The rapid development of the Internet,mobile and social media has brought many new online advertising and marketing channels to e-commerce enterprises.Under the background of integrated marketing and multi-channel online advertising,users may not only contact the online advertising channels once before purchase,and the channels contacted successively will affect consumers jointly but not independently.Therefore,in the effectiveness study of multi-channel online advertising,it is also necessary to consider the multi-channel combination effect of online advertising.The click-stream data of this study comes from the third party insurance agency website in Nanjing.After extracting,cleaning and transforming,the basic variables of this study are obtained.To explore the combinatorial effect of multi-channel online advertising,according to the choice set theory,related research hypotheses based on the classified combination effects of online advertising channels are proposed,and then the COX model is constructed.Based on the channel click path data of individual users in e-commerce enterprises,the relevant variables are extracted,and the COX model is verified by empirical analysis.Then the channel combination effect of the same channel,cross-channel and channel classification is analyzed.The results show that the combination of advertising channels from firm-initiated chan-nels to brand search of customer-initiated channels will have a positive combination effect on purchases,while the combination of advertising channels from customer-initiated channels to firm-initiated channels will have a negative impact on purchases.In addition,the order of channels that move from general-purpose search to brand-type search has a positive combination effect on purchases.The data are grouped by gender to explore the differences in advertising effects of users of different genders.The results can help enterprises establish their marketing mix of advertising channels and mitigate the cost of ineffective advertising channel mix.
Omni-channel retailing is a new model that combines online store and offline physical store.In the context of omni-channel retailing,retailers provide consumers with more options for return channels,including same-channel return mode(offline/online purchase,offline/online return)and cross-channel return mode(online store purchase,offline physical store return).For retailers,although the cross-channel return service can reduce the logistics cost of returns to an extent and increase the consumer traffic of offline physical store,the choice of return channels and the re-sale of returned products brings challenges to the inventory management.If the inventory is higher than consumer demand,it will bring the pressure of inventory holding cost to retailers.If inventory falls short of consumer demand,retailers will incur out-of-stock cost.Based on the analysis,the inventory decision under omni-channel retail considering returns is studied in two cases:same-channel returns and cross-channel returns.On the basis of considering consumer returns and the resale of returned products,the newsvendor model is used to construct inventory decision models in the case of same-channel returns and cross-channel returns aiming at maximizing retailers'profit,and the models are analyzed.The numerical analysis shows that,the proportion of online consumers using ROPS(reserve-online and pick-up-and-pay-in-store)to purchase when out of stock,the proportion of resale of returned products,the proportion of online returns,and the proportion of cross-channel returns have a significant impact on the inventory of the offline physical store and online store and profit.Specifically,under the cross-channel returns mode,the inventory decision quantity of offline physical store should be directly proportional to the proportion of online consumers using ROPS to purchase when out of stock or the proportion of online returns,and inversely proportional to the proportion of resale of returned products or the proportion of cross-channel returns;the inventory decision quantity of the online store should be directly proportional to the proportion of online consumers using ROPS mode to purchase when out of stock,the proportion of online returns or the proportion of resale of returned products,and inversely proportional to the proportion of cross-channel returns.The difference between the same-channel returns model and the cross-channel returns model is that the proportion of online returns or the proportion of resale of returned products has no significant influence on the inventory decision in the offline store.In particular,when the cross-selling profit is large,the profit of the retailers will increase with the proportion of cross-channel returns,but the profit of the retailers may decrease with the proportion of online returns.In terms of practical application,although there is still a certain gap between the model construction and the reality,the relevant conclusions can also provide a certain theoretical foundation and decision-making reference for the retail-ers'omni-channel retailing operation management.
It is of great significance to study the risk spillover effect between soybean futures markets in China and the United States under extreme circumstances.A dynamic model based on the average method and the method of the bureau of digit conditional value at risk are combined to study.The risk spillover effects of Chinese soybean futures market influenced by policy and economic environment change is studied.And it analyses the risk spillover effects influenced by spot market,downstream products of soybean,macro-economic variables,international market trading,etc.It is found that different policies and economic environments affected the soybean futures market risk spillover effects in different ways,such as reserve policy stability of China's soybean futures price fluctuations,the night trading system to increase the interaction of the agricultural product futures market of China and the United States to increase the degree of impact on the domestic market in the international market.At the same time,the study shows that each control variable has a different contribution to the change in the risk spillover effect of the Chinese and American agricultural futures markets in different periods.For example,soybean oil futures price and WTI crude oil price have a great influence on the risk spillover effect in the early stage of the night trading system.The impact of shipping indices,exchange rates and WTI crude oil prices was at a low level during the COVID-19 pandemic.
创业可以激发北方经济活力,解决南北方经济差异持续扩大的问题.但在缺乏全行业和中小微企业数据背景下,尚无研究对所有创业群体行为进行深入分析,导致区域发展不平衡问题始终缺乏一个稳妥且科学的应对方案.本文首度采用高频全量企业大数据,构建了我国277个城市的产业集聚指标,深入研究了集聚对南北方全量企业创业活动的影响,发现集聚能够促进城市创业.南方集聚边际效应要大于北方.这与集聚在南北方的作用机制有关.制度环境对集聚效应具有调节作用.在生产要素不断"南流"趋势下,北方地区的政府需要积极主动地创造合理的制度环境,引导企业家和劳动力"回流"集群,实现专业化协同下的产业发展与结构升级.本文是海量数据助力政府实现治理现代化和区域发展科学布局的一个重要尝试.本文结论还对推进大众创业万众创新和多渠道就业具有启发性意义.
When multi-individual learns from experience, the matching between information and analytic methods will affect the learning efficiency. The concepts of the first matching and the second matching between information and analytic methods are put forward in this paper, when multi-individual learns from experience, the analytic method needs to match the information under the learning goal, that is, both the first matching and the second matching are satisfied. In this paper, a multi-individual learning process mechanism model based on the matching between information and analytic methods is constructed. It is considered that under the constraints of the available resources, multi-individual can improve the first matching and second matching between information and analytic methods to achieve the desired results, by dynamically switching between the two ways of searching information based on analytic methods and searching analytic methods based on information, thus improving the learning efficiency. Some practical tools, such as Delphi technique and nomial group technique, are also listed. The process that multi-individual improves the matching of information and analytic methods to improve learning efficiency are paid attention to, and theoretical basis and practical suggestions are provided for improving learning efficiency.
How investors’ information acquisition behavior affects their trading decisions is a basic issue in the capital market. Research in the field of behavioral finance holds that attention is a scarce cognitive resource for individuals. Therefore, investors usually selectively pay limited attention to a few assets, which results in the phenomenon of “attention-driven trading”. Although abundant existing literature supports investor attention’s driving effect on trading, whether such an effect is asymmetric under different market situations is far from being revealed. To fill this gap, this study examines the asymmetric driving effects of investor attention frequency on market trading at good and bad market times.Based on data on users’ daily adoption of securities service mobile applications in mainland China, Cai Wenwu and Lu Jing（2019） are followed and investors’ financial attention frequency（IFAF） is measured from two aspects: the number of start-up times and the online duration. Consistent with Cai Wenwu and Lu Jing（2019）, IFAF significantly promotes trading activity in the market, which supports the “attention-driven trading” hypothesis. However, compared with bad market times, such as low-return periods, bear cycles and low-sentiment stages, the driving effect of IFAF on trading activities is significantly weaker during the good market times, i.e., high-return periods, bull cycles, and high-sentiment stages. This finding supports the “utility of information” hypothesis, suggesting that investors obtain financial information frequently to achieve more psychological pleasure at good market times, which leads to a lower marginal driving effect of unit attention frequency on transactions. For comparison, the asymmetric impact of investor attention measured by Baidu search volume index（BSVI） on market trading is also examined. BSVI shows a more substantial marginal driving impact on trading at good market times than at bad market times. Since BSVI reflects the number of investors searching for specific information through the Internet, the above result indicates that investors are stronger risk-seeking under good market situations. Finally, a series of robustness tests are made by using instrumental variables, adopting other measures of main variables, and investigating the asymmetric incremental effect of IFAF on trading volume, etc. The findings remain unchanged.It helps to make up for the gap in the asymmetric driving effect of attention on trading in the existing literature and to deepen the understanding of the relationship between investors’ information acquisition activities and trading activities. Meanwhile, our findings reveal the essential difference between investor attention indexes measured from the quantitative and frequency dimensions, which has a certain significance for exploring the internal mechanism of how attention drives trading. More importantly, given the additional psychological utility of information to investors, practical enlightenment for investors is also provided to optimize information acquisition decisions under different market conditions, thus reducing irrational trading decisions.
近年来,在更新产品与旧产品存在竞争关系的市场环境下,更新产品的延期投放成为许多企业的产品运营策略.现有竞争扩散研究重点关注外部竞争下一种产品的扩散最大化问题,尚没有考虑内部竞争下(如旧产品与更新产品的竞争)全部产品的扩散最大化问题.本文研究非退市条件下更新产品投放时机和种子优化问题:在一个已存在旧产品的社会网络G(N,E)中,产品以竞争扩散模型的P形式传播其影响力,更新产品投放时,新旧产品同时扩散,如何选择投放阶段 t和p个更新产品的种子使得新旧产品利润之和最大化.本文提出了一种基于竞争的确定阈值模型,并构建了该问题的整数规划模型,设计了求解大规模问题的多阶段贪婪算法.计算实验显示,该算法具有较高求解效率,比传统贪婪算法提高了 88％;该算法具有较高求解质量,比随机算法提高了 651％,比度数下降算法提高了 9.5％.同时,发现更新产品种子数量多、计划阶段限制大、单位利润大时,延期投放使得产品利润更高.
二手电子产品市场中产品的质量信息在零售商与消费者之间、零售商与第三方保修商(the third warranty provider-3WP)之间均存在着严重的不对称现象,且由于消费者个体决策的分散性,很难以其为激励主体进行有效激励策略的设计.针对这一问题,在分析二手电子产品质量信息的真实性与准确性对第三方保修商收益影响的基础上,考虑零售商拥有产品检测质量及检查努力的私有信息下,以3WP为激励主体,结合利润分享契约机制,构建3WP与零售商之间的委托-代理模型,以激励零售商披露产品质量的真实信息.研究发现,3WP通过适当设定分享计划中的激励参数,可以对零售商产品质量信息的真实披露起到有效激励作用,从而促进二手电子产品市场的健康发展.最后用数值算例验证了理论结论.
为研究企业社会责任(corporate social responsibility,CSR)对技术授权以及企业产品定价的影响,本文构建了技术授权与产品分销两阶段动态博弈模型.通过模型求解,得到了技术提供商最优技术授权合同设计以及品牌制造商、零售商的最优产品定价决策,分析了品牌制造商承担SR对技术授权、产品分销以及消费者福利的影响.研究发现:品牌制造商承担SR不会影响技术提供商对技术授权合同形式的选择,其总是会选择固定收费形式的技术授权合同或者"版税提成+固定授权费"形式的技术授权合同;品牌制造商承担SR会导致产品批发价格、零售价格的降低和产品需求的增加,有利于提升零售商、供应链系统的利润水平和消费者福利;品牌制造商承担SR能激励企业让渡部分利润来提升消费者福利,但也会导致品牌制造商利润水平的降低;不对称信息会导致技术提供商的利润损失,但有利于品牌制造商获得额外信息租金.
政府在面临大规模传染疫情应急管理时存在应对能力不足的情形,这与没有开展关键因素识别有关.结合新冠疫情,提出基于前景理论的大规模传染疫情应急管理决策方法.该方法将专家给出的决策矩阵作为参考点,将应急管理实际参与方给出的决策矩阵作为决策评价点,其中决策评价点是在充分考虑评价者的决策心理和知识结构基础上,采用实数和区间数构建的混合评价信息,然后利用二元语义将混合评价信息进行转化,保障评价信息的一致性,接着建立前景初始直接关联矩阵,通过决策步骤识别出五个关键因素,同时比较了专家评价者、所有评价方综合和新决策方法的决策结果,提出了健全疫情响应直报机制、增强防疫物资储备能力、提高疫情物资调度能力、提高应急决策指挥能力和加强基层网格化防控能力的决策建议.
本文建立了由制造商和电商平台组成的供应链,制造商在电商平台上采用分销和直销两种渠道,分销是指电商平台从制造商处批发产品进行销售;而直销是指制造商在电商平台上直接销售,电商平台分享一定的收益.针对具有资金约束的制造商可采取内部融资(电商平台)或外部融资(银行),进一步构建了基于电商平台的线上双渠道融资模型,研究了不同融资模式下供应链定价策略及最优利率决策,探讨了电商平台提供融资服务的先决条件以及制造商融资策略选择.研究表明:当电商平台和银行贷款的相对利率在一定范围内变化时,制造商和电商平台在相同融资模式下实现利润最大化,进而实现"双赢";当两种融资模式下的贷款利率相同时,电商平台融资模式下的批发价格更高,但分销渠道的零售价格在此融资模式下更低;在最优贷款利率下,当电商平台收益分成比率、交叉价格弹性系数或分销渠道消费者所占比例较大时,制造商会选择电商平台融资;在电商平台融资模式下,制造商和电商平台都愿意提供较低的零售价格,促进市场需求增加.
通过声誉机制缓解信息不对称是网络借贷行业的核心问题.在经典的两方借贷模型中引入声誉机制,运用演化博弈理论,构造了包括P2P平台-商业银行-投资者的三方博弈模型,分析系统的演化稳定策略,以及声誉信号在三方动态演化博弈中的作用.研究表明,商业银行对P2P平台的认证有助于形成正反馈的声誉机制,而能否形成稳定的分离均衡的关键在于商业银行能否发挥严格的筛选效应.增加商业银行的声誉损失、增加商业银行对P2P平台的认证费用,均有助于提高商业银行选择"积极认证"行为策略的概率,并最终实现分离均衡.实证结果表明,获得商业银行存管能够改善P2P平台的生存状况,但受限于积极认证的商业银行比例较低、且多为中小型商业银行,抑制了商业银行的第三方认证作用,不利于声誉信号发挥筛选作用.本文为理解互联网金融领域的声誉机制作用提供了一个新的研究视角.
竞价上网是电力行业实现市场化的基础条件,竞价机制设计是深化市场化改革亟需探讨的议题.基于离散多物品逆向拍卖的理论框架对电力市场竞价上网的交易机制进行建模,在考虑需求状态、市场结构和报价区间等现实情形下刻画出统一价格(UPA)和差别价格(DPA)两种常用竞价机制下发电厂商的均衡报价策略,并从期望收益和生产效率两个视角进行机制比较.结果表明:(1)在低需求状态下,两种机制的市场均衡价格均为未被调用的最高效率发电厂商(机组)的边际成本;在高需求状态下,UPA存在多重纯策略均衡,DPA出现混合策略均衡.(2)在需求确定(较短报价区间)的情形下,DPA比UPA更可能形成更低的均衡价格;在需求不确定(较长报价区间)的情形下,对称发电厂商在两种机制下具有相同的期望收益.(3)两种机制在生产效率方面的比较是不确定的,具体取决于模型参数和UPA多重均衡的选择.本文的研究结论为完善电力竞价上网交易机制和深化电力体制改革提供理论基础和决策借鉴.
契约机制是供应链合作管理的主要方式之一,智慧供应链管理需要新的基于人工智能的智慧契约的参与.本文尝试把人工智能技术融入供应链合作管理,基于深度强化学习来设计依托"机制学习、行为约束和反馈惩罚"的人机协同的智慧决策机器人(核心算法),并用于提供智慧契约(批发价契约).通过开展行为特征、合作能力和环境适应三类智慧实验,研究发现:利用人机协同思想的智慧决策机器人具备设计批发价契约的能力,并且依托大数据训练,智慧契约不依赖于直接共享需求信息;智慧契约显示出牺牲个人(制造商)利润以提高供应链整体收益的合作管理意向;同时,智慧契约对环境改变和扰动具有良好的适应性.本文的研究框架与人机协同思想为供应链合作管理提供了新途径.
针对绿色供应链中参与主体采用柔性期权、远期或二者组合情况下的最优决策过程进行了建模分析,进一步通过仿真对比研究了不同合约条件下,产品回收价值、远期合约及期权合约执行价格对决策主体的协同影响.研究发现,在供应商提供混合合约订购方式前提下,其仅提供柔性期权合约方式较仅提供远期合约方式的期望收益更高;供应商可以采取将远期合约执行价格调高的策略,引导零售商选择柔性期权合约方式.在供应商仅提供柔性期权合约订购方式的前提下,当期权合约执行价格较高、产品回收价值较低时,采用远期合约方式为零售商最优订购策略.但零售商采用柔性期权合约方式会使其收益降低,供应商可通过收益共享机制以激励零售商采用该种方式.
在两级供应链中,零售商需要向供应商采购产品以满足随机的市场需求,供应商需要在市场需求观测到之前进行备货.供应商的自有资金是有限的,在必要时可向银行借款.在完全竞争的资本市场中,银行会根据贷款数量以及相应的风险决定其利率,使其期望回报率等于无风险利率.为了降低供应商的融资成本,促使其提高备货量,零售商有动机为供应商提供贷款担保.供应商和零售商进行Stackelberg博弈,首先由零售商决定采购价格和担保比例,然后由供应商决定备货量.供应商和零售商均为风险中性的决策者,以最大化自身期望利润为目标.研究结果表明:对给定的采购价格和担保比例,供应商的期望利润是其备货量的拟凹(quasi-concave)函数,其最优备货量是唯一的.零售商的最优策略是提供全额担保,零售商的最优期望利润是供应商自有资金的减函数.本文还讨论了存在破产成本的情形,发现破产成本不利于零售商,但不会改变零售商的最优担保策略.
在对效率与绩效概念进行甄别的基础上,给出了一种兼顾效率与效果的决策单元投入产出绩效评价方法.有别于决策单元的技术效率评价,该方法一并考虑了决策单元的前沿技术实现与投入产出目的达成.在将决策单元投入产出绩效的影响因素归纳为技术因素、投入配置因素和投入规模因素的基础上,分别构建了决策单元的绩效型综合效率模型、绩效型技术效率模型和绩效型投入配置效率模型.这些模型的解给出了决策单元的投入产出绩效值、决策单元投入产出绩效形成过程中各种影响因素的贡献以及决策单元基于不同影响因素的绩效改进目标,从而为决策单元在现实环境约束下进行绩效提升途径选择提供了较为充分的信息.
本文提出了一种生物物理经济学思想的碳排放权价值区间测算模型,使用温室气体环境容量价值解释碳排放权内在价值,以区别于使用企业边际减排成本或一级、二级市场价格等博弈结果的解释方法.虽然气候与经济动态综合模型(DICE模型)使用碳排放的社会成本(SCC)概念部分地实现了这个目标,但如果模型经历"排放—碳循环—气候变化—经济损失"的传导后对贴现率过于敏感,那么结果可能会包含较大的主观性.本文首先研究温室气体环境容量的价值形成过程,通过概率分布描述所有影响碳排放权价值的因素;其次使用蒙特卡洛方法对这些因素进行10000次试验,最终得到碳排放权价值概率分布和模型的敏感性分析结果.本文还提出了碳排放权参照价格模型,用于评估每单位碳排放应为本国环境容量生产以及国际碳机制对本国的货币损失所支付的货币量.参照价格是符合《巴黎协定》中"公平以及共同但有区别的责任和各自能力原则"的理论碳价,考虑了不同国情.本文的模型计算完全根据中国实际情况获得关键参数和数据,为中国碳市场健康发展以及气候变化谈判提供理论依据.
随着短视频直播平台的发展和壮大,网红直播带货成为消费者购物的新形态.然而这一过程中网红带货产品质量差和维权难的问题也不断涌现.本文基于此背景,使用一个三方演化博弈模型研究了网红、短视频直播平台、消费者的三方策略选择与演化,并且进一步讨论了直播网红风险态度变化对带货和选品努力水平的影响.研究发现:随着消费者从网红直播带货的产品中获得的功能性收益增加,消费者越倾向于选择积极追责;而随着情感收益增加,消费者更倾向于选择消极追责.这种虚拟亲密关系带来的情感收益在一定程度上解释了当下网红直播带货产品质量问题泛滥的现象.其次,收益类参数对均衡状态的影响远比系数类参数的影响作用显著,这应该作为监管的重点方向.再次,消费者面对网红直播带货产品质量问题,会受到产品种类的影响,产品的功能性收益越高,消费者维权动力更充足.最后,直播带货网红的风险态度会影响其选品和带货努力,风险厌恶型网红的带货和选品努力水平比确定情形下努力水平更低.
针对现有决策方法理想解确定不合理和秩反转的问题,提出一种基于q阶orthopair模糊集(q-rung orthopair fuzzy set,q-ROFS)和参考理想法(reference ideal method,RIM)的多准则决策方法.在q-ROFS概念基础上分别定义了 q-ROF可能度和q-ROFS交叉熵,并讨论其相关性质;将传统RIM在q-ROF环境下进行扩展,构建一种基于q-ROFRIM的多准则决策模型,利用q-ROF可能度确定准则最优权重以及作为q-ROF信息差异程度测度的q-ROF交叉熵替代传统RIM中距离测度.最后通过实例分析说明该方法可行和有效,方法对比结果表明,所提方法不仅使决策过程更加科学和合理,而且能够解决秩反转问题.
