Managerial and Decision Economics最新文献

Existing research has paid little attention to the impact of tax enforcement independence on corporate financial asset allocation. To fill this gap, we use the merger of the State Tax Bureau and Local Tax Bureau as an exogenous shock, and our DID analysis shows that greater tax enforcement independence reduces corporate financial asset holdings. The mechanism analysis shows that the effect is primarily driven by intensified liquidity constraints and enhanced corporate information disclosure. Moreover, the effect is stronger for firms with greater financing constraints and weaker corporate governance, particularly affecting short-term financial assets.

Participation in the global value chain is crucial for integrating small and medium enterprises (SMEs) into the global economy. However, participation alone does not guarantee success. Other factors may catalyze true internationalization. The digital economy and advancements in information and communication technologies have enabled SMEs to remain competitive. By leveraging digital technologies like cross-border e-commerce (CBEC), SMEs can overcome stagnation at the lower end of the value chain and enhance international market performance. Compared with traditional entry modes, CBEC offers high control and low costs, making it an effective tool for overcoming barriers to internationalization. To investigate how CBEC aids SMEs in ascending global value chains, we developed hypotheses based on organizational learning theory and tested them on a panel dataset of 781 SMEs. Regression analysis results indicate that engaging in CBEC significantly improves an SME's position in the global value chain. We also identified two mediating factors: the firm's entrepreneurial orientation and its ability to reconfigure value chains. These findings underscore the role of CBEC in enhancing the global value of SMEs, offering valuable insights for academics and SME managers alike.

Green-biased technological progress is a critical pathway for achieving low-carbon development. This study investigates how command-and-control and market-based low-carbon regulations influence green-biased technological progress from the perspective of policy combinations, providing policy recommendations for reducing global carbon emissions and designing efficient policy synergy pathways. The findings reveal a significant synergistic effect between command-and-control and market-based low-carbon regulations. Compared with single-policy approaches, synergistic regulations more effectively promote green-biased technological progress. The conclusion is robust across various tests. Mechanism analysis indicates that the synergistic regulations primarily enhance green-biased technological progress by augmenting firm green investment intensity and facilitating industrial structure upgrading. Furthermore, command-and-control and market-based low-carbon regulations exhibit asymmetric impacts on green-biased technological progress in cities with different characteristics. Compared to market-based low-carbon regulations, command-and-control low-carbon regulations have a more pronounced effect in cities facing greater fiscal pressure, higher proportions of state-owned assets, and a larger share of high-energy-consuming industries. These findings provide actionable insights for maximizing the combined effects of synergistic regulations, accelerating the green-biased technological progress dividend through targeted collaborative governance models, and enhancing the diversified implementation of heterogeneous low-carbon regulations.

The information aggregation operator in a fuzzy environment effectively reduces information errors and enhances the accuracy of decision-making. In this paper, considering the preference of decision makers (DMs) for dense distribution of evaluation information, we introduce density intermediate operators and propose interval-valued spherical fuzzy density intermediate operators, which include the interval-valued spherical fuzzy density weighted arithmetic average (IVSFDWAA) operator and the interval-valued spherical fuzzy density weighted geometric average (IVSFDWGA) operator, to achieve effective aggregation of information and study their related properties. In addition, the distance measure and entropy theory in the interval-valued spherical fuzzy environment are extended, and the weight of attributes is determined by comprehensive fuzzy information entropy. On this basis, a three-stage multi-attribute group decision-making method combining TOPSIS is proposed. Finally, an example is provided to illustrate the effectiveness of the proposed method, and its sensitivity and comparative analysis are carried out.

This paper investigates the impact of artificial intelligence (AI) on the technological innovation (TI) of enterprises and explores its mechanisms of action based on data from Chinese enterprises. This study finds that AI significantly promotes enterprise TI. The reliability of these conclusions is validated through instrumental variable regression and robustness tests. Key mechanisms through which AI enhances enterprise innovation include total factor productivity (TFP) improvement, human capital structure optimization, and digital transformation. The level of internal R&D focus within enterprises plays a positive moderating role in the incentive effects of AI on TI. AI can not only enhance the quantity of technological innovation but also promote the diffusion of technology. Enterprises in the service industry exhibit stronger effects. Therefore, it is recommended that governments increase support for AI technologies, promote the integration of traditional industries, and improve talent training mechanisms. This would allow AI to play a more positive role in fostering enterprise TI and driving high-quality economic development.

This paper investigates the interplay between a firm's product quality investment and subsequent product quality information acquisition. We consider two quality information acquisition scenarios: non-transparent acquisition scenario and transparent acquisition scenario, depending on whether the quality information acquired by the firm is observable to the public. We show that under the non-transparent acquisition scenario, the firm's equilibrium quality investment decision and quality information acquisition decision are strategic complements, and these decisions can significantly affect consumers' inferences on product's quality level. More importantly, we uncover that in comparison with the scenario where firm's quality information acquisition is transparent to the consumers, when the quality information acquisition is non-transparent to the consumers, the firm chooses a higher quality investment level and has more incentive to conduct the acquisition of precise quality information by incurring an extra cost after the quality investment decision-making, which eventually leads to an increase in consumer surplus but may result in a decrease in social welfare.

This study investigates principal-agent coopetition (PAC) by developing a comprehensive stochastic dynamical framework that captures the intricate interactions and associated risks between principals and agents in uncertain environments. The research emphasizes the importance of dynamic incentive strategies, focusing on key factors such as equity structure, driving capital, risk aversion degree, and task complexity. By defining and analyzing the dual risks of cooperation and competition within the PAC process, we introduce a novel performance evaluation metric, coordination at risks (CaR), to assess the effectiveness of these strategies. Through extensive numerical simulations, our results highlight the critical need to balance these risks to optimize PAC performance. The study provides actionable insights and practical guidelines for designing dynamic incentive mechanisms that not only align the interests of principals and agents but also mitigate potential risks, fostering sustainable cooperation and enhanced organizational performance in principal-agent relationships.

This study explores the impact of oil price uncertainty on working capital management for 21,051 nonfinancial Indian firms throughout 2000–2023. This study is based on the panel data set of 154,550 firm-year observations over 23 years from 2000 to 2023. We have proceeded with a fixed effect panel data regression model and robustness analysis to arrive at the results. Using the working capital ratio and industrial-adjusted WCR as two measurements of working capital management, we find that working capital management is positively associated with oil price uncertainty. Moreover, the intensity of this impact is subject to firm heterogeneity. Financially constrained and publically listed firms are exposed to greater risk during times of uncertainty. Our findings suggest that such firms must hold greater short-term capital to mitigate the associated risks. The originality of our research can be summed up into two aspects: First, our study is the first in the literature that explicitly investigated the intricacies of oil price uncertainty on working capital management. Second, our research expands the existing literature by probing in-depth analysis at various firm-specific heterogeneity levels and is probably among the rare studies that leverage extensive datasets to provide a thorough view of the perspective.

Driven by strategic direction, strategic emerging industry convergence clusters are a complex evolutionary process of increasing technological innovation levels and optimizing industrial structure through cross-sectoral organizational collaboration and cooperation among various stakeholders. Given the dynamic and long-term nature of this process, alongside factors such as technological heterogeneity and integration capabilities, we use a differential game approach to compare the optimal innovation strategies across three scenarios: centralized decision-making, Stackelberg leader–follower, and Nash noncooperative game models. This analysis explores how different innovation entities within strategic emerging industry clusters can coordinate and cooperate to achieve converged cluster development. The results indicate that (1) innovation levels in convergence clusters and the returns of individual actors are lowest under the Nash noncooperative game model, followed by the Stackelberg leader–follower model. The optimal strategy for convergence cluster development is centralized decision-making and collaborative development. (2) While technological heterogeneity inhibits the benefits of innovation entities, technological integration capabilities increase them. Additionally, the growth of convergence clusters is more strongly impacted by technical heterogeneity, with higher levels of heterogeneity having a negative impact on their development. (3) Under centralized decision-making, government subsidies have the strongest incentive effect; nevertheless, as compared to other characteristics, their influence on increasing convergence cluster returns is weaker. Findings here may provide theoretical support for enhancing innovation efficiency and promoting strategic emerging industry convergence clusters.