The issue of environmental protection and sustainable development is a key research focus across multiple fields. Employee green behavior is considered to be an important micro-activity to address this. Researchers in the field of organizational behavior and sustainable development have been focusing on the influencing factors of employee green behavior. However, few have explored the beneficial effects of employee green behavior on behavioral implementers. The objective of this study is to investigate the relationships among employee green behavior, self-esteem, perceived organizational support for employee environmental efforts, and employee well-being, and to explore a new dimension of employee green behavior. We empirically examined the underlying framework by conducting two surveys to collect data from 900 employees working in manufacturing, construction, and the service industry in China. We performed multilevel path analysis using SPSS and AMOS software, and confirmed that employee green behavior includes four dimensions: green learning, individual practice, influencing others, and organizational voices. Further, employee green behavior has a significant positive impact on self-esteem, which in turn is converted into employee well-being. Finally, perceived organizational support for employee environmental efforts not only positively moderated the relationship between employee green behavior and self-esteem, but was also confirmed as a moderated mediation model. This study enriches the current literature on the measurement framework and variables of employee green behavior.

Background: With the emergence of the gig economy as a new economic form, the influence of algorithmic technology control on gig workers’ perceptions and engagement has become a topic of academic concern. This study explores the emotional impact of perceived algorithmic control on gig workers and how it affects their work engagement. Methods: This study takes gig workers as the research object to build a structural equation model. Based on the background of gig economy and the Job Demands-Resources model, this paper constructs a mechanism model of the influence of perceived algorithmic control on the work engagement of gig workers. The research data in this paper are collected by questionnaire, and the research hypothesis is tested by the SEM structural model. Results: The gig workers in this study believed that perceived algorithmic control positively affects employee work engagement. In addition, burnout was positively correlated with employee work engagement. Burnout played a partial mediating role in the relationship between perceived algorithmic control and employee work engagement. And flow experience played a moderating role through the indirect effect of burnout on employees’ work engagement. Conclusion: Perceived algorithmic control causes burnout among gig workers, but strong algorithmic technology support provides them with rich work resources that can help them meet their work needs. That is, the gig workers may still demonstrate a high level of work engagement even if they experience burnout symptoms.

Risk management is an essential way for farmers to reduce uncertainty. In this research, a stratified random sampling method was used to survey 350 maize farmers in four different agro-ecological regions in Bangladesh. Using the multivariate probit model, this study explored the possible correlation between farmers’ perceptions of catastrophic risks and their attitudes towards risk sources—as well as the possible correlation between contract farming, diversification and precautionary savings as risk management strategies. The results confirm the relevance of risk management adoption decisions and reveal that the use of one risk management tool may simultaneously influence the use of another risk management tool. In addition, the research results also show that age, education level, extension experience, monthly household income, farming areas, land ownership and risk aversion nature are the most important factors that affect the adoption of risk management strategies. The research results provide further explanation and information and provide a platform for decision-makers to predict appropriate risk management strategies.

In the backdrop of growing public concerns about climate change and air pollution, and the contribution of petroleum combustion in the transport sector to these issues, China has introduced a range of policy incentives for promoting the uptake of electric vehicles (EVs). These incentives have resulted in a rapid increase in EV stocks since the early 2010s, and this trend is expected to continue, or even accelerate, in the years to come, which would, in turn, lead to a policy-oriented EV market. The primary objective of the project is to develop an understanding of the effect of various policy incentives on EV uptake based on annual data of 43 cities from 2011 to 2017, and these policies mainly refer to those that focus on EV purchasing, registering, driving, and charging stages. The novelty of this paper is that we focused on policy effect on EV adoption behavior, rather than adoption intention which has widely been analyzed in previous studies. Results showed that the abolishment of purchasing and driving restriction, government subsidies, purchase tax exemption, and dedicated license plates significantly promote EV diffusion, and the effect of the two restrictions are more powerful. Thus, this study suggested that abolishment of purchasing and driving restrictions can be insisted to drive EV market share, meanwhile improving policies that can guide consumers through various motivations are also necessary.

In order to mitigate energy consumption and greenhouse gas emission in the transportation sector, countries around the world have generally adopted electric vehicles (EVs) as a new development direction of the automobile industry. Although the Chinese government has issued a series of incentive policies to promote EVs, the ownership of EVs is still insufficient due to low public purchasing enthusiasm. Thus, to better realize the promotion goal of EVs, public preference for EV incentive policies is worth investigating. Based on a large sample survey (N = 1039), this study investigated public preference for various incentive policies by using the conjoint analysis method. The results suggest that less than one third of consumers have a better understanding of the incentive policies, while more than half of the consumers know little about these policies. For consumers, the relative importance of different policy categories is ranked as follows: charging incentive policies, driving incentive policies, vehicle registering incentive policies, and purchasing incentive policies. As for different socio-demographic groups, consumers aged 26–30 years, with a monthly income higher than RMB 20,000, with high school, special secondary school, and masters (or above) educational levels regarded the relative importance of driving incentive policies as the highest; consumers from two-member families ranked purchasing incentive policies as the first one; consumers with a monthly income of RMB 15,001–20,000 and those from three-member families place registering incentive policies first; other consumers put charging incentive policies first. Based on the above results, this paper offers policy recommendations for improving consumer knowledge level of incentive policies as well as full consideration of their policy demands.