玉石直播电商中消费者退货行为影响因素研究

doi:10.16381/j.cnki.issn1003-207x.2024.0523

摘要/Abstract

摘要：

抖音、快手等大众直播平台，以及翡翠王朝、对庄翡翠等垂类直播平台，为玉石产品（如和田玉、翡翠等）开辟了全新的销售渠道。然而，随着玉石直播电商行业的迅速发展，产品的高退货率（约80%，显著高于其他类别产品）成为一个突出问题。本文基于直播平台特征、产品特征和商家运营策略3个维度，结合翡翠王朝APP和抖音平台的数据，运用Logit回归等实证分析方法，系统分析了玉石直播中消费者退货行为的影响因素，包括是否退货和退货时间间隔。研究结果表明：在平台特征维度，大众直播平台中的玉石产品退货率显著高于垂类直播平台；在产品特征维度，产品价格对退货率的影响呈倒U型，且手镯等匹配不确定性较高的产品退货率显著高于其他类型产品；在运营策略维度，价格折扣显著提高了退货率，且白天直播的退货率显著高于夜间直播。此外，直播平台类型对产品特征和运营策略对退货行为的影响具有显著的调节作用。进一步分析退货时间间隔，本文识别了“冲动型退货”和“体验型退货”两种退货行为模式，并揭示了产品特征及直播运营策略对退货时间间隔的影响。研究结果为玉石直播电商从业者优化直播调度、产品选择及平台策略提供了指导，以期有效降低消费者退货率。

关键词: 直播电商, 玉石产品, 退货行为, 影响因素

Abstract:

Live broadcasting platforms， encompassing both general platforms （e.g.， TikTok and Kuaishou） and specialized platforms （e.g.， Jade Empire）， have emerged as innovative sales channels for jade products such as Hotan Jade and Jadeite. These platforms have revolutionized the marketing of high-value， non-standardized products like jade， offering consumers immersive and interactive shopping experiences. However， the rapid growth of the jade live-streaming has introduced several operational challenges， most notably the exceptionally high return rate. According to data provided by Jade Empire Co.， Ltd.， the return rate for jade products in live-streaming can reach up to 80%， significantly exceeding other product categories. This issue poses critical challenges to both merchants and platforms in managing costs and maintaining customer satisfaction. The determinants of consumer return behavior in the jade live-streaming context is investigated， focusing on two key dimensions of return behavior： the likelihood of returns and the return time interval. Drawing on data from two distinct platforms， Jade Empire （a specialized jade platform） and TikTok （a general live-streaming platform）， the influence of platform characteristics， product features， and merchant operation strategies on return behavior is examined. Employing empirical methods， including logit regression and linear regression models， the following key insights are derived：（1） Jade products sold on TikTok exhibit significantly higher return rates compared to those on Jade Empire. This difference can be attributed to the broader， less specialized audience of TikTok， which includes a higher proportion of impulsive buyers with limited knowledge of jade products. （2） The relationship between product prices and return rates follows an inverted U-shaped curve. Low-priced and high-priced jade products are associated with lower return rates， while mid-priced products exhibit the highest return rates. Additionally， bracelet products， characterized by high matching uncertainty （e.g.， size， fit）， show a notably higher return rate compared to other jade product types. （3） Operational factors such as price discounts and live-streaming schedules significantly influence return rates. Offering price discounts increases return rates by encouraging impulsive purchases. Furthermore， daytime live-streaming events are associated with higher return rates compared to nighttime events， as daytime viewers often face external distractions that impair their ability to make informed decisions. （4） The type of live-streaming platform moderates the relationship between product features， operational strategies， and return behavior. General platforms like TikTok amplify the effects of product and operational characteristics on return rates， while specialized platforms like Jade Empire mitigate these effects by attracting a more knowledgeable and engaged audience. （5） By analyzing return time intervals， two distinct return behavior patterns are identified： impulsive returns （occurring before product delivery） and experiential returns （occurring after product receipt and usage）. Mid-priced products and bracelet products are more likely to prompt impulsive returns due to heightened expectations or mismatched features. Conversely， price discounts and nighttime live-streaming extend return time intervals by encouraging consumers to experience the product before deciding to return it. It contributes to the literature on live-streaming commerce and product return behavior by integrating insights from platform-specific dynamics and consumer decision-making processes in this paper. The findings offer valuable insights into live-stream scheduling， product assortment management， and platform selection， all of which can help reduce return rates and enhance consumer experience.

Key words: live-streaming e-commerce, jade products, consumer returns, impact factors

中图分类号:

F724.6

孔德梅,肖勇波,杨钊, 等. 玉石直播电商中消费者退货行为影响因素研究[J]. 中国管理科学, 2026, 34(4): 205-217.

Demei Kong,Yongbo Xiao,Zhao Yang, et al. Impact Factors of Consumer Return Behavior in Jade Products Live-streaming E-commerce[J]. Chinese Journal of Management Science, 2026, 34(4): 205-217.

图/表 6

图1

表1

主要变量的描述性统计"

Variable	N	Mean	Std	Min	Max
全部订单
$R e t u r n i$	89992	0.14	0.347	0	1
$P l a t f o r m i$	89992	0.11	0.313	0	1
$l n P r i c e i$	89992	6.034	2.717	0.039	11.513
$P r o d u c t i$	89992	0.091	0.288	0	1
$D i s c o u n t i$	89987	0.286	0.452	0	1
$N i g h t i$	89992	0.492	0.5	0	1
翡翠王朝APP：2019年7月-2020年2月
$R e t u r n i$	43937	0.135	0.341	0	1
$l n P r i c e i$	43937	6.625	2.682	0.039	11.513
$P r o d u c t i$	43937	0.04	0.196	0	1
$D i s c o u n t i$	43937	0.415	0.493	0	1
$N i g h t i$	43937	0.527	0.499	0	1
翡翠王朝APP：2020年7月-2021年2月
$R e t u r n i$	36161	0.071	0.257	0	1
$l n P r i c e i$	36161	5.108	2.599	0.693	11.513
$P r o d u c t i$	36161	0.04	0.197	0	1
$D i s c o u n t i$	36156	0.19	0.393	0	1
$N i g h t i$	36161	0.476	0.499	0	1
手镯之家：2023年7月-2024年2月
$R e t u r n i$	9894	0.411	0.492	0	1
$l n P r i c e i$	9894	6.8	2.289	0.693	11.391
$P r o d u c t i$	9894	0.505	0.5	0	1
$D i s c o u n t i$	9894	0.058	0.234	0	1
$N i g h t i$	9894	0.399	0.49	0	1

表1

表2

图2

图3

图4

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	（1）	（2）	（3）	（4）	（5）	（6）
	Return	Return	Return	Return	Return	lnReturnTime
Platform	1.313^***	-1.319^***	1.309^***	1.423^***	1.254^***
	(0.039)	(0.39)	(0.045)	(0.04)	(0.047)
lnPrice	0.99^***	0.887^***	0.99^***	0.981^***	0.993^***	0.509^***
	(0.037)	(0.037)	(0.037)	(0.037)	(0.037)	(0.037)
lnPrice²	-0.059^***	-0.055^***	-0.059^***	-0.059^***	-0.06^***	-0.032^***
	(0.003)	(0.003)	(0.003)	(0.003)	(0.003)	(0.003)
Product	1.119^***	0.95^***	1.111^***	1.096^***	1.129^***	-0.097^**
	(0.038)	(0.044)	(0.054)	(0.038)	(0.038)	(0.048)
Discount	0.799^***	0.823^***	0.8^***	0.883^***	0.796^***	0.493^***
	(0.029)	(0.029)	(0.029)	(0.03)	(0.029)	(0.034)
Night	-0.069^***	-0.051^**	-0.069^***	-0.067^***	-0.105^***	0.094^***
	(0.025)	(0.025)	(0.025)	(0.025)	(0.029)	(0.031)
lnPrice×Platform		0.646^***			0.126^**
		(0.11)			(0.056)
lnPrice²×Platform		-0.036^***
		(0.008)
Product×Platform			0.014
			(0.076)
Discount×Platform				-0.989^***
				(0.103)
Night×Platform					0.126^**
					(0.056)
月份固定效应	Yes	Yes	Yes	Yes	Yes	Yes
_cons	-6.113^***	-5.651^***	-6.109^***	-6.137^***	-6.105^***	2.22^***
	(0.14)	(0.139)	(0.141)	(0.14)	(0.14)	(0.135)
Observations	53831	53831	53831	53831	53831	5919
R²	0.17	0.172	0.17	0.172	0.17	0.092