Managerial and Decision Economics最新文献

Advancing new quality productivity (NQP) is essential to deviate from the traditional paths of productive forces and economic growth. However, further research is needed to uncover effective development strategies for NQP. By applying dual machine learning (DML) on panel data for 282 Chinese cities from 2009 to 2022, this study examines how digital economy (DE) influences NQP. Several key findings emerge from the analysis. First, China's NQP has considerable potential for improvement, with the development of new factors and technologies being the main barriers. Second, DE positively influences the advancement of NQP as confirmed by various robustness checks. Third, DE promotes NQP by promoting green innovation, facilitating human resource agglomeration, and improving urban informatization levels. Fourth, the effects of DE on NQP are heterogeneous. From a regional heterogeneity perspective, DE aids NQP advancement across the eastern, central, and western regions, while from an economic heterogeneity perspective, the DE in the Yangtze River Delta, central Yangtze River, and Beijing–Tianjin–Hebei economic areas significantly drives NQP progress. Theoretically, this study innovatively applies the DML method to examine how DE influences NQP, thereby effectively addressing endogeneity concerns and deepening the present theoretical understanding of their mechanisms and heterogeneity. Practically, this study provides policy insights for governments to design differentiated DE strategies and promote regionally coordinated development.

With the rapid development of the digital economy, digital industry agglomeration has become a key driver of firm innovation. This study develops a theoretical framework distinguishing between complex innovation and simple innovation from the perspective of data elements and examines the mechanisms through which digital industry agglomeration affects innovation. Using panel data of Chinese A-share listed firms from 2013 to 2021, we find that digital industry agglomeration significantly promotes firms' overall innovation, as well as both complex innovation and simple innovation. Moreover, firms' absorptive capacity positively moderates this relationship, with a stronger effect on complex innovation than on simple innovation. The innovation-enhancing effect of digital industry agglomeration is more pronounced in regions with lower marketization, varies with the intensity of environmental regulation—promoting complex innovation under stricter regulation and simple innovation under looser regulation—and is stronger in regions with higher government intervention. In addition, digital industry agglomeration generates significant positive spatial spillovers that enhance innovation in neighboring regions. These findings provide new theoretical and empirical evidence on the relationship between digital industry agglomeration and firm innovation, highlighting the importance of fostering digital industry agglomeration and strengthening firms' absorptive capacity to stimulate innovation.

Amid global economic restructuring, enterprise digital transformation has emerged as a critical strategy for enhancing competitiveness and upgrading industrial structures. As a major policy instrument to foster the digital economy, the effects of China's smart city pilot program on enterprise digital transformation remain insufficiently understood. Drawing on data from Chinese A-share listed firms over the period 2007–2024, this study employs a multiperiod difference-in-differences (DID) design to evaluate the policy's impact and to investigate its underlying mechanisms and heterogeneity. The findings indicate that the smart city initiative significantly accelerates enterprise digital transformation, primarily through three production factors: talent agglomeration, technological innovation, and capital support. Heterogeneity analyses further reveal that the effects differ across firms, industries, institutional contexts, and regions. These results provide new insights for optimizing smart city policy design to promote more effective digital transformation of enterprises.

We revisit the managerial delegation game by considering the optimal weight placed on the rival's profit under relative performance evaluation with an overconfident manager. In economic studies, the effect of biased managers is investigated in several situations with the delegation game. In contrast to the classical studies where owners set a negative weight on rivals' profit under quantity competition, we demonstrate that the owner who hires an overconfident manager may set a positive weight to avoid excessive supply under specific conditions. Additionally, overconfidence improves consumer surplus and social welfare in specific economic environments by increasing the supplied quantities. Our results suggest the bright and dark sides of overconfident managers with relative performance evaluations.

This study employs a Stackelberg game-theoretic framework to analyze three scenarios of product quality disclosure: honest disclosure of product quality to both consumers and influencers; deceptive disclosure of the actual quality to both influencers and consumers by brand manufacturers; and collusion between brand manufacturers and influencers in misleading consumers about the true quality. By modeling these scenarios using a multistage decision-making game, equilibrium solutions for price, promotional effort, and penalties are derived. The findings indicate that low-quality brand manufacturers encounter both “honesty” and “collusion” situations when marketing through single channels, whether self-operated or via micro influencers. Mega influencers in hybrid channels can generate positive spillover effects that benefit self-operated live streams more than low-quality ones. When the power structure shifts, the operational strategy of truthfully disclosing product quality to consumers manifests a more distinct long-term stability advantage. This study makes a significant contribution to the selection of market channels under conditions of information asymmetry, while also exploring strategies to enhance spillover effects through the adoption of hybrid channels.

The application of autonomous vehicles (AVs) offers a promising avenue to address the profitability challenges of ride-hailing platforms. However, in a mixed operational model combining human drivers (HDs) and AVs, the high operating costs of AVs raise concerns about their feasibility for large-scale commercial deployment and the design of optimal pricing strategies. To address these issues, this study develops a cooperative game-theoretic model involving three parties: a ride-hailing platform, an AV manufacturer, and a jointly established company. We first examine changes in platform profitability before and after AV application to identify the conditions under which adopting AVs becomes viable. We then compare the payoffs of all market participants under different pricing strategies to determine the optimal scheme that facilitates multiparty cooperation. The results indicate that introducing AVs can strengthen user-platform stickiness and reduce wage costs for HDs. However, the platform is incentivized to adopt AVs only when the operating cost coefficient is sufficiently low. Moreover, once this cost falls below a certain threshold, a premium pricing strategy yields higher platform profitability and enables a win–win–win outcome among the platform, joint company, and manufacturer. This study advances the literature on economic decision-making for AV deployment in the ride-hailing sector and offers theoretical and practical guidance for pricing strategies and partnership design in the era of intelligent mobility.

Numerous e-commerce marketplaces are establishing their own resale platforms. Some e-commerce marketplaces gain profits from resale transactions, while others use the resale platform as a traffic pool for their new products. For example, Taobao.com places many advertisements for new products on its self-built resale platform, Idle Fish, which is called a promotion strategy. Considering the interaction between the two sources of profit, this study develops an analytical model to investigate how the e-commerce marketplace with a self-built resale platform strategically sets resale transaction commission and promotion strategy to maximize profit. The main conclusions are as follows. Firstly, the marketplace may set a low resale transaction commission when the sales price of new products is high when adopting no-promotion strategy, but it is completely reversed when the promotion strategy is used. Secondly, when the promotion strategy is adopted, the resale transactions will always decrease, but the market clearing price may increase. Lastly, the marketplace should adopt the promotion strategy when the sales price of new products is high, or the durability of products is low. Finally, we examine the impact of repurchase behaviour and the intrinsic impact of promotion on consumer valuation, the main conclusions derived from the baseline model remain valid under both extensions. This conclusion may explain why JD.com does not place any new product advertisements on its resale platform, Paipai.com, which focuses on the resale transactions of pricey products such as laptops and smart phones.

Forest ecological benefit compensation mechanisms are critical for aligning private landowner incentives with forest ecosystem service objectives. However, traditional area-based schemes in China neglect the additionality of the ecosystem services and fail to capture dynamic strategic interactions between policymakers and forest managers. To address this gap, this study develops a two-player evolutionary game model, in which the government chooses between equalization and compensation incentive compensation, whereas forest farmers decide whether to conserve. The model then analyzes the evolutionary stability of the strategy selection of the game players and explores the impacts of different parameter changes on the evolutionary stable strategy. The results show that the evolutionary stable states of the strategy selection of both parties in the game exhibit complexity due to different initial states. Increases in compensation fees, forest farmers' production revenue, and silvicultural capacity, as well as reductions in forest farmers' risk aversion coefficient, fairness preference coefficient, government free-riding coefficient, and information asymmetry coefficient, will promote the evolution of the system to the optimal stable equilibrium point. Notably, the forest farmers' conservation effort level under incentive compensation on system evolution presents a significant “inverted U-shaped” feature. This research provides a new analytical perspective and scientific basis for the optimal design of the forest ecological benefit compensation mechanism.

While the United States has imposed trade restriction policies on Xinjiang cotton from China, few studies have examined their economic impacts. This research theoretically clarifies the mechanism through which these restrictions affect the economic system and utilizes a global multiregional, multisector dynamic computable general equilibrium model to quantitatively assess their international economic impacts. Results show that the restrictions adversely impact both the Chinese and US economic growth and welfare. Initially, China's real GDP decreased by 0.0232%–0.0629%, with social welfare deteriorating by $2433–6678 million, while the US real GDP declined by 0.0202%–0.0217%, with welfare losses of $3886–4347 million. Over time, as Chinese products are substituted in the US and European markets, China's economic losses and welfare deterioration tend to intensify, while the US situation gradually improves. The Southeast Asian countries may benefit from this trade conflict, showing some economic growth and welfare improvements. The impacts vary across Chinese industries, with the textile and apparel sectors most severely affected. This paper offers a quantitative assessment of the international macroeconomic impacts of the Xinjiang cotton trade restrictions, revealing the gains and losses across countries and providing valuable empirical evidence for understanding the economic impacts of such trade restrictions.