社交电商中考虑有价市场信息的拼团规模与区别定价决策研究

doi:10.16381/j.cnki.issn1003-207x.2023.1996

Abstract

Abstract:

The rise of social e-commerce has benefited from innovative marketing strategies. Among them， group-buying model is a precise marketing strategy that targets consumers with different characteristics. It has combined social attributes and differentiated pricing properties， making it the most effective marketing model today. An optimal decision-making model for merchants and platforms is provided under information asymmetry by comparing uniform pricing models with differential pricing models that provide group buying channels. The model has taken into account consumer heterogeneity and valuable consumer feature information to offer optimal group buying conditions， pricing， and scale. It has also categorized consumers into group-buying preference type and waiting aversion type， analyzing the impact of potential consumer scale， consumer composition， and differences in group formation costs on business decisions. After obtaining precise decision-making on optimal pricing combinations and group-buying scales for each mode， it is found that （1） when the proportion of waiting aversion consumers is slightly higher， providing group-buying options not only does not reduce brand value but also increases both the profits of businesses and platforms. （2） The more pronounced the heterogeneity of consumers， the more profit can be gained from opening group-buying channels. （3） Information on consumer proportions collected by platforms has a more significant impact on large-scale businesses. As the proportion of impatient consumers increases， businesses should widen pricing differentials and set larger group-buying scales.（4） Within a certain range， platforms can obtain additional profits through selling valuable information. However， when businesses have low market forecasting capabilities， choosing to share information with them for free can actually achieve a win-win situation for both the platform and the businesses.

Key words: social e-commerce, group-buying pricing, information value, collaborative decision-making, Stackelberg game

CLC Number:

C934

Chuan Zhao,Ziyang Guo,Kun Wang, et al. Research on the Scale of Group-buying and Differential Pricing Decisions in Social E-commerce Considering Market Information Value[J]. Chinese Journal of Management Science, 2026, 34(3): 242-252.

Figures/Tables 11

参数	说明
a	电商平台中商家的潜在消费者规模
β_(0<_β_<1)	等待厌恶型消费者占潜在消费者比例，表征两类消费者占比
θ_H	等待厌恶型消费者的成团成本系数
θ_L	拼团偏好型消费者的成团成本系数
Δθ	两类消费者成团成本系数的比值，表征两类消费者的异质性
1-k	商家成交单位商品的平台抽成比例
D_L	拼团偏好型消费者的需求
D_H	等待厌恶型消费者的需求
p^U	U模式的最优价格
p_i	i=1拼团价格；i=2单售价格
n	拼团规模
π_m	商家利润
π_p	平台利润
w	平台售卖信息的价格
γ	商家不购买信息时对β的预测值
e	平台获取消费者信息付出的努力程度
v	商家购买信息的概率
F	平台收集信息付出的固定成本
$σ$	商家信息预测偏差
$β ˜$	平台付出努力获得的β预测值

$σ$	平台信息有价	商家购买信息	双赢价格范围 (自由度)	范围外最优模式
0.05	>1057.25	<349.43	0	无信息
0.07	>962.93	<1406.22	443.29	无信息
0.11	>465.08	<2564.67	2099.59	无信息
0.13	>1072.48	<5132.61	4060.13	无偿共享
0.15	>1534.08	<7382.61	5848.53	无偿共享
0.17	>2057.04	<9940.08	7883.04	无偿共享
0.19	>2638.82	<12789.50	10150.68	无偿共享

$σ$	平台信息有价	商家购买信息	双赢价格范围 (自由度)	范围外最优模式
0.05	>1734.09	<862.31	0	无信息
0.07	>1706.36	<795.325	0	无信息
0.11	>1591.17	<1314.72	0	无信息
0.13	>1368.51	<2633.22	1264.71	无信息
0.15	>2194.21	<4415.15	2220.94	无偿共享
0.17	>1620.92	<4690.80	3069.88	无偿共享
0.19	>5710.09	<12031	6320.91	无偿共享

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Research on the Scale of Group-buying and Differential Pricing Decisions in Social E-commerce Considering Market Information Value

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