消费者异质性下基础产品及其附加品的供应链定价策略研究

建立一个由制造商和销售商组成的二级供应链模型,在销售商向消费者销售制造商基础产品的同时提供可选附加品的供应链多产品定价背景下研究了制造商及销售商的定价策略。以消费者对附加品价值增值存在的异质性将基础产品及其附加品的消费市场细分为基础产品单独消费市场及产品共同消费市场。基于市场细分比例以及附加品价值增值程度,讨论了不同条件下制造商及销售商的定价策略以及所形成的供应链定价均衡,分析了产品定价均衡与市场细分比例、附加品价值增值程度之间的关系。     

考虑策略型消费者和退货的零售商降价促销决策研究

基于零售商降价促销问题,引入策略型消费者,考虑到异质性消费者有可能对商品不满意,构建两期决策模型,旨在从退货和价格路径优化两方面提高零售商利润。研究给出(不)允许退货时,零售商面对策略型消费者的定价建议,指出零售商制定价格要在一定程度上参考商品类型。订货量相同时,给出策略型消费者降低零售商的期望利润的条件;面对短视型或者策略型消费者时,允许退货可在特定条件下提升零售商利润。消费者退货成本越高,对策略型消费者消极影响的抑制作用越明显,零售商的利润增长越显著。最后,通过数值算例分析了在两种退货决策以及不同退货成本下产品类型对零售商定价的影响,以及退货措施对策略型消费者消极影响的作用。     

考虑跟随企业搭便车行为的创新企业定价策略研究

文章通过构建博弈模型探讨了存在跟随企业搭便车行为时创新企业的定价策略及定价模式(稳定定价或动态定价),并分析了忠实消费者规模及需求溢出效应等特征对创新企业定价决策与利润的影响.结果表明,无论创新企业采用稳定定价或动态定价模式,跟随企业搭便车行为均会降低创新企业利润,需求溢出效应的增强总能使创新企业和跟随企业均获得更高的...     

考虑传统双渠道和BOPS双渠道的消费者和零售商决策研究

本论文在全渠道零售的BOPS模式下,考虑允许退货研究消费者渠道选择问题。首先研究传统双渠道和实施BOPS的双渠道下的消费者和零售商决策。然后加入退货设定,对二者的决策进行重新研究和对比分析。研究发现:不考虑退货的情况下,引入BOPS模式能够提升实体店对零售商及消费者的吸引力;退货情况下,引入BOPS使消费者更愿意选择线下购买和销售,但不一定总能带来销售总量的增加;实施BOPS时,退货允许让零售商更愿意在实体店销售,同时消费者的购买渠道选择也变得复杂。     

Predicting Fraud Victimization Using Classical Machine Learning

Protecting financial consumers from investment fraud has been a recurring problem in Canada. The purpose of this paper is to predict the demographic characteristics of investors who are likely to be victims of investment fraud. Data for this paper came from the Investment Industry Regulatory Organization of Canada’s (IIROC) database between January of 2009 and December of 2019. In total, 4575 investors were coded as victims of investment fraud. The study employed a machine-learning algorithm to predict the probability of fraud victimization. The machine learning model deployed in this paper predicted the typical demographic profile of fraud victims as investors who classify as female, have poor financial knowledge, know the advisor from the past, and are retired. Investors who are characterized as having limited financial literacy but a long-time relationship with their advisor have reduced probabilities of being victimized. However, male investors with low or moderate-level investment knowledge were more likely to be preyed upon by their investment advisors. While not statistically significant, older adults, in general, are at greater risk of being victimized. The findings from this paper can be used by Canadian self-regulatory organizations and securities commissions to inform their investors’ protection mandates.     

EXISTENCE OF EQUILIBRIUM FOR ECONOMIES WITH INFINITELY MANYCOMMODITIES AND INFINITELY COUNTABLE CONSUMERS

1.IntroductionManyreseachershavebeendoingresearchesoneconomieswithindnitelydimensionalcommodityspacesandmadeagreatdealofachievementssinceBewleyl2]definedcommodityspaceasLoo(M,M,p).ThefaCtthatmostoftheorderedvectortopologicalspacesconsideredascommodityspaceshaven'tinteriorpointsintheirpositiveconesraisesduncultiesinprovingtheealstenceofequilibriumforthistakeofmodel.TOovercomethemainobstacle,Mas-Colelll31proposeduniformpropernessonpreferenceweakerthantheassumptionof"nonemptinessofinferiorpoil…     

我国城市消费者寿险购买行为的影响因素及预测

通过文献回顾、专家访谈和问卷调查,确定了8种对消费者寿险购买行为有重要影响的个体态度变量。根据在全国10个城市进行问卷调查所获得的态度变量和人口统计学变量数据,采用判别分析和Logistic回归的方法分别建立了消费者寿险购买行为的预测模型。对模型的评价表明,2个模型都有较好的预测精度;若将两个模型的联合应用,能取得更好的预测效果,对寿险营销管理具有重要的参考价值。     

考虑消费者行为转化的零售商两阶段动态定价研究

考虑消费者呈现的不同行为特征,将其划分为两类:策略型和短视型。不同类型的消费者对同一产品会给出差别估价,假设这一估价呈随机分布,研究存在消费者行为转化的产品两阶段动态定价问题。引入消费者剩余对策略型和短视型消费者的市场响应特征进行描述,考虑贴现率,建立两阶段动态定价模型,分析不同类型消费者的差别决策过程,并且采用逆推法求解动态定价模型,得到最优价格策略。研究表明消费者期望购买数量与降价幅度都和转化率成负相关,此外,通过对比考虑转化和不考虑转化的两种情形下零售商总的期望利润,发现如果零售商没有考虑转化率而仅根据市场初期调查的消费者构成来进行产品定价将会给零售商带来损失,并且两种情形的利润差值随转化率的升高呈先上升后下降的变化趋势。     

基于电子优惠券的多渠道整合策略研究

在由制造商的网络直销渠道和混合零售商的实体渠道和网络渠道所构成的多渠道供应链中,考虑存在消费者无缺陷退货和消费者对购买渠道偏好的异质性,研究了混合零售商的基于电子优惠券的多渠道整合策略问题。探明了混合零售商愿意采用电子优惠券实现多渠道整合的临界条件,揭示了多渠道整合策略对市场均衡的影响,并且构建了基于零售商订货量和接受消费者退货量的转移支付机制来协调多渠道供应链。     

Clustering electricity consumers using high‐dimensional regression mixture models

A massive amount of data about individual electrical consumptions are now provided with new metering technologies and smart grids. These new data are especially useful for load profiling and load modeling at different scales of the electrical network. A new methodology based on mixture of high‐dimensional regression models is used to perform clustering of individual customers. It leads to uncovering clusters corresponding to different regression models. Temporal information is incorporated in order to prepare the next step, the fit of a forecasting model in each cluster. Only the electrical signal is involved, slicing the electrical signal into consecutive curves to consider it as a discrete time series of curves. Interpretation of the models is given on a real smart meter dataset of Irish customers.