The role of socially rich photos in generating favorable donation behavior on charity websites

While online retail sites succeed in reinforcing perceptions of human warmth on their websites thanks to more and more interactive technologies, the charitable sector cannot, for lack of human, technical and financial resources, make use of such technologies. This research paper demonstrates the potential of a simple tool: the socially rich photo. Although the literature has extensively documented how photos impact visitor behavior in online environments, research in the context of donations through charity websites is rare and has provided contradictory results. By focusing more closely on the role of the individual represented in such photos (the recipient of a donation versus the donor), our studies, conducted in France, offer additional insights on the subject as well as managerial advice for organizations.     

川西北高原藏区农村贫困化分异机制探测

[目的]客观揭示农村贫困化地域分异机制,为科学推进川西北高原藏区精准扶贫创新途径、综合战略和认真贯彻落实中央治藏方略提供参考依据。[方法]应用地理探测器模型、GIS空间分析与地统计方法等方法,探测川西北高原藏区贫困村单位面积GDP分异的主导因素,揭示农村贫困化分异机制,提出不同贫困化地域类型的扶贫政策措施。[结果]影响川西北高原藏区农村贫困化分异的主导因素包括到主要交通道路距离、归一化植被指数(NDVI)、土地利用、年均温度和海拔高程等,各因素对贫困村经济发展分异的解释力分别为80. 76%、12. 82%、8. 82%、5. 45%和3. 96%;贫困村经济发展贫困化的分异机制存在明显差异,可归纳为交通区位约束型、自然环境约束型和经济区位约束型等三大类型;农村贫困化驱动机制下的贫困村精准扶贫政策措施亟需因地制宜、科学推进、讲求实效,有序推进精准扶贫战略。[结论]精准扶贫应分类指导、重点突破,尤其加强贫困村交通、水利、公共基础设施建设和特色农牧业产业培植,注重多种扶贫模式的综合集成。     

京津冀生态涵养区生态与产业协调发展影响因素分析

[目的]京津冀协同发展国家重大战略将冀北地区的发展确定为承担生态保障与水源涵养功能,文章基于长期以来该区域生态脆弱、经济落后的特征,研究了该生态涵养区生态与产业协调发展的影响因素,目的是为该区域生态建设与产业经济协调发展提供参考。[方法]设置了影响生态建设与产业经济协调发展的规模因素、经济因素、社会因素与政策因素四大类37个影响因素,借助于统计数据,在对数据进行标准化处理的基础上,应用计量经济模型,通过因子分析法将各个因素进行分类归纳为5个公因子,利用SPSS软件回归模拟结果显示各个因素对生态建设与产业经济协调发展的影响作用。[结果]将四大类通过因子分析法的主成分分析,应用计量经济模型模拟,拟合优度较高,影响因素通过分组具有较强的解释力,结果能够很好地反映这些影响因素对生态建设与产业经济协调发展的影响方向与影响程度。[结论]依据定性分析假设与计量模拟分析结果,针对存在的问题与发展机遇,提出对策建议:继续加强生态建设项目的推进、适度扩大产业经营规模、加大生态建设与产业发展投资力度、实施资源节约型生产创新模式以及健全和完善生态与经济协调发展制度。     

Risk aversion,prudence and temperance: An experiment in gain and loss

We characterize the individual's attitude towards risk, prudence and temperance in the gain and loss domains. We analyze the links between the three features of preferences for a given domain and between domains for each feature of preferences. Consequently, the reflection effect, the mixed risk aversion and the risk apportionment, are key concepts of our study. We also display some determinants for risk aversion, prudence and temperance in each domain. To do this, we conducted a lab experiment with students eliciting risk aversion, prudence and temperance in the two domains, and collected information about each subject's characteristics.     

Forecasting unknown-unknowns by boosting the risk radar within the risk intelligent organisation

This theoretical perspective paper interprets (un)known-(un)known risk quadrants as being formed from both abstract and concrete risk knowledge. It shows that these quadrants are useful for categorising risk forecasting challenges against the levels of abstract and concrete risk knowledge that are typically available, as well as for measuring perceived levels of abstract and concrete risk knowledge available for forecasting in psychometric research. Drawing on cybersecurity risk examples, a case is made for refocusing risk management forecasting efforts towards changing unknown-unknowns into known-knowns. We propose that this be achieved by developing the ‘boosted risk radar’ as organisational practice, where suitably ‘risk intelligent’ managers gather ‘risk intelligence information’, such that the ‘risk intelligent organisation’ can purposefully co-develop both abstract and concrete risk forecasting knowledge. We also illustrate what this can entail in simple practical terms within organisations.     

Deep learning for modeling the collection rate for third-party buyers

This study evaluates a wide range of machine learning techniques such as deep learning, boosting, and support vector regression to predict the collection rate of more than 65,000 defaulted consumer credits from the telecommunications sector that were bought by a German third-party company. Weighted performance measures were defined based on the value of exposure at default for comparing collection rate models. The approach proposed in this paper is useful for a third-party company in managing the risk of a portfolio of defaulted credit that it purchases. The main finding is that one of the machine learning models we investigate, the deep learning model, performs significantly better out-of-sample than all other methods that can be used by an acquirer of defaulted credits based on weighted-performance measures. By using unweighted performance measures, deep learning and boosting perform similarly. Moreover, we find that using a training set with a larger proportion of the dataset does not improve prediction accuracy significantly when deep learning is used. The general conclusion is that deep learning is a potentially performance-enhancing tool for credit risk management.     

The influencing factors of satisfaction and lending intention in online lending investment: an empirical study based on the Chinese market

This paper develops a platform‐based influencing factors model which considers value perception, risk prevention measure, non‐default experience, trust and incentive gap, to better examine the impact of platforms on investors’ satisfaction and lending intention based on the Chinese market. The results reveal that the first four factors positively influence the satisfaction of the investors, while the incentive gap has a negative impact, and there is a positive association between investors’ satisfaction and lending intention. Some specific features of China’s online lending market are identified, which provides valuable insights for online lending platforms and the government.     

Hedging and temporal permit issuances in cap-and-trade programs: The Market Stability Reserve under risk aversion

Cap-and-trade programs such as the European Union's Emissions Trading System (EU ETS) expose firms to considerable risks, to which the firms can respond with hedging. We develop an intertemporal stochastic equilibrium model to analyze the implications of hedging by risk-averse firms. We show that the resulting time-varying risk premium depends on the size of the permit bank. Applying the model to the EU ETS, we find that hedging can lead to a U-shaped price path, because prices initially fall due to negative risk premiums and then rise as the hedging demand declines. The Market Stability Reserve (MSR) reduces the permit bank and thus, increases the hedging value of the permits. This offers an explanation for the recent price hike, but also implies that prices may decline in the future due to more negative risk premiums. In addition, we find higher permit cancellations through the MSR than previous analyses, which do not account for hedging.     

政策不确定性、财政扩张与区域商业银行风险——基于中国地方官员变更和银行业的证据

文章利用2007-2017年我国93家区域商业银行的面板数据，并结合省级宏观经济数据和地方官员变更数据，实证考察了中国地方官员变更引起的经济政策不确定性对区域商业银行风险的重要影响。研究结果表明：（1）地方官员变更引起的政策不确定性增大了区域商业银行风险；（2）地方官员变更引起的政策不确定性，通过提高银行的资产收益率降低了商业银行风险；而不确定性时期的财政扩张，通过降低银行资产收益率增大了商业银行风险；并且财政扩张的负面影响大于政策不确定性的正面影响；（3）各省的市委书记发生职位更替引起的政策不确定性对区域商业银行风险的影响更大。文章研究为新时代背景下的金融供给侧结构性改革、政府宏观经济政策的制定以及财政体制与金融体制之间的联系提供参考。