The role of self‐regulation in the relationship between abusive supervision and job tension

Trait and state self‐regulation both have critical influences on workplace behavior, but their influences are thought to operate quite differently. We draw from social exchange and ego depletion theories to investigate the relationship between trait and state self‐regulation, as well as how they differentially affect the relationship between subordinates' perceptions of abusive supervision and job tension. Specifically, we examine (a) how the interaction between abusive supervision and trait self‐regulation affects job tension and (b) how state self‐regulation mediates the relationship between abusive supervision and job tension. Using 3 studies that include an experiment (n = 81) and 2 field studies with cross‐sectional (n = 157) and time‐separated (n = 109) data, we demonstrate that the interaction between abusive supervision and trait self‐regulation increases experienced job tension for subordinates who report higher levels of abusive supervision and trait self‐regulation than others. Also, we provide evidence that abusive supervision is indirectly associated with job tension through state self‐regulation. This study's findings have important implications for abusive supervision and self‐regulation research, as well as social exchange and ego depletion theories, because we extend our understanding of how trait and state self‐regulation affect cognitive responses associated with abusive supervision.     

基于机器学习的典型社会安全事件发生规律研究及对雄安新区的启示

针对雄安新区建设和发展过程中对社会安全事件的防控需求,以盗窃作为典型社会安全事件,提出基于机器学习模型的社会安全事件分析预测方法,并以A市2012—2016年的实际盗窃犯罪数据为基础,提取发案时间、发案地点、实施手段和损失金额作为分类特征,通过比较多种机器学习算法,研究盗窃前科人员的预测方法,并根据预测结果挖掘盗窃前科人员的作案规律。研究结果表明:随机森林算法表现最优,查准率、查全率和F1均达到了0.85以上;对于盗窃这一典型社会安全事件,其前科人员倾向于选择下午时段和人流量大的地区实施,盗窃金额明显高于初犯和惯犯。最后,基于前述研究,提出构建数据驱动的社会安全事件预测预警和综合研判系统,并针对该系统的前期建设和后期使用,给出“制定统一的数据格式”、“实现数据实时接入”的建议。相关研究成果可为雄安新区社会安全事件预测预警以及治安防控工作的开展提供参考和借鉴。     

Mixed oligopoly,foreign penetration,and environmental taxes

This paper examines the impact of foreign penetration on the public firm in a mixed oligopolistic market. Through the establishment of a mixed double oligopoly market, this paper analyzes how the share of foreign investment affects the environmental policy, the pollutant emission, and the social welfare under the condition of the state tax. The results show that: first, the introduction of foreign investment has some crowding-out effects on the social level of output. Second, the entry of foreign investment increases the profit of the public firm by a large margin, and the maximum profit of the private firm has been reduced. Third, the increase of foreign investment in public firm does not necessarily improve the environment.     

How can social enterprises contribute to sustainable pro-poor tourism development?

“Tourism-Assisting the Poor” has been an important approach in anti-poverty strategy. Studies suggest that the conventional tourism model fails to generate benefits for the poor and to some extent enlarges the gap between the poor and the rich, which subsequently causes more social problems. Pro-poor tourism (PPT) is regarded as an alternative model. Precisely targeted assistance to the poor is the key to initiating this model, while the institutional change and implementation mechanism are essential to run it successfully and sustainably. Social entrepreneurship focusing on social values while adopting modern business philosophy has been introduced recently as an instrument in helping disadvantage population. This paper takes the Smangus community in Taiwan as an example to explore the role of social entrepreneurship and social enterprises in PPT development. It also discusses their implications for the anti-poverty practice in other regions including ethnic areas in China.     

The Anti-Fracking Movement in Ireland: Perspectives from the Media and Activists

This article explores social mobilization in the controversy over hydraulic fracturing (i.e. “fracking”) in Ireland from the perspectives of agenda setting in national and local Irish newspapers as well as framing by anti-fracking activists and journalists. We analysed all articles referencing hydraulic fracturing (and related terms) from 11 Irish national newspapers and 128 Irish local newspapers from April 2013 to April 2014, and interviewed 19 journalists, activists, and government officials affiliated with fracking issues in Ireland. Based on the analytical salience and resonance across prominent empirical themes in conjunction with theoretical insights, we conclude that social mobilization against fracking in Ireland is challenged by a frame war on the credibility of activists, diverse economic interests across national and local scales, and the need for procedural legitimacy in the contribution of science. In conclusion, this research demonstrates the importance of agenda setting and framing in structuring the capacity for social mobilization.     

Assessing community vulnerability to floods and hurricanes along the Gulf Coast of the United States

Hurricanes and flooding have affected millions of people and generated massive economic losses over the past several decades. Geographic information system (GIS) methods are employed in this paper to analyse coastal communities' vulnerability to these two hazards along the Gulf Coast of the United States. Specifically, two types of quantitative indicators are developed: (i) exposure to hurricanes and flooding, based on information from multiple sources; and a social vulnerability index, constructed using census data. These indices are combined to depict the spatial patterns of overall community vulnerability to hurricanes and flooding along the US Gulf Coast. The results of this study can potentially inform disaster management agencies, county governments, and municipalities in areas at heightened risk. Furthermore, the demonstration of the geographic distribution of community vulnerability can assist decision-makers in prioritising to-do items and designing policies and plans for the more effective allocation of resources. The paper ends by discussing the study's limitations and its practical implications.     

Fundamental insights on when social network data are most critical for conservation planning

As declines in biodiversity accelerate, there is an urgent imperative to ensure that every dollar spent on conservation counts toward species protection. Systematic conservation planning is a widely used approach to achieve this, but there is growing concern that it must better integrate the human social dimensions of conservation to be effective. Yet, fundamental insights about when social data are most critical to inform conservation planning decisions are lacking. To address this problem, we derived novel principles to guide strategic investment in social network information for systematic conservation planning. We considered the common conservation problem of identifying which social actors, in a social network, to engage with to incentivize conservation behavior that maximizes the number of species protected. We used simulations of social networks and species distributed across network nodes to identify the optimal state-dependent strategies and the value of social network information. We did this for a range of motif network structures and species distributions and applied the approach to a small-scale fishery in Kenya. The value of social network information depended strongly on both the distribution of species and social network structure. When species distributions were highly nested (i.e., when species-poor sites are subsets of species-rich sites), the value of social network information was almost always low. This suggests that information on how species are distributed across a network is critical for determining whether to invest in collecting social network data. In contrast, the value of social network information was greatest when social networks were highly centralized. Results for the small-scale fishery were consistent with the simulations. Our results suggest that strategic collection of social network data should be prioritized when species distributions are un-nested and when social networks are likely to be centralized.     

公众信任及水灾风险认知的区域对比

通过在长江流域不同区域开展问卷调查(获885份样本),统计、比较公众对社会减灾能力的信任态度及水灾风险认知状况,并探讨信任与认知之间的关系。结果显示,公众对社会减灾能力基本持信任态度,信任度依次为:灾害监测预报预警>政府应急>防灾工程能力,其中下游地区公众的信任度较高。公众对于水灾的风险感知普遍偏弱,尤其是下游地区公众对发生水灾、受灾可能性的评估偏低(小于25%)。公众对于区域减灾能力的信任主要受到受灾经历(如灾情损失、灾后救援等)的影响,区域本底的灾害风险高低导致公众水灾风险认知的差异。风险较高区域(上、中游)公众更愿意采取较多的、积极的具体防减灾措施。     

利用ARIMA(自回归移动平均)模型对跑道侵入事件的分析及预测

综合运用具有相当精度的时间序列分析方法,建立美国民航运输安全中的机场跑道侵入的AR IMA(自回归移动平均)模型,克服了样本空间总是有限带来的不足,揭示出民航跑道侵入的动态变化规律,并对未来美国民航跑道侵入事故发生次数进行较准确的预测,为我国民航部门科学地制定飞行计划、人员培训、提高安全管理水平,提供可靠的依据。