高管薪酬、董事会治理与分类转移

本文研究了高管薪酬和董事会治理对分类转移的影响。研究表明,货币薪酬会诱使高管向上转移利得,股权激励和缺乏区分度的薪酬制度会诱使高管同时向下转移费用和向上转移利得;而勤勉、独立和两职分离的董事会能够在一定程度上识别并抑制这两种不同方式的分类转移;但董事会对分类转移的抑制程度小于高管薪酬对分类转移的诱发程度。进一步地,良好的市场、法制和媒体监督环境能够有效缓解高管薪酬对分类转移的诱发作用,而董事会能够有效弥补法制建设和媒体监督不足导致的分类转移治理缺陷,并在较高质量的审计环境下对分类转移发挥更强的抑制作用。本文首次结合制度背景和媒体监督研究了公司核心治理机制对不同分类转移方式的影响和原因,并创新性地对向上转移利得的程度进行了衡量,为监管部门及公司治理机制、会计准则的制定者提供经验证据。     

论物保与人保并存纠纷中债权实现之顺位——以近五年来类案实证研究为基础

当前,物保与人保并存纠纷不断涌现，相关裁判时常冲突，引发社会关注。围绕当前物保与人保并存纠纷债权实现条款模式、关于物保与人保并存债权实现顺位之理解、债权人是否放弃物保之认定等相关问题，对近五年来最为主要的类案进行实证分析与比较研究可见，处理物保与人保并存纠纷，应当根据物保相对优先的精神，对保证人是否就放弃物保优先抗辩权重点作出判断，据此就物保与人保提供者的责任顺位作出正确裁决。     

The influence of managers' awareness of climate change,perceived climate risk exposure and risk tolerance on the adoption of corporate responses to climate change

Uncertainties posed by climate change limit companies' ability to understand implications of global warming on business and society at large, hampering the adoption of tangible organizational responses to climate change. Understanding climate action thus requires to investigate influential factors of decision-making under uncertainty, which implies acknowledging managerial interpretations and perceptions about climate issues. Drawing insights from the literature on climate inaction and from corporate sustainability literature, the present study examines awareness of climate change and perceived exposure to climate risks as antecedents of corporate responses to climate change, drawing on a survey of managers of Italian manufacturing companies. In addition, the study tests the moderation of risk tolerance on the relation between perceived climate risk exposure and climate action, suggesting that risk attitudes are a significant factor of decision-making under climate uncertainty. The results support the hypothesis of the model and thus provide several contributions to the literature on business and climate change. Managerial implications and avenues for future research are also discussed.     

新一代车险“理赔难”

保险作为现代服务行业之一,服务质量问题始终贯穿其中。其中,“理赔难”的现象层出不穷。从1929年11月在中国上海成立第一家保险公司太平保险,到目前保险行业已发展90余年,中国的车险理赔还难吗?依然难,只是难点变了,老矛盾得到部分解决,新矛盾层出不穷,唯独对理赔服务质量的评价参差不齐是永恒的。     

Shariah compliance and corporate cash holdings

This study investigates the influence of Shariah compliance status on cash holding levels and the speed of adjustment of non-financial listed firms in six Gulf Cooperation countries from 2005 to 2016. The results show that Shariah compliance status has a significant effect on firms’ cash holding decisions. Shariah-compliant firms have significantly higher cash holding levels than non-Shariah-compliant firms. Further, Shariah-compliant firms adjust more quickly towards their target cash holdings than their conventional counterparts. In our view, Shariah-compliant firms are subject to multiple restrictions that limit their external financing channels. Therefore, holding larger cash reserves is important as it helps gain from the transaction cost motive of holding cash. The findings of this study have important implications for regulators, investors and managers. To the best of our knowledge, this study is the first to compare the effect of Shariah compliance on firms’ cash holdings and the speed of adjustment towards the trade-off theory’s optimal cash holding target.     

智慧居家养老服务协同供给的优化路径研究——以广西钦州市智慧居家养老项目的实践经验为例

智慧居家养老服务的协同供给可以形成社会联动效应，降低公共服务成本。运用公共服务协同供给分析框架，通过交叉分类方法从协同主体的利益与目标两个变量的耦合性出发分析广西钦州市智慧居家养老项目中养老服务主体间不同种类的协同关系，可以从加强政府领导、形成合作联盟、建立市场标准、创新扶持政策4个方面完善政府、企业、社会组织之间不同类型的协同供给，实现社会资源的高效利用以及向智慧居家养老服务的发展和转变。     

论数据权益的刑法保护——民刑交错视角下的法益保护论探讨

关于“数据权益的刑法保护”问题，在理论层面上，可从“刑法解释论”和“刑法立法论”两种路径进行探讨。“刑法解释论”探讨路径以现行《刑法》罪名体系为出发点，主要任务是揭示哪些罪名的犯罪构成之设置是以“数据权益”为规范保护目的；“刑法立法论”探讨路径是在刑法立法层面上探讨立法者为在刑法政策上实现数据权益的刑法保护这一目的，动用刑罚禁止何种行为类型才具正当性的问题。然而，两种探讨路径都将依赖于“数据权益”的内涵确定以及法益格的认定问题，换言之，是刑法法益理论在数据权益刑法保护问题上的具体运用。《民法典》第127条并非是关于数据的确权性规定，且在民法理论界关于数据权益的法律属性存在诸多争议的状况下，“数据权益”内涵不明、法律属性不清导致其并不具备作为刑法保护客体的“法益”格。当前刑法理论界存在的关于“数据权益”的刑法类法益保护模式探讨路径，是对刑法法益保护方式的误读，并未触及“数据权益刑法保护”之规范层面问题的实质。“数据安全”法益观的探讨路径则不仅混淆了现象层面与规范层面上的“数据犯罪”概念的界分，也由于没有论证“数据安全”如何能够构成刑法的保护法益，而导致结论过于武断。在理论探讨上，应该明确作为行为客体的物理层面上的“数据”概念与作为保护客体的法益层面上的“数据权益”概念之间的区别，据此明确“数据权益的刑法保护”在规范层面上是否构成新问题。     

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.     

Is the system reliability profitable for retailing and consumer service of a dynamical system under cross-price elasticity of demand?

Retailing strategy is one of the most crucial factors for industries. A proper retailing strategy can help to enhance consumer service and increase the industry's profit. An improved approach to retailing is suggested in this research to deliver superior customer service while maximizing profits in a dynamic system. The study analyzes a retailing strategy for a demand with cross-price elasticity upon the retail price. A product's cross-price elasticity and the system reliability are critical factors in retailing. Understanding the cross-price elasticity of demand between products helps retailers to make pricing decisions that maximize profits by maintaining demand. Imperfect products are produced due to an imperfect production system. The imperfect ones must be adjusted with some costs to make them perfect for better retailing. The system failure rate is crucial for retailing under cross-price elasticity of demand patterns. Production system reliability, cross-price elasticity of demand, and consumer service are all essential factors that can impact a company's success in the market. The production rate is considered time- and system failure rate-dependent. Contradictory to the literature, a dynamical system is proposed for improved retail management, which is solved using the Euler-Lagrange theory. Finally, one can achieve the expected maximum profit for this retail system with optimum selling prices for different products by reducing the system failure rate. Some numerical illustrations with graphical representations are provided to validate the current study. Numerical examples show that applying cross-price elasticity of demand for more than two identical products provides 35% more profit for the retail industry than a single type of product.     

Examining artificial intelligence (AI) technologies in marketing via a global lens: Current trends and future research opportunities

Artificial intelligence (AI) has captured substantial interest from a wide array of marketing scholars in recent years. Our research contributes to this emerging domain by examining AI technologies in marketing via a global lens. Specifically, our lens focuses on three levels of analysis: country, company, and consumer. Our country-level analysis emphasizes the heterogeneity in economic inequality across countries due to the considerable economic resources necessary for AI adoption. Our company-level analysis focuses on glocalization because while the hardware that underlies these technologies may be global in nature, their application necessitates adaptation to local cultures. Our consumer-level analysis examines consumer ethics and privacy concerns, as AI technologies often collect, store and process a cornucopia of personal data across our globe. Through the prism of these three lenses, we focus on two important dimensions of AI technologies in marketing: (1) human–machine interaction and (2) automated analysis of text, audio, images, and video. We then explore the interaction between these two key dimensions of AI across our three-part global lens to develop a set of research questions for future marketing scholarship in this increasingly important domain.