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。     

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.     

创新战略下的英国研发组织创新——Catapults的组织管理模式研究

Catapults（弹射中心）作为一种新型研发组织形式，在英国创新发展战略的实施中扮演着重要角色。精准的组织功能定位、高效的组织管理运作模式和紧密的网络化组织关系，是Catapults组织管理模式创新的重要三大重要内容。为我国新型研发机构的培育和发展提供了良好的参考样本。     

The Impact of Public Expenditure and International Trade on Agricultural Productivity in China

The “industry nurturing agriculture” reforms and World Trade Organization accession led to dramatic growth in public expenditure and international trade in China’s agricultural sector. This article aims to estimate the effects of public expenditure and trade on agricultural productivity in China for 2004–2015. A semi-parametric production function with shape constraints is introduced to derive more accurate productivity before the productivity determinants are analyzed with an emphasis on public expenditure and trade. The empirical result shows that public expenditure and exports can effectively improve agricultural productivity, while imports have no significant effects. Policy implications are discussed in the context of supply-side reforms.     

The effect of place attachment of geographical indication agricultural products on repurchase intention

[Objective] This study aims to investigate how place attachment and the quality of experience affect consumers' intention to repurchase Geographical Indication (GI) agricultural products. [Methodology] Based on the special functional value and symbolic meaning of GI agricultural products, we constructed a five-dimensional model of place attachment for GI agricultural products consumers. Expanding on the original two-dimensional place attachment model, we introduced three connection concepts: nature, society, and emotion. We also combined this model with the quality of experience model and consumer purchase intention model to form a research model of repurchase intention for GI agricultural products. A questionnaire survey was used to collect data (n = 340), and a partial least squares structural equation model was used to test the empirical evidence. [Research results] (1) Place attachment to Geographical Indication agricultural products significantly affects the quality of experience. (2) Quality of experience significantly affects consumer trust and repurchase intention. (3) Consumer trust significantly affects repurchase intention. (4) There is a mediating effect of experience quality between place attachment and repurchase intention; there is a mediating effect of trust in the relationship between quality of experience and repurchase intention. (5) There is a chain mediating effect of “place attachment→quality of experience→consumer trust→repurchase intention” in the model.     

产业共性技术政府支持性研发外包合同影响因素研究

建立产业共性技术研发外包博弈模型，从政府视角研究以共性技术产出最大化为目标的最优政府支持性研发外包合同，并对最优外包合同影响因素进行分析。研究表明：足够多的政府预算是共性技术政府支持性最优研发外包合同存在的前提，最优合同条款随政府预算的增大而增多，企业保留收益增大倒逼政府预算增加；企业最优知识技术投入与共性技术价值系数以及从研发中分享的收益份额正相关，与政府支付的固定研发费用负相关；政府最优知识技术投入与固定研发费用及单位投入成本系数负相关；企业最优投入与政府投入成本系数负相关，与政府投入对研发成功的贡献系数正相关，且企业最优投入随其研发能力的增强而加大。最后，根据研究结论，从政府预算、项目前景及支持性资源分配3个方面提出共性技术研发外包合同实施的政策建议。     

War and economy. Rediscovering the eighteenth-century military entrepreneur

Abstract

The detrimental effects traditionally assigned to warfare in the development of pre-industrial economies have obscured the prominent role that military entrepreneurs played in economic development in this period. Historiography minimises the extent to which war and the concomitant strengthening of the central state provided a whole new range of opportunities for capital investment, a tendency that has been strengthened by the paradigm of Redlich’s ‘decline of the soldier-entrepreneur’ and the technological determinism of the debate on the Military Revolution among others. The aim of this introduction is to look into the background of this relative lack of interest and to reaffirm the mutual dependence of eighteenth-century state-formation and the business of war.     

中国高端装备制造业自主技术创新效率稳定性及影响因素研究

在全面实施创新驱动发展战略的时代背景下，揭示中国高端装备制造业自主技术创新效率稳定性，测度相关因素的影响强度与方向，对揭示中国高端装备制造业自主技术创新规律，制定其发展策略具有重要指导意义。聚焦中国高端装备制造业自主技术创新效率稳定性及影响因素，采用DEA-Malmquist指数方法和Tobit回归模型进行实证研究，结果表明，中国高端装备制造业自主技术创新效率存在一定波动性，且各细分行业技术创新效率内部稳定性较差，产业自主技术创新效率整体稳定性不高；自主技术创新投入因素和环境影响因素对产业自主技术创新效率的影响具有较高的异质性，其中创新资金投入和人力资源投入对自主技术创新综合效率、纯技术效率及规模效率具有正向影响，而新产品开发投入和企业规模对自主技术创新综合效率、纯技术效率和规模效率的影响具有异质性，政策因素与市场因素分别具有显著和非显著负向影响，外源性创新因素具有显著正向影响，且影响强度较大。在此基础上，提出调整政府对高端装备制造业创新支持模式、加强外源性创新与智力引进等策略建议。     

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.     

Non-linear Gaussian sovereign CDS pricing models

Prior literature indicates that quadratic models and the Black–Karasinski model are very promising for CDS pricing. This paper extends these models and the Black [J. Finance 1995, 50, 1371–1376] model for pricing sovereign CDS’s. For all 10 sovereigns in the sample quadratic models best fit CDS spreads in-sample, and a four factor quadratic model can account for the joint effects on CDS spreads of default risk, default loss risk and liquidity risk with no restriction to factors correlation. Liquidity risk appears to affect sovereign CDS spreads. However, quadratic models tend to over-fit some CDS maturities at the expense of other maturities, while the BK model is particularly immune from this tendency. The Black model seems preferable because its out-of-sample performance in the time series dimension is the best.