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Assisted training using upper limb rehabilitation robots is beneficial for flaccid paralysis patients in recovering their functional abilities. In the assisted training mode, the patient’s motor ability is limited by factors such as limb muscle tension, and it is prone for the rehabilitation robot to deviate from the prescribed training trajectory. A sliding mode control method based on a fixed time observer is proposed to address the problem of delayed trajectory tracking response of upper limb rehabilitation robots caused by external disturbances such as patient limbs.

First, aiming at the problem of estimating and compensating for external disturbances in the upper limb rehabilitation robot system, a fixed time observer was designed based on the robot’s dynamic model. Second, the composite sliding mode reaching law combining the smooth function and the power-exponential function is proposed to shorten the convergence time of system states in the startup phase, thereby reducing chattering in the control process and realizing the real-time tracking of the training trajectory by the control system.

The proposed method provides a solution for the trajectory tracking speed of upper limb rehabilitation robot controllers. In the circular trajectory tracking control, compared to the sliding-mode control method combined with the variable-exponential composite reaching law based on the fixed-time observer, the method in this paper reduces the time for the system state to reach the sliding surface by 0.89 s and improves the response speed by 0.66%.

The composite sliding mode approach law based on smooth function and power exponent function can reduce the time it takes for the system state to reach and remain on the sliding surface and improve the trajectory tracking speed of upper limb rehabilitation robots. This controller improves the accuracy of trajectory control and ensures the robustness of auxiliary rehabilitation training.

In the practice of live streaming e-commerce, the consumer demand is usually uncertain, and the inventory and prices can be decided by brand owners or streamers. To this end, this study examines the inventory and pricing decisions of the brand owner and streamer in a live streaming e-commerce supply chain under demand uncertainty.

In this study, four scenarios are considered, i.e. the brand owner determines the inventory and price (Scenario BB), the brand owner determines the inventory and the streamer determines the price (Scenario BS), the streamer determines the inventory and the brand owner determines the price (Scenario SB), and the streamer determines the inventory and price (Scenario SS).

The results show that the inventory and prices, as well as the profits of the brand owner and streamer increase with the consumer sensitivity to streamer’s sales effort level under the four scenarios. The inventory (price) is the highest under Scenario SS (SB), while that is the lowest under Scenario BB (BS). In addition, when the sensitivity is low, the brand owner’s profit is the highest under Scenario BB, otherwise, the profit is the highest under Scenario SS. Regardless of the sensitivity, the streamer’s profit is always the highest under Scenario SS.

Few studies focused on the inventory and pricing decisions of brand owners and streamers in live streaming e-commerce supply chains under demand uncertainty, while this work bridges the research gap. This study can provide theoretical basis and decision support for brand owners and streamers.

This paper aims to investigate the vertical cooperative relationship between the core enterprise and the manufacturer within the platform ecosystem, specifically analyzing the optimal decision-making processes of both parties under the original equipment manufacturer (OEM) and original brand manufacturer (OBM) modes.

This paper uses game theory to analyze the problem, considering factors such as brand value difference, cross-selling and platform empowerment. It constructs the game models for both OEM and OBM modes and discusses the selection strategies for the cooperation mode.

The results indicate that the choice of cooperation mode by the manufacturer and the core enterprise depends on the relative size of their brand values. In cases of inconsistent choices, cooperation can be improved by designing a transfer payment contract. When the brand value is constant, the product price is comprehensively affected by cross-selling revenue, price elasticity coefficient, cost coefficient of sales effort and cost coefficient of platform empowerment. The enterprise reduces the price only when the potential revenue brought by increasing product sales exceeds the marginal profit brought by increasing product pricing; otherwise, it raises the sales price.

The platform ecosystem is emerging as a future direction for business mode development. However, there is a paucity of research on the cooperation modes between manufacturers and core enterprises within the platform ecosystem.

The main purpose of this study is to examine the effect of corporate digital transformation on bond credit spreads. Additionally, it also explores the two potential channels, information asymmetry and default risk, through which digital transformation can influence bond credit spreads.

We use the bond issuance data of Chinese listed companies over the period 2008–2020. Corporate digital transformation of these companies is measured with textual analysis of the management discussion and analysis part of annual reports. We employ a panel regression model to estimate the effect of digital transformation on bond credit spreads.

We find robust evidence that companies with higher digital transformation experience lower bond credit spreads. We further observe that credit spread reduction is higher for firms that are smaller, non-state-owned, have lower credit ratings and have less analyst coverage. We also find evidence that digital transformation reduces credit spreads by reducing the information asymmetry between firms and investors with enhanced information transformation mechanisms and lowering corporate default risk by strengthening operating efficiency.

To the best of our knowledge, this study is the first attempt to understand the impact of corporate digital transformation on bond credit spreads. Our findings help to understand the effect of digital transformation on firms’ credit worthiness and access to capital.

ESG issues are gaining increasing attention from investors, but the environmental, social and governance (ESG) rating disagreement caused by different standards of rating agencies misleads investors' investment decisions. This can lead to an increased risk of stock price crashes, causing turbulence in the financial markets and reducing investors' confidence. The paper investigates whether ESG rating disagreement of the current period increases stock price crash risk and the mechanism to mitigate this impact.

With the sample of the listed companies of Shanghai and Shenzhen Stock Exchanges from 2010 to 2022 this paper examines the impact of ESG rating disagreement itself on stock price crash risk. Moreover, this paper examines the mechanisms by analyzing the moderating effect of distraction of investors; digital economy and corporate intelligence maturity.

This paper finds that ESG rating disagreement itself would amplify the stock price crash risk. When exploring the moderating effect of institutional investors' distraction, digital economic development level and corporate intelligence, the paper found that they would mitigate the impact of ESG rating disagreement on stock price crash risk. The relationship between ESG rating disagreement and stock price crash risk is more pronounced in the context of heavily-polluted, state-owned enterprises (SOEs) and enterprises with star analysts.

Currently, few articles discuss ESG rating disagreement, especially the impact of current ESG rating disagreement on stock price crash risk. This paper focuses on this topic and provides strategies to mitigate the impact of current ESG rating divergence on stock price crash risk.

This paper aims to reveal the green governance role played by stock connect in transition economies from the perspective of corporates’ environmental violations and provides implications for the coordination and optimization of subsequent stock market liberalization and green transformation policies in pursuit of carbon peaking and carbon neutrality goals.

With the data of Chinese listed enterprises, this paper takes the Shanghai-Hong Kong Stock Connect or Shenzhen-Hong Kong Stock Connect in China as a quasi-natural experiment and applies the multi-period difference-in-difference (DID) model to identify the impact of stock market liberalization on the corporates’ environmental violations.

The findings reveal that the stock market liberalization significantly restrains the corporates’ environmental violations. These findings are robust to a series of sensitivity tests, including excluding two-way effects, adjusting the year of policy implementation, replacing the core variables, introducing the regional fixed effects and excluding the interference effect of other relevant policies during the sample period. Furthermore, the stock market liberalization is beneficial for upgrading information disclosure quality, improving internal governance capability, strengthening environmental protection incentives, and thus restrains corporates’ environmental violations. Meanwhile, heterogeneity tests show that the inhibitory effects are more significant in those grouped samples which is large scale, state-owned nature, located in eastern region, with poor evaluation performances and heavy tax burden.

We make two marginal contributions to the current literature. First, this paper enriches the literature on the factors influencing corporate environmental violations by focusing on how the macro-level financial policy influences the micro-level corporate environmental violations. One the one hand, prior studies mainly focused on the consequences of corporate environmental violations; however, there is still a puzzle that the effect of stock market liberalization cannot be fully justified to influence corporate environmental violations. The findings help explain this puzzle by examining that stock market liberalization can restrain corporate environmental violations. Moreover, prior studies mainly focused on corporate share price (Yunsen Chen et al., 2022), market liquidity (Han Kim and Singal, 2000), information disclosure (Liang, Lin, and Chin 2012), corporate governance (Bae and Goyal, 2010) and corporate violations (Lingyun Xiong et al., 2021), but not on corporate environmental violations. We assume that the suppression effect of stock market liberalization on corporate environmental violations can help reduce corporate environmental violations, improve corporates’ awareness of environmental compliance. Second, this paper contributes to a better understanding of the literature on stock market liberalization by investigating the restraining effect of Stock Connect on corporate environmental violations from the perspective of information channel, corporate governance channel and motivation channel, which is of practical significance. Moreover, we investigate the differences in the inhibitory effects of stock market liberalization on different enterprises' environmental violations, from firm size, property rights, enterprise assessment results, tax burden to geographical location, which is conducive to the construction of a green financial system and the promotion of sustainable economic development. Our results show that firms which are large scale, state-owned nature, located in eastern region, with poor evaluation performances and heavy tax burden tend to compliance with environmental laws. These findings emphasize the importance and benefits of Stock Connect.

Promoting electric vehicles (EVs) is an effective way to achieve carbon neutrality. If EVs are widely adopted, this will undoubtedly be good for the environment. The purpose of this study is to analyze the impact of network externalities and subsidy on the strategies of manufacturer under a carbon neutrality constraint.

In this paper, the authors propose a game-theoretic framework in an EVs supply chain consisting of a government, a manufacturer and a group of consumers. The authors examine two subsidy options and explain the choice of optimal strategies for government and manufacturer.

First, the authors find that the both network externalities of charging stations and government subsidy can promote the EV market. Second, under a relaxed carbon neutrality constraint, even if the government’s purchase subsidy investment is larger than the carbon emission reduction technology subsidy investment, the purchase subsidy policy is still optimal. Third, under a strict carbon neutrality constraint, when the cost coefficient of carbon emission reduction and the effectiveness of carbon emission reduction technology are larger, social welfare will instead decrease with the increase of the effectiveness of emission reduction technology and then, the manufacturer’s investment in carbon emission reduction technology is lower. In the extended model, the authors find the effectiveness of carbon emission reduction technology can also promote the EV market and social welfare (or consumer surplus) is the same whatever the subsidy strategy.

The network externalities of charging stations and the subsidy effect of the government have a superimposition effect on the promotion of EVs. When the network effect of charging stations is relatively strong, government can withdraw from the subsidized market. When the network effect of charging stations is relatively weak, government can intervene appropriately.

Comparing previous studies, this study reveals the impact of government intervention, network effects and carbon neutrality constraints on the EV supply chain. From a sustainability perspective, these insights are compelling for both EV manufacturers and policymakers.

This study examines how default bias, driven by the default positive review (DPR) rule – which automatically classifies reviews not provided by consumers within a specified period as positive – and rebate bias, associated with the conditional rebate strategy (CRS), where sellers offer rebates exclusively to consumers who submit positive reviews, distort the distribution of online product reviews over time and impact consumer satisfaction.

A key aspect of our method lies in developing latent variable models that capture the relationship between biased online reviews and consumer satisfaction levels. By applying our models to a panel dataset from Taobao – a leading Chinese e-commerce platform – and using insights from online consumer feedback surveys, we assess the extent of the biases introduced by DPR and CRS in a given feedback system. A hierarchical regression model was employed to investigate the impact of the proposed biases on consumer satisfaction.

Consumers who have previously written online reviews experience satisfaction outcomes 72.9% of the time with DPR and up to 81.3% when CRS is included. Implementing DPR may boost product sales to some extent, but it would significantly amplify consumer dissatisfaction, whereas offering a rebate could effectively alleviate consumer discontent, even though the rebate is conditional.

Our findings reveal the extent of biases introduced by CRS and DPR in online reviews and inform the consumer satisfaction debate regarding the phenomenon of excessive positive reviews resulting from these practices.

This study aims to address the problem of low-temperature wave soldering in industry production with Sn-9Zn-2.5 Bi-1.5In alloys and develop qualified process parameters. Sn–Zn eutectic alloys are lead-free solders applied in consumer electronics due to their low melting point, high strength, and low cost. In the electronic assembly industry, Sn–Zn eutectic alloys have great potential for use.

This paper explored developing and implementing process parameters for low-temperature wave soldering of Sn–Zn alloys (SN-9ZN-2.5BI-1.5 In). A two-factor, three-level design of the experiments experiment was designed to simulate various conditions parameters encountered in Sn–Zn soldering, developed the nitrogen protection device of waving soldering and proposed the optimal process parameters to realize mass production of low-temperature wave soldering on Sn–Zn alloys.

The Sn-9Zn-2.5 Bi-1.5In alloy can overcome the Zn oxidation problem, achieve low-temperature wave soldering and meet IPC standards, but requires the development of nitrogen protection devices and the optimization of a series of process parameters. The design experiment reveals that preheating temperature, soldering temperature and flux affect failure phenomena. Finally, combined with the process test results, an effective method to support mass production.

In term of overcome Zn’s oxidation characteristics, anti-oxidation wave welding device needs to be studied. Various process parameters need to be developed to achieve a welding process with lower temperature than that of lead solder(Sn–Pb) and lead-free SAC(Sn-0.3Ag-0.7Cu). The process window of Sn–Zn series alloy (Sn-9Zn-2.5 Bi-1.5In alloy) is narrow. A more stringent quality control chart is required to make mass production.

In this research, the soldering temperature of Sn-9Zn-2.5 Bi-1.5In is 5 °C and 25 °C lower than Sn–Pb and Sn-0.3Ag-0.7Cu(SAC0307). To the best of the authors’ knowledge, this work was the first time to apply Sn–Zn solder alloy under actual production conditions on wave soldering, which was of great significance for the study of wave soldering of the same kind of solder alloy.

Low-temperature wave soldering can supported green manufacturing widely, offering a new path to achieve carbon emissions for many factories and also combat to international climate change.

There are many research papers on Sn–Zn alloys, but methods of achieving low-temperature wave soldering to meet IPC standards are infrequent. Especially the process control method that can be mass-produced is more challenging. In addition, the metal storage is very high and the cost is relatively low, which is of great help to provide enterprise competitiveness and can also support the development of green manufacturing, which has a good role in promoting the broader development of the Sn–Zn series.

This study examines the effect of local government debt (LGD) on corporate earnings management using 25,624 firm-year observations from 2007 to 2019.

Pooled ordinary least squares (OLS) regression is used to examine the impact of LGD on earnings management. A difference-in-differences (DID) method is also used to alleviate potential endogeneity.

Results show that LGD motivates firms to increase earnings management, especially income-decreasing earnings management. Findings are robust to DID method and robustness tests. Heterogeneity analyses show that the positive effect of LGD on earnings management is pronounced in firms with political dependence and moderated by external governance mechanisms. Further discussions indicate that tax enforcement is an underlying channel for LGD to affect earnings management. Firms engage in downward real earnings management by increasing their abnormal discretionary expenditures and higher LGD leads to a greater book-tax difference in those firms that manipulate income-decreasing earnings management.

This study contributes towards examining the political costs hypothesis, the microeconomic effects of LGD and the determinants of earnings management.