three-level cross-border supply chain model using game theory to analyze how tariff policies influence multinational firms' decisions. The results show that firms can benefit from relocating production to third countries only when the tariff differential between preferential and original tariffs reaches a moderate level. Small tariff differentials provide insufficient motivation. However, when the tariff gap becomes significant, multinational manufacturing firms must decide whether to relocate their production base, considering both the tariff gap and rules of origin constraints. Additionally, product substitutability and market size significantly affect decisions. Less competitive products are more suited for supply chain restructuring, while larger markets raise the threshold for tariff differentials. This study highlights the core mechanisms of how tariff policies impact supply chain restructuring and provides practical guidance for firms navigating complex international trade environments.

When a product receives increasingly negative online opinions from the public, its producer will suffer trust crisis, which will then spill over into the supply chain system, thus undermining the sustainable development of the supply chain. Based on the perspective of blockchain traceability, this paper reveals the interaction mechanism between trust repair and cooperation, constructs three cooperation models:SG (collaborative), NC (division of labor) and QI (division of labor and sponsorship), explores their applicable conditions, and gives the optimal equilibrium strategy. The research shows that the SG cooperation strategy is optimal under the influence of public opinion. Although the QI cooperation strategy can encourage suppliers to make more efforts in quality improvement, the overall profit of the supply chain will decline. From a dynamic point of view, the product quality level and the goodwill level eventually tend to be stable, but the goodwill level tends to be stable later. The supply chain equilibrium strategy is more sensitive to consumers' traceability preferences, but the deeper the influence of public opinion, the weaker the positive impact of blockchain traceability. Supply chains should dynamically adapt evolutionary game strategies based on public opinion intensity. During normal periods, they should establish close collaboration and resource-sharing mechanisms to create synergistic effects that jointly enhance market demand; during crisis periods, they should incentivize suppliers to improve quality and reduce excessive reliance on reputation repair. It is crucial to be vigilant about marginal cost constraints under high-impact scenarios, achieving a dynamic equilibrium between supply chain resilience enhancement and operational efficiency through optimized resource allocation. This study provides new micro-empirical evidence and paradigmatic support for supply chain resilience governance theories in the digital economy era.

In recent years, the research and application of the agent-based modeling and simulation method in supply chain risk management have become increasingly deeper. By adopting the agent-based modeling (ABM), scholars in the field of supply chain risk management can simulate the agent behavior in the supply chain in response to different risk events, establish simulation models to demonstrate the dynamics of supply chain operations, and thus deduce the impact of risk events on the supply chain, evaluate the effectiveness of risk management strategies, identify potential sources of risk, and predict the consequences of different risk events. This paper reviews the development of ABM and the current status of supply chain risk management research based on three significant types of supply chain risks (environmental, network, and organizational risks). The main research content and problems of combining ABM with different risks are elaborated. Finally, this paper proposes to pay attention to the internalization of risk factors in ABM, explore the combination of ABM with other methods, and strengthen the resilience simulation research of industrial-chain-and-supply-chains.

Aiming at the distributed green flexible job shop scheduling problem with deterioration effect, a high-dimensional mixed integer programming model is constructed to minimize makespan, the total energy consumption, the total machine load and the total delay time. An improved NSGA-III algorithm is proposed. To represent the problem reasonably, a parallel three-layer encoding approach based on factories, operations and machines is designed. A multi-objective joint optimization method is used to initialize the population. In the decoding stage, a greedy insertion decoding method with machine deterioration is designed. The strategy of dynamically controlling cross-mutation parameters is designed to improve the optimization efficiency of the algorithm. Five kinds of neighborhood search mechanisms based on critical factories and critical paths are proposed to improve the local search ability of the algorithm. Finally, simulation experiments based on multiple scale cases are conducted to verify the superior performance of the improved algorithms for solving the problem.

Overcoming the "bottleneck" constraints in the high-end equipment manufacturing industry chain is crucial for China to achieve technological self-reliance and strength. Based on the Multiple Helix Innovation Theory, this paper takes the high-end equipment manufacturing enterprise Beijing Jingyi Equipment Technology Co., Ltd. as the research object to explore its pathway and mechanisms for breaking through core key technologies and achieving domestic substitution. By constructing a theoretical analytical framework for hard-tech enterprise innovation, it extends and enriches the Multiple Helix Innovation Theory. The paper clarifies the new ecological relationships within the multi-level sci-tech finance ecosystem of "universities-industry-government-venture capital-banks-other financial institutions-capital market" that drive the development of hard-tech enterprises. This provides an in-depth analysis for the sustainable innovation of high-end equipment manufacturing enterprises and also offers reference and new perspectives for similar enterprises to achieve breakthroughs in key core technologies.

Solidly promoting the process of common prosperity has become an important mission of China in its pursuit of modernization. Enterprises play a crucial role in promoting shared value creation and achieving common prosperity. They actively prioritize low-income groups and make indispensable contributions to fostering regional inclusive development. Based on a study of Yitai North Pastoral, this paper obtains data through the "three-step coding method", and reveals the internal mechanism of digital technology enabling inclusive innovation in enterprises. The research finds that:(1) The identification of available resources and the reconstruction of resources serve as pivotal mechanisms for inclusive innovation within enterprises. Digital technology enables three core aspects:industrial strategic allocation (through smart planting systems), business model design (via breeding support service systems), and platform collaboration (by means of online marketing systems) to facilitate inclusive innovation within enterprises. (2) Enterprises enhance the efficiency of "resource reconstruction" by leveraging such digitally-driven capabilities as control capability (digital monitoring and feedback for accurate decision-making), collaboration capability (digital production traceability management for scientific breeding practices), and response capability (encompassing product demand forecasting to gain market insights), thus promoting inclusive innovation practices. The conclusions not only enrich the theory regarding inclusive innovation but also provide valuable managerial inspiration for Chinese enterprises seeking to harness the power of digital technology in empowering their own inclusive innovation practices.