小世界创新网络动态演化及其效应研究

低度、中度和高度开放的创新网络在创新集群创立和解体的动态演化过程中,其表现出的小世界性对创新绩效的效应有着较大差异． 创新绩效同创新网络小世界性间往往呈现倒U形关系,但该关系只在特定开放度下的创新网络演化到特定阶段才会显现: 低度开放的网络的小世界性同创新绩效的倒U 形关系在创新集群建立阶段显著,而在创新集群解体阶段不显著; 高度开放的网络则恰好相反; 在网络演化的其他阶段,小世界性对创新绩效并未构成显著影响． 这很可能是由于在创新绩效显著变化的阶段,创新个体的能动性较强,更倾向于接受新知识: 在低度开放的网络中,创新绩效显著上升的阶段主要集中于新的创新集群建立的阶段,而在高度开放的网络则主要集中于原有创新集群解体的阶段． 对中国专利合作发明数据的回归分析结果为该研究结论提供了实证依据． 因此,应当保持一个适度集聚的创新网络,以保证其不会传播过多冗余信息; 应在创新网络演化的不同阶段采用不同的开放策略,以充分利用高度和低度开放的创新网络演化到不同阶段所发挥的优势,同时,应当主动按照新兴技术下的新形势适当调整创新网络结构关系．     

谦卑领导行为与下属工作投入：有中介的调节模型

谦卑领导行为是近年来新出现的一个广受研究者和管理实务者关注的领导主题,其有效性和作用机制有待更多实证研究的深入探讨.基于自我概念衍生理论视角,采用问卷调查法收集375份员工数据,在检验共同方法偏差和问卷信效度的基础上,通过有中介的调节作用检验程序,考察谦卑领导行为对下属工作投入的影响,并探讨下属组织自尊的中介作用和权力距离的调节作用.研究结果表明,谦卑领导行为能够显著正向预测下属的工作投入;下属的权力距离在谦卑领导行为与下属工作投入关系间发挥有中介的调节作用,对于高权力距离的下属,谦卑领导行为对其工作投入的积极影响更强;下属权力距离的调节作用通过下属组织自尊的中介实现,当下属权力距离较高时,谦卑领导行为通过下属组织自尊的中介对其工作投入产生的积极影响更加强烈.     

消费者偏好背景下的买方抗衡势力形成与影响

与以往研究关注产业链内部因素不同,对于厂商利用消费者偏好因素的买方抗衡势力形成机理和影响的研究考虑了市场环境因素作用的买方抗衡势力理论研究新视角。将消费者偏好的市场差异建模为同一产品在不同市场中消费者效用差异,基于外部选择博弈和Hotelling博弈的基本原理,构建综合反映上、下游厂商纵向关系和市场内厂商间横向关系的博弈模型。对下游厂商跨市场并购前后均衡结果的变化进行考察,得到下游厂商利用消费者偏好特征形成自身买方抗衡势力的机理以及该行为对厂商本身和竞争对手谈判势力、收益水平和规模带来的影响。研究结果表明,存在消费者偏好差异的市场间厂商并购行为不仅可以提升合并厂商自身买方抗衡势力和收益,也不会对竞争对手造成负面影响,甚至可以提高部分竞争对手的买方抗衡势力、收益和规模,产生与水岸效应相反的结果。同时,对均衡结果进行福利分析,表明下游厂商的并购行为将提高总产业利润,但会降低消费者剩余和社会福利。     

非正式知识网络关系强度分布与知识流动小世界

针对非正式知识网络上的知识流动小世界现象,利用多主体建模与仿真方法,基于经典WS小世界网络建立知识流动模型,研究了知识流动小世界现象的发生机理.结果表明,知识流动小世界现象的内在机理为网络宏观关系强度分布呈现指数衰减;网络知识流动效率对于网络宏观关系强度分布具有高度敏感性;网络平均关系强度均值的不同会影响网络关系强度分布的演化速度,但不影响其演化模式.     

论管理学与意识形态——兼与李平商榷

"话语"是承载了一定的意识形态的"陈述",中国管理学界完全可以创建自己的学派,可是如果仍然采用主流的方法,虽然也可以称之为中国学派,但谈不上话语权问题。《试论中国管理研究的话语权问题》基于"话语能力"的所谓"策略"实际上是将"中国管理研究话语权策略"演变成"西方主流管理理论在中国进行扩散的策略"。当然,若将学术本土化演变成学术民族主义,将重点放在对西方主流的价值评判与道德批评上,同样无助于"管理的中国理论"话语权的取得——学术研究的话语权应在学术市场的竞争中取得——因此,其策略就只能是理解、宽容与构建学术竞争的公平市场。     

ANP-Fuzzy方法在电力企业绩效考核中的应用研究

本文针对我国电力企业绩效考核指标间具有相互影响关系,且大多定性指标难以精确界定的特点,给出了一种ANP-Fuzzy的综合评价法。首先,建立系统的绩效考核指标体系及具体的考核标准,为考核员工提供规范化的评价框架。其次,设计能够准确反映考核指标间相互关系的ANP网络结构,以此确定具有联系性的指标权重。在此基础上,设计能够精确界定定性指标的Fuzzy多层次评价结构,以此评价具有模糊性的员工绩效。最后,运用Super Decisions软件和Matlab软件,给出了基于ANP-Fuzzy方法的电力企业员工绩效考核实证研究。结果表明,该方法合理可行,能够真实反映员工绩效,并且能够有效区分员工自身不同方面的绩效差异以及员工之间的绩效差异。这对帮助员工发现绩效改进空间,不断提升自身绩效具有明显效果。同时,该方法可以借助软件实现,将其应用于电力企业绩效考核具有较强的操作性。     

Bayesian approach for optimal PV system sizing under climate change

This paper proposes a novel statistical approach for optimally sizing a stand-alone photovoltaic (PV) system under climate change. Traditionally, the irradiation profile of a typical day or year is used to size PV systems. However, facing the global warming crisis as well as the fact that no two years would have the same weather condition for a single site, this often makes the traditional way failed in the extreme weather conditions. This paper presents a method to statistically model the trend of climate change year by year and put it into the sizing formula, so that the results are optimal for the current weather condition and confidential for the future as well. Hence, the suitable sizes for the PV array and the number of batteries are obtained by pure computation. This is different from the traditional simulation-based sizing curve method. An economic optimization procedure is also presented. In addition to the capital and maintenance costs, a penalty cost is introduced when service fails. A new statistic-based reliability index, the loss of power probability, in terms of threshold-based Extreme Value Theory is presented. This index indicates the upper bound reliability for applications and provides rich information for many extreme events. A technological and economic comparison among the traditional daily energy balance method, sizing curve method and the proposed approach is conducted to demonstrate the usefulness of the new method.     

Quantifying the Hurricane Catastrophe Risk to Offshore Wind Power

The U.S. Department of Energy has estimated that over 50 GW of offshore wind power will be required for the United States to generate 20% of its electricity from wind. Developers are actively planning offshore wind farms along the U.S. Atlantic and Gulf coasts and several leases have been signed for offshore sites. These planned projects are in areas that are sometimes struck by hurricanes. We present a method to estimate the catastrophe risk to offshore wind power using simulated hurricanes. Using this method, we estimate the fraction of offshore wind power simultaneously offline and the cumulative damage in a region. In Texas, the most vulnerable region we studied, 10% of offshore wind power could be offline simultaneously because of hurricane damage with a 100‐year return period and 6% could be destroyed in any 10‐year period. We also estimate the risks to single wind farms in four representative locations; we find the risks are significant but lower than those estimated in previously published results. Much of the hurricane risk to offshore wind turbines can be mitigated by designing turbines for higher maximum wind speeds, ensuring that turbine nacelles can turn quickly to track the wind direction even when grid power is lost, and building in areas with lower risk.     

Inference Based on Conditional Moment Inequalities

In this paper, we propose an instrumental variable approach to constructing confidence sets (CS's) for the true parameter in models defined by conditional moment inequalities/equalities. We show that by properly choosing instrument functions, one can transform conditional moment inequalities/equalities into unconditional ones without losing identification power. Based on the unconditional moment inequalities/equalities, we construct CS's by inverting Cramér–von Mises‐type or Kolmogorov–Smirnov‐type tests. Critical values are obtained using generalized moment selection (GMS) procedures. We show that the proposed CS's have correct uniform asymptotic coverage probabilities. New methods are required to establish these results because an infinite‐dimensional nuisance parameter affects the asymptotic distributions. We show that the tests considered are consistent against all fixed alternatives and typically have power against n^−1/2‐local alternatives to some, but not all, sequences of distributions in the null hypothesis. Monte Carlo simulations for five different models show that the methods perform well in finite samples.