Observation of other's preferential treatment as a constructive catalyst for improved repatronize intentions

Observing others receive preferential treatment has been found to provoke bystanders' negative reactions. However, seeing others treated better may also inspire bystanders to increase their repatronize intentions. Drawing upon theories of social comparison and goal-setting, this study investigates the condition when watching another's preferential treatment acts as a catalyst to enhance bystanders' repatronize intentions and the mechanism by which the constructive effect appears. The results indicate that when bystanders witness similar others' preferential treatment and believe that they can attain comparable treatment, their goal-setting motivation is more activated and consequently they are more committed to goal-relevant behavior (i.e., repatronize intentions).     

互联网视角下供应链金融的新发展

在这个网络社会愈发深化的时代,通过互联网科技完善和发展线上供应链金融成为必要的发展方向。本文首先梳理了传统供应链金融的基本模式,发现了其存在的不足;然后重点介绍了当前三种主要的线上供应链金融模式,总结了线上供应链金融的优势;最后,提出相关政策建议,认为金融机构应该不断升级线上供应链金融产品,逐步完善供应链金融线上操作系统,监管机构需要出台更加严格的监管规则对线上供应链金融进行有效监管。     

Challenging the obsession with local level institutions in country ownership of climate change adaptation

Working with local level actors to enable country ownership is applauded within the multilateral climate finance landscape. However, are emerging adaptation interventions equitable by reflecting the priorities of local level vulnerable populations? This research sought to find out whether the engagement of local institutions in projects that seek to achieve country ownership enabled local level vulnerable groups to participate in and influence adaptation decision-making processes and outcomes, thereby enabling them to have a voice in local level adaptation. It used a case study of a Global Environmental Facility-managed coastal adaptation project in Tanzania, which sought to restore and protect mangroves to enable adaptation to sea level rise. Data was generated from 13 Focus Group Discussions and survey questionnaires administered to 629 individuals in three locations on the mainland of Tanzania and in Zanzibar. The findings indicate that community-based organizations were used to facilitate the implementation of project activities at the community level. However, participation spaces created in the project and facilitated by these local institutions were exclusionary and failed to enable vulnerable community groups to have a voice on mangrove restoration and protection. Use of these local institutions altered local level power relations and disempowered other pre-existing and (in)formal local resource management institutions. Community members questioned legitimacy of actions implemented by these local institutions. These findings suggest that working with local level stakeholders to generate country ownership does not automatically guarantee that actions will address the needs of local vulnerable groups. Multilateral climate finance institutions should acknowledge these risks and implement measures to address them.     

Brexit: Tracking and disentangling the sentiment towards leaving the EU

On 23 June, 2016, the UK held a referendum to decide whether to stay in the European Union or leave. The uncertainty surrounding the outcome of this referendum had major consequences for public policy, investment decisions, and currency markets. We discuss some of the subtleties involved in smoothing and disentangling poll data in light of the problem of tracking the dynamics of the intention to Brexit, and propose a multivariate singular spectrum analysis method that produces trendlines on the unit simplex. The trendline yield via multivariate singular spectrum analysis is shown to resemble that of local polynomial smoothing, and singular spectrum analysis presents the nice feature of disentangling the dynamics directly into components that can be interpreted as changes in public opinion or sampling error. The merits and disadvantages of some different approaches for obtaining smooth trendlines on the unit simplex are contrasted, in terms of both local polynomial smoothing and multivariate singular spectrum analysis.     

Crime prediction by data-driven Green’s function method

We develop an algorithm that forecasts cascading events, by employing a Green’s function scheme on the basis of the self-exciting point process model. This method is applied to open data of 10 types of crimes happened in Chicago. It shows a good prediction accuracy superior to or comparable to the standard methods which are the expectation–maximization method and prospective hotspot maps method. We find a cascade influence of the crimes that has a long-time, logarithmic tail; this result is consistent with an earlier study on burglaries. This long-tail feature cannot be reproduced by the other standard methods. In addition, a merit of the Green’s function method is the low computational cost in the case of high density of events and/or large amount of the training data.     

Predicting bank insolvencies using machine learning techniques

Proactively monitoring and assessing the economic health of financial institutions has always been the cornerstone of supervisory authorities. In this work, we employ a series of modeling techniques to predict bank insolvencies on a sample of US-based financial institutions. Our empirical results indicate that the method of Random Forests (RF) has a superior out-of-sample and out-of-time predictive performance, with Neural Networks also performing almost equally well as RF in out-of-time samples. These conclusions are drawn not only by comparison with broadly used bank failure models, such as Logistic, but also by comparison with other advanced machine learning techniques. Furthermore, our results illustrate that in the CAMELS evaluation framework, metrics related to earnings and capital constitute the factors with higher marginal contribution to the prediction of bank failures. Finally, we assess the generalization of our model by providing a case study to a sample of major European banks.     

Behavioral equilibrium and evolutionary dynamics in asset markets

This paper analyzes a dynamic stochastic equilibrium model of an asset market based on behavioral and evolutionary principles. The core of the model is a non-traditional game-theoretic framework combining elements of stochastic dynamic games and evolutionary game theory. Its key characteristic feature is that it relies only on objectively observable market data and does not use hidden individual agents’ characteristics (such as their utilities and beliefs). A central goal of the study is to identify an investment strategy that allows an investor to survive in the market selection process, i.e., to keep with probability one a strictly positive, bounded away from zero share of market wealth over an infinite time horizon, irrespective of the strategies used by the other players. The main results show that under very general assumptions, such a strategy exists, is asymptotically unique and easily computable.     

Managers’ exploration activities and individual unlearning: the mediating role of learning orientation and reflection

Abstract

The main goal of this study was to investigate the effects of managers’ exploration activities on learning orientation, reflection, and unlearning of team members. A questionnaire survey was conducted among 115 employees in 23 teams from a Japanese pharmaceutical company. The results of multi-level analyses suggest that managers’ exploration activities had a direct influence on team members’ learning orientations, which subsequently had a positive influence on their unlearning, with and without the mediation of reflection. These findings suggest that a subordinate’s unlearning is driven by managers’ activities through motivational and cognitive processes. The theoretical and practical implications of the findings are discussed.     

Partner‐diversity effects on alliance termination in the early stage of green alliance formation: Empirical evidence from carbon‐emission reduction projects in Latin America

Partner diversity is a key influencer in interorganizational alliances, and several empirical studies have shown that its outcomes are contingent on alliance‐specific factors. We extend this research as well as the growing literature on green alliances, in which partner diversity is uniquely high. Specifically, we examine partner‐diversity effects on alliance termination in the early stage of green alliance formation. We hypothesize that in this context, size disparity increases termination likelihood, whereas organizational variety and cultural separation have the opposite effect. To test our hypotheses, we use a sample of 366 alliance projects located in Latin America and submitted to the Kyoto Protocol's Clean Development Mechanism for evaluation, validation, and registration from 2004 to 2014. Our findings contribute to several research streams and provide practical guidance for successful formation of alliances focused on environmental protection.