开放度组合策略与创新绩效类型匹配关系研究——吸收能力的调节效应

根据创新开放广度与深度，提出高广度-高深度、高广度-低深度、低广度-高深度与低广度-低深度4种组合策略。以吸收能力为调节变量，构建开放度组合策略与创新绩效关系模型，以我国三大行业201家企业为调查对象，采用多元线性回归分析方法进行实证检验。研究表明：实施高广度-高深度组合策略的企业倾向于追求突破式创新绩效，实施其它三类组合策略的企业倾向于追求渐进式创新绩效；吸收能力不仅能够积极调节高广度-高深度、高广度-低深度、低广度-高深度组合策略与突破式创新绩效的关系，而且能够积极调节3种组合策略与渐进式创新绩效的关系，但是无法调节低广度-低深度组合策略与突破式创新绩效、渐进式创新绩效的关系。     

新冠肺炎疫情对居民消费的影响及对策

2020年春节期间,湖北省武汉市爆发的由新型冠状病毒感染引起的肺炎疫情正在对中国经济和消费带来负向冲击。从逻辑上看,除了直接限制消费外,疫情还会通过收入效应、财富效应、实际购买力效应对消费形成不利影响。从历史上看,十七年前的"非典"疫情对经济和消费的影响是短期的,但此次疫情影响更大,持续期存在不确定性。稳定消费的关键是稳定居民收入,要稳定居民收入就需要稳定非农产业就业。基于分析,本文提出了一揽子政策建议。     

大豆大杂粉碎及重杂灭活工艺改造

随着国家对环保的重视,改变进口大豆大杂(豆荚、豆杆)焚烧处理的传统做法尤为重要,采用风选、粉碎等手段,区分处理轻重杂,杂质分别采用生产线高温高压和蒸汽湿热灭活法进行处理后,可充当纤维类物质掺入到成品豆粕中。根治环境污染的同时降低厂内安全风险,充分利用进口大豆秸秆类纤维物质,减少大豆损耗,以较小的投入实现经济效益和环境保护。     

Trust‐based decisions in commission–agency relationships

In commission–agency case, an agent has an incentive to increase the recommended order quantity (ROQ) to maximize the expected profit. This study refers to a retailer's degree of dependence on the agent's ROQ as “trust.” We aim to formulate the agent and the retailer's decisions with trust‐based optimization models. To conduct the study, we first build the demand prediction and income model of the retailer and the agent with a trust‐updating model. Furthermore, we investigate the facts of the retailer's trust value by some experimental studies. Some managerial insights are thereby given and helpful to practice.     

农产品直播效果影响因素分析

随着互联网技术、网络技术等与农产品深度融合,新一轮农产品销售模式孕育兴起.农产品直播销售为各行业带来巨大的商机,农产品直播已经成了农民的"新农具",各大直播平台都开展了各种直播活动.在前人研究的基础上,结合直播实践,通过因子分析法分析农产品直播效果的影响因素,以期改善农产品直播效果,促进农产品销售.     

创新生态系统视角下价值共创如何影响企业创新绩效——一个有调节的中介模型

以创新生态系统为视角，引入创新资源互动与生态系统规范机制为中介变量和调节变量，探究价值共创如何影响企业创新绩效。基于307家企业样本数据，运用结构方程模型、Bootstrap和层级回归方法对数据进行分析。研究发现：创新生态系统中，价值共创不完全直接影响企业创新绩效；创新资源互动部分中介价值共创对企业创新绩效的影响；生态系统规范机制部分正向调节价值共创对创新资源互动的影响。最后提出，创新生态系统调控方与企业要差异化地制定提升创新绩效的实施方案，营造活跃的创新资源互动氛围，形成并完善系统内生态规范机制。     

Is bank staff interaction associated with customer saving behavior in banks?

Major changes are underway in the U.S. retail banking sector toward heavy investments in technology and fewer in personnel. Using the 2017 survey of household economics and decision‐making (SHED) (n = 11,359), we examine the relationship between saving behavior related to emergency, long‐term and periodic expenses and personal, technological, and hybrid bank account access methods. Binary logistic regression models were used to estimate the odds of reporting various saving behaviors in relation to various banking access methods. Findings suggest that the personal access method is positively associated with savings behavior for periodic expenses for the general population, and negatively associated with emergency savings in people with lower education attainment. Technology is associated with all types of saving behavior, while the hybrid access method is associated only with saving for periodic expenses. As investments in self‐service technology increase, the importance of access methods to savings behavior must be considered.     

结构性违约、信用分层与评级机构的策略性反应——来自中国信用债市场的证据

2014年以来，以主要集中于民企的"结构性违约"宣告了中国信用债市场刚性兑付的"结构性打破"，债券市场进入了新的发展阶段。利用2013-2017年债券数据，本文深入分析了结构性违约对评级机构行为和效率的影响，主要有如下发现：首先，信用评级的市场公信力会因政府隐性担保导致的"刚性兑付"而削弱；其次，结构性违约爆发之后，信用评级的整体效率有所提升，但在不同发行主体呈现分化，对于非城投类企业，信用评级对发行利差的影响显著增大，意味着评级公信力的显著提升，而对于城投类企业，评级效率并未明显改善；进一步研究表明，出现这种情况的原因在于评级机构在违约后采取了差异化的评级策略，对于违约风险较大的非城投类债券，评级机构倾向采取"收紧评级"的策略；对于违约风险较低的城投债，评级机构倾向于采取"放宽评级"的策略。这种策略性行为是导致市场"信用分层"的重要原因，并有可能推升民营企业的融资成本。     

Abuse of Market Dominance Under China’s Anti-Monopoly Law: The Case of Tetra Pak

Review of Industrial Organization - In this paper, we analyze a recent antitrust case of abuse of dominance that was decided by a Chinese administrative enforcement agency under China’s...     

Bubbles and crises: Replicating the Anundsen et al. (2016) results

This paper both narrowly and widely replicates the results of Anundsen et al. (Journal of Applied Econometrics, 2016, 31(7), 1291–1311). I am able to reproduce the same results as theirs. Furthermore, I find that allowing for time‐varying parameters of early warning system models can considerably improve the in‐sample model fit and out‐of‐sample forecasting performance based on an expanding window forecasting exercise.