慧学（30）：精读博士论文直播背景下的平台销售模式选择（1）

摘要：In this issue, the editor will introduce the Platform sales model selection considering consumer return behavior in the context of

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“慧学（30）：精读博士论文《考虑消费者行为的平台供应链销售策略优化研究》直播背景下考虑消费者退货行为的平台销售模式选择（1）”

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"Hui Xue (30): Intensive reading of doctoral dissertation

‘Optimization of Platform Supply Chain Sales Strategies Based on Consumer behavior’ platform sales model selection considering consumer return behavior in the context of live streaming (1)”

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本期推文小编将从思维导图、精读内容、知识补充三个方面为大家介绍博士论文《考虑消费者行为的平台供应链销售策略优化研究》直播背景下考虑消费者退货行为的平台销售模式选择（1）。

In this issue, the editor will introduce the Platform sales model selection considering consumer return behavior in the context of live streaming (1) of the doctoral dissertation "Optimization of Platform Supply Chain Sales Strategies Based on Consumer behavior" from three aspects: mind mapping, intensive reading content, and knowledge supplement.

一、思维导图（Mind mapping）

二、精读内容（Intensive reading content）

1、模型构建（Model building）

文章首先利用三个关键参数来描述消费者的退货行为特征：一是传统渠道中的退货比例，二是退货造成的损失，三是直播情境下因质量不匹配而产生的增加率。在线下或常规销售中，退货往往源于商品质量未能达到消费者的预期；而在直播销售中，由于过度包装以及消费者对商品价值的高估，退货概率会进一步提高。在现实情况下，退回的商品通常没有残值，消费者也无需为退货支付任何费用，相应的成本大多由平台、商家或保险机构承担。

The article first characterizes consumer return behavior using three key parameters: the return rate in traditional channels, the losses incurred through returns, and the rate of increase due to quality mismatch in livestreaming. In offline or conventional sales, returns often stem from product quality failing to meet consumer expectations; in livestreaming, the probability of returns is further increased due to excessive packaging and consumers' overestimation of the product's value. In reality, returned goods typically have no residual value, and consumers do not incur any return fees; the corresponding costs are largely borne by the platform, the merchant, or insurance agencies.

随后，文章对两类模式进行了对比分析。在模式R下，退货费用由平台承担，平台的利润来源于销售收入，扣除批发支出、退货损失以及直播相关开销后所得；而供应商的主要收益则来自批发环节。相比之下，在模式A中，直播和退货的成本转由供应商负担，平台则按照扣除退货后的净销售额收取一定比例的佣金，这与淘宝、京东等电商平台的实际运作方式相符。

The article then compares and analyzes the two models. In Model R, the platform covers the cost of returns, with profits derived from sales revenue after deducting wholesale expenses, return losses, and livestreaming-related expenses. Suppliers, on the other hand, primarily earn revenue from wholesale. In contrast, in Model A, the costs of livestreaming and returns fall to the supplier, while the platform receives a commission based on net sales after deducting returns. This aligns with the actual operating practices of e-commerce platforms like Taobao and JD.com.

表3-2展示了在引入退货情境时，不同定价策略对应的最优解，其中包括可行域、最优直播投入水平以及最优批发价格。接着，引理3.1通过严格证明表明，在退货模型下，只有当相关参数处于某一阈值范围内，平台或供应商才会选择开展直播，否则最优决策为不进行直播。最终的结论指出，除直播成本及其系数外，退货率也是决定是否开展直播的重要因素。在定价策略SH和D中，仅当退货率低于阈值时才会进行直播，因此，消费者的退货行为在很大程度上影响了平台的销售模式选择和供应链决策。

Table 3-2 shows the optimal solutions for different pricing strategies when introducing a return scenario, including the feasible domain, the optimal level of live streaming investment, and the optimal wholesale price. Next, Lemma 3.1 rigorously proves that, under the return model, a platform or supplier will only choose to conduct live streaming when the relevant parameters are within a certain threshold range; otherwise, the optimal decision is not to conduct live streaming. The final conclusion indicates that, in addition to the live streaming cost and its coefficient, the return rate is also an important factor in determining whether to conduct live streaming. In pricing strategies SH and D, live streaming will only be conducted when the return rate is below the threshold. Therefore, consumer return behavior significantly influences the platform's sales model selection and supply chain decisions.

推论3.1的证明分为数学推导与经济含义两部分。首先，在数学推导中，给出了最优解相对于关键参数及批发价格系数的偏导数关系，结果表明：当退货率或货率处于较低水平时，随着退货率的上升，最优批发价格呈下降趋势。推导过程中进一步给出了不同条件下的最优解边界及对应的判别式，并证明了在各个区间内最优批发价格的单调性。其次，在经济层面的解释中指出：当退货率或货率较低时，供应商倾向于降低批发价，以激励零售商扩大销量；而当相关参数较高时，过大的退货压力会增加零售商的经营负担，使其不愿继续扩大需求，此时供应商可能维持较高的批发价格来保障自身利润。

The proof of Corollary 3.1 is divided into two parts: mathematical derivation and economic implications. First, in the mathematical derivation, the partial derivative relationship of the optimal solution with respect to key parameters and wholesale price coefficients is given. The results show that when the return rate or the return rate is at a low level, the optimal wholesale price tends to decrease as the return rate increases. The derivation process further gives the optimal solution boundaries and corresponding discriminants under different conditions, and proves the monotonicity of the optimal wholesale price in each interval. Secondly, the economic explanation points out that when the return rate or the return rate is low, suppliers tend to lower wholesale prices to encourage retailers to expand sales; when the relevant parameters are high, excessive return pressure will increase the operating burden of retailers, making them unwilling to continue to expand demand. At this time, suppliers may maintain a higher wholesale price to protect their own profits.

2、消费者退货对销售模式选择的影响（The impact of consumer returns on sales model selection）

引理3.2的推导结果表明，平台或供应商是否开展直播及其努力水平均取决于退货率与相关参数的区间分布。在策略SH和D下，证明部分给出了相应的边界条件与判别式：当退货率偏高时，直播努力水平呈下降趋势；而在退货率较低或退货成本较小的情形下，平台则倾向于提高直播投入。总体而言，模式R下的直播努力水平通常高于模式A，但在高退货率与低成本并存时，模式R的投入可能低于模式A。该结论揭示了退货因素对平台销售模式选择及直播努力水平具有决定性影响。

The derivation of Lemma 3.2 shows that whether a platform or supplier engages in live streaming and the level of effort involved in it depend on the return rate and the interval distribution of related parameters. Under strategies SH and D, the proof provides the corresponding boundary conditions and discriminants: when the return rate is high, the level of effort involved in live streaming tends to decrease; whereas when the return rate is low or the return cost is low, the platform tends to increase its investment in live streaming. Overall, the level of effort involved in live streaming under model R is generally higher than that under model A, but when a high return rate and low cost coexist, model R may involve less effort than model A. This conclusion reveals that the return factor has a decisive influence on the platform's choice of sales model and the level of effort involved in live streaming.

定理3.1表明，当直播渠道与常规渠道的退货率相同，供应商的模式选择主要依赖于佣金率：在较高佣金下更倾向于模式R，而在退货率及直播成本系数较大时则偏向模式A。平台的决策则同时受到佣金率与退货成本系数的影响，当退货成本或佣金水平较高时，更可能选择模式R。

Theorem 3.1 shows that when the return rate for live streaming channels is the same as that for conventional channels, suppliers' model selection depends primarily on the commission rate: they are more inclined to choose Model R when the commission rate is higher, while they tend to choose Model A when the return rate and live streaming cost coefficient are higher. Platforms' decisions are influenced by both the commission rate and the return cost coefficient; when the return cost or commission level is higher, they are more likely to choose Model R.

进一步考虑直播引致的质量不匹配增长率后，表3-3的结果显示：尽管定理3.1的核心结论依旧成立，但质量不匹配的增加加剧了供应商与平台在模式选择上的分化——供应商更趋向于模式A，而平台则偏好模式R。这一现象的原因在于：在高退货情境下，平台通过模式R可将风险转移至供应商，并在需求下降和成本上升的情况下仍维持利润；而供应商选择模式A则有助于减少直播投入，缓解退货压力与成本负担。总体来看，质量不匹配的存在进一步强化了双方在模式选择上的分歧。

Further considering the growth rate of quality mismatch caused by live streaming, the results in Table 3-3 show that while the core conclusion of Theorem 3.1 remains valid, the increasing quality mismatch exacerbates the divergence in model choice between suppliers and platforms—suppliers tend to favor Model A, while platforms prefer Model R. This phenomenon arises from the fact that, in a high-return scenario, platforms can transfer risk to suppliers through Model R and maintain profits despite declining demand and rising costs. Suppliers, on the other hand, can reduce their investment in live streaming, alleviating the pressure and cost burden of returns. Overall, the existence of quality mismatch further exacerbates the divergence in model choice between the two parties.

在定价策略D下，当直播渠道与常规渠道的退货率相同，供应商与平台的模式选择主要受到佣金率、退货率、L型消费者估值以及直播成本系数的综合影响。总体来看，供应商更倾向于选择模式A；而平台则在低佣金率和低退货率条件下，会依据L型消费者的估值水平及直播成本系数，在模式A与模式R之间做出权衡。

Under pricing strategy D, when the return rates for live streaming and conventional channels are the same, the model choice between suppliers and platforms is primarily influenced by a combination of commission rates, return rates, valuation of L-shaped consumers, and the cost coefficient for live streaming. Overall, suppliers tend to prefer Model A; while platforms, given low commission rates and low return rates, will weigh Model A against Model R based on the valuation of L-shaped consumers and the cost coefficient for live streaming.

观察3.1表明，供应商与平台的模式选择受L型消费者估值、佣金率、退货率以及直播成本系数的综合影响。当L型消费者估值较低时，供应商在模式A下利润更高，而平台在模式R下收益更大；当估值较高时，佣金率成为核心因素，高佣金推动供应商偏向模式R、平台则选择模式A，而低佣金条件下结果则相反；当估值与退货率均处于中等水平时，直播成本系数与退货率的相对大小决定了双方偏好，若二者水平均较高，则供应商更倾向模式A，而平台更倾向模式R。

Observation 3.1 shows that the model choices of suppliers and platforms are influenced by a combination of L-shaped consumer valuation, commission rate, return rate, and the live streaming cost coefficient. When L-shaped consumer valuation is low, suppliers profit more under Model A, while platforms profit more under Model R. When valuation is high, the commission rate becomes the core factor, with high commissions driving suppliers toward Model R and platforms toward Model A. The opposite is true under low commission conditions. When valuation and return rates are both at moderate levels, the relative magnitude of the live streaming cost coefficient and return rate determines both parties' preferences. If both are high, suppliers tend to favor Model A, while platforms tend to favor Model R.

图3-2直观展示了消费者估值处于低、高及中等水平时的不同模式选择结果。结合定理2.3、定理3.1与观察3.1，可以发现所谓的“反直觉现象”多发生在估值或退货率处于中间区间时。进一步情形分析表明，定理3.2与观察3.1的核心结论依然成立：当直播渠道的退货率较高时，更容易出现供应商偏向模式A而平台选择模式R的情形，这一结果也在表3-4的数值分析中得到验证。

Figure 3-2 visually illustrates the different model selection outcomes when consumer valuations are low, high, and moderate. Combining Theorem 2.3, Theorem 3.1, and Observation 3.1, we find that the so-called "counterintuitive phenomenon" often occurs when valuations or return rates are in the intermediate range. Further scenario analysis shows that the core conclusions of Theorem 3.2 and Observation 3.1 still hold true: when the return rate of live streaming channels is high, suppliers are more likely to favor Model A while platforms choose Model R. This result is also confirmed in the numerical analysis in Table 3-4.

命题3.1表明，在退货率相同的条件下，平台与供应商的模式选择依赖于定价策略及相关参数。在策略SH中，当佣金率、消费者接受度和直播成本处于特定区间时，双方均会选择模式R；而在策略D下，当L型消费者估值适中、佣金率与退货率较高且直播成本较低时，也存在双方同时选择模式R的均衡情形。这一结果不同于既有文献普遍认为模式A存在均衡而模式R缺乏稳定性的结论。本文在引入退货因素后发现，模式R在一定条件下更具优势：在模式A中，供应商需承担较高的佣金和退货成本，导致直播努力下降并压缩利润；而在模式R中，尽管平台需承担更高的成本，但供应商可通过降低批发价分担压力，从而提升平台利润，使模式R在部分情境下成为双方更具吸引力的选择。

Proposition 3.1 indicates that, given the same return rate, the model choice between platforms and suppliers depends on pricing strategies and related parameters. Under Strategy SH, when commission rates, consumer acceptance, and livestreaming costs are within a specific range, both platforms will choose Model R. Under Strategy D, when L-shaped consumer valuations are moderate, commission rates and return rates are high, and livestreaming costs are low, an equilibrium exists where both platforms simultaneously choose Model R. This result differs from the prevailing literature, which generally holds that Model A is in equilibrium while Model R lacks stability. After introducing the factor of returns, this paper finds that Model R is more advantageous under certain conditions: In Model A, suppliers bear higher commission and return costs, leading to a decline in livestreaming efforts and compressed profits. In contrast, in Model R, although the platform bears higher costs, suppliers can share the burden by lowering wholesale prices, thereby increasing platform profits, making Model R a more attractive option for both parties in some scenarios.

三、知识补充（Knowledge supplementation）

1、产品估值概述（Product valuation overview）

产品估值是对某一产品价值进行综合性判断的过程，其依据通常涵盖质量、功能特征、市场需求以及竞争环境等多方面因素。通过对这些要素进行全面分析与比较，可以较为准确地确定产品的价值水平。在产品开发与市场推广过程中，科学的估值具有重要意义：它不仅帮助企业明确市场定位、制定合理的定价方案，还能指导产品设计和功能优化，以更好地满足消费者需求；同时，估值结果也是投资决策和战略规划的重要参考。

Product valuation is the process of comprehensively assessing a product's value, typically based on a variety of factors, including quality, functional characteristics, market demand, and the competitive landscape. By comprehensively analyzing and comparing these factors, a product's value can be accurately determined. Scientific valuation plays a crucial role in product development and marketing: it not only helps companies clarify their market positioning and formulate reasonable pricing plans, but also guides product design and feature optimization to better meet consumer needs. Valuation results also serve as a crucial reference for investment decisions and strategic planning.

2、消费者后悔情绪概述（Overview of consumer regret）

消费者后悔情绪，又称买家懊悔或购物后悔，指的是消费者在完成购买后产生的一种负面心理反应，常见于重大消费决策情境中。其形成原因主要在于消费者对自身决策的怀疑，包括担心选择不当、因高额支出产生愧疚感，或怀疑受到销售方误导等多重因素的综合作用。这类情绪的出现通常需要特定的决策环境，尤其是在面对多种吸引力相近的高价商品时更为明显。2013年的相关研究指出，消费者在商品上的资源投入、决策过程中的参与程度、商品与购买目标的契合度，以及交易完成后所获得的正负反馈，都是决定后悔强度的关键变量。

Consumer regret, also known as buyer's remorse or purchaser's regret, refers to a negative psychological reaction consumers experience after completing a purchase, often occurring in critical consumer decision-making situations. This emotion stems primarily from consumer doubts about their decision, including concerns about making a poor choice, feelings of guilt over high spending, and suspicions of being misled by the seller. The emergence of this emotion typically requires a specific decision-making context, particularly when faced with a variety of high-priced products with similar appeal. A 2013 study indicated that the intensity of regret is determined by the consumer's investment in the product, their level of involvement in the decision-making process, the fit between the product and their purchase goals, and the positive and negative feedback received after the transaction.

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参考资料：百度、Chatgpt