ABSTRACT
Objectives. Although some studies have shown role stressors can lead to unsafe behaviors, it is unclear how role stressors induce delivery riders' unsafe behaviors. We found that delivery riders suffered from tremendous role stressors during the delivery process and had to conduct frequent smartphone interactions. This study aimed to explore the effects of role stressors and smartphone interactions on delivery riders' unsafe behaviors. Methods. First, a questionnaire survey (N = 326) was used to collect data, and correlation and regression analyses were conducted to explore the relationship between role stressors, smartphone interaction frequency and delivery riders' unsafe behaviors. Second, a scenario survey (N = 35) was conducted, and comparative analysis was used to further explore how smartphone interactions affect delivery riders' unsafe behaviors. Results. The questionnaire survey revealed that role stressors, smartphone interaction frequency and delivery riders' unsafe behaviors were positively correlated. In addition, the role stressors forced delivery riders to conduct necessary and unnecessary smartphone interactions. The scenario survey found that smartphone interactions reduced delivery riders' motion speed and motion ability, and increased their psychology, so they had a risk-taking mentality, which led to an increase in unsafe behaviors.
ABSTRACT
Population aging poses challenges to the immature elderly care service system in many countries. The strategic behaviors of different participants in the provision of elderly care services in a long-term and dynamic situation have not been well studied. In this paper, an evolutionary game model is developed to analyze the strategic behaviors of two types of participants-the government sectors and the private sectors in provision of elderly care services. Firstly, eight scenarios are analyzed, and the evolutionary process and stable strategies are identified. Then, the behavioral strategies of the two types of participants under demand disturbance and dynamic subsidy strategy are analyzed. Simulation experiments are conducted to explore the influence of different initial conditions and parameter changes on the evolutionary process and results. The obtained observations are not only conducive to a systematic understanding of the long-term dynamic provision of elderly care services but also to the policymaking of the government.
Subject(s)
Biological Evolution , Private Sector , Aged , Aging , Government , Humans , Policy MakingABSTRACT
As the world's largest energy production and demand country, China's energy security is a hot issue concerned by the whole society. Most existing research focused on China's energy security performance from the point of view of the country. But, as a huge geographical country, provinces energy security performance varies hugely in different regions. So, this paper aims at evaluating Chinese provinces energy security, analyzing reasons and providing policy implications. Firstly, a comprehensive evaluation criteria system, including four dimensions: energy supply, energy using, energy economy and energy environment, is proposed. The criteria consist of 14 indicators. Secondly, Mahalanobis-Taguchi Gram-Schmidt is presented to obtain criteria weights, which not only considers subjective and objective information, but also eliminates the overlap information in criteria. Thirdly, considering that TOPSIS method ignores the correlation between the two distances (alternatives to ideal and to negative ideal), an improved TOPSIS model with set pair analysis is proposed to assess the China's regional energy security performance from 2013 to 2017. From the results, 30 provinces' energy security performance improves in general, but there is still a huge gap among different regions. The north reaches of yellow river and northwest regions are the most energy-secure, while the northeast and central regions are least energy-secure. SHAANXI, INNERMONGOLIA and SHANXI are always top three in the ranking, while the energy security performance of NINGXIA is the lowest.
