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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.

This study aims to establish a model for rapid and accurate emotion recognition in restaurant online reviews, thus advancing the literature and providing practical insights into electronic word-of-mouth management for the industry.

This study elaborates a hybrid model that integrates deep learning (DL) and a sentiment lexicon (SL) and compares it to five other models, including SL, random forest (RF), naïve Bayes, support vector machine (SVM) and a DL model, for the task of emotion recognition in restaurant online reviews. These models are trained and tested using 652,348 online reviews from 548 restaurants.

The hybrid approach performs well for valence-based emotion and discrete emotion recognition and is highly applicable for mining online reviews in a restaurant setting. The performances of SL and RF are inferior when it comes to recognizing discrete emotions. The DL method and SVM can perform satisfactorily in the valence-based emotion recognition.

These findings provide methodological and theoretical implications; thus, they advance the current state of knowledge on emotion recognition in restaurant online reviews. The results also provide practical insights into intelligent service quality monitoring and electronic word-of-mouth management for the industry.

This study proposes a superior model for emotion recognition in restaurant online reviews. The methodological framework and steps are elucidated in detail for future research and practical application. This study also details the performances of other commonly used models to support the selection of methods in research and practical applications.

Start-ups in emerging markets, entrepreneurship, business models and strategy.

The case is suitable for MBA and EMBA graduate and undergraduate students in strategic management, finance and the relevant areas.

This case provides a real-life entrepreneurial situation in agricultural industry in China. The protagonist of the case is the founder and CEO of the start-up Harvest Agricultural Technology and Development Company Limited (Harvest). From his perspective, the case depicts the current business environment for private companies in China, and presents the opportunities and challenges a new start-up faces in this environment. Agricultural industry plays an important role in the Chinese economy. Especially because in China land is owned by the state or collective, agricultural industrialization has more significance and experiences greater difficulties. The company in the case explores the situation of integrating the different stakeholders of agricultural production and delivery given the current political and economic environment. The case describes the characteristics and quality that a typical Chinese entrepreneur has and questions why such factors matter so much in China. The case emphasizes the strategic planning process of Harvest and its unprecedented business model design. The case also touches upon the growth pattern of entrepreneurial companies in China. All the above issues deserve discussion and in-depth analysis.

After studying this case, students should be able to: describe the business environment in China and identify the stakeholders of the agricultural industry in China; describe the process and value chain of agriculture production and delivery by adopting management models if necessary; discuss the personality and quality of the founder and CEO and compare his characteristics with that of western entrepreneurs and analyse why these characteristics are helpful (or detrimental) to the start-up company; analyse the development of business model designs, and identify the merits, drawbacks and risks of each version of business model; analyse the competitive advantages of Harvest, and identify the key resources and capacities with management models if necessary; discuss different possibilities of Harvest's future with evidence and process analysis; discuss whether the business model and the development strategy of Harvest are applicable to other companies or industries; discuss how setting the goal of going public on the first day Harvest was founded will affect the development of the company; and compare the business models of Harvest with other companies serving as a platform in a different industry (i.e. Taobao marketplace).

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With the development of e-commerce and mobile payment, platform sales become unstoppable, and many manufacturers also encroach on online market by establishing direct selling channels. Channel conflict intensifies in online market and quality differentiation and is widely used in business practice as an effective way to alleviate such a competition. The authors study a retail platform's sales strategy and interactions with an upstream manufacturer's encroachment strategy in this paper. Unlike most online marketplace and encroachment research, product quality selection is also engaged in the present research to capture the motivation above.

The authors analyze a game-theoretical model that the platform as the first/second mover participates in strategic decision-making, and then jointly decides the product quality level with manufacturer.

The authors find that encroachment always profits the manufacturer and almost hurts the platform. Interestingly, the first-mover advantage can help the platform guide the manufacturer encroachment and promote a “win–win” situation when product quality level is relatively slight or obvious. Nevertheless, the second-mover advantage can help the platform alleviate the profit loss caused by encroachment when product quality level is moderate. Furthermore, suffered from encroachment loss, the platform can make a credible threat by sales termination to restrain manufacturer encroachment.

This paper innovatively explores the strategic interaction between manufacturer encroachment and quality differentiation in a platform supply chain, and further analyzes the first-mover advantage in this interaction, which fills the gaps of previous platform research and has great significances to enterprise production and operational decision in business practice.

The purpose of this paper is, from a resource accumulation and resource allocation perspective, to examine the variant effects of government subsidies among firms with varying levels of market power and to test how industry competition moderates the relationship between market power and allocative efficiency of government subsidies.

This study explores the relationship between government subsidies and firm performance from a resource-based view. The authors study the moderating role of market power and three-way interaction between subsidy, market power and industry competition on firm performance. The authors test their hypotheses using a sample of Chinese A-share manufacturing firms from 2006–2019. The authors apply firm-level panel data regressions and conduct a series of robustness tests. The marginal effect of market power and industry competition is explored via three-way moderator effect models.

This study finds that government subsidies are negatively related to firm performance. Market power, on average, strengthens the negative effect of government subsidies on performance, but such a reinforcement effect is neutralized when industry competition is intense. Government subsidies are least efficiently used when firms have market power and industry competition is low. In addition, the authors use different forms of firm performance and a various of robustness tests to verify their assumptions.

This paper contributes to the literature as follows. First, the authors look into subsidy–performance problem from the perspective of the resource-based view and contribute to explaining and mitigating the divergence of current findings on the subsidy–performance relationship. Second, the authors introduce market power and industry competition as moderators to study how resource allocative efficiency affects the subsidy–performance relationship. Third, the authors propose that managerial incentives have played an important role in the allocation of government subsidies, which enriches management practices.