数据确权的理论逻辑与路径设计

我国"数据战略"有效实施的前提之一即是数据确权与交易,数据财产权的产生,与人的劳动物化其中有着因果关系.本文试图以马克思劳动价值论证明数据这一新生产要素的产权来源及其归属与物化劳动的相关性,并从社会契约集合的公意论述了大多数社会成员对财产权利安排的共同承认,然后讨论了以国家赋权的方式来塑形和定格数据财产权法律创制.最后提出:适用法律选择、数据权属分层、应用机制创新,构成了数据确权路径设计的主要内容.     

大数据能力对企业创新绩效的影响——IT-业务融合与双元环境的调节作用

基于动态能力理论和权变理论，以IT-业务融合、双元环境为调节变量，探究大数据能力与企业创新绩效间的作用关系，结果发现：大数据能力对创新绩效具有正向促进作用；IT-业务融合调节大数据能力与创新绩效的关系，IT-业务融合水平越高，大数据能力对创新绩效的正向影响就越显著；双元环境能够调节大数据能力与创新绩效间关系。其中，环境动态性在大数据能力与创新绩效关系间起正U型调节作用，而环境竞争性在大数据能力与创新绩效关系间起倒U型调节作用。从内外部情境视角解释大数据能力的创新转化，对中国企业大数据应用具有指导意义。     

社会网络视角下的创客资本研究

揭示创客资本的内涵与特征，有助于深层次挖掘创客与经济社会发展之间的关系。运用关键词共现、社会网络分析和扎根理论方法，对148篇文献、21位由创客与众创空间负责人等构成研究对象的访谈资料和网络文本资料展开分析。社会网络分析发现互联网、数字经济、分享经济等是创客发展的重要驱动力，创客可借助众创空间社会网络跳出时空局限，运用网络资源参与大众创新活动，并与社会网络形成价值共创关系，推动协同创新和平台生态系统构建；扎根理论分析结果表明，创意制造、开放协同、网络共生、边际非稀缺是创客资本的4个核心特征，并印证了社会网络分析结果。基于上述分析结果，进一步基于社会网络理论界定了创客资本的内涵，提出未来需要深度挖掘创客资本与网络场景之间的交互机制及其结果效应。     

Corporate Social Responsibility and NGO Directors on Boards

Journal of Business Ethics - In the years 2009 to 2016, approximately 35% of Standard & Poor’s (S&P) 500 firms had at least one director with a professional background in...     

Do goodwill impairments affect audit opinions? Evidence from China

There has been a steady growth of goodwill impairments in the Chinese stock market since the adoption of the impairment approach in accounting. The influence of goodwill impairments on a firm’s financial position and profitability give reason to doubt its current and future performance. We examine whether auditors, as a crucial external monitor, identify the information risks of goodwill impairments and express their concerns about financial reporting quality in their audit opinions. Using a sample of firms listed on China’s A-share market from 2007 to 2017, we test the association between goodwill impairments and the type of audit opinion received in the same financial period. Our findings are as follows. First, the probability of receiving a modified opinion increases with the amount of goodwill impairments. Second, the positive association between goodwill impairments and modified audit opinions is driven primarily by earnings management risks. Third, this positive association is more salient when auditors are industry experts and there is no auditor–client mismatch. Fourth, auditors are more sensitive to the amount of goodwill impairments than to their mere existence. Overall, we document that auditors perceive goodwill impairments as a signal of information risks and communicate their concerns to investors to avoid litigation.     

从利益共同体到命运共同体:地方政府隐性债务的合作治理

在地方政府性债务的治理中,隐性债务因其增长迅速、规模不清和风险不确定,成为未来债务治理的重点和难点。隐性债务挟裹主体众多,关联利益甚广,因此,基于共同体视角,明确治理主体,有利于化解风险。在隐性债务生产阶段,因利而合的共同体大肆攫取利益导致隐性债务激增;在债务风险凸显阶段,利益共同体的本能选择不是"安危与共",而是"大难临头各自保"。但债务风险的"飞去来器效应"和群体性焦虑,将促使利益共同体走向命运共同体。命运共同体既是利益共同体,更是治理共同体。在隐性债务治理阶段,共同体应形成"共生"意识、采取"共治"行动、构筑"共担"保障,实现有效共治。     

中国水稻加工环节成本结构及影响因素分析

［目的］“产业兴旺”是乡村振兴战略中的重要内容，探究中国水稻加工环节成本结构及影响因素，对促进我国农业一、二、三产业的融合，实现农业价值链转型升级，提升我国农产价值链竞争力具有重要的意义。［方法］通过微观调研数据，构建超越对数成本函数和要素需求函数，采用似不相关回归法（Seemingly Unrelated Regression）实证分析我国水稻加工厂的成本及要素投入的影响因素。［结果］（1）稻米加工厂存在规模报酬经济，扩大规模可以降低稻米加工平均成本。（2）建立品牌、与农户形成合同关系、鼓励多元主体经营、公司成立时间、企业能够获得政府支持对降低加工厂的平均加工成本具有正向显著影响。（3）建立品牌、与稻农签订合同、企业负责人的年龄、受教育年限越高对提高加工厂资本投入，促进结构升级具有显著影响。［结论］应采取相关政策措施鼓励稻米加工产扩大加工规模，与上下游形成更为紧密的利益共同体，加强稻米加工企业的品牌意识进而提高我国稻米加工产业的成本竞争力，并鼓励具有更为丰富管理经验、接受更高教育水平的管理者担任企业负责人，在促进我国稻米加工行业的升级转型的同时，提高我国稻米副产品的深度开发利用。     

The optimal level of corporate social responsibility based on the duopoly model

This paper constructs a duopoly model considering corporate social responsibility (CSR) and market's sensitivity to CSR (e) and analyzes the equilibrium results, the condition of CSR implementation and the optimal CSR level (β^* ) of Model CC (two enterprises implement CSR) and Model CN (only one enterprise implements CSR). The results show that β^* is affected by competitors and e. e, marginal cost (c) and cost difference affect the equilibrium results and the comparative results. Reducing c and improving e can promote social welfare. Consumer surplus under Model CC is highest. CSR has a negative effect on social welfare under certain conditions.     

Doing more and doing better are two different entities: Different patterns of family control and environmental performance

Family firms bear two types of agency costs, including type I and type II agency problems, in corporate environmental practices: (1) Outside executives at family firms hesitate to engage in environmental strategies, which can lead to drops in profits; (2) Controlling families employ opportunistically environmental management to achieve their interests. We argue that a primary cause for the agency problems lies on ineffective internal corporate governance at family firms, which can cause loss of managerial (or power) balance between outside executives and family executives. Our findings show that family firms with ownership and strategic control (FSC), which family executives and outside executives monitor and constrain each other, can achieve the highest environmental performance. Moreover, external controls, including product market competition and provincial environmental regulations, substitute effective internal control of FSC. The environmental performance premium of FSC is more prevalent when the production market competition is lower. Family firms with ownership, operational, and strategic control (FOSC) can achieve higher environmental performance within a province with more stringent environmental regulations.     

深度学习方法在作物遥感分类中的应用和挑战

[目的]准确估算作物的面积和分布对粮食安全至关重要。与传统的机器学习方法相比,深度学习具有多种优势,如端到端训练、可迁移性。为有效利用高时空数据进行作物识别提供了新的机遇。已有多种模型被应用于作物分类任务中,针对不同的分类任务,如何有效地选择模型,并对其进行训练和使用已成为关键问题。[方法]文章回顾了利用深度学习模型对作物分类的主要研究。N维卷积神经网络(N-D CNN)(N=1、2、3)和递归神经网络(RNN)已被有效用于作物分类任务。长短期记忆RNN(LSTM RNN)和门控循环单元RNN(GRU RNN)是RNN的变体,解决了随着时间序列增加RNN出现的梯度消失或爆炸问题。此外,还有研究使用CNN和RNN(我们称为RCNN)的混合模型对作物进行分类。该文首先阐述了使用深度学习方法进行作物制图的背景和意义,并介绍了CNN和RNN模型结构。然后回顾了一些典型的研究,包括模型的结构、遥感数据源、数据处理方法和分类精度。最后,总结了使用深度学习方法进行作物分类的挑战以及现有解决方案的局限性。[结果](1)1-D CNN可用于提取时间特征,或时间+光谱特征,分类效果良好;2-D CNN已被广泛应用于单时相数据的空间特征提取,分类精度依赖于数据源;3-D CNN应用较少,但具有很大的潜力,尤其是时间+空间维度的特征提取;(2)相同条件下(架构、数据源、研究区域、类别),LSTM RNN和GRU RNN分类效果通常高于普通RNN,而前两者的效果差距不大,但GRU RNN训练时间较短;(3)CNN+RNN混合模型(RCNN)用RNN比3-D CNN更适合提取时间特征。这主要是由于RNN建立了对序列数据的长期依赖,而3-D CNN卷积核是局部计算的。[结论]通过分析,认为深度学习技术是作物遥感分类的有效工具。此外,与其他模型相比,RCNN,3-D CNN和GUR RNN具有更大的潜力。