Managerial and Decision Economics最新文献

This study investigates how elections affect banking efficiency in an African emerging economy (Ghana), by inferring from the political choice theory and further decoupling the ownership effects of elections on diverse efficiency proxies. The study employs Tobit regression models and instrumental variable models (Tobit and fixed effect) as robustness models with year control effects for 29 banks between 2000 and 2020. While banking efficiency in the forms of technical, pure-technical, scale, cost, allocative, and revenue is measured using DEA across private, state, listed, unlisted, domestic, and foreign banks, election is measured as a dummy. The study reveals that elections significantly lower banking efficiencies. However, the reducing effect of elections varies across types of banking efficiency indicators and ownership structures. Specifically, while elections adversely affect technical, cost, scale, allocative, and revenue efficiencies of banks, private, state, unlisted, listed, and foreign banks were mostly affected. These suggest that the decline in banking efficiencies during electioneering periods is attributable to political, policy, and fiscal uncertainties and differs significantly depending on bank ownership and efficiency types. While the study presents significant insights, future studies may investigate the effect of elections in phases to include the pre-, during-, and post-election phases to understand the temporal dynamics of election effects in other emerging economies other than Ghana. The results explain the need for political stability and transparency, fostered by electoral and policy reforms, to ensure continuity and a reduction in election-related inefficiencies. Also, bank managers must recognize and factor elections into their efficiency decision-making. To the best of our knowledge, this study is the first to provide empirical evidence on how elections affect the diverse array of banking efficiencies across different ownership types and nature in an African emerging economy context.

Employment is the cornerstone of people's livelihood. It is closely tied to the fundamental interests of the public and serves as the foundation of social well-being. Using the 2012 launch of the Intellectual Property (IP) Demonstration City pilot program as a quasi-natural experiment, this paper applies a multi-period difference-in-differences (DID) approach to empirically examine the impact of IP protection on enterprise employment size. The findings are as follows: (1) The IP Demonstration City pilot significantly promotes enterprise employment expansion through channels such as increased production, alleviated financing constraints, and enhanced physical investment, and the results are robust to various tests; (2) Heterogeneity analysis shows that the pilot has a significantly positive effect on the employment size of enterprises located in inland regions, in high-tech industries, and in areas with relatively weak IP protection environments; (3) Further analysis reveals that the pilot facilitates human capital upgrading and increases wage levels of workers in pilot cities, but has a negative effect on labor efficiency. The employment-promoting effect of IP protection is more pronounced in firms that receive higher government subsidies and tax incentives. This study provides theoretical and empirical evidence on how to encourage enterprises to expand employment and improve the quality and adequacy of jobs, offering valuable policy implications for the government to achieve stable and high-quality employment.

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.

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.

Effective near-miss reporting is important for achieving high reliability in safety management. To enhance the effectiveness of near-miss reporting, this paper examines the dynamic evolution of stakeholders' decision-making behaviors using coal mines as a case study. A tripartite evolutionary game model is developed, involving enterprises, management, and employees, based on prospect theory and mental accounting theory (PT-MA theory). Stability analysis and simulations under varying parameters yield the following insights: (i) A higher initial probability of active decision-making promotes positive strategy evolution, with sensitivity ranked as enterprise $�>$ management $�>$ employee. (ii) Near-miss reporting is mainly driven by cost considerations, with stakeholders' sensitivity to cost changes ranked as employee $�>$ management $�>$ enterprise. Lowering the perceived cost of active strategies and raising that of passive strategies encourages active decisions. (iii) A combined reward–punishment strategy is more effective than either alone in motivating employees and management. (iv) The psychological stress experienced by management when the enterprise adopts passive strategies is greater than that employees feel in response to management's passive strategies. Reducing such stress facilitates active strategy evolution. (v) Modifying reference points for perceived benefits and costs, reducing risk preference, and increasing sensitivity to loss aversion can facilitate active strategy evolution. This research offers practical implications for improving near-miss reporting management in the coal mine industry, including optimizing incentive mechanisms, implementing role-specific psychological interventions, and adjusting cost–benefit perceptions. These insights may also be applicable to other high-risk industries facing similar challenges, contributing to more effective and sustainable safety management practices.

The open-source platform emerges as a complex and dynamic system, shaped by the intricate interplay and interdependence of a diverse array of stakeholders. Existing studies mostly focus on micro-level aspects such as individual open-source entities, projects, or platforms, and rarely rise to the ecosystem level to conduct dynamic evolutionary analysis on the governance mechanisms of highly heterogeneous stakeholders within open-source platforms. This paper, therefore, constructs an evolutionary game model based on innovation ecosystem theory, with platform governors and complementors as the primary players. By quantifying the strategic choices of both parties under different profit scenarios, the study explores the system's stability characteristics. Additionally, MATLAB simulation analysis is used to examine the impact of parameter changes on the evolution of strategies and paths, elucidating the evolutionary stability of stakeholder strategies within the open-source platform ecosystem (OSPE). The study finds that: First, incentives for complementors can effectively drive their active contributions, but continuous strong stimuli will instead cause governors to strategically reduce their intervention inputs. Second, the exposure of potential risks from governance inaction has a corrective effect on the behaviors of both parties: It not only forces governors to turn to active performance of duties to safeguard the long-term value of the OSPE, but also prompts complementors to accelerate behavioral standardization based on direct constraint rules. Third, a high level of open knowledge sharing can create the dual dividends of a systematic reduction in contribution costs and a multiplier effect on the value of the governance ecosystem. This paper unpacks the governance mechanism from the stakeholders' perspective, providing a clearer understanding of the evolutionary dynamics of OSPE and practical insights for formulating its governance mechanisms.