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The number of farms engaged in grain production in China has been declining in recent years. Limited efforts have been devoted to examine why producers quit from grain production and how such exits affect China's grain output. Such information, however, is invaluable in understanding whether the exit from grain production should be encouraged and if so, how. The purpose of this paper is to identify the factors that influence farmers' decision to quit from grain production, with a view to drawing implications for devising policies to deal with such exits.

Both descriptive statistics and econometric techniques are used to analyse a set of unique and comprehensive farm‐level survey data to identify key factors that affect farmers' decision to quit from grain production.

Key factors that influence a farm to quit from, or stay in, grain production include: family size, the share of farming labour out of total family labour, per capita arable land, the proportion of land used for grain production, the share of family income from grains. It was also found that the level of grain prices and the sunk cost in farming, chiefly in grain production, also affect the likelihood that a household will stay or exit from grain production. Further, farmers in more economically developed regions are more likely to quit from grain production.

The paper's findings clearly indicate that farms with a larger scale of grain production and earning higher income from grain are the major contributors to China's grain production. Potential exists for China to raise its total grain output if the land from those exiting farmers is readily made available to larger producers, enabling them to further benefit from the economies of scale.

Digitally driven virtual streamers are increasingly utilized in live-streaming commerce, possessing distinct advantages compared to human streamers. However, the applicable scenarios of virtual streamers are still unclear. Focusing on product attribute variances, this paper compares the livestreaming effects of virtual and human streamers to clarify the applicable scenarios for each and assist companies in strategically choosing suitable streamers.

We conducted four experiments utilizing both images and video as stimulus materials, and each experiment employed different products. To test the proposed model, a total of 1,068 valid participants were recruited, encompassing a diverse group of individuals, including undergraduates and employed workers.

The results indicate no significant difference between virtual and human streamers in increasing consumers’ purchase intention for utilitarian products. In contrast, human streamers are more effective in enhancing consumer purchase intention for hedonic products, with a mediating role of mental imagery quality. Consumers’ implicit personality variances also influence their willingness to accept virtual streamers.

This paper is the first to compare the effects of virtual and human streamers in promoting different products to enhance our comprehension of virtual streamers. Given the potential risks associated with human streamers, a comprehensive understanding of the role of virtual streamers is imperative for brands when deploying live-streaming commerce activities.

This paper aims to propose a general framework of shared control for human–robot interaction.

Human dynamics are considered in analysis of the coupled human–robot system. Motion intentions of both human and robot are taken into account in the control objective of the robot. Reinforcement learning is developed to achieve the control objective subject to unknown dynamics of human and robot. The closed-loop system performance is discussed through a rigorous proof.

Simulations are conducted to demonstrate the learning capability of the proposed method and its feasibility in handling various situations.

Compared to existing works, the proposed framework combines motion intentions of both human and robot in a human–robot shared control system, without the requirement of the knowledge of human’s and robot’s dynamics.