在线点评场景中考虑消费者损失厌恶的动态定价

doi:10.16381/j.cnki.issn1003-207x.2023.0375

Abstract

Abstract:

Consumers are generally loss averse and are often reluctant to purchase emerging goods of uncertain quality. Before purchasing a good， consumers may browse the rating information displayed on the review platform to form a clearer perception of the quality of the good， mitigating the impact of loss aversion. Then， after purchase and use， consumers may return to the review platform to give their ratings. A multi-stage dynamic pricing model is constructed for a monopoly firm under the online review scenario that considers consumers' loss aversion. The firm maximizes its revenue by dynamically adjusting its price to balance earning higher current revenue and accumulating rating information more quickly. Due to the complex inter-period impacts of pricing and the stochastic and non-linear properties of the evolution process of the state variables， the exact optimal strategy for the problem is difficult to find. Therefore， a deterministic treatment based on the fluid approximation method is carried out， from which a pricing strategy with asymptotic optimality is found. Furthermore， the impact of consumer loss aversion on pricing is discussed. Finally， the effectiveness of the policy is verified through numerical experiments based on real data from Amazon. The experimental results find that the dynamic price strategy can enhance revenue by about 10% compared to the fixed price strategy. On the basis of the dynamic price strategy， further adding the portrayal and treatment of consumer loss aversion can further enhance revenue by about 1%. In addition， the dynamic fluid matching strategy proposed in this paper has the form of a semi-closed solution and thus has the advantage of high computational efficiency. It contributes both in theory and application. In theory， it is proved that the pricing strategy based on the fluid approximation method is asymptotically optimal. In terms of application， the results of this paper can provide guiding suggestions on pricing for related firms.

Key words: loss aversion, online reviews, dynamic pricing, fluid approximation

CLC Number:

F274

Xiao Wang,Chaolin Yang. Dynamic Pricing with Consumer Loss Aversion and Online Reviews[J]. Chinese Journal of Management Science, 2025, 33(12): 253-263.

Figures/Tables 9

参数		情形(a)	情形(b)	情形(c)	情形(d)
是否存在损失厌恶		×	√	×	√
消费者对质量的先验认知		高估	高估	低估	低估
消费者先验认知的均值	$μ 0$	10	10	6	6
消费者损失厌恶系数	$k$	0	2	0	2
评分的均分	$μ$	8
消费者先验认知的标准差	$σ 0$	1
消费者学习速率	$γ$	1
需求函数截距项	$β$	-1
需求函数价格系数项	$α$	0.1
购买后填写评分的概率	$u$	1
消费者总数	$T$	40

参数		情形(a)	情形(b)	情形(c)
消费者对质量的先验认知		无偏	高估	低估
消费者先验认知的均值	$μ 0$	8	10	6

算法缩写	价格是否可变	是否将损失厌恶纳入算法
$F N$	固定	否
$D N$	浮动	否
$D Y$	浮动	是

算法	$D N$	$D Y$
最小值	0.853	0.871
第一四分位数	1.006	1.007
中位数	1.029	1.057
第三四分位数	1.186	1.225
最大值	2.065	2.068
平均值	1.147	1.156
标准差	0.247	0.233

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模型序号	转化率(CR)
模型序号	1%	2%	3%	4%	5%
1	0.752	0.765	0.776	0.785	0.798
2	0.420	0.571	0.630	0.673	0.703
3	0.711	0.728	0.731	0.742	0.741
4	0.456	0.579	0.652	0.674	0.68
5	0.440	0.443	0.445	0.448	0.450
6	0.384	0.398	0.388	0.396	0.397

Dynamic Pricing with Consumer Loss Aversion and Online Reviews

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Figures/Tables 9

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模型序号	是否考虑价格	是否考虑评分数和均分	是否考虑损失厌恶	函数形式
1	√	√	√	Logistic
2	√	√	√	线性
3	√	√	×	Logistic
4	√	√	×	线性
5	√	×	×	Logistic
6	√	×	×	线性

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