科技企业孵化器何以激活区域创新——风险投资与孵化基金的中介作用

完善创新生态系统、提升区域创新能力是我国科技企业孵化器建设的重要目标。基于2013-2018年中国（内地）30个省份面板数据，采用面板数据模型实证检验科技企业孵化器是否促进了区域创新能力提升，并基于中介效应模型探讨风险投资和孵化基金在其中的间接作用。结果发现：①科技企业孵化器建设显著提升了区域创新水平，但主要增加的是实用新型和外观设计专利申请授权数总量，对发明专利申请授权数并没有显著促进作用。上述结果在剔除直辖市样本、采用随机效应模型及空间计量模型的稳健性检验后依然成立；②通过中介效应模型检验发现，区域风险投资和孵化基金集聚效应是科技企业孵化器影响区域创新水平的主要机制；③科技企业孵化器对区域创新的影响在不同区域间差异较大，在东部地区的创新激励效应更加显著，而且政策工具强度对科技企业孵化器与区域创新水平的关系具有正向调节作用。     

基于区块链技术的建筑材料供应链质量数据安全共享方法

传统方法在建筑材料供应链质量数据安全共享中应用效果不佳，不仅数据误码率比较高，而且数据共享时延比较长，无法达到预期的数据安全共享效果。为此提出基于区块链技术的建筑材料供应链质量数据安全共享方法。利用区块链技术对建筑材料供应链质量数据属性加密，建立建筑材料供应链质量数据安全共享链，整合建筑材料供应链质量数据加密、解密程序，通过对用户身份验证实现对质量数据安全共享，以此完成基于区块链技术的建筑材料供应链质量数据安全共享。实验证明，设计方法的数据误码率在1%以内，数据共享时延在1s以内，具有良好的数据安全共享效果。     

理性人的人情味：快手“老铁经济”的内在机制和逻辑

以中国用户规模最大短视频平台之一快手头部主播直播带货推广项目的案例和数据分析为基础,构建解释快手直播电商经济底层逻辑的社会学机制模型.提出快手直播电商经济背后的3种社会学机制:关键影响者逻辑、身份认同逻辑和社会网络逻辑.通过LDA文档主题生成模型将快手主播的直播语义字段进行文档主题生成归类,发现情感因素和理性因素词交互影响受众的购买决策,它背后的社会学作用机制是关键影响者逻辑和身份认同逻辑.2020年快手直播电商半年交易额已达1044亿元,理解快手直播背后的影响机制,能够持续优化推广模型,在互联网流量广告领域具有重要的应用价值.     

协调度视角下长江流域产业转移效率研究

实现区域经济协调发展是当前我国在块集聚与点极化双重压力下面临的现实难题，长江经济带尤其是欠发达省份承接发达地区产业转移是解决该难题的必然路径选择。产业转移活动因区域主客体特征变量不同，呈现出典型的多发式转移和门槛转移特征，使得区域政策制定缺乏科学指导，陷入盲目拼政策红利的状态。结合效率模型及考虑熵权的经济社会发展协同度分析，测算了2004-2016长江经济带各省（市）年综合产业转移效率，并考察了门槛效应值及变化趋势。结果表明：在考察期内，长江经济带各省（市）产业转移效率提升较为明显，但不少省（市）存在不协调的阶段性平衡关系，且这种关系还未显露经济总量驱动下的协同发展能力。     

品牌认同视角下“生产商-经销商-终端商”价值共创的逻辑再构

随着产业链与供应链的深入与完善,以品牌认同为引领的价值创造再一次引起关注,基于"生产商-经销商-终端商"三位一体的价值共创思维是重塑品牌形象强化品牌认同的关键.研究认为:经销商层面中人口基数、人均可支配收入、配送能力等是实现价值增值的因素,生产商层面中市场费用支持、分销政策支持、品牌战略提升是促进价值提升的关键,终端商层面中客情维护、货龄管理、品牌维护是保障价值实现的途径.     

CEO hometown identity and firm green innovation

Drawn on the upper echelons theory, this study investigates how chief executive officer (CEO) hometown identity drives firm green innovation. We propose that CEO hometown identity has a positive impact on a firm's green innovation performance. Furthermore, we explore the moderating role of managerial discretion determined by organizational and environmental factors (i.e., institutional ownership and market complexity). We propose that institutional ownership negatively moderates the positive relationship between CEO hometown identity and green innovation, but market complexity plays a positive moderating role. Using Chinese publicly listed firms from 2002 to 2016 in heavily polluting industries, our findings support these hypotheses. Our research contributes to the upper echelons theory and corporate social responsibility literature and has substantial practical implications.     

Analysis of territorial compatibility for Seveso-type sites using different risk assessment methods and GIS technique

The increasing size of chemical plants and rapid growth in residential areas has led to many incompatible land-use scenarios in the last 100 years. In this context, the authors assume that assessment and planning have their significant role in preventing the juxtaposition of hazards and population, and that, in this field, there is a broad recognition of the need for legislative and policy consistency across the European Union. The paper presents a comparative case study in which the Romanian land-use planning (LUP) criteria and the risk-based quantitative approach for a chemical plant are applied. Accident scenarios involving chlorine and propylene arecomprehensively analyzed using consequence and risk modelling software and GIS technique for the territorial compatibility assessment. The objective of the paper is threefold. Firstly, it presents an overview about current risk analysis methods; secondly, the authors advance an understanding of risk assessment practices used in several countries for the prevention and control of major industrial accidents involving dangerous substances and, also, for LUP. Thirdly, a method targeting an improved risk assessment framework for LUP, encompassing Romania’s determinants is outlined. The results obtained using the two different approaches indicate significant differences regarding the possibly affected areas and territorial compatibility. Furthermore, based on the findings, the paper ends with a set of recommendations that can be transformed into the foundation for future enactments of new safety standards that cover risk assessment for LUP. Consequently, the present study aims to become a frame of reference for decision-makers towards more sustainable and updated risk assessment practices in the field of industrial activities.     

东南亚国家社交商务与非本土节日消费行为分析——以中国春节为例

在世界范围内,中国春节越来越受消费者欢迎,特别是东南亚消费者会在社交商务中参加中国春节活动。然而,现实中却存在一个矛盾的现象,即一些东南亚消费者春节期间热衷于购买中国商品,而春节过后却抵制中国商品。实证研究发现,东南亚消费者并未完全从态度上偏好中国春节和中国商品,只是出于社交商务的需要才参与中国春节和购买中国商品。东南亚消费者可区分为文化杂食型和节日身份型两个细分市场。其中,文化杂食型消费者包容并喜欢多元文化,会长期偏好中国节日和中国商品;节日身份型消费者仅在中国节日期间会参与中国节日和购买中国商品。这样的结论对指导中国企业实践具有重要价值,首先,为更好地响应“一带一路”倡议,应树立中国品牌意识,在“一带一路”沿线国家培育偏好中国文化和产品的朋友圈,转变消费者态度;其次,为更好地实施“走出去”战略,应在国际市场进行文化杂食和节日身份的细分,对文化杂食型消费者以文化为纽带培育其长期稳定的消费习惯,对节日身份型消费者通过营销策划刺激其消费;其三,为成功进入东南亚市场,对在东南亚运营的社交商务,应侧重培育消费者亲中国文化与产品的态度,并增加其使用社交商务平台的愉悦感。     

非互惠的零关税待遇是否促进了受惠国的出口多样化——基于中美对非洲贸易政策的比较研究

非互惠的优惠贸易待遇是世界贸易组织和国际社会支持发展中国家尤其是最不发达国家发展的重要工具。为了评估该政策的效果,本文以中美对非洲实施的零关税待遇为例,采用2001—2017年HS8位贸易数据从整体、行业和区域层面评估了两种政策对受惠国出口多样化产生的影响。结果显示,美国零关税待遇分为AGOA-GSP和AGOA-服装两个条款,中国零关税待遇(FOCAC)在遵循WTO规则、受惠商品范围、受惠国家标准、原产地标准及政策有效期方面均优于AGOA-GSP,但在原产地标准灵活度和政策优惠力度方面低于AGOA-服装。这些特征导致三类政策对受惠国出口多样化的影响产生显著的差异。整体上,FOCAC和AGOA-服装对受惠国出口多样化均产生了显著的积极影响,而AGOA-GSP的影响不显著;行业层面,FOCAC对受惠国制造业、矿业和农业的出口多样化均产生了显著的促进作用,而AGOA的两个条款对受惠国三个细分行业出口多样化的影响均不显著;区域层面,三类政策对受惠国出口多样化的影响仅在部分区域发挥效果。     

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.